Federico - Master Thesis

POLITECNICO DI TORINO DEPARTMENT OF ELECTRONICS AND TELECOMMUNICATIONS College of Electronic Engineering, Telecommunications and Physics (ETF)

Master of Science in Telecommunication Engineering

Master’s Degree Thesis

Advanced receivers for LTE/LTE-A systems with interference cancellation capabilities

Supervisor Prof. Marina Mondin Ing. Bruno Melis Candidate Federico Pacifici

March 2014

INDEX INTRODUCTION

4

CHAPTER 1: From LTE to LTE-Advanced - PHY Overview

6

1.1 Main concepts

6

1.2 Multi antenna techniques

9

1.3 Transmission modes and transmission schemes

10

1.4 Modulation and multiple access technique

13

1.5 Downlink signals

14

1.6 Downlink multi-antenna transmission

17

1.6.1 Layer mapping

18

1.6.2 Transmit diversity

19

1.7 User Equipment categories

20

1.8 A limiting factor of spectrum efficiency

22

1.9 Inter cell Interference modeling: DIP values

23

1.10 Channel profiles

25

CHAPTER 2: ACTIVITIES IN 3GPP ON ADVANCED RECEIVERS

27

2.1 feICIC

28

2.2 CRS-IM

29

2.3 NAICS

30

2.3.1 MMSE

31

2.3.2 MMSE-IRC

31

2.3.3 E-MMSE-IRC

33

2.3.4

34

Symbol level SIC

1

2.3.5

Bit level hard SIC

35

2.3.6

Soft Turbo SIC

36

2.3.7

ML receiver

36

2.3.8

R-ML

38

CHAPTER 3: MMSE-IRC RECEIVER IN REAL SCENARIOS 3.1 Overview

40

3.1 General Architecture of the MIMO OFDMA simulator

41

3.1.1 Data region Mapping/Demapping

43

3.1.2 Subcarrier Mapping/Demapping

45

3.1.3 IFFT/FFT calculation and cyclic prefix insertion/removal

46

3.1.4 Pilots compensation

47

3.1.5 Channel Estimation

48

3.1.6 Space-time Encoder/Decoder

50

3.2 Modeling MMSE-IRC

51

3.2.1 MMSE-IRC for SFBC transmit diversity

51

3.2.2 Building the MMSE-IRC receiver

58

3.3 Performance of MMSE-IRC receiver

61

3.3.1 Interfering signal modeled as Gaussian noise

61

3.3.2 Real Interference signal – Colliding pilot case

63

3.3.3 Real Interference signal – No Colliding pilot case

67

CHAPTER 4: SUCCESSIVE INTERFERENCE CANCELLATION RECEIVERS

2

39

69

4.1 Introduction and comparison

69

4.2 SLIC implementation

71

4.3 BLIC implementation

76

4.4 SLIC and BLIC performance analysis

79

CONCLUSION

85

BIBLIOGRAPHY

86

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INTRODUCTION The Master Thesis was written after an internship period at Telecom Italia S.p.A (Wireless Access Innovation group). The objective of this Master Thesis is analyzing and simulating advanced receiver schemes with interference rejection capabilities that represent one of the next innovative step in the physical layer of LTE/LTE-A systems providing higher throughput especially at the cell edge. Several receiver schemes are analyzed and some of them are simulated to obtain performance results in terms of Throughput and Raw BER. The selected receivers are chosen considering the trade-off between complexity and expected gains. A low complexity version of the MMSE-IRC receiver has been implemented in a link level simulator specific for the LTE system, so performance results have been obtained showing interesting features and using a low complexity technique for the estimation of the interference covariance matrix. MMSE-IRC has been implemented as an independent block to simplify the development of innovative receivers that use it as elementary building block. The MMSE-IRC receiver outperforms the classical detection schemes that treat the inter-cell interference as Gaussian noise, especially in case of no colliding pilots between the serving and interfering cells. MMSE-IRC is also a fundamental block of successive interference cancellation receivers operating at symbol level (SLIC, Symbol Level Interference Cancellation) and bit level (BLIC, Bit Level Interference Cancellation). In a second step of the analysis SLIC and BLIC receivers have been implemented in a simplified link level simulator based on MATLAB and the simulated performances are compared with the other considered receivers.

The

analysis showed that, even if the SLIC receiver complexity is higher than MMSE-IRC one, it provides some gain especially in the low SINR region, while for higher SINR, the successive interference cancellation functionality must be switched off to avoid the error propagation effect. BLIC is more powerful, but its complexity is very high because it performs the channel decoding also for the interfering signals. The Master Thesis is structured into four chapters. In the first chapter, a Physical Layer overview of LTE and LTE-Advanced systems is provided, focusing on the aspects that have been considered

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for the receiver implementations. Chapter two gives an overview of the activity carried out by 3GPP on advanced receivers. The third chapter shows the algorithm and the implementation of the MMSE-IRC receiver in the LTE link level simulator, discussing also the performance results. The last chapter describes the SLIC and BLIC receivers, showing simulation performance in terms of throughput and Raw BER.

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CHAPTER 1: From LTE to LTE-Advanced - PHY Overview 1.1 Main concepts Long Term Evolution (LTE) is a mobile telecommunication system designed to drive the evolution from 3G to 4G wireless communication technologies. These developments include all the newest techniques that can provide new services to many users in complex scenarios ensuring the growing users expectation. Many technical aspects are standardized and there are a lot of research groups and companies that invest in these fields, moreover the evolution tracking and the dominant standards are the result of many partnerships inside 3GPP. In the last years, there was a strong evolution in terms of competition between mobile operators, new frequencies allocation, new advanced technologies, creating an innovative and revolutionary market. In this context, the natural development of mobile communication was driven by the necessity to enable internet connectivity for mobile users, creating the mobile broadband. This is the major driver for the evolution of LTE that provide internet protocol services. Packet switched services and IP are guidelines for a radio interface that support new design parameters such as: high data rate (close to Gbit/s), low latency and high capacity. Note that, from the mobile system operator perspective, it is not only important the peak data rate to end users, but also the total data rate that can be provided on average from each deployed base station and per hertz of licensed spectrum, so the spectral efficiency. Another important constrain that has to be satisfied is the Quality of Service for the end users. All of these design parameters influenced the development of LTE, moreover there is an increasing demand for more spectrum resources, so innovative mobile systems need to operate in different frequency bands with spectrum allocation of different size and fragmentation. One main target for the evolution of mobile communication is to provide the possibility for higher user data rates compared to what is achievable with 3G

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standards. Another important target is to provide higher data rates over the entire cell area, including users at the cell edge. Theoretically, the maximum rate is limited by the channel capacity that depends on the channel bandwidth and on the signal to noise ratio, in presence of AWGN noise. This is a noise limited scenario, in which, the data rates are always limited by the available received power or by the received signal power to noise power ratio. When the bandwidth utilization is low, so the data rate is lower than the available bandwidth, increasing the data rate requires a higher received power, so an increase in the available bandwidth does not substantially impact what received signal power is required for a certain data rate. On the other hand, in the case of high bandwidth utilization, when the data rates is equal or higher than the available bandwidth, an increase of data rate requires a much larger increase in the received signal power, so an increase in the bandwidth will reduce the received signal power required for a certain data rate. In conclusion, the transmission bandwidth should at least be of the same order as the data rates to be provided. Fixing a transmit power, to increase the received one, it is possible reduce the attenuations, decreasing the distance, planning small cells and increasing the number of cells. At the receiver side, another useful technique to provide high data rates is using additional antennas, known as receive antenna diversity. Even at transmit side it is possible to use multiple antennas, so combining signals received at the different antennas the signal to noise ratio can be increased in proportion to the number of antennas, allowing higher data rates. Multiple transmit or receive antennas techniques are efficient up to a certain level beyond which there is only a marginal increase in the data rates. This limit can be avoided using multiple antennas at both the transmitter and the receiver side, using the spatial multiplexing or MIMO. There are also other techniques, for examples focusing the total transmit power in the direction of the receiver or reducing the noise power density improving the receiver design. In the previous cases, the AWGN noise is the main negative contribution, but in real scenarios, especially in mobile communication fields, the interference from transmissions in neighboring cells, called inter cell interference, is the dominant source of radio link impairment that usually occurs with a high traffic load. In addition to inter

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cell interference, there could be another kind of interference, called intra cell interference in which the useful signal is interfered by other signals within the current cell. In this case, the maximum data rate that can be achieved in a given bandwidth is limited by the SINR (Signal power to Interference and Noise Ratio). One important difference between interference and noise is that interference, in contrast to noise, typically has a certain structure which makes it, at least to some extent, predictable and thus possible to further suppress or even remove completely. More advanced topics about interference cancellation will be addressed carefully in the next chapters, emphasizing some aspects that are the main job of this Master Thesis, focusing on the implementations and performances of advanced receivers able to cancel interferences in various scenarios. From the operator point of view, bandwidth is a scarce and expensive resource, so telecom operator would like to provide very high data rates within a limited bandwidth. One way to increase the data rate is to use higher order modulations. In 3G systems (i.e. WCDMA) is used the QPSK modulation, nowadays high order modulations such as 16QAM or 64QAM are used in HSPA to improve the bandwidth utilization, providing higher data rates within a given bandwidth at the cost of reduced robustness to noise and interference. Higher order modulation are normally combined with channel coding giving more efficiency, paying attention that an additional channel coding applied by using a higher order modulation scheme such as 16QAM may lead to an overall gain in power efficiency compared to the use of QPSK. Setting a SINR there is an optimal choice of modulation and channel coding to obtain the highest bandwidth utilization. Wider band transmissions are subjected to frequency channel selectivity that corrupt the frequency domain structure of the signal, leading to higher error rates for a given SINR. It is necessary to design a transmission scheme that avoids frequency channel selectivity with low complexity. This goal can be reached by OFDM. This scheme provides a lot of other benefits such as robustness against Intersymbol Interference (ISI) through cyclic prefix insertion, IFFT/FFT digital processing, user multiplexing, multi access etc.

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Using an OFDM scheme, it is possible to estimate the frequency-domain channel taps directly inserting known reference symbols or pilot symbols at regular intervals within the OFDM time-frequency grid. Knowing the reference symbols, the receiver can estimate the channel coefficients around the location of the reference symbols. The reference symbols are mapped in time and frequency domain in a grid with a high density to combat high frequency and time selectivity. In the next chapters an advanced channel estimation algorithm will be explained.

1.2 Multi antenna techniques Transmission with multiple transmit and receive antennas (MIMO) is supported in the downlink with two or four transmit antennas and two or four receive antennas, which allow for multi-layer transmissions with up to four layers. Both Single User MIMO (SUMIMO) and Multi-user MIMO (MU-MIMO) are supported in the 3GPP specifications. In the case of SU-MIMO, the transmission resources over the different antennas are allocated to one user only, while in case of MU-MIMO the transmission resources are allocated to different users. The SU-MIMO is then used in order to increase the user peak data rate (or coverage), while MU-MIMO is used to increase the average data rate per sector. In particular the following multi-antenna transmission techniques are supported in the LTE Release 8 downlink standard: Transmit Diversity (SFBC), Spatial Multiplexing with a single use (SU-MIMO), Spatial Multiplexing with two users (MU-MIMO), CDD (superimposed to open loop spatial multiplexing), Linear Precoding (both for single layer or multiple layer transmission), single layer Beamforming. Transmit diversity is based on the so called Space-Frequency Block Coding (SFBC), complemented with Frequency Shift Transmit Diversity (FSTD) in case of four transmit antennas (MIMO 4 x n). Transmit diversity is used by common downlink control channels to provide additional diversity, as for these channels dynamic scheduling and H-ARQ are not applicable. However, transmit diversity is also applied to user-data

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transmission, in particular for cell edge users that experience low Signal to Interference plus Noise Ratio (SINR) values. In case of spatial multiplexing, up to four antennas at both the transmitter (base station) and the receiver (terminal) side are used to provide simultaneous transmission of multiple parallel data streams, also known as layers, over a single radio link, thereby significantly increasing the peak data rates that can be provided over the radio link. As an example, with four base-station transmit antennas, and a corresponding set of (at least) four receive antennas at the terminal side, up to four layers can be transmitted in parallel over the same radio link, effectively quadrupling the peak data rate with respect to a single antenna system (i.e. SISO).

1.3 Transmission modes and transmission schemes In LTE Release 8 and LTE Release 10 (i.e. LTE Advanced), nine transmission modes are defined and two different transmission schemes are allowed in each transmission mode. The reference transmission scheme is what is intended for the transmission mode and the other is for fallback operation. In 3GPP specifications the term antenna port is often used instead of antenna since, by means of antenna virtualization, two/multiple physical antennas can transmit the same information and hence make one antenna port. An antenna port is defined by its associated Reference Signal (RS) pattern. The following antenna ports are defined in Release 10: 

Cell specific RS (antenna ports 0,1,2,3);



Multicast/Broadcast over Single Frequency Network (MBSFN) RS (antenna ports 4);

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

UE-specific RS for single layer beamforming (antenna ports 5);



Positioning RS (antenna ports 6);



UE-specific RS for multi-layer beamforming (antenna port 7,8,9,10,11,12,13,14)



Channel state Information RS (CSI-RS) (antenna port 15,16,17,18,19,20,21,21);

Table 1 summarizes the transmissions schemes corresponding to each transmission mode.

Downlink

Reference Transmission Scheme

Fallback

Transmission

Transmission

Mode

Scheme

Mode 1

Single antenna port

Mode 2

Notes

LTE Rel.8

Transmit diversity

Single antenna port Transmit diversity

Mode 3

Open-loop spatial multiplexing

Transmit diversity

LTE Rel.8

Mode 4

Closed-loop spatial multiplexing

Transmit diversity

LTE Rel.8

Mode 5

Multi-user MIMO

Transmit diversity

LTE Rel.8

Mode 6

Closed-loop rank=1 precoding

Transmit diversity

LTE Rel.8

Mode 7

Single-antenna port; port 5

LTE Rel.8

Mode 8

Dual layer transmission or single layer

Transmit diversity or single-antenna port Transmit diversity

Mode 9

Up to 8 layer transmission

Transmit diversity

LTE Rel.10

LTE Rel.8

LTE Rel.9

Table 1: Downlink Transmission Mode

Among the transmission modes defined in the 3GPP standard, Transmit Diversity (Mode 2) and Open Loop Spatial Multiplexing (Mode 3) are supported in the first equipment and terminal implementations and thus are of importance for the initial roll-out of the LTE network. Switching between these two modes is decided by the network as a function of the channel conditions, which is known to the eNode B through the channel state information reported by the UE (CQI and RI). The accuracy of RI reporting, which indicates the estimated number of simultaneous layers that can be received by the UE, is a critical information for the optimal usage of TxD and SM in a real LTE network.

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This figure shows how is convenient to switch in a transmit diversity mode when SINR is low.

Figure 1: Transmit Diversity and Spatial Multiplexing modes

The LTE physical layer offers data transport services to higher layers. The access to these services is through the use of a transport channel via the MAC sub-layer. The physical layer is designed to perform the following functions: 

Error detection through CRC and indication to higher layers



FEC encoding/decoding of the transport channel



Rate matching



Hybrid ARQ (with soft-combining at the receiver)



Power weighting of physical channels



Modulation and demodulation of physical channels



Mapping onto physical channels



Multiple Input Multiple Output (MIMO) antenna processing



RF processing

Figura 2: LTE Physical Layer

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1.4 Modulation and multiple access technique The LTE radio interface adopts the S-OFDMA (Scalable OFDMA) as modulation and multiple access technique with fixed subcarrier spacing ∆f equal to 15 KHz. The total number of subcarriers (i.e. the FFT size NFFT) is proportional to the channel bandwidth. For example, in case of a channel bandwidth BW=10 MHz the FFT size is 1024. In this case the number of subcarriers used for transmission (e.g. data, pilots or control) is equal to 600, while the remaining subcarriers are left unused, for the DC subcarrier and for the guard subcarriers positioned at the edges of the transmission spectrum. Table 2 summarizes the LTE numerology for different channel bandwidths.

Table 2: LTE numerology

An important characteristic of the LTE radio interface is that the frame duration and Transmission Time Interval (TTI) are harmonized with those of UMTS/HSDPA system. In particular the frame duration is equal to 10 ms while the subframe period, which corresponds to the Transmission Time Interval (TTI), is equal to 1 ms (compared to the 2 ms of HSPA). Each subframe is divided in two slots, where each slot has a duration of 0.5 ms. Also the sampling frequency of the baseband (BB) signals are harmonized: for UMTS/HSPA the baseband signal is sampled at 3.84 MHz, while for LTE the baseband sampling frequency is equal to m/n3.84 MHz, where m and n are integer factors that

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depend on the LTE channel bandwidth. These features reduce the complexity and the cost of dual mode terminals that will support both radio interfaces.

1.5 Downlink signals A downlink signal corresponds to a set of resource elements used by the physical layer but does not carry information originating from higher layers. The following downlink physical signals are defined in the standard: Reference signal and Synchronization signal. Three types of downlink reference signals (RS) are defined: Cell-specific reference signals (CRS), MBSFN reference signals, associated with MBSFN transmission, UEspecific reference signals. The cell specific downlink reference signals (CRS) consist of known reference symbols inserted in the first and third last OFDM symbol of each slot in the case of Normal CP.

Figure 3: Pilot pattern for a SISO system

There is one reference signal transmitted per downlink antenna port. The number of downlink antenna ports P equals 1, 2, or 4. The RS of different antenna ports are orthogonal among each other because resource elements used for RS transmission of one antenna port are not used for any transmission by the other antennas (i.e. are set to zero power for the other antennas).

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Figure 3, Figure 4 and Figure 5, respectively show, the pilot pattern for a SISO case when a normal or an Extended prefix cyclic is used, the CRS signals for two transmit antennas (MIMO 2x2) and finally CRS signals for four transmit antenna (MIMO 4x4). The cell specific RS sequence is a PN (pseudo random) sequence defined by a length-31 Gold sequence. The pseudo-random sequence generator is initialised with a value that depends on the cell identity (cell-ID) so that different PN sequences are associated to different cells. In this way the RSs of different cells have low values of cross-correlation and thus the interference from neighboring cells can be reduced by proper averaging on frequency adjacent reference symbols received at the UE.

Figure 4: MIMO 2x2 CRS pattern

Frequency hopping (FH) can be applied to the cell-specific reference signals. The frequency hopping pattern has a period of one frame (10 ms). Each frequency hopping pattern corresponds to one cell identity group. The LTE standard foresees also UE-specific reference signals, also denoted in the technical documents as DeModulation Reference Signals (DM-RS). The DM-RSs are introduced for the support of beamforming techniques. The eNode B can semistatically configure a UE to use the dedicated reference signal as the phase reference for data demodulation of a single codeword. DL control signalling is located in the first n OFDM symbols (n  3) of a subframe and consists of: 

Number n of control OFDM symbols per subframe (PCFICH);

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

Transport format, resource allocation and hybrid-ARQ information (PDCCH);



Uplink scheduling grant (PDCCH)



ACK/NACK in response to uplink transmission (PHICH)

Note that there is not mixing of control signaling and shared data in an OFDM symbol. Figure 6 shows the mapping between Control and Data symbols.

Figure 5: MIMO 4x4 CRS pattern

Control channels are formed by aggregation of control channel elements (CCE), each control channel element consisting of a set of resource elements. The modulation used for all control channels is QPSK.

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Multiple physical downlink control channels are supported and a UE monitors a set of control channels.

Figure 6: Control and Data REs

1.6 Downlink multi-antenna transmission Spatial multiplexing (SM) of multiple symbol streams to a single UE using the same time frequency resources, also referred to as Single-User MIMO (SU-MIMO) is supported in the LTE standard. Spatial multiplexing of multiple symbol streams to different UEs using the same time frequency resources, also referred to as MU-MIMO, is also supported. In general SU-MIMO is beneficial for increasing user throughput or coverage, whilst MU-MIMO is exploited for increasing the aggregate cell throughput. In addition to SU-MIMO and MU-MIMO, the following spatial processing techniques are also supported in the LTE Release 8 standard: Codebook based precoding, Transmit antenna diversity based on SFBC (Space-Frequency Block Coding), Single layer Beamforming and Cyclic Delay Diversity (CDD). In the following a short description of these multi-antenna transmission techniques is provided.

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1.6.1 Layer mapping

Multi-antenna transmission with 2 and 4 transmit antennas is supported. The maximum number of codeword is two, irrespective to the number of antennas, with fixed mapping of codewords to layers. In the MIMO terminology one codeword represents one data stream that is independently encoded and modulated under the control of the AMC (Adaptive Modulation and Coding) procedure. The mapping of the codewords to the layers depends on the rank of the channel and is performed by a specific block denoted as layer mapping. The layer mapping operation is depicted in Figure 7, where CW1 and CW2 are the first and the second codeword respectively and the layer mapping block is represented by the dotted box in blue colour. The output of the layer mapping operation (e.g. the layers) is provided to the block that performs the precoding.

Figure7: Layer mapping for two transmit antennas

The Figure 8 shows the layer mapping operation for the case of four transmit antennas.

Figure 8: Layer mapping for four transmit antennas

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1.6.2 Transmit diversity

Transmit antenna diversity (TxD) is designed to improve transmission reliability and coverage and is typically used for cell edge users that experience low values of SINR and for which it is not advantageous the use of spatial multiplexing. The LTE standard includes two different techniques based on SFBC (Space Frequency Block Coding) for the case of two and four transmit antennas respectively: SFBC for 2Tx antennas, SFBC combined with FSTD for 4-Tx antennas. In case of two transmit antennas the SFBC technique is basically the Alamouti code applied in the frequency domain over two adjacent OFDM subcarriers. The Figure 9 shows the principle of SFBC encoding where S1 and S2 are the modulated symbols coming from the layer mapping block. It must be noted that only one codeword is transmitted when the TxD technique is used.

Figure 9: SFBC technique for two transmit antennas

An important feature of the Alamouti code is that only simple linear operations are needed at the receiver for decoding. In case of four transmit antennas the LTE standard adopts a combination of the Alamouti code and the Frequency Switching Transmit Diversity (FSTD) technique. The Figure 10 shows the principle of SFBC+FSTD encoding where S1,…,S4 are the modulated symbols coming from the layer mapping block. Notice that also in this case only one codeword is transmitted. Basically the SFBC + FSTD technique consists in the application of the Alamouti code over pair of antennas.

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Figure 10: SFBC+FSTF technique for four transmit antennas

The Alamouti code is applied over the antennas 1 and 3 for symbols S1 and S2, while for symbols S3 and S4 the code is applied over the antennas 2 and 4. The antenna pairing (1,3) and (2,4) is done in order to balance the different pilot density that is lower for antenna 3 and antenna 4 compared to antenna 1 and antenna 2.

1.7 User Equipment categories From the release 8 to the release 10 user terminals support different features having different physical layer capabilities. In LTE release 8/9, for example, the low-end category 1 does not support spatial multiplexing, while the category 5 supports the full set of features in the release 8/9 physical layer specifications. In LTE release 10, more useful interesting techniques are used (i.e. carrier aggregation), providing higher performance.

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In Table 3 are showed the eight categories from 1-5 (LTE Release 8/9/10) to 6-8 (LTEAdvanced Release 10).

Table 3: UE Category

In more detail, for categories from 1 to 5, it is showed a Table 4 containing the downlink physical layer parameters for each category. The second column in Table 4 defines the maximum number of DL-SCH transport blocks bits that the UE is capable of receiving within a DL-SCH TTI of 1 ms. In case of spatial multiplexing, this is the sum of the number of bits delivered in each of the two transport blocks. The third column represents the maximum number of DL-SCH transport block bits that the UE is capable of receiving in a single transport block within a DL-SCH TTI. The fourth column represents the total number of soft channel bits available for H-ARQ processing while the last column gives the maximum number of supported layers for spatial multiplexing per UE.

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Table 4: Downlink Physical Layer parameters

It is possible to notice that a Category 3 user equipment is capable of supporting a downlink peak throughput of about 102 Mbit/s (i.e. 102048 received bits in one 1 ms) and that it can support spatial multiplexing with a maximum of two layers. The highend terminals correspondent to the category 5 can support a peak throughput of about 300 Mbit/s with spatial multiplexing over four layers (these terminals thus need to be equipped with four receive antennas).

1.8 A limiting factor of spectrum efficiency The ever increasing user density in cellular systems coupled with the unitary frequency reuse factor selected for the Long Term Evolution (LTE) standard have made interference (both inter-cell and intra-cell) the main limiting factor of spectrum efficiency in LTE-Advanced (LTE-A) system, and Interference Cancellation (IC) one possible solution that need to be addressed in LTE-A receivers. In this Master Thesis I focus my attention to Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) schemes and Space Frequency Block Code (SFBC) encoded schemes, and I will describe the corresponding receiver structures. As far as the interference is concerned, depending on the transmission conditions and the constraints imposed by the transmission standard, a receiver affected by interference may have different degrees of knowledge of the interference signals, and

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as a consequence different IC strategies will be possible. In general, the more complete the knowledge of the characteristics of the interfering signals, the more elaborate the IC strategies that can be implemented, the better the achievable performances. I am dealing with a multi-antenna scheme, with typically 2 antennas at the user terminal and 2 or 4 antennas at the base station. The availability of multiple transmission antennas will be typically used to improve throughput and capacity, transmitting multiple information streams that will be received overlapped at the receiving antennas. For this reason the considered receivers will have, as a first step, to be able to perform what we will denote as MIMO equalization, i.e. to separate the individual information streams, cancelling the mutual interference (this scenario exists also in absence of any intra-cell or inter-cell interference), and generating an initial estimate of the transmitted symbols, that will then be fed to the subsequent softdemapper. In presence of prohibitive transmission conditions, like at the cell boundary, the presence of multiple antennas may also be used to improve performances by introducing redundancy on the space and frequency (or time) dimensions, using a so called Space Frequency Block Code (SFBC). In presence of a SFBC, the inter-stream interference is typically null or minimal, and, as a consequence, non-iterative receivers are often employed.

1.9 Inter cell Interference modeling: DIP values Network interference statistics are computed using geometry factor G, defined as:

G

Iˆor1  I oc

Iˆor1 N BS

 Iˆ j 2

orj

 2

where Iˆorj is the average received power from the j-th strongest base station implies ( Iˆor1 is the serving cell average received power), 2 is the thermal noise power over the

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received bandwidth, and NBS is the total number of base stations considered including the serving cell. In addition to geometry, another measure, referred to as the Dominant Interferer Proportion (DIP) ratio, was agreed as a key parameter for defining the interference profiles. DIP was defined as the ratio of the power of a given interfering cell over the total other cell interference power. DIP of synchronized, and asynchronized interference, DIPi s , DIPi a is expressed as follows:

DIPi s 

Iˆors (i1)

DIPi a 

I oc

Iˆora i I oc

where the total inter-cell interference plus noise is given by:

Ns

Na

j 2

j 1

a I oc   Iˆors j   Iˆor j N and NBS = NS + Na is the total number of eNodeBs considered including the serving cell. DIP ratio statistics have been derived obtaining both unconditional DIP CDFs and conditional median DIP values, the latter conditioned on various geometry values. An interference profile was defined on the basis of averaging unconditional median DIP values submitted by the different companies. DIP values conditioned to the geometry values have also been submitted by the different companies. Starting from these values, the interference profiles that have been defined as part of the 3GPP feasibility study to assess link level performance of MMSE-IRC receivers, are showed in Table 5.

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Profile Based on conditional median values

Geometry 0 dB geometry -3 dB geometry

Synchronized NW DIP1 DIP2 -3.1 -5.4 -2.8

-7.3

Asynchronized NW DIP -3.1 -2.8

-2.5 dB geometry

Table 5: Conditional DIP values

1.10 Channel profiles Three channel profiles have been defined in 3GPP for UE and BS conformance testing. These profiles are also used in the link level simulations. The delay profiles are selected to be representative of low, medium and high delay spread environments. The frequency selectivity of the channel is proportional to the delay spread.

Table 6: 3GPP Channels

The link level simulations have been done for the Extended Pedestrian A (EPA) channel profile defined by the 3GPP.

Table 7: EPA Channel

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Table 8: EVA channel

The channel is assumed constant in each TTI (Transmission Time Interval of 1 ms). The correlation of the fading processes is set according to the three cases defined in 3GPP for the conformance tests (TS 36.101 and TS 36.104) of the equipments (low, medium and high correlation):

Table 9: Correlaton of fading processes

The  and  correlation values are, respectively, the correlation coefficient at the transmitter and the correlation coefficient at the receiver. The fading correlation is mainly determined by two factors: antenna characteristics (e.g. distance, polarization) and propagation environment (e.g. number and position of the scatterers, presence of LoS, angle of arrival and angle spread of the electromagnetic waves). The transmission schemes of LTE are differently affected by the correlation. In particular transmit diversity (TxD) appears rather robust whilst Spatial Multiplexing (SM) suffers a severe performance degradation as the correlation increases.

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CHAPTER 2: ACTIVITIES IN 3GPP ON ADVANCED RECEIVERS This chapter provides an overview of activities carried out in 3GPP on the topic of advanced receivers with interference cancellation/mitigation capabilities. The activities have been carried out mainly in the RAN4 group under the framework of four study/work items: IR, feICIC, CRS-IM, NAICS.

Study Item IR (Rel.11)

Description Interference Rejection

Focus Focus on receiver structures targeting spatial domain

interference

mitigation.

IRC

considered as a starting point. feICIC

Further enhanced ICIC

(Rel. 11)

Heterogeneous network scenarios where the interference is mainly caused by the CRS and Control Channels of the macro cell on the UEs connected to the small cells

CRS-IM

CRS Interference Mitigation

Analyses the cancellation of the interference

(Rel. 12)

(RP-130393)

caused by CRS in synchronized homogenous network scenarios

NAICS

Network Assisted

NAICS is similar to the approach of CRS-IM.

(Rel. 12)

Interference Cancellation

The major difference for NAICS is that the

Suppression

interference mitigation is now targeted not

(RP-130404)

only for interfering CRS but also for interfering PDSCH considering also possible improvements

deriving

from

“network

assistance”

27

2.1 feICIC In the feICIC case the focus is on the heterogeneous network scenarios where the interference is mainly caused by the CRS and Control Channels of the macro cell on the UEs connected to the small cells. The main IC candidate techniques for the implementation at the UE side include: 

Interference cancellation: signal regeneration and subtraction applicable to CRS, PBCH, PSS/SSS;



Puncturing; receiver that punctures REs of the wanted signal of the serving cell that are interfered by CRS REs received from one or more dominant interfering cells.

In the case of CRS interference cancellation, the procedure requires: the channel estimation from the interfering cells, regeneration of all the interfering cells CRS signals and subtraction. Puncturing is not applicable in several scenarios, e.g. with colliding CRS in non-MBSFN ABS because CRE REs of the serving cell cannot be punctured. For SFBC and SFBC-FSTD, two REs used should be punctured simultaneously when one of them was contaminated. In the other cases, it sets the LLR of bits of REs undergoing strong interference as zero. The results show the better and robust performance and versatility of the CRS cancelling receiver over the CRS puncturing receivers. Also for PDSCH demodulation the CRS cancelling receiver outperforms the CRS puncturing receivers. CRS puncturing receiver performs reasonably for single non colliding interferer, but for the other scenarios it does not perform well. The relatively poor performance of the CRS RE puncturing receiver for transmission mode 2 is because strong interference on one RE affects demodulation of the two symbols that are transmitted through the affected RE via SFBC encoding.

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2.2 CRS-IM Interference Mitigation (IM) of Cell-Specific Reference Signals (CRS) has been studied in the Rel-11 Work Item on feICIC, where interference from CRS is dominant assuming data RE muting in ABS subframes. A new study item has been started in 3GPP on CRS interference mitigation (IM) in homogeneous network deployments. The main objectives of this Study Item are: 

identify the partial traffic loading levels, other realistic system level parameters (e.g. traffic and interference models, time and frequency offset between cells) and performance metrics for studying the feasibility of CRS-IM in a synchronized homogenous network;



identify the baseline receiver which can be used for evaluating the gain of CRS IM in a synchronized homogenous network considering the reuse of CRS-IM receiver assumed for Release 11 feICIC and the reuse of MMSE-IRC receiver as the baseline receiver;



evaluate the system level and link level gains of CRS-IM with respect to the baseline MMSE-IRC receiver in a synchronized homogenous network deployment under the various loading levels identified (e.g. gains of CRS-IM from 1 and 2 aggressor cells CRS shall be evaluated and compared).

The objectives of the study item explicitly indicate that only Release 11 CRS assistance information should be assumed to be available. It can be seen that the CRS assistance information consists of a list of cells which are to be considered as candidates for CRS interference mitigation. Therefore, for each cell the information related to the CRS transmission (i.e. the physical cell ID, antenna port count and MBSFN configuration) are provided to the UE. A way forward on CRS-IM performance evaluation has been agreed. The first proposal is the reuse of CRS-IM receiver assumed for Release 11 feICIC to mitigate CRS interference of up to two cells. The second solution is the reuse of MMSE-IRC based receiver with interference covariance matrix estimation, here the receiver does not differentiate CRS or data interference when suppressing them.

29

The proposed receiver scheme for the execution of the link level simulations is the MMSE-IRC with/without CRS-IM. Concerning the CRS-IM part of the receiver, basically it consists in the regeneration and subtraction of the CRS signal from only the 1 st or both the 1st and 2nd strongest interfering cell. A possible receiver implementation is depicted below. A possible work item on this activity can follow.

Figure 1: MMSE-IRC with CRS-IM

2.3 NAICS NAICS is similar to the approach of CRS-IM. The major difference for NAICS is that the interference mitigation is now targeted not only for interfering CRS but also for interfering PDSCH. Objectives of this Study Item for RAN4 are: 

Identify reference IS/IC receivers with and without network assistance, and evaluate

their

performance/complexity

trade-off

and

implementation

feasibility; 

Analyze complexity and feasibility of basic receiver structures: based on linear MMSE-IRC, successive interference cancellation, and maximal likelihood detection are considered as a starting point for reference IS/IC receivers;



Based on the RAN1 scenarios agree on co-channel inter and intra-cell interference models for link-level simulation;



Evaluate the link-level gain over baseline Rel-11 linear MMSE-IRC receivers and Rel-11 non-linear receivers required for feICIC;

30



Indicate (to RAN1) assumptions on the network assistance information for the evaluated receivers under possible network coordination.

In the following part of this chapter, it will be shown a brief description of the main advanced receivers with interference cancellation/mitigation capabilities.

2.3.1 MMSE

The Rel-8/Rel-9 baseline receiver, MMSE receiver, ignores the fact that interfering signals are spatially colored signal. MMSE receivers treat interference as white noise. Along with the channel matrix H for the desired signal, only interference-plus-noise power  I2 n needs to be estimated by the MMSE receiver. The MMSE receiver can be expressed as: sˆ  H H HH H   I2n I  x 1

The complexity of the Rel-8/Rel-9 MMSE receiver is given by: the channel estimation and the matrix inversion.

2.3.2 MMSE-IRC

Using a proper spatially colored interference model, an MMSE interference rejection/combining receiver (MMSE-IRC) is expected to outperform the MMSE receiver in strong interference scenarios. In Rel-11 advanced receiver SID, RAN4 studied two approaches of the MMSE-IRC receiver realization. One approach is to use data REs to estimate overall signal-plusinterference-plus-noise covariance matrix Rs  I  n . In this case, The MMSE-IRC receiver has the form of: 1 sˆ  H H Rs I n  x

31

A second approach to realize the MMSE-IRC receiver is using the CRS or DMRS from the serving transmitter to estimate the channel matrix H of the desired signal, and using the differences of the received reference signal and the re-constructed reference signal with the estimated desired channel on the CRS or DMRS REs to estimate interference-plus-noise covariance matrix RI  n : sˆ  H H HH H  RI n  x 1

RI n 

 y

k ,lRS

k ,l



 Hˆ  xk ,l yk ,l  Hˆ  xk ,l



H

The RAN4 Rel-11 advanced receiver study shows that CRS or DMRS-based MMSE-IRC receiver outperforms data RE-based MMSE-IRC receiver. The above MMSE-IRC approaches can be applied to intra-cell interference suppression in MU-MIMO scenarios as well as to inter-cell interference suppression. For the Rel-12 NAICS SID, it would be a logical extension to study the possible performance gain of an MMSE-IRC receiver when the system assists UEs in performing better channel state information estimation, for both desired and interference signals. For example: 

In case of dominant interference cell exists e.g. in HetNet case, UE may explicitly estimate the channel of dominant interference cell. Thus, the covariance matrix RI  n of inter-cell interference could be calculated based on the channel estimation of dominant interference cell;



the accuracy of covariance matrix may also be improved by allowing averaging across multiple RBs, so the estimated received symbol is: 1

P   sˆ  H  HH H   H i H iH   I2 I  x i 1   H

32

2.3.3 E-MMSE-IRC

Enhanced MMSE-IRC is an MMSE-IRC that considers different interferer channel estimates and new interference knowledge from network signaling or trough blind techniques. E-MMSE-IRC could achieve significant throughput gain over MMSE-IRC receiver for both CRS-based and DMRS-based transmissions, given the assistance for UE to perform channel estimation on interference signals and knowing the number of layers. A disadvantage of this receiver is the performance gain since it is lower than others receivers (ML, SLIC, CWIC) when SINR is low. In contrast, there are several advantages using E-MMSE-IRC: 

limited complexity;



throughput gain is significant for high SINR;



other receivers require more additional assistance information, introducing more complexity and less robustness (e.g. ML and SLIC receiver need modulation of interference signals trough blind detection or DCI/RRC signaling).

In this context, the received signal is given by the superposition of one useful signal and N-1 interferer signals with different precoding matrix and different amplitudes:

N 1

yk 

 H i

i , k Pi xi , k

 nk

i 0

where, ρi is the amplitude of the signal transmitted from i-th cell, H i ,k is the channel matrix of the i-th cell on the k-th tone / resource element (RE), xi ,k is the symbol transmitted by the i-th cell in the k-th tone and

Pi is

the spatial precoding matrix used

by the i-th cell and K is the total number of observed tones. The number of cells in this case is N with one serving cell and N – 1 interferers.

The operations can be subdivided in core receiver processing (Channel estimation, CRS-IC, Detection, Decoding) and parameter extraction.

33

Core receiver processing includes symbol level detection of the desired cell’s signals and Turbo decoding. At the detector stage, Rel-11 MMSE-IRC receivers suppress the transmission from interfering cells before detecting the desired symbols. The nulling operation is performed by a front end MMSE filter, W, and Wy is the linear estimate of the transmitted symbols. For Rel-11 MMSE IRC receivers, W is constructed using: the channel estimation of serving cell and the total interference and noise estimated using CRS or DMRS. In contrast, even if E-MMSE-IRC receiver perform some similar functions, there are some key differences: 

the interfering signals are modelled using the estimated channels of the interferers, using CRS-IC;



for each signal the precoded matrix is needed and it is obtained using UE-side blind estimation or network signaling;



the interferer signal strength is extracted from network signaling or blind detection at the UE.

In the E-MMSE-IRC receiver the complexity is calculated considering the channel estimation complexity (CCE), the MMSE-IRC detection complexity (CFE), the FEC decoding complexity (CBE) for the core receiver and the parameter extraction. The complexity is N(CCE) + CFE + CBE , while the complexity of MMSE-IRC is CCE + CFE + CBE. The MMSE-IRC complexity is lower than the E-MMSE-IRC one, since the channel estimation is made without CRS-IC, while in E-MMSE-IRC the channel estimation with CRS-IC scales linearly with the number of interferers. To completion, CFE is the detection and interference cancellation complexity and CBE is the FEC decoding and turbo decoding.

2.3.4 Symbol level SIC

There are two types of successive interference cancellation (SIC) receivers: in the first one only symbol demodulation is involved in the SIC process and in the other one the FEC decoding is involved. It can be expected that, if the FEC decoding is involved in the SIC process, the performance will be improved compared to the one only using symbol demodulation. However, FEC decoding will require that all detailed coding information

34

and resource allocation information of the interference signal be available to the UE receiver, this requires a lot of system coordination and signalling overhead. The symbol level SIC receiver can be expressed as:

P 1   sˆ  H H HH H   n2 I   y   H i ~ si  i 1  

where ~ si is the quantized estimation of the interference signal. The symbol level SIC receiver needs to know the modulation order of the interference signal, power offset and (an estimate of) the channel matrix of the interferers as well. This requires system assistance in providing the interference modulation order and providing means to estimate the interference channel matrix. It is a general understanding that an SIC receiver can perform well in case that the interference signal is much stronger than the desired signal. Therefore, SIC receivers are well suited for some inter-cell interference scenarios (like range extension in HetNet, or intra-cell interference in some MU-MIMO cases). However, for inter-cell interference in homogeneous networks, the interference signal can generally be expected to be weaker or not much stronger than the desired signal. In this case, the performance advantage of SIC receiver over MMSE-IRC receiver may be questionable.

2.3.5 Bit level hard SIC

The receiver attempts to detect and decode one by one the interferers of interest, also in case of MU-MIMO and/or inter-cell interference cancellation. The decoded interferers are subtracted step-by-step to the overall signal, obtaining at the end the decoded useful signal. This receiver takes advantage of the CRC attached to each transport block before channel coding: if CRC check is successful, the block has been correctly decoded and the interfering signal can be reconstructed (minor the channel estimation errors). The

35

bit level hard SIC to be efficient needs to find at least one interferer that can be decoded without error (in order to subtract its interference from the useful signal). As a result, the situations where the interference power is much higher than the useful signal power and/or when the interference has a robust MCS are favourable situations where it brings significant gains. In case the interference and useful signal have similar powers, the Hard SIC imposes the constraint that the MCS used by the first interferer be more robust than the MCS used for the signal of interest, as it will need to be decoded under the interference of the latter.

2.3.6 Soft Turbo SIC

This receiver scheme performs the soft detection and the Turbo decoding of the UE signals which are repeatedly subtracted from the received signal. An important parameter of these receivers is the number of Turbo-code iterations for each detection and decoding step. In the case of Turbo-SIC receivers (also in the Hard SIC), the victim UE needs to know the following transmission parameters of the interferers: 

PRB assignments;



MCS;



RNTI;



DMRS sequence (if demodulation is based on DMRS);



Precoding information (if demodulation is based on CRS).

Up to Release 11, a UE cannot access any of these pieces of information related to another UE. Some mechanisms (e.g. a new signalling) then need to be introduced into the standard in order to provide this information to the victim UE.

2.3.7 ML receiver

This receiver treats the interference as un-known deterministic QAM signal. ML receivers can jointly estimate the desired signal and the interference signals. It is

36

generally understood that ML receivers provide an optimal performance compared to other receiver structure. SIC receivers can be viewed as sub-optimal realizations of ML receivers with less computational complexity but some performance degradation as compared to ML receivers. The ML receiver, like the SIC receiver, requires information of the modulation order and channel matrix of the interference signals. The ML receiver can be expressed as:

P

sˆ, sˆ1, sˆ2 ,..., sˆP   arg s,s ,min x  Hs   H i si s ,..,s  1

2

P

2

i 1

where,  is the set of constellation points of the used modulations. It can be expected that the ML receiver would provide good performance in both intracell and inter-cell interference mitigation. However, when the number of layers of the desired signal plus interference signals is large and when the modulation orders are high, the full ML receiver is very computationally complex and may not be feasible to implement. For example, a total of NS=4 layers with M=64 constellation size will require about MNs=644=16 million hypotheses. This is a very large number of possible combinations for a UE receiver to check them. Some performance-complexity tradeoff has to be taken for this high order modulation and large number of layers. Some well-known sub-optimal ML-type receivers, for example, sphere detectors, could be considered as candidate. ML receiver can be easily extended to joint detection on desired and interfering signals with limited Network Assistance (NA) information. For example if the channel knowledge and modulation order of the interference is available, interfering signals could be treated as desired signals and joint detected by ML receiver. There is no difference in ML receiver processing procedure. Assuming UE has the ML detection capability up to 2 layers receptions, when UE is in cell centre area (high SNR region), ML receiver can be used to detect the scheduled Rank 2 transmission. When UE move to cell edge area (low SNR region) and scheduled

37

with Rank 1 transmission, the dominant interfering signals could be jointly detected with limited additional NA information.

2.3.8 R-ML

This advanced receiver is a reduced complexity maximum likelihood receiver. It is based on the joint detection of useful and interference modulation symbols in accordance to the ML criterion (e.g. sphere decoding, QR-MLD, MLM, etc.). . Assuming that there is only one strong interferer, the received signal is: y  H1W1x1  H2 W2 x2  n

where,

is the useful channel matrix and

is the interferer channel matrix.

The ML can be expressed as:

H H    y  Hx  R1  y  Hx    y  Hx  R1  y  Hx   LLR(bi )  log   e  log e      x0 ( bi )   x1 ( bi ) 

where  k (bi ) denotes the set of transmit vectors with bi  k,  k  0 ,1 , and R is the noise covariance matrix. Using a Rel.11 MMSE-IRC receiver, the interferer term (the second one inserted in the received signal) can be used to calculate the interferer plus noise covariance matrix R in this way: R  H2 W2 W2H H 2H  E nn H  .

Finally, about the R-ML, LLRs can be also represented by max-log approximation:



LLR  bi   min  y  Hx x0 ( bi ) 

38



H







R1 y  Hx   min  y  Hx  x1( bi ) 



H





R 1 y  Hx  

where H  H1W1 H2 W2  , x   x1 x2  , R is the interferer plus noise covariance T

matrix, y is the received symbols 2x1 matrix and  k (bi ) denotes the set of transmit vectors with bi  k,  k  0 ,1 . R-ML is a reduced complexity version of ML, but it is more complex than the previous receiver schemes, even if it provide sub-optimal performance.

CHAPTER 3: MMSE-IRC RECEIVER IN REAL SCENARIOS

39

3.1 Overview This chapter provides a detailed vision of all the aspects that led to a low complexity implementation of the MMSE-IRC receiver in real scenarios. Moreover, performance results are showed and explained carefully, taking care to select relevant results that best show the behavior of the receiver. At the starting point, a brief analysis of the simulation platform is provided, focusing on some key blocks that are the core of a MIMO OFDMA link level simulator and that are useful to understand the MMSE-IRC implementation inside it. It is not possible to describe the overall architecture, since this simulator is composed by a very large number of blocks. The link level simulator is designed for the simulation of MIMOOFDM based wireless communication systems like LTE/LTE-A and represents an effective tool for the research and development of innovative physical layer system components. Simulations are obtained adding an independent block, the MMSE-IRC receiver, into the physical layer simulator, developed using CoCentric System Studio. MMSE-IRC block is intentionally implemented as a unique block, putting inside the corresponding functionalities, with the objective to have an interferer cancellation receiver that can be accessible and modifiable quickly. The designed MMSE-IRC is a unique simulation block implemented in C language. The main implementation constraint for our MMSE-IRC is the low complexity. Some techniques are used to reduce the computation burden: reducing the complexity of the matrix inversion, averaging and weighting coefficients computation. Interfering scenarios are selected, first of all, to test the MMSE-IRC code and after to visualize the performance in terms of Raw BER, BLER and Throughput in presence of single or double interfering cells selecting different spatial correlations and DIPs. Performance results are compared with the baseline receiver based on the Alamouti detection scheme [ref. paper di Alamouti], using ideal implementations developed in MATLAB and also with the more realistic simulator based on CoCentric System

40

Studio, showing how in the most cases MMSE-IRC provides a performance gain with respect to Alamouti. In the next chapter are also shown two other advanced receiver schemes that exploit the MMSE-IRC algorithm and are based on the symbol level interferer cancellation (SLIC) and bit level interferer cancellation (BLIC) concept, comparing them and showing interesting features in order to develop an adaptive receiver that is able to switch or adapt the interference cancellation algorithm as a function of the channel and interference conditions.

3.1 General Architecture of the MIMO OFDMA simulator The general architecture of an MIMO OFDMA based system like LTE/LTE-A is described by the block diagrams in the figure below. The models (blocks) described in this document are highlighted in green color. The corresponding input data files (data sets) that allow them to be configured according to a specific standard are also shown, with the relation they have with each block. The same data set can be used by different functional blocks; this was intended in order to reduce as much as possible the number of data sets. The design of the reconfigurable simulation models was done with the aim of having blocks as flexible as possible and the source code in the CoCentric simulation platform as simple as possible, based on the use of the provided input data files (data sets). In this sense, the complexity of the functions performed by these blocks is “implicit” in the data sets.

41

Figure 2: MIMO OFDMA system architecture

42

3.1.1 Data region Mapping/Demapping

The explanation will be concentrated in the mapping block. The demapping block basically performs the inverse operations, so just the most important differences will be pointed out.

Figure 3: Data regions mapping block

The basic resource unit is a structure constituted by logical subcarriers, with rectangular dimensions defined by the parameters BRU_freq_size and BRU_time_size, given in number of subcarriers and OFDMA symbols, respectively. The numbering of the subcarriers inside the BRU is shown in the Figure 3. Not necessarily all the subcarriers are filled with data, being possible to reserve some subcarriers for other purposes. For

43

example, in the LTE system, the BRU (in this case called Resource Elements) has some positions reserved for the pilot subcarriers. For this reason, to describe the internal structure of the BRU, it is defined a data set containing the indexes of the subcarriers that can be used for data transmission. By means of this data set, it is also determined the filling order of the structure, such as frequency-first, time-first or any other order, depending on the order the subcarriers indexes are listed.

Figure 4: Basic Resource Unit (BRU) structure

The generic resource grid (GRG) represents all the allocable resources within a time/frequency zone, being constituted by BRUs. It is important to remark that all BRUs within a GRG must have the same structure and filling order, as previously explained. The GRG has rectangular dimensions defined by the parameters GRG_freq_size and GRG_time_size, given in number of BRUs in frequency and in time, respectively. The numbering of the BRUs inside the GRG is shown in Figure 4. Regarding the implementation of the block, it is also useful to view the GRG in terms of subcarriers, with the correspondent dimensions and numbering shown in Figure 5.

44

Figure 5: Generic Resources Grid (GRG)

The BRUs inside the GRG are allocated by the specification of GDRs, as will be explained in the following. BRUs not allocated have all their subcarriers filled with zeroes.

Figure 6: Resources Grid

3.1.2 Subcarrier Mapping/Demapping

The purpose of the mapping block is to map the symbols of different types (data, pilots, other signals) that arrive organized in a logical manner (logically indexed), into theirs correspondent physical resources, given a mapping rule. A physical resource is defined as a physical subcarrier (i.e., a given position in the IFFT/FFT) at a given time (in terms of OFDMA symbol offset). The physical resources are positioned over a grid with

45

dimensions NFFT x Nsymb. NFFT is the IFFT/FFT size and Nsymb corresponds to the maximum between the pilots pattern repetition period and the extension in time where the mapping rule applies (i.e., the maximum offset in time between a logical index and its correspondent physical index). The numbering of the physical resources in the grid is done as shown in the Figure 6. In general, a logically indexed subcarrier at the input can be mapped into any physical resource in the grid.

Figure 7: Physical resources grid for subcarrier mapping

Besides NFFT and Nsymb, other additional parameters shall be provided to the model: 

Ndata: total number of data subcarriers in the grid, also equivalent to the rate of the data input port;



Npilot: total number of pilot subcarriers in the grid, also equivalent to the rate of the pilots input port;



Nnull: total number of null subcarriers in the grid, including guard, DC, and other null subcarriers (when using MIMO, for example);



Nother: total number of subcarriers in the grid dedicated to other signals, such as synchronization signals or control channels in LTE.

3.1.3 IFFT/FFT calculation and cyclic prefix insertion/removal

In transmission, the Generic IFFT & Cyclic Prefix Insertion model, as its name already states, performs the IFFT calculation of the spectrum defined by the input subcarriers.

46

The FFT size depends on the channel bandwidth being considered. In sequence, the cyclic prefix is inserted taking a copy of a given number of samples (Cyclic Prefix length) at the end of the useful OFDM symbol (just after the IFFT calculation) and inserting them before it.

Figure 8: IFFT and Cyclic Prefix insertion block

In reception, considering that the system is ideally synchronized and that time windowing is not performed over the OFDMA symbol, the Generic FFT & Cyclic Prefix Removal model performs the inverse operations done in transmission. First, it removes the beginning of the OFDMA symbol corresponding to the cyclic prefix. Finally, it performs the FFT calculation of the useful OFDM symbol.

Figure 9: FFT and Cyclic Prefix removal block

3.1.4 Pilots compensation

The purpose of the generic pilots compensation model is to compensate the received pilots to remove the power boost and the specific pilot sequence, based on the knowledge of the transmitted (reference) pilot sequence. After doing that, the value of each pilot symbol represents an estimate of the channel seen by the pilot subcarrier itself.

47

The block operates over the same grid of the subcarriers mapping (see the Figure 3), therefore using the same parameters and data sets (just the necessary ones) to know the location of the pilots subcarriers.

3.1.5 Channel Estimation

The purpose of the generic channel estimation block is to estimate the channel coefficients correspondent to the received data symbols. These estimated values are used in the subsequent blocks of the chain to perform some data processing over the data symbols. The channel estimation is based on the received pilot subcarriers that should be already compensated prior to enter in the block to remove power boost and the specific pilot sequence. The estimation of the channel coefficients is performed using linear interpolation, linear extrapolation and the hold operation (which is indeed a particular case of linear extrapolation).

Figure 10: Channel estimation block

48

First of all, it is defined an interpolation grid, with frequency length equal to the IFFT/FFT size and time duration Nsymb, equal to the periodicity of the interpolation rules. The parameter Nsymb is not necessarily the same defined in the generic subcarriers mapping model. The contents of the grid are the channel estimates of the correspondent subcarriers. An interpolation rule is a linear operation involving 3 points in the grid, where the channel estimate of a “destination” subcarrier is obtained from the known estimates of the two “source” subcarriers, considering the 3 points are positioned over a straight line. Therefore, it is possible to calculate the channel estimate related to a given subcarrier (destination), providing the channel estimates of the two “source” subcarriers and the proper weights. The weights are a function of the subcarriers indexes and can be precalculated for every defined interpolation rule. This information is then provided to the block by the data sets shown in the Figure 9, which contain all the interpolation rules (meaning first operand indexes and weights, second operand indexes and weights, and destination indexes) to be performed in the grid. The channel estimation is done in steps, starting from the step 0, where at the beginning just the received pilot symbols are known. The pilots are assumed to be already compensated to remove sequence and power boost. A step includes all the interpolation rules that can be defined using all channel estimates known at the end of the previous step. New steps should be included until all the required channel estimates are obtained.

49

Figure 11: Interpolation rules

3.1.6 Space-time Encoder/Decoder

The purpose of using the technique of space-time coding and decoding is to support Multiple Input Multiple Output (MIMO) antenna systems in order to also exploit the spatial dimension. As consequence, an improvement in the capacity (throughput) or in the reliability (coverage range) of a wireless communication system can be obtained.

Figure 12: Space-time encoder (MIMO 2x2)

Two possible transmission modes of MIMO systems, the spatial multiplexing (SM) and the space-frequency block coding (SFBC). The spatial multiplexing is based on the transmission of different data streams across the different transmitting antennas with

50

the goal of increasing the overall throughput, while the space-frequency coding techniques transmit redundant data streams over the multiple antennas for increasing the link reliability and extending the coverage range.

3.2 Modeling MMSE-IRC Before implementing a simulation model in C code and creating a block in CoCentric with the associated ports, it is very important to provide a mathematical explanation of the MMSE-IRC receiver. It is useful to realize how complexity can be managed and the importance of adapting our mathematical model to a real implementation.

3.2.1 MMSE-IRC for SFBC transmit diversity

MMSE-IRC receiver is based on the MMSE criteria, but the interference rejection combining require highly accurate channel estimation and covariance matrix estimation that includes inter cell interference. In this scheme, the covariance matrix is used in a modified version that provides lower complexity, avoiding the 4x4 matrix inversion (MIMO 2x2 SFBC), leading to a trivial 2x2 matrix inversion. Let’s consider a scenario where there is an UE and some interfering cells, the received signal by UE antennas is:

where, considering a MIMO 2x2 SFBC transmit diversity, Y is the 4x1 matrix containing the received signals by UE,

is the channel response in frequency domain 4x2 matrix

between the serving cell and the UE,

is the transmitted useful signals 2x1 matrix, I is

the total interference received 4x1 matrix, N is the 4x1 noise matrix. In a real context, the UE receives the summation of many signals plus noise composed by the useful signal (from the serving cell) and interferences (from interfering cells):

51

∑

where: UE,

is the 4x2 channel frequency response matrix between the c-th cell and the is the 2x1 transmitted signal matrix and

serving cell and the other

is the 4x1 noise matrix,

is the

cells are interferer cells.

This matrix formulation can be extended, considering the SFBC transmit diversity in a 2x2 MIMO fashion. When SFBC is enabled, considering two antennas in transmission and two in reception (2x2 MIMO), the transmitted symbols are Alamouti coded exploiting two adjacent subcarriers and the two antennas, sending for each time instant four symbols mapped in subcarrier k (even) and k+1 (odd). So, the previous matrix equation can be expanded as:

[

= [

] [

]

] [

]

The UE, implementing the MMSE-IRC, estimates the useful signal, in particular the 2x1 matrix composed by two estimated serving cell symbols transmitted by the two antennas:

̂

Where, ̂

[

̂ ̂

]

is the estimated received symbol at the antenna 1 port and ̂

inverted and conjugated estimated received symbol at the antenna 2 port.

The ̂ vector is obtained applying this relation: ̂

52

is the sign

where,

is the 2x4 receiver weight matrix calculated considering both

code and spatial domains. This matrix is generated considering the estimated channel matrix of the useful signal and the interferers plus noise covariance matrix. can be calculated using one of the two following methods:

1th Method

where,

is the estimated useful channel matrix and

is the estimated

interferences plus noise covariance matrix. From simulation tests, using this method it was noted that the estimate received symbol have to be normalized through the following normalizing symbol the matrix element

function, considering for the antenna 1 port received and for the antenna 2 port received symbol

:

so, ̂ ̂

and

̂

̂

53

2nd Method

In this case, the normalization process is not necessary as the estimated symbols are already normalized (i.e. the amplitude is correctly scaled for the subsequent symbol to bit demapping operation). Both methods provide the same result; the first one is a reduced complexity method because the matrix inversion is done considering only one operand (

with

eventually a splitting zero-adding operation. The second method can be used when does not contain zero values. The covariance matrix

include the interferences and noise components, in

general, it is defined as:

where I is the total interference received by the UE. It can be expressed neglecting the received useful signal (

:

∑ At the UE receiver, the total interference received by the UE on the subcarriers k and k+1 can be expressed as:

[

54

]

where

is the total received interference at antenna 1 port for the k-subcarrier,

is the total received interference at antenna 2 port for the (k+1)-subcarrier. Expanding

: |

| |

| |

| |

[

|

]

the main diagonal represents the received interferer powers at antenna port 1 and port 2, the other expectation terms are the correlation functions between the interfering signals at antenna port 1 and port 2, the null terms are the auto-correlation function of the interference calculated over two adjacent subcarriers that can be assumed equal to zero. Besides, also the terms

,

],

,

are statistically zero and thus it is possible to avoid their estimation, so leading to the final matrix: |

| |

| |

| |

[

|

]

Obviously, the complexity is much lower in terms of matrix inversion, but the price to pay is a very small performance degradation. For simplicity, expectations can be expressed as:

[

]

55

The last matrix can be rewritten as:

[

]

Considering that the interference characteristic changes slowly in time and frequency domains, it possible to write:

So, [

]

This is an important approximation, because to calculate knowing

and its transpose. Now, calculating

, it is only needful is a simpler operation,

moreover in presence of zero matrix elements, the

4x4 matrix can viewed as the

composition of two 2x2 matrix. In this case:

The matrix inversion of

[

]

[

]

is simply:

[

]

In the following, the relative sub-carrier indexes k and k+1 are omitted, considering:

56

Calculating the inverse matrix, it is possible to show the low complexity procedures:

[

]

[

]

and,

where

.

Using, the first method for MMSE-IRC:

[

̂ ̂

]

[

][

] [

Imposing that

]

, the estimated received symbol at the UE antenna 1 port

is:

̂ The coefficients a, b, c and d are:

57

Moreover, the estimate received symbol at the UE antenna 2 port (

:

̂

It is important to note that, the above values are complex symbols, so they must be divided in real parts and imaginary parts, doing complex operations. Extending above formulas, considering complex values, there are not important simplifications, so it is not convenient splitting real part and imaginary part, but sometimes it is the only way to proceed. Fortunately, CoCentric is able to treat complex values and operations using a specific complex data format. So, in the following all the implemented variables are treated as complex values.

3.2.2 Building the MMSE-IRC receiver

As already mentioned, the description of the main employed simulator blocks and the math procedure, is fundamental to build an independent block that accurately represents the MMSE-IRC receiver. The math description appears very simple, because the approximations and calculus are simple to understand and to realize on paper, but a real realization into a real LTE/LTE-A simulator or in a real LTE/LTE-A chipset has to be done opportunely, solving several implementation problems, engineering some calculus to respect the LTE/LTE-A standard and the simulator software context. Choosing to implement all the operations inside the MMSE-IRC receiver (e.g. covariance matrix estimation), the input and output ports are:

58

INPUT PORT

OUTPUT PORT

float symbols_in1_I;

float symbols_out_I;

float symbols_in1_Q;

float symbols_out_Q;

float symbols_in2_I;

float reliability;

float symbols_in2_Q; float reference_pilots_in1_I; float reference_pilots_in1_Q; float reference_pilots_in2_I; float reference_pilots_in2_Q; float h11_I; float h11_Q; float h12_I; float h12_Q; float h21_I; float h21_Q; float h22_I; float h22_Q; Table 10: Input/Output MMSE-IRC block data

Moreover some data set files have to be loaded to know the position of useful data (e.g. pilot subcarrier indexes). In order to estimate the covariance matrix, it is very important know exactly the CRS positions, so mapping CRS indexes in a data file it is possible to extract the interested data from a PRB. So, to estimate the covariance matrix ̂

, it is considered that the estimation of

the total received interferer is obtained subtracting the estimated received signal to the total received signal, for each OFDM symbol and subcarrier that belong to the CRS resource elements:

̂

where, ̂

∑

̂

̂

[̂

is the 2x2 estimated covariance matrix,

the serving cell at k-th subcarrier and l-th OFDM symbol, ̂

̂

]

is the CRS sequence of is the received symbol

59

by UE at k-th subcarrier and l-th OFDM symbol, ̂ is the estimate channel response of the serving cell,

is the number of averaged samples.

The average operation can be done considering a sliding windows to select the number of PRB and so the number of CRS inside the sliding windows. The parameter K establishes the size of the sliding window: for example, if K=1 the estimated covariance matrix, the interferer power at the UE antenna 1 and the interferer power at the UE antenna 2 are calculated for each PRB. The following C-code shows the implementation of the sliding window and the estimations of covariance matrix and interferer powers at the UE antenna 1 and 2: for(i = 0; i < Nsymb*NFFT; i++) { for(i = 1; i <=Nprb-K+1; i++) { idx_low = G_LEFT + (i-1)*12; /* First index within SW */ idx_high = G_LEFT + (i-1)*12 + Nrew_f-1; /* Last index */ if(idx_low >= DC_POSITION) idx_low++; /* Skip DC */ if(idx_high >= DC_POSITION) idx_high++; /* Vector with the pilot indexes within the sliding window */ m = 0; x = 0; for(n=0; n= idx_low && j <= idx_high) { p_idx_w1[m] = n; m++; } if(f >= idx_low && f <= idx_high) { p_idx_w2[x] = n; x++; } } for(n=0; { l x j f

n
p_idx_w1[n]; p_idx_w2[n]; pilot_subcarriers_indexes_1[l]; pilot_subcarriers_indexes_2[x];

/* Power of the interference on antenna 1 */ z11 = sig1[j] - (c11[j] * p1[l]); z12 = sig1[f] - (c12[f] * p2[x]); pz_1 += z11*conj(z11) + z12*conj(z12);

60

/* Power of the interference on antenna 2 */ z21 = sig2[j] - (c21[j] * p1[l]); z22 = sig2[f] - (c22[f] * p2[x]); pz_2 += z21*conj(z21) + z22*conj(z22); /* Correlation of the interference between antenna 1 and 2 */ corrz += (z21)*conj(z11) + (z22)*conj(z12); // r21 } r11[(i-1)+(K-1)/2] = pz_1/(2*Npw); r22[(i-1)+(K-1)/2] = pz_2/(2*Npw); r21[(i-1)+(K-1)/2] = corrz/(2*Npw); } }

3.3 Performance of MMSE-IRC receiver Performance analysis are performed comparing throughput and Raw BER results between the MMSE-IRC and the Alamouti detection scheme. Several schematics are created, considering Gaussian interference and real interference. The selected simulation scenario includes the following parameters: number of interferences, DIP values, IMCS, useful and interferers signal correlations, Angle of Arrivals (AoA), colliding or not colliding pilots, modulation, channel types (EPA,EVA) etc.

3.3.1 Interfering signal modeled as Gaussian noise

As starting point, to test the MMSE-IRC block, it is possible to consider a Gaussian Interference, some useful simulation parameters are: 

Extended Pedestrian Channel A (v = 3 km/h);



DL control channel mapping (n=2);



IMCS 7;



QPSK modulation;



Allocated PRB (50 for a 10 MHz bandwidth);



TBS=6200 bits;



2D-MMSE channel estimation;



Medium Correlation useful signal (

);

61



Interfering signal modelled as Gaussian noise (correlation=0.7);



Pilot boost = +3dB;

The simulated results are shown in the following table:

ALAMOUTI SINR [dB] -12

MMSE-IRC

THR [Kbit/s] 3

THR [Kbit/s] Gain [%] 5,4304 74,99919

-8 147,397 259,884 76,31566 -4 1582,582 1975,125 24,80396 0 3673,298 4321,846 17,65574 4 5765,565 5964,164 3,444571 8 6187,587 6186,036 -0,02507 12 6200 6200 0 Table 11: MMSE-IRC and Alamouti THR

MMSE-IRC, in presence of a Gaussian interferer, provides a significant gain in the low SINR region, that is when the interference level is very high as happen when the UE is at cell edge. The following figure shows graphically the MMSE-IRC gain respect to Alamouti receiver, the relevant gains happen in the SINR region from -7dB to +3dB:

MMSE-IRC MIMO 2x2 performance - Med. correlation 7 000 Throughput [kbit/s

6 000 5 000 4 000 3 000

ALAMOUTI

2 000

MMSE-IRC

1 000 0 ALAMOUTI

-12 3

-8

-4

0

4

8

12

147.397 1582.58 3673.3 5765.57 6187.59 6200

MMSE-IRC 5.4304 259.884 1975.13 4321.85 5964.16 6186.04 6200 SNR [dB] Figure 13: THR comparison (Gaussian Interference)

62

A script matlab is created to verify if the C code implementation in Cocentric is correct, the throughput results show the same trend and match the expectations. For completeness, the next figure shows the Raw BER obtained through a MATLAB  script that represent an ideal implementation of the realistic MMSE-IRC algorithm implemented in Concentric, comparing the ideal channel and interference estimation with the real one:

Figure 14: Raw BER comparison (Gaussian Interference)

3.3.2 Real Interference signal – Colliding pilot case

After having done the preliminary simulations, the real simulator is extended adding two transmission chains to insert real interference cells. The simulator schematic is showed in the next figure. In the case of one interferer signal, DIP1=-0.1dB and DIP2=0.001, so the first interferer power is very high and the second one is about zero. Moreover, several simulations are performed changing the angle of arrival (AoA) of the interferer, from 0 degree (worst case) to 45 degree (best case). Fixing the AoA of the useful signal at 0 degree, if the AoA of interferer is 0 degree, the interferer signal is perfectly aligned to the useful signal so the spatial filtering of the interference is very difficult. In contrast, if the AoA of the interferer is 45 degree and AoA of the useful

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signal is 0 degree, the spatial filtering can provide some interference rejection. In general, it is very important knowing performances in different network scenarios (i.e. physical layer simulations for different IMCS), also to optimize the network planning. Adjacent cells can use the same pilot pattern in their transmitted frames or a planning can be done selecting different pilot positions for each cell.

64

Figure 15: CoCentric MMSE-IRC Double Interferer schematic

Moreover, several simulations are performed changing the angle of arrival (AoA) of the interferer, from 0 degree (worst case) to 45 degree (best case). Fixing the AoA of the

65

useful signal at 0 degree, if the AoA of interferer is 0 degree, the interferer signal is perfectly aligned to the useful signal so the spatial filtering of the interference is very difficult. In contrast, if the AoA of the interferer is 45 degree and AoA of the useful signal is 0 degree, the spatial filtering can provide some interference rejection. In general, it is very important knowing performances in different network scenarios (i.e. physical layer simulations for different IMCS), also to optimize the network planning. Adjacent cells can use the same pilot pattern in their transmitted frames or a planning can be done selecting different pilot positions for each cell. Obviously, the UEs, subjected to a reception of interferer signals, in the first case receives interfered pilots (colliding pilots case), in the second case receives non interfered pilots (no colliding pilots case). Both the cases are simulated, showing interesting results. Let’s start with the colliding single interferer case, the simulation parameters are: 

Extended Pedestrian Channel A (v = 3 km/h);



DL control channel mapping (n=2);



IMCS 7;



QPSK modulation;



Allocated PRB (50 for a 10 MHz bandwidth);



TBS=6200 bits;



2D-MMSE channel estimation;



Medium Correlation useful signal (



Real Interfering signal, specific correlation matrix as a function of the

);

considered AoA

66



DIP1=-0.1dB and DIP2=-0.001;



AoA1=0, 10, 30, 45;



Angle spread = 10;



Array elements = 2;



Normalized array element distance =



Pilot boost = +3dB;



Colliding pilots between useful and interfering signals.

The comprehensive simulated throughput figure is:

7 000

LTE MIMO 2x2 - Alamouti vs. MMSE-IRC receiver IMCS 7 - EPA channel - Single interfering cell - Colliding pilots AoA useful = 0° - AoA interf = variabile - AS = 10°

Alamouti (AoA interf=45) Alamouti (AoA interf=30)

Throughput [kbit/s]

6 000

Alamouti (AoA interf=10)

5 000 4 000

Alamouti (AoA interf=0)

3 000

MMSE-IRC (AoA interf=45)

2 000

MMSE-IRC (AoA interf=30)

1 000

MMSE-IRC (AoA interf=10)

0 -10 -8 -6 -4 -2

0

2

4

6

8

10 12 14

MMSE-IRC (AoA interf=0)

SINR [dB] Figure 16: THR comparison (Real Interference) - Colliding pilots

The above figure shows that the MMSE-IRC receiver provides a gain of 6-7 dB at about 3.5 Mbit/s when the interferer AoA is 45 degree. The gain decreases when the direction of the interference is close to the useful signal one and becomes negative when the interferer is perfectly aligned to the useful signal.

3.3.3 Real Interference signal – No Colliding pilot case

The simulation scenario is about the same, changing only the pilot pattern for the interferer signals, modifying appropriately the CRS position inside the resource blocks (i.e. a pilot pattern shift is applied to the interfering signal). Since pilot are not colliding, channel estimation is more accurate than in the colliding case, so a better estimation of interfering signal permits to estimate the interferer covariance matrix with higher accuracy. This facts are reflected by the simulation results, in fact, comparing the colliding case with the no colliding case, when AoA of the interferer signal is 45 degree, the gain of no colliding case is about 6 dB at 5 Mbit/s. For different interferer AoA the gain and throughput are always consistent.

67

In the colliding case, if the interfering AoA is between 0 and 10 degree, the Alamouti receiver seems to provide better performances than MMSE-IRC. Conversely, about the no colliding CRS case, the MMSE-IRC receiver always provides a gain respect to Alamouti receiver, as shown in the figure below:

Throughput [kbit/s]

LTE MIMO 2x2 - Alamouti vs. MMSE-IRC receiver IMCS 7 - EPA channel - Single interfering cell - Non Colliding pilots AoA useful = 0° - AoA interf = variabile - AS = 10°

Alamouti (AoA interf=45)

7 000

Alamouti (AoA interf=30)

6 000

Alamouti (AoA interf=10)

5 000

Alamouti (AoA interf=0)

4 000 3 000

MMSE-IRC (AoA interf=45)

2 000

MMSE-IRC (AoA interf=30)

1 000

MMSE-IRC (AoA interf=10)

0 -10 -8 -6 -4 -2

0 2 4 SINR [dB]

6

8

10 12 14

MMSE-IRC (AoA interf=0)

Figure 17: THR comparison (Real Interference) - No Colliding pilots

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CHAPTER 4: SUCCESSIVE INTERFERENCE CANCELLATION RECEIVERS 4.1 Introduction and comparison After having implemented and analyzed the performance of the MMSE-IRC receiver, it is very interesting to extend the research to a class of successive interference cancellation receivers operating at symbol level (SLIC) and at bit level (BLIC). These receivers are expected to overcome the MMSE-IRC in terms of performance, even if the complexity due to presence of N detecting stages is higher than MMSE-IRC complexity, where N-1 is the number of cancelled interfering signals. Symbol Level Interferer Cancellation receiver (SLIC) and Bit Level Interferer Cancellation receiver (BLIC) use both non-linear IC (Interferer Cancellation) techniques to detect/decoding serving/interfering cells. The main difference between SLIC and BLIC is that the UE can either use detected symbols to cancel out the interference (SLIC) or it can perform the full decoding of the signals from the i-th interfering cell. BLIC receivers compared to SLIC receivers, require the exact encoding scheme information and exact rate matching information (including RB allocation) of each interferer. Such information may not be easily available unless provided by network signaling, which may incur significant signaling overhead as well as scheduling constraints. Moreover, BLIC introduces additional complexity to perform bit-level decoding and re-encoding, also introducing larger latencies in the receiver chain. Compared to the baseline MMSE-IRC receivers, the IC receiver achieves better performance due to its ability to cancel interferers and demodulate potentially cleaner signals when decoding the serving cell. The class of IC receivers is especially well suited to handle strong interferers which can be accurately demodulated and regenerated avoiding the error propagation effect. The performance gain is a function of the amount of interference received by the the UE and thus depends on the actual value of the Signal to Interference plus Noise Ratio (SINR). For example, in order to be

69

able to detect/decode a strong interferer, the UE needs to either know or estimate some of its transmission parameters. The error propagation that may occur in IC receivers due to errors in each stage can contribute to the performance loss compared to ML receivers. On the other hand, the complexity of IC receiver is lower than ML. The SLIC and BLIC complexities are mainly calculated as:

where,    

is the channel estimation complexity; is the detection complexity; is the interferer cancellation complexity; is the channel decoding complexity (i.e. Turbo decoding).

About SLIC, channel estimation, detection and interferer cancellation are performed N times (N is the number of transmitting cells including serving plus N-1 interfering cells) so the complexity scales linearly; the decoding procedure is perfomed only once for the serving cell. In contrast, even the BLIC decoding is made N times, typically N turbo decoding operations, so the BLIC complexity is much higher. Moreover, interference parameter estimation is significantly more complex for BLIC, since the encoding scheme and entire RB allocation information are needed for all the interferers. Finally, SLIC receivers are a good choice for interference cancellation because their interference cancellation capability gives a reasonable complexity that scales linearly with the number of interferers. In contrast, the BLIC receiver complexity is higher than SLIC one, but BLIC can give higher performance due to the turbo decoder that operates on each interferer. In the next paragraphs will be presented the description of SLIC and BLIC receivers, showing implementation details and performance simulation results. The simulation

70

results are presented considering single and double interferers for different spatial characteristics expressed in terms of Angle of Arrivals (AoAs). The results show the SLIC gain respect to MMSE-IRC and the SINR window in which it is possible apply the dynamic on/off switching of the interference cancellation functionality. BLIC simulation results are compared to the others one, showing that in terms of performance (Throughput and Raw BER), BLIC receiver is the optimal choice to reach the highest throughput when the UE is at the cell edge.

4.2 SLIC implementation SLIC receiver is implemented using a MATLAB simulator for LTE system that consists of several parts that correspond to the LTE transmission chain. This simulator is simplified compared to the one developed in CoCocentric, but includes: Turbo encoder/decoder, multi-antenna transmission, Alamouti encoder/decorder etc. A new block implementing the SLIC receiver is created and all the operations are done inside. In this implementation, only two interferers are considered. The next figure shows the block diagram of the SLIC receiver, all the main operations are showed and in the following it is provided a mathematical description with the relevant MATLAB instructions. The scope of the SLIC receiver is to subtract step-by-step the regenerated interfering signals. When a UE receives two interfering signals, two estimation steps are made to estimate the useful signal.

The SLIC receiver, viewed as a black box, can be

represented in MATLAB as a function that returns the SFBC decoded symbols r and their reliability: [r,reliability] = slic(y,y1,y2,y3,Heff1,Heff2,Heff3,Nr,Nlay,Cmod);

The input values are:    

y is the total received signal (useful plus interferences and AWGN noise)< y1 is the useful signal; y2 is the first interfering signal; y3 in the second interfering signal;

71

     

72

Heff1 is the estimated channel matrix of the serving cell; Heff2 is the estimated channel matrix of the first interfering cell; Heff3 is the estimated channel matrix of the second interfering cell; Nr is the number of UE receive antennas (i.e. MIMO 2x2); Nlay is the number of layers; Cmod is a vector of size M (cardinality of modulation) that contains the constellation.

At the output of the first step an estimation of the first interferer is provided (the

Figure 18: SLIC receiver

73

strongest). As shown in the Figure, for the first dominant interferer estimation, the MMSE-IRC receiver receives as input the estimated channel matrix of the second interfering signal Heff2, the total received signal y and the estimated covariance matrix considering the residual interference. The covariance matrix is calculated considering the low complexity formulation treated in the chapter 3, and it is calculated considering the residual interferer signal viewed excluding the first dominant interferer: y_calc1=y-y2; P1=1/length(y)*sum(abs(y_calc1(1,:)).^2); P2=1/length(y)*sum(abs(y_calc1(2,:)).^2); r12=1/length(y)*sum(y_calc1(1,:).*conj(y_calc1(2,:))); r21=conj(r12); Ri=[P1 r12; r21 P2]; Ri is a 2x2 matrix that is provided as input to the MMSE-IRC: [r2,reliability] = mmse_irc(y,Heff2,Nr,1,Ri); The MMSE-IRC receiver at the first step acts on the total received signal by UE (denoted as y), knowing the estimated channel matrix of the first interfering signal and the covariance matrix of the residual interference signal. The symbols r2 represent a rough estimation of the first interferer symbols. To improve the estimation, the proposed SLIC receiver regenerates the 1th interferer making the following operations:

Soft Demodulation (Max log-MAP algorithm) w = soft_demodulator(r2,Nr,Nlay,Cmod,reliability); Hard decision w_hard = zeros(size(w)); idx = w>0;

74

w_hard(idx)=0; idx = w<0; w_hard(idx)=1; Modulation (x-QAM) r2 = modulation(1,Cmod,w_hard); Alamouti encoding s_2 = alamouti_enc(r2);

So, knowing the regenerated Alamouti encoded symbols s2 and the estimated channel matrix of the first interferer, it is possible to calculate the regenerated first interferer as: y2_est=Heff2*s_2; This signal, subtracted to the total received signal y, permits to calculate the received signal without the presence of the strongest interferer. y_2=y_1-y2_est; This is the input of the second step, in which the second interferer is estimated. At the second step, MMSE-IRC acts on the second interfering signal knowing its channel matrix and the covariance matrix of the residual interferer: y_calc2=y-y3-y2_est; P1=1/length(y)*sum(abs(y_calc2(1,:)).^2); P2=1/length(y)*sum(abs(y_calc2(2,:)).^2); r12=1/length(y)*sum(y_calc2(1,:).*conj(y_calc2(2,:))); r21=conj(r12); Ri=[P1 r12; r21 P2]; [r3,reliability] = mmse_irc(y_2,Heff3,Nr,1,Ri);

75

After MMSE-IRC, the same procedure is made: soft demodulation, hard decision, modulation and alamouti encoding. So, the second interference can be estimated and regenerated: y3_est=Heff3*s_3;

Having estimated, regenerated and subtracted the first and the second interfering signals, the remaining part is composed by the useful signal and thermal noise. So, the useful signal plus noise, is: y_3=y_2-y3_est; Applying the MMSE-IRC to this signal, knowing the estimated channel matrix of the useful signal (Heff1) and the covariance matrix calculated only for the noise, it possible to obtain the alamouti decoded useful symbols: y_calc3=y-y1-y2_est-y3_est; y_3=y_2-y3_est; P1=1/length(y)*sum(abs(y_calc3(1,:)).^2); P2=1/length(y)*sum(abs(y_calc3(2,:)).^2); r12=1/length(y)*sum(y_calc3(1,:).*conj(y_calc3(2,:))); r21=conj(r12); Ri=[P1 r12; r21 P2]; [r4,reliability] = mmse_irc(y_3,Heff1,Nr,1,Ri); The useful symbols r4 is then soft demodulated and turbo decoded. Finally, the performance measurements can be done, they are showed comparing Throughput and Raw BER of BLIC, SLIC, MMSE-IRC and Alamouti receivers.

4.3 BLIC implementation Bit level interference cancellation receiver is implemented in similar way, but for each step the interfering signal is re-encoded using a turbo encoder.

76

The method used for the SLIC receiver applied to MMSE-IRC are the same, but instead of applying the hard decision after the soft demodulation, in the BLIC receiver the soft demodulated bits are turbo decoded: w = soft_demodulator(r3,Nr,Nlay,Cmod,reliability); z = layer_demapping(w,Ncw,Nlay,M); [soft_bits_1 soft_bits_2] = channel_decoding(Ncw,Lp,f1,f2,n_iter,interf_power,z); After the turbo decoder, a hard decision is done: w_hard = zeros(size(soft_bits_2)); idx = soft_bits_2>0; w_hard(idx)=1; idx = soft_bits_2<0; w_hard(idx)=0;

So, the re-generated i-th interfering transmitted symbol is turbo encoded, modulated and SFBC encoded. Multiplying the channel matrix of the i-th interfering signal it is possible to obtain the regenerated i-th interferer signal. For example, considering the first step or in other word, the regeneration of the strongest interferer signal: enc_bits_interf_2, data_bits_interf_2] = channel_encoding_data(Ncw,Lp,rate,f1,f2,w_hard); r3 = modulation(1,Cmod,enc_bits_interf_2); s_3 = alamouti_enc(r3); y3_est=Heff3*s_3; Having regenerated the first interference signal correctly, the remaining parts are the same, paying attention to apply the MMSE-IRC correctly as mentioned before. In the next page is showed in detail the block diagram of the proposed BLIC receiver.

77

Figure 19: BLIC receiver

78

4.4 SLIC and BLIC performance analysis The SLIC and BLIC simulations are done, considering a single interferer and double interferer cases. Moreover, for each case, simulation results are provided varying the AoAs, considering:  

Single Interferer case: AoA=0°, 10°, 30°, 45°; Double Interferer case: AoA1=10° and AoA2=10°, AoA1=10° and AoA2=30°, AoA1=30° and AoA2=10°, AoA1=30° and AoA2=30°.

The simulation parameters are the following:         

Number of transmitting antennas = 2; Number of receiving antennas = 2; Number of codeword = 1; Number of layers = 1; Number of transmitted packets = 20000; Symbol period = 0.0714; Angle spread = 10°; Normalized array element distance = 0.5; Medium correlation

The single interferer case simulation results are showed in the figures 3 and 4. In this case a single interferer is consider, this means that DIP1=-0.1 and DIP2=-0.001. Considering negative SINR, BLIC and SLIC receivers shows a similar throughput, also the Throughput of MMSE-IRC when AoA1=30 and AoA1=45 is comparable to the BLIC and SLIC one. When SINR is about 2dB, the SLIC (AoA1=10 and AoA1=0) and BLIC AoA1=0 throughput start to decrease and starting from 3dB MMSE-IRC provides higher throughput. From this simulations seems that SLIC receiver is the optimal choice when the SINR is less than about 3dB, while the MMSE-IRC is optimal (in terms of throughput and complexity) when SINR is higher than about 3dB. Regarding the Raw BER figure, SLIC and BLIC reach a minimum that shifts varying the AoA, so increasing the SINR, SLIC and BLIC Raw BER becomes higher than MMSE-IRC ones. The double interferer case simulation results are shown in the figures 5 and 6, as expected when there are two

79 Figure 20: Raw BER - Single Interferer

interfering signals SLIC and BLIC provide higher gain respect to MMSE-IRC and Alamouti receiver in the low SINR region. In this case, for negative SINR, BLIC receiver is the optimal choice, while SLIC performances are comparable to the MMSE-IRC ones. When SINR is higher than about 1dB, the MMSE-IRC becomes the best receiver because, rather than SLIC and BLIC, does not suffer from the error propagation effect in the regeneration of the interfering signals.

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Figure 3: Throughput - Single Interferer

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Figure 4: Raw BER - Single Interferer

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Figure 5: Throughput - Double Interferer

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Figure 6: Raw BER - Double Interferer

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CONCLUSION Symbol level successive interference cancellation and interference rejection combining receivers represent a good trade-off between complexity and performance results. Bit level successive interference cancellation is optimal in presence of two interfering signal when SINR is low (at cell edge), but in presence of single interfering signal or for a UE close to the base station the switching on-off procedure between SLIC and MMSE-IRC represents the optimal choice to respect the trade-off between complexity and performances. From the analysis and simulation tests, it follows that there is no an optimal receiver useful for all the interferer scenarios, but surely a class of receivers becoming to the NAICS (i.e. SLIC and BLIC that include MMSE-IRC) are very promising. Some research activities must be done about the topic of NAICS, in particular to clarify the type of network assistance and to find new network parameters useful to improve the UE interference cancellation capabilities. Clearly, since UE will become more powerful, the research must be address to receiver side, limiting the spectrum usage foe transmitting assistance from the network. UE could be able to sense the interfering signals to switch their IC receivers to the optimal one. This capacity could be reached implementing in software or directly in the chipset hardware a set of IC receivers, for example starting from: MMSE-IRC, SLIC and BLIC. As analyzed in the chapter 4, it is possible to subdivide the SINR range in regions in which the selection of the best receiver can be done on performance basis. Moreover, identifying accurately (tagging) each interferer signals and tracking their received powers, would be possible to order and filter each interferer signals in the most suiable way.

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BIBLIOGRAPHY 1. Complexity and link level performance analysis for feICIC CRS-IC receiver. Ericsson. s.l. : 3GPP TSG-RAN WG4 Meeting # 64, 2012. R4-124460. 2. Discussion on the reference receiver for FeICIC. Huawei. s.l. : 3GPP TSG-RAN WG4 Meeting #63 , 2012. R4-122479. 3. FeICIC baseline receiver assumptions. Qualcomm. s.l. : RAN4 #62bis, 2012. R4122185. 4. Link level simulations for FeICIC with 9dB cell range expansion. Qualcomm. s.l. : 3GPP TSG-RAN WG4 #63, 2012. R4-123313. 5. CRS Interference Mitigation For Homogeneous Deployments. Ericsson, ST-Ericsson, NEC, MediaTek, Sony Mobile, Verizon, Orange, Softbank, Alcatel-Lucent, LG Electronics, Renesas. s.l. : 3GPP TSG-RAN Meeting # 59. RP-130393. 6. WF on CRS-IM performance evaluation. Ericsson. s.l. : 3GPP TSG-RAN WG4 #66bis. R4-132020. 7. Investigation on Advanced Receiver Employing Interference Rejection Combining. s.l. : Yusuke Ohwatari, Vol. NTT DOCOMO. 8. 4G LTE/LTE-A for Mobile broadband. s.l. : AP. 9. Analsysis of LTE physical layer and its evolution . s.l. : Telecom Italia Lab, 2013. 10. Reconfigurable OFDMA Simulation Platform - Inner Modem V 1.0. s.l. : Telecom Italia Lab, 2007.

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