MIMO detector algorithms and their implementations for LTE/LTE-A

Transcription

1 GIGA seminar MIMO detector algorithms and their implementations for LTE/LTE-A Markus Myllylä and Johanna Ketonen

2 Outline Introduction System model Detection in a MIMO-OFDM system SIC and K-best LSD implementation MMF LSD Implementation Summary and Conclusions

3 Introduction Orthogonal frequency division multiplexing (OFDM), which simplifies the receiver design, has become a widely used technique for broadband wireless systems Multiple-input multiple-output (MIMO) channels offer improved capacity and potential for improved reliability compared to singleinput single-output (SISO) channels MIMO technique in combination with OFDM (MIMO-OFDM) has been identified as a promising approach for high spectral efficiency wideband systems 3GPP LTE, WiMax

4 A MIMO-OFDM system OFDM based multiple antenna system with N T transmit and N R receive antennas Received signal y=hx+h where H is the channel matrix, x is the transmitted symbol vector, h is a noise vector. IFFT S/P x QAM Mod Int b Encoder Channel L A1 L E2 Int + - FFT SISO detector - + LE1 DeInt SISO decoder y L D1 L A2 L D2 Figure: A MIMO-OFDM system model

5 Detection for MIMO-OFDM Detection means that the detector calculates an estimate of the transmitted signal vector x as an output of the detector Transmitted signal vector x includes N T different symbols The OFDM technique simplifies the receiver structure by decoupling frequency selective MIMO channel into a set of parallel flat fading channels Different data is sent in different subcarriers However, the reception of the signal has to be done seperately for each subcarrier E.g. in 3GPP LTE standard 512 subcarriers (300 used) with 5MHz bandwidth (BW) and the the interval of OFDM symbol is 71 s Thus, detector must calculate an estimate of 300 x N T symbols in 71 s CWC Centre For Wireless Communications 5

6 The use of maximum a posteriori (MAP) detector is the optimal solution for soft output detection In practice coded systems are used, i.e., soft output detection is applied The calculation of maximum likelihood (ML) and MAP solutions with conventional exhaustive search algorithms is not feasible with large constellation and high number of transmit antennas Suboptimal linear minimum mean square error (LMMSE) and zero forcing (ZF) criterion based detectors feasible with reduced performance The LMMSE performance can be improved by performing successive interference cancellation (SIC) between the MIMO streams, i.e., detecting the strongest signal first and cancelling its interference from each received signal. CWC Centre For Wireless Communications 6

7 Sphere detectors (SD) calculate ML solution with reduced complexity List sphere detector (LSD) is an enhancement of SD that can be used to approximate the MAP detector Sphere detectors more complex compared to linear detectors Linear detectors calculate a weight matrix W which can be possibly be used for multiple subcarriers and OFDM symbols Depending on the channel coherence time and frequency SD and LSD execute a tree search always separately for each subcarrier and OFDM symbol List sphere detector executes a tree search Gives a list L of candidate symbol vectors as an output, which is used to approximate the soft output information L D (b k ) The list size L affects the quality of the approximation and depending on the list size, the LSD provides a tradeoff between the performance and the computational complexity CWC Centre For Wireless Communications 7

8 Tree search algorithms The tree search algorithms are often divided according to their search strategy into different categories: Breadth first (BF) Fixed complexity can be easily determined Depth search (DF) More efficient than BF in terms of visited nodes and variable complexity Metric-first (MF) Optimal in the sense of visited search tree nodes and variable complexity Visited nodes should be maintained in metric order to ensure the optimality -> requires the nodes to be stored in memory H Preprocessing Q, R LSD algorithm L LLR calculation L D1 (b k ) Figure: A high level architecture of LSD ~ y

9 The K-best LSD algorithm The K-best LSD is a breadth-first search algorithm 2 transmit antennas, 4 quadrature amplitude modulation (QAM), real valued 1-1 y - 4 r 4,4x4 2 Root layer 1-1 y - - r x r x 3 3,4 4 3, layer layer y - - r x 1 1,1x1 - r1,2x 2 - r1,3x3 r1,

10 Soft successive interference cancellation (SIC) W H 2 1 H ( H H IM ) H 2. stream 1. stream xˆ logp xˆ re sgn(logp i) S tanh(logp i 2), S {1,3,5,7}

11 Performance comparison in a 4x4 system The LSD receiver outperforms the SIC receiver and LMMSE receivers. In an uncorrelated channel, the SIC receiver performs better. 11

12 Implementation results Mentor Graphics Catapult C tool used for RTL generation VHDL generated from C code A field programmable gate array (FPGA) implementation Xilinx Virtex-4 chip Synthesis with Precision RTL An application specific intrated circuit (ASIC) implementation 0.18um CMOS technology Synthesis with Design Compiler

13 Complexity comparison in a 2x2 system 2x2 16-QAM results for complexity in equivalent gates, power consumption and maximum decoding rate 140 Mb/s is the required decoding rate of coded bits in LTE for a 20 MHz bandwidth for 2x2 16-QAM. The goodput is the detection rate times (1-FER) at the given SNR. Receiver Complexity Power Decoding rate Goodput 16 db Goodput 20 db LMMSE 66 k ge 59 mw 140 Mb/s 0 Mb/s 45 Mb/s SIC 85 k ge 83 mw 140 Mb/s 5.3 Mb/s 84 Mb/s 8-best LSD 197 k ge 175 mw 140 Mb/s 21.4 Mb/s 128 Mb/s 8-best, 2 it. 236 k ge 251 mw 140 Mb/s 63.3 Mb/s Mb/s 13

14 Complexity comparison in a 4x4 system 4x4 16-QAM Decoding rate was set to meet the LTE requirements for a 20 MHz bandwidth The iterative K-best gives the highest goodput at low SNRs Receiver Complexity Power Decoding rate Goodput 22 db Goodput 28 db LMMSE 480 k ge 376 mw 280 Mb/s 0 Mb/s 101 Mb/s SIC 540 k ge 449 mw 280 Mb/s 0 Mb/s 246 Mb/s 8-best LSD 582 k ge 568 mw 280 Mb/s 14 Mb/s 280 Mb/s 8-best, 2 it. 634 k ge 700 mw 280 Mb/s 68 Mb/s 280 Mb/s 14

15 MMF-LSD architecture design Modified metric first (MMF) - LSD The architecture includes four different units Tree pruning unit Final candidate memory Partial candidate memory Control logic unit Architecture operates in a sequential fashion Two tree nodes calculated in one iteration Figure: The MMF-LSD algorithm architecture

16 FER Implementation trade-offs x4 MIMO, LSD with list 15, Winner B1, 60kmph 16-QAM QAM MMF,logMAP,D max = 10-2 MMF,maxlog,D max = MMF,maxlog,D max =120/300it MMF,maxlog,D max =100/150it MMF,maxlog,D max =80/100it maxlog, ML LMMSE DF,maxlog,D max =750it SNR (db) Figure: FER vs. SNR: Performance of the real LSD based receivers in a 4x4 antenna system

17 MMF-LSD synthesis results Architecture units SEE-LSD synthesis results Depth first comparison LLR calculation Max-log-MAP solution 4x4 system with 16-QAM

18

19 Summary Successive interference cancellation (SIC) Improves the LMMSE performance with cancellation step Simple implementation K-best List Sphere Detector (LSD) Parallel tree search algorithm Implementation can be parallel and easy to pipeline Modified Metric First (MMF) LSD Dijkstra s algorithm Metric first search Modified to be feasible for implementation

20 Conclusions In a 2x2 system SIC performs well with low complexity For high data rates at low SNRs, a more complex LSD receiver can be used In a 4x4 system The K-best LSD gives goodput at low SNRs In better channel conditions, SIC also performs well Implementation of MMF-LSD presented 4x4 MIMO system with 16-QAM FPGA synthesis results ASIC synthesis results LSD feasible for practical systems Design comparable to the state-of-the-art