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

GIGA seminar 11.01.2010

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

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

© Centre for Wireless Communications, University of Oulu

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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

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A MIMO-OFDM system  OFDM based multiple antenna system with NT transmit and NR receive antennas  Received signal y=Hx+h where

H is the channel matrix, x is the transmitted symbol vector, h is a noise vector. x IFFT

S/P

QAM Mod

Int

Int

FFT

y

SISO detector

Encoder

LE2

LA1

Channel

b

+

-

+

LD1 LE1

DeInt

LA2

SISO decoder

LD2

Figure: A MIMO-OFDM system model © Centre for Wireless Communications, University of Oulu

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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 NT 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 NT symbols in 71s

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 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.

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 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 LD(bk)  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

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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

~ y

Figure: A high level architecture of LSD

LD1(bk)

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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 3 - r 3,4 x 4 - r 3,3 x3

-1

y 4 - r 4, 4 x 4

-1

2

2 Root layer

layer 4 1

-1

layer 3 1

-1

1

-1

2 1

-1

1

-1

1

y1 - r1,1x1 - r1,2 x 2 - r1,3 x3 - r 1, 4 x 4

2

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Soft successive interference cancellation (SIC) W  (H H H   2I M ) 1 H H 2. stream

1. stream

xˆ

logP

xˆre  sgn(logPi )S | tanh(logPi  2) |, S  {1,3,5,7} 10

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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.

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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

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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

139.6 Mb/s

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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

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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

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Implementation trade-offs 4x4 MIMO, LSD with list 15, Winner B1, 60kmph

0

10

FER

16-QAM

64-QAM

-1

10

MMF,logMAP,Dmax =  MMF,maxlog,Dmax =  MMF,maxlog,Dmax =120/300it MMF,maxlog,Dmax =100/150it MMF,maxlog,Dmax =80/100it

-2

10

maxlog, ML LMMSE DF,maxlog,Dmax =750it 10

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15 16 17 SNR (dB)

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19

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21

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Figure: FER vs. SNR: Performance of the real LSD based receivers in a 4x4 antenna system © Centre for Wireless Communications, University of Oulu

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MMF-LSD synthesis results – Architecture units

•

SEE-LSD synthesis results – Depth first comparison

•

LLR calculation – Max-log-MAP solution

•

4x4 system with 16-QAM

© Centre for Wireless Communications, University of Oulu

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© Centre for Wireless Communications, University of Oulu

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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

© Centre for Wireless Communications, University of Oulu

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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 © Centre for Wireless Communications, University of Oulu