Iterative tree search detection for MIMO wireless systems

Jong, Y.L.C. de; Willink, T.J.

doi:10.1109/vetecf.2002.1040761

Cited by 57 publications

(46 citation statements)

References 13 publications

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“…The M-algorithm is a breadth-first, sorted tree search algorithm that may be adapted for MIMO detection [16,18]. The algorithm reduces the search complexity by storing only the best M branches at a time.…”

Section: Decoders Based On the M-algorithmmentioning

confidence: 99%

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Low complexity maximum-likelihood detector for DSTTD architecture based on the QRD-M algorithm

et al. 2018

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This paper presents a new decoder algorithm for the double space-time transmit diversity (DSTTD) system. The decoder is based on the QRD-M algorithm, which performs a breadth-first search of possible solutions tree. The search is simplified by skipping unlikely candidates, and it is stopped when no promising candidates are left. Furthermore, the search is divided into three concurrent iterations, making possible a fast, parallel implementation either in hardware or software. After presenting an analysis of the capacity and diversity of DSTTD, we present performance results showing that the proposed decoder is capable of achieving near maximum likelihood performance. We also show that the proposed algorithm exhibits lower computational complexity than other existing maximum likelihood detectors.

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Section: Decoders Based On the M-algorithmmentioning

confidence: 99%

“…Recently, tree-search algorithms have been applied to STBC decoding [18]; these have enabled near-ML decoding performance with reduced complexity compared to the sphere decoder [8,[19][20][21][22]. Good results have been obtained with decoders based on the M-algorithm combined with the QR decomposition of the channel matrix.…”

Section: Introductionmentioning

confidence: 99%

Low complexity maximum-likelihood detector for DSTTD architecture based on the QRD-M algorithm

et al. 2018

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“…Finally, to measure how much improvement our MMT detector gains from the additional extension steps, we compared MTT against our first GPU MIMO detector, a one-pass trellis detector (OT), which does only reduction steps through the trellis once to perform detection. To mitigate inaccuracies in LLR computation due to the small list, we apply the LLR clipping technique to the K-Best detector [5] and OT. It should be noted that in the K-Best and one-pass trellis detector algorithm the cumulative distance for a particular bit can be missing due to the small list, so the LLR clipping is necessary in the K-Best algorithm.…”

Section: Mtt Detector Performancementioning

confidence: 99%

Implementation of a High Throughput Soft MIMO Detector on GPU

Sun

Gupta

et al. 2010

J Sign Process Syst

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Multiple-input multiple-output (MIMO) significantly increases the throughput of a communication system by employing multiple antennas at the transmitter and the receiver. To extract maximum performance from a MIMO system, a computationally intensive search based detector is needed. To meet the challenge of MIMO detection, typical suboptimal MIMO detectors are ASIC or FPGA designs. We aim to show that a MIMO detector on Graphic processor unit (GPU), a low-cost parallel programmable co-processor, can achieve high throughput and can serve as an alternative to ASIC/FPGA designs. However, careful architecture aware software design is needed to leverage the performance offered by GPU. We propose a novel soft MIMO detection algorithm, multi-pass trellis traversal (MTT), and show that we can achieve ASIC/FPGAlike performance and handle different configurations in software on GPU. The proposed design can be used to accelerate wireless physical layer simulations and to offload MIMO detection processing in wireless testbed platforms.

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“…An existing implementation of the Flexsphere detector [6] for WiMAX shows that the V-BLAST-like preprocessing block uses significant amount of FPGA resources due to matrix inversions, while the QR decomposition and detection blocks combined use significantly less FPGA resources. In addition, to generate soft-output, the candidate list generated by Flexsphere does not guarantee bit-level reliability information, the log likelihood ratio (LLR), cannot be found for all bits, leading to the need for LLR clipping [7][8][9]. In addition, soft-output values should be generated with squared l 2 -norm.…”

Section: Introductionmentioning

confidence: 99%

Low complexity scalable MIMO sphere detection through antenna detection reordering

Dick

Sun

et al. 2012

Analog Integr Circ Sig Process

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This paper describes a novel low complexity scalable multiple-input multiple-output (MIMO) detector that does not require preprocessing and the optimal squared l 2 -norm computations to achieve good bit error (BER) performance. Unlike existing detectors such as Flexsphere that use preprocessing before MIMO detection to improve performance, the proposed detector instead performs multiple search passes, where each search pass detects the transmit stream with a different permuted detection order. In addition, to reduce the number of multipliers required in the design, we use l 1 -norm in place of the optimal squared l 2 -norm. To ameliorate the BER performance loss due to l 1 -norm, we propose squaring then scaling the l 1 -norm. By changing the number of parallel search passes and using norm scaling, we show that this design achieves comparable performance to Flexsphere with reduced resource requirement or achieves BER performance close to exhaustive search with increased resource requirement.

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Iterative tree search detection for MIMO wireless systems

Cited by 57 publications

References 13 publications

Low complexity maximum-likelihood detector for DSTTD architecture based on the QRD-M algorithm

Low complexity maximum-likelihood detector for DSTTD architecture based on the QRD-M algorithm

Implementation of a High Throughput Soft MIMO Detector on GPU

Low complexity scalable MIMO sphere detection through antenna detection reordering

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