Research on low-complexity breadth-first detection for multiple-symbol differential unitary space–time modulation systems

Jin, Ning; Jin, Xiaoping; Ying, Yuxiang; Wang, S.; Lou, Xizhong

doi:10.1049/iet-com.2010.0736

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…For instance, for a MIMO K-best detector with 16-QAM modulation, a bubble sorter needs at least 128 pairwise comparisons if K is set to 8. To reduce the complexity of K-best algorithm, [8] introduces a dynamic K-best method by selecting different K values in different layers, it has achieved certain effect but not very effective.…”

Section: Introductionmentioning

confidence: 99%

Research on Low Complexity K-best Sphere Decoding Algorithm for MIMO Systems

Lou

Zhou

Chen

et al. 2015

Wireless Pers Commun

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In order to reduce the complexity of hard-output K-best decoding algorithm in multiple-input multiple-output (MIMO) systems and guarantee performance of the system, we propose a bit-sort (BS) strategy based on bit counting operation in hardware implementation for the K-best decoder. The proposed BS K-best algorithm finds out the smallest K paths by scanning and counting the bits of every candidate, which is much simpler than the pairwise comparison operation in conventional K-best algorithm. Moreover, we proposed a dynamic bit-sort (DBS) strategy for the K-best decoder based on the BS strategy. The DBS K-best algorithm further reduces the complexity of BS K-best algorithm by selecting a dynamic K value that depends on the candidates. The complexity analysis shows the complexity of bit counting operations in proposed BS K-best algorithm is much less than that of the pairwise comparisons in conventional K-best algorithm, and the DBS K-best algorithm can further reduce about 50 % counting complexity of BS K-best algorithm. The simulation results show both the BS K-best decoder and DBS K-best decoder can achieve the same performance as that of hard-output sphere decoding algorithm if a proper K is selected.

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Section: Introductionmentioning

confidence: 99%

Research on Low Complexity K-best Sphere Decoding Algorithm for MIMO Systems

Lou

Zhou

Chen

et al. 2015

Wireless Pers Commun

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“…One is the depth-first tree-search algorithm such as sphere decoding (SD) [1,2] which is able to achieve near-optimum SISO detection performance, and the other is breadth-first tree-search algorithm such as K-best detection [3,4]. Several SD SISO detectors and K-best SISO detectors have been developed for the practical implementation of iterative MIMO receivers in [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Bit-Clusters Trellis Search Based Iterative MIMO Detection Algorithm

Lou

Peng

Zhou

et al. 2014

Wireless Pers Commun

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A bit-clusters trellis-search (BCTS) based soft-input soft-output detection algorithm is proposed for iterative Multiple-Input Multiple-Output (MIMO) receivers. Compared to the symbol nodes trellis-search (SNTS) based MIMO detection algorithm, which significantly reduces the search complexity by limiting the number of candidates in each trellis stage compared to sphere decoding algorithm, the BCTS algorithm proposed in this article further reduces the number of candidates in the trellis. In the BCTS algorithm, we replace the symbol node in SNTS algorithm with bit-cluster that represents several relative symbols whose corresponding bit is 0 or 1 while the other bits are not concerned. Because the bit-cluster number is fewer than the symbol node number, the search complexity of proposed BCTS algorithm is much less than that of the SNTS algorithm. The simulation results show, by transforming symbol nodes to bit-clusters in trellis-search detection, the performance is improved nearly 0.5 dB when the bit-error-ratio reaches 10 −4 ; the complexity analysis shows the number of partial-Euclidian-distance (PED) comparisons and PED computations are approximately three and one times fewer than that of the SNTS algorithm respectively when constellation size is beyond 16. And this tendency will be more obvious with the transmit antenna number and constellation size increasing.

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