The application of lattice-reduction to the K-Best algorithm for near-optimal MIMO detection

Shabany, Mahdi; Gulak, P.G.

doi:10.1109/iscas.2008.4541418

Cited by 50 publications

(55 citation statements)

References 5 publications

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“…This requires only 2K-1 paths to be extended for selection of K-paths, and eliminates the need for a sorter. This approach has been first reported by the authors in [10] and [11], and also independently in [12] and [13]. In this paper, we study the complexity of the WPE approach versus traditional extension and sorting.…”

Section: Winner Path Extension (Wpe): a Sort Free Approachmentioning

confidence: 97%

“…We propose a novel reduced cost (in terms of power and area) WPE which is used as the core of a K-Best detector and quantify improvement in cost and performance. Finally, it is important to note that the method presented in [12] and [13] require exact sorting among the set of first children, which is not a requirement in our proposed approach.…”

Section: Winner Path Extension (Wpe): a Sort Free Approachmentioning

confidence: 99%

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Design and Implementation of a Sort-Free K-Best Sphere Decoder

Mondal

Eltawil

Shen

et al. 2010

IEEE Trans. VLSI Syst.

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Abstract:-This paper describes the design and VLSI architecture for a 4x4 breadth first K-Best MIMO decoder using a 64 QAM scheme. A novel sort free approach to path extension, as well as quantized metrics result in a high throughput VLSI architecture with lower power and area consumption compared to state of the art published systems. Functionality is confirmed via an FPGA implementation on a Xilinx Virtex II Pro FPGA. Comparison of simulation and measurements are given and FPGA utilization figures are provided. Finally, VLSI architectural tradeoffs are explored for a synthesized ASIC implementation in a 65nm CMOS technology.

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Section: Winner Path Extension (Wpe): a Sort Free Approachmentioning

confidence: 97%

Section: Winner Path Extension (Wpe): a Sort Free Approachmentioning

confidence: 99%

Design and Implementation of a Sort-Free K-Best Sphere Decoder

Mondal

Eltawil

Shen

et al. 2010

IEEE Trans. VLSI Syst.

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show abstract

“…The K-Best search with lattice reduction proposed in [8] and [9] belongs to a particular subset of the family of breadth-first tree search algorithms. At a high algorithmic level of abstraction, the LR aided K best search is performed sequentially, solving for the symbol at each antenna.…”

Section: A K-best Searchmentioning

confidence: 99%

“…The LR-aided K-best tree search algorithm was first proposed in [8] and [9] and improved in [10]. Compared to a conventional K-Best search, the LR-aided K-best algorithm has no boundary information about the symbols in the lattice-reduced domain.…”

mentioning

confidence: 99%

“…Practically, this means that the set of children in a given layer is infinite, as it is unconstrained. This infinite set is replaced by a finite subset in [8] and [9] developing a so-called "on demand" child expansion based on the Schnorr-Euchner (SE) strategy [10] for reducing the complexity of generating the subset. The algorithm is further improved by the addition of a priority queue and on demand child expansion in [11].…”

mentioning

confidence: 99%

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An Improved Soft Decision Based MIMO Detection Using Lattice Reduction

Rahman¹,

Rohani²,

Xu³

et al. 2014

IJCCE

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Abstract-This paper presents a performance analysis of soft decision based Lattice Reduction (LR) aided Schnorr-Euchner (SE) multiple-input-multiple-output (MIMO) detection algorithm with that of a hard one. We develop an advanced soft decoding scheme, for 4×4 MIMO with different modulation schemes and it provides 1.3 to 1.5 dB improvement compared to the LR-aided hard decision based detection method. The optimum parameters for K and saturation limit are further derived using extensive simulation. In the conventional K-best algorithm limiting the LLR values is not beneficiary in reducing the optimum size of candidate list, whereas applying the saturation limit on the LLR values in LR-aided algorithm will result in more than 8x reduction in list size as well as in the complexity of detector and LLR calculation unit.Index Terms-MIMO, soft decoding, lattice reduction, and k-best detector.

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Real Domain Iterative K-Best Detector

Rahman

Choi

2016

K-Best Decoders for 5G+ Wireless Communication

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The application of lattice-reduction to the K-Best algorithm for near-optimal MIMO detection

Cited by 50 publications

References 5 publications

Design and Implementation of a Sort-Free K-Best Sphere Decoder

Design and Implementation of a Sort-Free K-Best Sphere Decoder

An Improved Soft Decision Based MIMO Detection Using Lattice Reduction

Real Domain Iterative K-Best Detector

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