Likelihood Estimation for Reduced-Complexity ML Detectors in a MIMO Spatial-Multiplexing System

This paper proposes an low-complexity soft-output multiple-input multiple-output (soft-MIMO) detection algorithm for achieving soft-output maximum-likelihood (soft-ML) performance under max-log approximation. The proposed algorithm is based on a parallel tree-search (PTS) applying a channel ordering by a sorted-QR decomposition (SQRD) with altered sort order. The empty-set problem that can occur in calculation of log-likelihood ratio (LLR) for each bit is solved by inserting additional nodes at each search level. Since only the closest node is inserted among nodes with opposite bit value to a selected node, the proposed node insertion scheme is very efficient in the perspective of computational complexity. The computational complexity of the proposed algorithm is approximately 37-74% of that of existing algorithms, and from simulation results for a 4×4 system, the proposed algorithm shows a performance degradation of less than 0.1dB. ※ 본 연구는 지식경제부 기술혁신사업(10035389)의 지원을 받아 수행되었습니다.주저자：연세대학교 전기전자공학과 IT-SoC

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Section: 모든 가능한 송신 심볼 벡터에 대한 유클리디언unclassified

Low-Complexity Soft-MIMO Detection Algorithm Based on Ordered Parallel Tree-Search Using Efficient Node Insertion

Kim

Park

Kim

2012

The Journal of Korean Institute of Communications and Informati

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“…The conventional method in [4] searches for MLS and IB-MLS individually. First, this method estimates MLS by a lowcomplexity algorithm such as the MMSE detection and the sphere decoding [5].…”

Section: Conventional Llr Calculationmentioning

confidence: 99%

“…In addition, we consider the one-dimensional search in the direction of the eigenvector having the largest eigenvalue with the MMSE detection as a starting point [7], which is indicated by PM. Note that SD-MMSE was proposed in [4]. Furthermore, the performance of LLR calculation by MLD were evaluated as a theoretical lower bound.…”

Section: A Simulation Conditionsmentioning

confidence: 99%

“…To solve such a problem, low-complexity algorithms that simplify the maximum likelihood detection (MLD) have been applied to the calculation of LLR [3], [4]. The conventional method in [4] first employs a low-complexity algorithm in order to find the transmitted signal candidate that maximizes the log likelihood function, that is the maximum likelihood sequence (MLS).…”

Section: Introductionmentioning

confidence: 99%

“…The conventional method in [4] first employs a low-complexity algorithm in order to find the transmitted signal candidate that maximizes the log likelihood function, that is the maximum likelihood sequence (MLS). Then, the method applies another lowcomplexity algorithm in order to find the transmitted signal candidate that maximizes the log likelihood function under a constraint that a coded bit be inverse to that of the estimated MLS, which is referred to as inverse-bit MLS (IB-MLS).…”

Section: Introductionmentioning

confidence: 99%

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Low-Complexity Algorithm for Log Likelihood Ratios in Coded MIMO-OFDM Communications

Zheng

Woo

Fukawa

et al. 2009

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

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This paper proposes a low-complexity algorithm to calculate log likelihood ratios (LLRs) of coded bits, which is necessary for channel decoding in coded MIMO-OFDM mobile communications. An approximate LLR needs to find a pair of transmitted signal candidates that can maximize the log likelihood function under a constraint that a coded bit be equal to either one or zero. The proposed algorithm can find such a pair simultaneously, whereas conventional ones find them individually. Specifically, the proposed method searches for such candidates in directions of the noise enhancement with the MMSE detection as a starting point. First, an inverse matrix which the MMSE weight matrix includes is obtained and then the power method derives eigenvectors of the inverse matrix as the directions of the noise enhancement. With some eigenvectors, one-dimensional search and hard decision are performed. From the resultant signals, the transmitted signal candidates to be required are selected on the basis of the log likelihood function. Computer simulations with 4 × 4 MIMO-OFDM, 16QAM, and a half-rate convolutional code demonstrate that the proposed algorithm requires only 1.0 dB more E b /N0 than that of the maximum likelihood detection (MLD) in order to achieve packet error rate of 10 −3 , while reducing the complexity to about 0.2% of that of MLD.

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Adaptive joint maximum‐likelihood detection and minimum‐mean‐square error with successive interference canceler over spatially correlated multiple‐input multiple‐output channels

Fan

Zhang

Jiang

et al. 2012

Wireless Communications

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We develop an efficient hard detector for multiple‐input multiple‐output (MIMO) channels, which adaptively combines maximum‐likelihood detection (MLD) and minimum‐mean‐square error with a successive interference canceler together. Unlike the conventional joint combination scheme, which may suffer from considerable degradation in bit‐error‐rate (BER) performance over correlated channels and where only one data stream is detected by MLD, our proposed scheme adaptively controls the number of data streams to be detected by MLD based on an analytical characterization of reliability for the detection. Simulation results illustrate that near‐optimal BER performance can be obtained at much lower computational complexity by the proposed method as compared with existing techniques, regardless of the spatial correlation of the MIMO channels. Copyright © 2012 John Wiley & Sons, Ltd.

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Likelihood Estimation for Reduced-Complexity ML Detectors in a MIMO Spatial-Multiplexing System

Cited by 8 publications

References 13 publications

Low-Complexity Soft-MIMO Detection Algorithm Based on Ordered Parallel Tree-Search Using Efficient Node Insertion

Low-Complexity Soft-MIMO Detection Algorithm Based on Ordered Parallel Tree-Search Using Efficient Node Insertion

Low-Complexity Algorithm for Log Likelihood Ratios in Coded MIMO-OFDM Communications

Adaptive joint maximum‐likelihood detection and minimum‐mean‐square error with successive interference canceler over spatially correlated multiple‐input multiple‐output channels

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