Optimal LLR Clipping Levels for Mixed Hard/Soft Output Detection

Zimmermann, Ernesto; Milliner, D.L.; Barry, John R.; Fettweis, Gerhard

doi:10.1109/glocom.2008.ecp.222

Cited by 12 publications

(11 citation statements)

References 5 publications

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“…Our simulation results reveal that large computational complexity to find a new list or a single ML solution do not evidently contribute to improve performance. Either in a LSD or STS based method, there may be a problem of estimating SBI during the iterative MIMO detection process because a part of the candidates are eliminated during the pruning process, and these were also reported in the previous studies [14,15]. In order to solve this problem, we propose an efficient method, and we refer to this method as the virtual candidate insertion method.…”

Section: Introductionmentioning

confidence: 91%

Efficient list‐sphere detection scheme for joint iterative multiple‐input multiple‐output detection

Ahmed

Kim

2014

IET Communications

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List-sphere detection (LSD) is a sub-optimal multiple-input multiple-output (MIMO) detection scheme which searches candidate symbol vectors that lie within a sphere of a given radius. This study presents an efficient LSD based method for a joint iterative MIMO detection scheme. The proposed method utilises a channel condition in order to define the list size. During the search process, the radius is adaptively updated to reduce the computational complexity. Owing to the list size and corresponding radius are adaptively determined by the channel condition, the authors can operate the detector at the most appropriate complexity to produce the required performance. Simulation results show that the proposed methods provide substantial complexity reduction without bit error rate performance degradation.

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Section: Introductionmentioning

confidence: 91%

Efficient list‐sphere detection scheme for joint iterative multiple‐input multiple‐output detection

Ahmed

Kim

2014

IET Communications

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“…Although we have proven the optimalilty of the bit-wise combiner (8) for only binary alphabets, we propose its use for all alphabets. As such, the function F( x | p) plays an important role in this paper.…”

Section: Definitionmentioning

confidence: 99%

Bit-wise combining for decode-and-forward relays

Kim

Barry

2012

2012 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)

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We consider the problem of combining the signals received from a decode-and-forward relay with those received from the source. As an alternative to the high-complexity maximum-likelihood combining strategy, we propose a bit-wise combining strategy in which the received signals are first converted to log-likelihood values before being combined. We show that, subject to the bit-wise constraint, the optimal combining strategy for binary alphabets -minimizing the probability of error -applies a sigmoidal nonlinearity to the relay log-likelihood values before adding to the source loglikelihood values. Unlike the maximal-ratio combining strategy, which has no diversity for the Rayleigh-fading decode-andforward relay channel, the proposed strategy exhibits the maximum diversity order of two.

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“…Several LLR clipping methods for different algorithms have been proposed, e.g., in [10,[27][28][29]. All the methods aim at reducing the detector algorithm complexity required to achieve certain performance.…”

Section: Clipping Methodsmentioning

confidence: 99%

“…The method cannot be applied as such for LSD algorithms due to the differences in the algorithms. The LLR clipping in [28,29] is based on the reliability information and the channel state information (CSI) to determine close to optimal LLR clipping values with additional complexity. We introduce and study two simple methods to process L D (b k ) information and the impact of the methods on the performance of a coded system.…”

Section: Clipping Methodsmentioning

confidence: 99%

Implementation aspects of list sphere decoder algorithms for MIMO-OFDM systems

2010

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a b s t r a c tA list sphere decoder (LSD) can be used to approximate the optimal maximum a posteriori (MAP) detector for the detection of multiple-input multiple-output (MIMO) signals. In this paper, we consider two LSD algorithms with different search methods and study some algorithm design choices which relate to the performance and computational complexity of the algorithm. We show that by limiting the dynamic range of log-likelihood ratio, the required LSD list size can be lowered, and, thus, the complexity of the LSD algorithm is decreased. We compare the real and the complexvalued signal models and their impact on the complexity of the algorithms. We show that the real-valued signal model is clearly the less complex choice and a better alternative for implementation. We also show the complexity of the sequential search LSD algorithm can be reduced by limiting the maximum number of checked nodes without sacrificing the performance of the system. Finally, we study the complexity and performance of an iterative receiver, analyze the tradeoff choices between complexity and performance, and show that the additional computational cost in LSD is justified to get better soft-output approximation.

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Optimal LLR Clipping Levels for Mixed Hard/Soft Output Detection

Cited by 12 publications

References 5 publications

Efficient list‐sphere detection scheme for joint iterative multiple‐input multiple‐output detection

Efficient list‐sphere detection scheme for joint iterative multiple‐input multiple‐output detection

Bit-wise combining for decode-and-forward relays

Implementation aspects of list sphere decoder algorithms for MIMO-OFDM systems

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