Channel state information based LLR clipping in list MIMO detection

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

doi:10.1109/pimrc.2008.4699774

Cited by 19 publications

(16 citation statements)

References 9 publications

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“…However, finding the optimal value is difficult because it may be different from modulation order, SNR, M , etc. [13].…”

Section: B Enhanced Soft Llr Calculationmentioning

confidence: 99%

Enhanced QRD-M algorithm for soft-output MIMO detection

Kim

2012

2012 IEEE Global Communications Conference (GLOBECOM)

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QR-decomposition with M algorithm is a promising technique for multi-input multi-output (MIMO) systems. When the number of candidates is insufficient, its performance is, however, degraded due to an inaccurate log likelihood ratio (LLR). To address this problem, an enhanced soft LLR calculation scheme is proposed for soft-output MIMO detection. Based on QR-decomposition, the proposed algorithm can efficiently obtain a soft information of minimum mean-squared error (MMSE) equalization. With this information, an optimal weighted combining method is derived in an MSE sense. In addition, we compute a more reliable clipping value from the soft information of MMSE equalization. Simulation results show that the proposed algorithm provides a considerable performance gain over conventional algorithms and its performance is close to optimal performance with insufficient candidates. Index Terms-Maximum-likelihood detection (MLD), Multiinput multi-output (MIMO), QR-decomposition, Log likelihood ratio (LLR) calculation

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“…However, finding the optimal value is difficult because it may be different from modulation order, SNR, M , etc. [13].…”

Section: B Enhanced Soft Llr Calculationmentioning

confidence: 99%

Enhanced QRD-M algorithm for soft-output MIMO detection

Kim

2012

2012 IEEE Global Communications Conference (GLOBECOM)

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“…The conventional SD, therefore, fails to generate a precise estimate of soft output for SD. On the other hand, the proposed modified LAS algorithm precisely estimates the counter hypothesis and outperforms the conventional method used in [23] by 2.0-3.0 dB as shown in Fig. 7a.…”

Section: Single Candidate Casementioning

confidence: 85%

Likelihood ascent search augmented sphere decoding receiver for MIMO systems using M‐QAM constellations

et al. 2020

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MIMO systems employing sphere decoding (SD) algorithm are known to achieve near maximum likelihood (ML) performance at a reduced complexity by restricting the candidate search space to a sphere of a certain radius. The performance of SD depends on the precise estimation of its soft output. In this paper, a low complexity modified Likelihood Ascent Search (LAS) algorithm is proposed to be used within a SD receiver in order to precisely estimate the counter-hypothesis for its winner candidates. The LAS algorithm is modified to search for the best counter-hypothesis in only one-half of the signal lattice thereby improving the performance of MIMO receiver. Our results challenge the popular perception that for a SD receiver a large number of candidates within the search sphere is essential for good performance. Instead, it is shown that accurate estimation of the counter-hypothesis is equally important and in fact, the performance of the proposed augmented SD receiver with only single candidate approaches that of a classical SD with multiple candidates. Bit error rate performance of the proposed method when compared with the existing research works on soft output generation for the same number of candidates shows that our proposed method outperforms them by upto 3 dB.

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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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Channel state information based LLR clipping in list MIMO detection

Cited by 19 publications

References 9 publications

Enhanced QRD-M algorithm for soft-output MIMO detection

Enhanced QRD-M algorithm for soft-output MIMO detection

Likelihood ascent search augmented sphere decoding receiver for MIMO systems using M‐QAM constellations

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

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