An improved list sphere decoder （ILSD） is proposed based on the conventional list sphere decoder （LSD） and the reduced- complexity maximum likelihood sphere-decoding algorithm. Unlike the conventional LSD with fix...An improved list sphere decoder （ILSD） is proposed based on the conventional list sphere decoder （LSD） and the reduced- complexity maximum likelihood sphere-decoding algorithm. Unlike the conventional LSD with fixed initial radius, the ILSD adopts an adaptive radius to accelerate the list cdnstruction. Characterized by low-complexity and radius-insensitivity, the proposed algorithm makes iterative joint detection and decoding more realizable in multiple-antenna systems. Simulation results show that computational savings of ILSD over LSD are more apparent with more transmit antennas or larger constellations, and with no performance degradation. Because the complexity of the ILSD algorithm almost keeps invariant with the increasing of initial radius, the BER performance can be improved by selecting a sufficiently large radius.展开更多
将二维映射下的软输入软输出解映射MAX-LOG-MAP算法扩展到多维映射比特交织编码调制及迭代译码(BICM-ID)系统,并首次结合多天线系统下的列表球形译码,提出一种简化MD-LSD(multi-dimensional list sphere decoding)算法。新算法大幅降低...将二维映射下的软输入软输出解映射MAX-LOG-MAP算法扩展到多维映射比特交织编码调制及迭代译码(BICM-ID)系统,并首次结合多天线系统下的列表球形译码,提出一种简化MD-LSD(multi-dimensional list sphere decoding)算法。新算法大幅降低了多维解映射的计算复杂度。仿真结果表明,列表长度满足一定范围时,采用简化MD-LSD算法解映射算法的性能损失可以忽略。展开更多
Recently, a new soft-in soft-out detection algorithm based on the Markov Chain Monte Carlo (MCMC) simulation technique for Multiple-Input Multiple-Output (MIMO) systems is proposed, which is shown to perform significa...Recently, a new soft-in soft-out detection algorithm based on the Markov Chain Monte Carlo (MCMC) simulation technique for Multiple-Input Multiple-Output (MIMO) systems is proposed, which is shown to perform significantly better than their sphere decoding counterparts with relatively low complexity. However, the MCMC simulator is likely to get trapped in a fixed state when the channel SNR is high, thus lots of repetitive samples are observed and the accuracy of A Posteriori Probability (APP) estimation deteriorates. To solve this problem, an improved version of MCMC simulator, named forced-dispersed MCMC algorithm is proposed. Based on the a posteriori variance of each bit, the Gibbs sampler is monitored. Once the trapped state is detected, the sample is dispersed intentionally according to the a posteriori variance. Extensive simulation shows that, compared with the existing solution, the proposed algorithm enables the markov chain to travel more states, which ensures a near-optimal performance.展开更多
基金The National Natural Science Founda-tion of China ( No 60496316)the National Hi-Tech Re-search and Development Program (863) of China (No2006-AA01Z270)
文摘An improved list sphere decoder （ILSD） is proposed based on the conventional list sphere decoder （LSD） and the reduced- complexity maximum likelihood sphere-decoding algorithm. Unlike the conventional LSD with fixed initial radius, the ILSD adopts an adaptive radius to accelerate the list cdnstruction. Characterized by low-complexity and radius-insensitivity, the proposed algorithm makes iterative joint detection and decoding more realizable in multiple-antenna systems. Simulation results show that computational savings of ILSD over LSD are more apparent with more transmit antennas or larger constellations, and with no performance degradation. Because the complexity of the ILSD algorithm almost keeps invariant with the increasing of initial radius, the BER performance can be improved by selecting a sufficiently large radius.
文摘将二维映射下的软输入软输出解映射MAX-LOG-MAP算法扩展到多维映射比特交织编码调制及迭代译码(BICM-ID)系统,并首次结合多天线系统下的列表球形译码,提出一种简化MD-LSD(multi-dimensional list sphere decoding)算法。新算法大幅降低了多维解映射的计算复杂度。仿真结果表明,列表长度满足一定范围时,采用简化MD-LSD算法解映射算法的性能损失可以忽略。
文摘Recently, a new soft-in soft-out detection algorithm based on the Markov Chain Monte Carlo (MCMC) simulation technique for Multiple-Input Multiple-Output (MIMO) systems is proposed, which is shown to perform significantly better than their sphere decoding counterparts with relatively low complexity. However, the MCMC simulator is likely to get trapped in a fixed state when the channel SNR is high, thus lots of repetitive samples are observed and the accuracy of A Posteriori Probability (APP) estimation deteriorates. To solve this problem, an improved version of MCMC simulator, named forced-dispersed MCMC algorithm is proposed. Based on the a posteriori variance of each bit, the Gibbs sampler is monitored. Once the trapped state is detected, the sample is dispersed intentionally according to the a posteriori variance. Extensive simulation shows that, compared with the existing solution, the proposed algorithm enables the markov chain to travel more states, which ensures a near-optimal performance.
