Partial a Posteriori Probabilities Based Soft Detection for Turbo-MIMO Systems

You Ming-hou; Tao Xiao-feng; Cui Qi-mei; Zhang Ping

doi:10.3724/SP.J.1146.2009.01037

Volume 32 Issue 7

Aug. 2010

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You Ming-hou, Tao Xiao-feng, Cui Qi-mei, Zhang Ping. Partial a Posteriori Probabilities Based Soft Detection for Turbo-MIMO Systems[J]. Journal of Electronics & Information Technology, 2010, 32(7): 1531-1537. doi: 10.3724/SP.J.1146.2009.01037

Citation:

You Ming-hou, Tao Xiao-feng, Cui Qi-mei, Zhang Ping. Partial a Posteriori Probabilities Based Soft Detection for Turbo-MIMO Systems[J]. Journal of Electronics & Information Technology, 2010, 32(7): 1531-1537. doi: 10.3724/SP.J.1146.2009.01037

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Partial a Posteriori Probabilities Based Soft Detection for Turbo-MIMO Systems

doi: 10.3724/SP.J.1146.2009.01037

You Ming-hou^{①②;陶小峰
,},
Tao Xiao-feng,
Cui Qi-mei,
Zhang Ping

Received Date: 2009-07-24
Rev Recd Date: 2009-11-23
Publish Date: 2010-07-19

Abstract

Abstract

Iterative Tree Search (ITS) is an efficient M-algorithm based soft MIMO detection scheme. However, ITS often faces the problem that Log-Likelihood Ratio (LLR) values of some detected bits can not be evaluated. Although it can be somewhat solved by setting the LLR magnitude for these bits to a constant valueLLR clipping, the system performance would be degraded. To overcome this problem, this paper presents a new M-algorithm based soft detection scheme. The scheme recursively calculates the a posterior probabilities of partial symbol sequences at each stage of the tree, based on which the LLRs of those bits from the first stage to the current one are approximately computed，and then, by using M-algorithm, retains partial symbol sequences and extends them to the next stage. The scheme can ensure that the LLR of each bit can be calculated, and provide highly reliable LLRs. Considering that the LLRs of some bits may be evaluated several times, a reduced-complexity implementation method is given in the paper. In addition, the paper suggests a simple approach for calculating the a posterior probabilities of symbol sequences. Finally, simulation results show that the proposed algorithm can obtain better performance than ITS and achieve good performance-complexity trade-off.

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References(1)

References

Zhang Ping, Tao Xiaofeng, and Zhang Jianhua, et al.. Thevision from FuTURE beyond 3G TDD[J]. IEEECommunications Magazine, 2005, 43(1): 38-44.[2]Haykin S, Sellathurai M, and de Jong Y, et al.. Turbo-MIMOfor wireless communications[J]. IEEE CommunicationsMagazine, 2004, 42(10): 48-53.[3]尤明厚, 陶小峰, 张平, 等. 一种新颖的低复杂度MIMOBICM迭代接收机设计[J].电子学报, 2007, 35(6A): 127-130.You Ming-hou, Tao Xiao-feng, and Zhang Ping, et al.. Novellow-complexity iterative receiver for MIMO-BICM systems[J].Acta Electronica Sinica, 2007, 35(6A): 127-130.[4]王军, 李少谦. 非理想信道估计条件下的软输出MMSEV-BLAST 算法[J].电子与信息学报.2009, 31(5):1225-1228浏览[5]Studer C, Burg A, and Bolcskei H. Soft-output spheredecoding: algorithms and VLSI implementation [J].IEEEJournal of Selected Areas in Communications.2008, 26(2):290-300[6]Barbero L G and Thompson J S. Extending a fixedcomplexitysphere decoder to obtain likelihood informationfor Turbo-MIMO systems[J].IEEE Transactions onVehicular Technology.2008, 57(5):2804-2813[7]Wang L, Xu L, and Chen S, et al.. MMSE soft-interference-cancellation aided iterative center-shifting K-best spheredetection for MIMO channels[C]. Proceedings of IEEE ICC,Beijing, China, May, 2008: 3819-3823.[8]Hagenauer J and Kuhn C. The list-sequential(LISS)algorithm and its application [J].IEEE Transactions onCommunications.2007, 55(5):918-928[9]de Jong Y and Willink T J. Iterative tree search detection forMIMO wireless systems[J].IEEE Transactions onCommunications.2005, 53(6):930-935[10]Wang R and Giannakis G B. Approaching MIMO channelcapacity with soft detection based on hard sphere decoding[J].IEEE Transactions on Communications.2006, 54(4):587-590[11]Zimmermann E and Fettweis G. Unbiased MMSE tree searchMIMO detection[C]. Proceedings of International SymposiumWPMC, San Diego, USA, September, 2006: 17-20.[12]Poor H V and Verdu S. Probability of error in MMSEmultiuser detection[J].IEEE Transactions on InformationTheory.1997, 43(3):858-871

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