基于半正定松弛的MIMO盲检测

李浩; 彭华

doi:10.11999/JEIT151444

基于半正定松弛的MIMO盲检测

doi: 10.11999/JEIT151444

李浩¹,
彭华¹

基金项目:

国家自然科学基金(61401511)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-12-22
- 修回日期: 2016-07-08
- 刊出日期: 2016-11-19

Blind Detection of MIMO via Semidefinite Relaxation

LI Hao¹,
PENG Hua¹

Funds:

The National Natural Science Foundation of China (61401511)

摘要

摘要: 为解决MIMO系统盲检测问题，该文以最大似然序列检测为估计准则，通过推导建立了一种新的半正定松弛(SemiDefinite Relaxation, SDR)求解模型，使得到的松弛解的秩等于发送天线数。为了解决了松弛解秩大于1时估计原始发送序列的难题，该文提出一种特征向量近似法和随机法相结合的方法。通过限定目标函数的取值上限，使算法能够根据目标函数值自适应判断求解发送序列个数，从而减少每次求解的约束个数和SDR的求解次数，分析表明算法的计算复杂度与发送天线数成线性关系。最后，通过仿真表明所提算法能够在与秩1的算法性能保持相当的条件下减少计算时间，并验证了算法计算复杂度与发送天线数成线性关系。
- 多输入多输出 /
- 盲检测 /
- 半正定松弛
Abstract: In order to solve the problem of blind detection of MIMO system, this paper takes maximum-likelihood sequence detection as the criterion and derives the formulas to get a model based on SemidDefinite Relaxation. The rank of SDR solution equals to the number of the transmit antennas. For the rank of SDR solution is greater than 1, a new method is proposed to approximate the solution of the original problem, which combines the eigenvector approximation method and randomization method. By setting the upper limit of objective function, the proposed method could judge the number of detection sequence adaptively and reduce constrains number and the number of solving SDR. The analysis shows that the computation complexity of proposed method has linear relationship with the number of transmit antennas. At last, simulation results indicate that compared with Rank-1 algorithm, the proposed detector could provide the same bit error performance with decrease of computation cost, and validate the linear relationship between the computation complexity and the number of transmit antennas.
- Multiple-Input Multiple-Output (MIMO) /
- Blind detection /
- SemiDefinite Relaxation (SDR)

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参考文献(16)

LUO Zhiquan, MA Wingkin, ANTHONY S M, et al. Semidefinite relaxation of quadratic optimization problems[J]. IEEE Signal Processing Magazine, 2010, 27(3): 20-34.

罗涛, 刘宏伟, 严俊坤, 等. 基于半正定秩松弛方法的稳健波束形成[J]. 电子与信息学报, 2014, 36(7): 1545-1551. doi: 10.3724/SP.J.1146.2013.01046.

LUO Tao, LIU Hongwei, YAN Junkun, et al. Robust beamforming via semidefinite rank relaxation[J]. Journal of Electronics Information Technology, 2014, 36(7): 1545-1551. doi: 10.3724/SP.J.1146.2013.01046.

MA Wingkin, DAVIDSON T N, WONG K M, et al. Quasi- maximum-likelihood multiuser detection using semi-definite relaxation with application to synchronous CDMA[J]. IEEE Transactions on Signal Processing, 2002, 50(4): 912-922.

STEINGIMSSON B, LUO Zhiquan, and WONG K. Soft quasi-maximum-likelihood detection for multiple-antenna wireless channels[J]. IEEE Transactions on Signal Processing, 2003, 51(11): 2710-2719.

NEKUII M, KISIALIOU M, DAVIDSON T N, et al. Efficient soft-output demodulation of MIMO QPSK via semidefinite relaxation[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(8): 1426-1437.

MA Wingkin, CHING Pakchung, and DING Zhi. Semidefinite relaxation based multiuser detection for M-ary PSK multiuser systems[J]. IEEE Transaction on Signal Processing, 2004, 52(10): 2862-2872.

WIESEL A, ELDAR Y C, and SHAMAI S. Semidefinite relaxation for detection of 16-QAM signaling in MIMO channels[J]. IEEE Signal Processing Letters, 2005, 12(9): 653-656.

SHAO Z Y, CHEUNG S W, and YUK T I. Comparison of semidefinite relaxation detectors for high-order modulation MIMO systems[J]. International Journal of Antennas and Propagation, 2014, 6(3): 1-8.

JALDEN J, MARTIN C, and OTTERSTEN B. Semidefinite programming for detection in linear systems-optimality conditions and space-time decoding[C]. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Hong Kong, 2003: 9-12.

KIM Minjoon, PARK Jangyong, KIM Kilhwan, et al. Exact ML criterion based on semidefinite relaxation for MIMO systems[J]. IEEE Signal Processing Letters, 2014, 21(3): 343-346.

JALDEN J and OTTERSTEN B. The diversity order of the semidefinite relaxation detector[J]. IEEE Transactions on Information Theory, 2008, 54(4): 1406-1422.

XU Zi, HONG Mingyi, and LUO Zhiquan. Probabilistic analysis of semidefinite relaxation for binary quadratic minimization[J]. SIAM Journal on Optimization, 2014, 24(3): 1265-1293.

韩飞. 正交空时分组码的最大似然盲检测算法研究[D]. [硕士论文], 西南交通大学, 2014.

LI Qingyu, BAI Erwei, and DING Zhi. Blind source separation of signals with known alphabets using epsi- approximation algorithms[J]. IEEE Transactions on Signal Processing, 2003, 51(1): 1-10.

MANUEL A and MIGUEZ V J. Maximum-likelihood sequence detection in time- and frequency-selective MIMO channels with unknown order[J]. IEEE Transactions on Vehicular Technology, 2009, 58(1): 499-504.

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