空间调制系统下改进的QRD-M检测算法

doi:10.11772/j.issn.1001-9081.2018030721

计算机应用 ›› 2018, Vol. 38 ›› Issue (10): 2950-2954.DOI: 10.11772/j.issn.1001-9081.2018030721

空间调制系统下改进的QRD-M检测算法

周围¹, 郭梦雨^1,2, 向丹蕾^1,2

1. 移动通信技术重庆市重点实验室(重庆邮电大学), 重庆 400065;
2. 重庆邮电大学通信与信息工程学院, 重庆 400065

收稿日期:2018-04-10 修回日期:2018-05-11 出版日期:2018-10-10 发布日期:2018-10-13
通讯作者: 郭梦雨
作者简介:周围(1971-),男,重庆人,教授,博士,主要研究方向:无线移动通信、通信系统及信号处理、多输入多输出天线技术;郭梦雨(1993-),女,河南平顶山人,硕士研究生,主要研究方向:无线移动通信、信号检测;向丹蕾(1994-),女,重庆人,硕士研究生,主要研究方向:无线移动通信、信号检测。
基金资助:
重庆市基础与前沿研究计划项目（cstc2015jcyjA40040）。

Improved QRD-M detection algorithm for spatial modulation system

ZHOU Wei¹, GUO Mengyu^1,2, XIANG Danlei^1,2

1. Chongqing Key Laboratory of Mobile Communications Technology(Chongqing University of Posts and Telecommunications), Chongqing 400065, China;
2. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2018-04-10 Revised:2018-05-11 Online:2018-10-10 Published:2018-10-13
Supported by:
This work is partially supported by Foundation and Frontier Research Project of Chongqing (cstc2015jcyjA40040).

摘要/Abstract

摘要： 空间调制（SM）系统中性能最优的最大似然（ML）检测算法复杂度很高，用基于信道矩阵QR分解的M算法（QRD-M）可以降低复杂度，但传统QRD-M算法检测时，每层都保留固定的M个节点，仍会造成额外的计算量。针对传统QRD-M算法中存在的问题，提出一种低复杂度的动态M值QRD-M检测算法——LC-QRD-dM。LC-QRD-dM算法利用设计的阈值与累积分支度量值进行比较，每层自适应地选择不超过M的保留节点数，相对于传统QRD-M算法以牺牲少量性能为代价大大降低了复杂度。接着又针对该改进算法在信道衰落较深时会产生较大误码率的问题，进一步提出一种基于信道状态的动态M值QRD-M检测算法——CS-QRD-dM。CS-QRD-dM利用LC-QRD-dM的原理，在低信噪比（SNR）时，每层根据阈值选择不小于M的保留节点数；在高信噪比时，每层则选择不超过M的保留节点数。理论分析和仿真结果表明：相比传统QRD-M，CS-QRD-dM在低信噪比时有约1.3 dB的信噪比增益（误码率为10^-2），以增加少量复杂度为代价，显著地改善了检测性能；在高信噪比时，其检测性能及复杂度与LC-QRD-dM相同。

关键词: 空间调制, 最大似然, 基于QR分解的M算法, 计算复杂度, 信道状态

Abstract: In Spatial Modulation (SM) system, the Maximum Likelihood (ML) detection algorithm with the best performance has high complexity, while the complexity can be reduced by M-algorithm based on QR decomposition (QRD-M) of channel matrix. However, when the traditional QRD-M algorithm is used, fixed M nodes were chosen at each layer, which leads to additional computation. Therefore, for the problem of the traditional QRD-M algorithm, a Low-Complexity QR-Decomposition M-algorithm with dynamic value of M (LC-QRD-dM) was proposed. In LC-QRD-dM, by comparing the designed threshold with the cumulative branch metrics, the number of reserved nodes that does not exceed M was adaptively selected at each layer, thus reducing the computational complexity with the cost of a small amount of performance. Then, concerning the high bit error rate of LC-QRD-dM with deep channel fading, QR-Decomposition M-algorithm with dynamic value of M based on Channel State (CS-QRD-dM) was further proposed. Based on the principle of LC-QRD-dM, the number of reserved nodes that do not less than M was selected by the threshold value at each layer when the Signal-to-Noise Ratio (SNR) is not high; and the number of reserved nodes that do not exceed M was selected by the threshold value at each layer when the SNR is high. Theoretic analysis and simulation results show that, compared with the traditional QRD-M algorithm, CS-QRD-dM achieves about 1.3 dB SNR advantage (when the bit error rate is 10^-2) at low SNR, which can significantly improve the detection performance at the cost of small complexity increase; and its detection performance and complexity are the same as LC-QRD-dM at high SNR.

Key words: spatial modulation, maximum likelihood, M-algorithm based on QR decomposition (QRD-M), computational complexity, channel state

中图分类号:

TN929.5

周围, 郭梦雨, 向丹蕾. 空间调制系统下改进的QRD-M检测算法[J]. 计算机应用, 2018, 38(10): 2950-2954.

ZHOU Wei, GUO Mengyu, XIANG Danlei. Improved QRD-M detection algorithm for spatial modulation system[J]. Journal of Computer Applications, 2018, 38(10): 2950-2954.

参考文献

[1] CLERCKX B, LEE H, HONG Y J, et al. A practical cooperative multicell MIMO-OFDMA network based on rank coordination[J]. IEEE Transactions on Wireless Communications, 2013, 12(4):1481-1491.
[2] RENZO M D, HAAS H, GHRAYEB A, et al. Spatial modulation for generalized MIMO:challenges, opportunities, and implementation[J]. Proceedings of the IEEE, 2014, 102(1):56-103.
[3] MESLEH R, HAAS H, AHN C W, et al. Spatial modulation-a new low complexity spectral efficiency enhancing technique[C]//Proceedings of the 2006 First International Conference on Communications and Networking. Piscataway, NJ:IEEE, 2006:1-5.
[4] LI C, HUANG Y Z, RENZO M D, et al. Low-complexity ML detection for spatial modulation MIMO with APSK constellation[J]. IEEE Transactions on Vehicular Technology, 2015, 64(9):4315-4321.
[5] MESLEH R, HAAS H, SINANOVIC S, et al. Spatial modulation[J]. IEEE Transactions on Vehicular Technology, 2008, 57(4):2228-2241.
[6] 张文彬, 王孝, 陶臣嵩, 等.采用格基规约算法的空间调制检测方案[J]. 哈尔滨工业大学学报, 2015, 47(11):63-68. (ZHANG W B, WANG X, TAO C S, et al. Spatial modulation detection schemes based on lattice reduction algorithm[J]. Journal of Harbin Institute of Technology, 2015, 47(11):63-68.)
[7] LEE K. Doubly ordered sphere decoding for spatial modulation[J]. IEEE Communications Letters, 2015, 19(5):795-798.
[8] 李小文, 薛尧, 张丁全.空间调制系统下改进的匹配滤波检测算法[J]. 光通信研究, 2017(1):61-64. (LI X W, XUE Y, ZHANG D Q. Improved matching filter detection algorithm in spatial modulation system[J]. Study on Optical Communication, 2017(1):61-64.)
[9] ZHENG J, YANG X, LI Z. Low-complexity detection method for spatial modulation based on M-algorithm[J]. Electronics Letters, 2014, 50(21):1552-1554.
[10] KIM B, CHOI K. A very low complexity QRD-M algorithm based on limited tree search for MIMO systems[C]//Proceedings of the 2008 IEEE Vehicular Technology Conference. Piscataway, NJ:IEEE, 2008:1246-1250.
[11] 门宏志, 金明录.低复杂度空间调制MPSK信号的最优检测[J]. 通信学报, 2015, 36(8):118-124. (MEN H Z, JIN M L. Low-complexity optimal MPSK detection for spatial modulation[J]. Journal on Communications, 2015, 36(8):118-124.)
[12] ZHENG J H, YANG X B, LI Z. Low complexity detection method for spatial modulation based on M-algorithm[J]. Electronics Letters, 2014, 50(21):1552-1554.
[13] 王奔, 张文彬, 赵洪林.空间调制信号的低复杂度球形译码算法[J]. 哈尔滨工业大学学报, 2017, 49(5):22-30. (WANG B, ZHANG W B, ZHAO H L. Low complexity sphere decoding algorithm for spatial modulation signals[J]. Journal of Harbin Institute of Technology, 2017, 49(5):22-30.)

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空间调制系统下改进的QRD-M检测算法

Improved QRD-M detection algorithm for spatial modulation system

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