Precoding based on improved conjugate gradient algorithm in massive multi-input multi-output system

doi:10.11772/j.issn.1001-9081.2019040638

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (10): 3007-3012.DOI: 10.11772/j.issn.1001-9081.2019040638

• Network and communications • Previous Articles Next Articles

Precoding based on improved conjugate gradient algorithm in massive multi-input multi-output system

BAI He, LIU Ziyan, ZHANG Jie, WAN Peipei, MA Shanshan

College of Big Data and Information Engineering, Guizhou University, Guiyang Guizhou 550025, China

Received:2019-04-16 Revised:2019-06-28 Online:2019-08-21 Published:2019-10-10
Supported by:
This work is partially supported by the Natural Science Foundation of China (61863006), the Science and Technology Foundation of Guizhou Province ([2016]1054), the Joint Natural Science Foundation of Guizhou Province (LH[2017]7226), the Key Project of Science and Technology Plan of Guizhou Province ([2019]1416),

基于改进共轭梯度的大规模多输入多输出预编码

白鹤, 刘紫燕, 张杰, 万培佩, 马珊珊

贵州大学大数据与信息工程学院, 贵阳 550025

通讯作者: 刘紫燕
作者简介:白鹤(1995-),女,吉林大安人,硕士研究生,主要研究方向:大规模MIMO预编码;刘紫燕(1974-),女,贵州都匀人,副教授,硕士,CCF会员,主要研究方向:无线通信系统、移动机器人、大数据挖掘分析;张杰(1995-),男,四川巴中人,硕士研究生,主要研究方向:移动机器人;万培佩(1994-),男,湖北安陆人,硕士研究生,主要研究方向:深度学习;马珊珊(1996-),女,贵州遵义人,硕士研究生,主要研究方向:信道估计。
基金资助:
国家自然科学基金资助项目（61863006）；贵州省科学技术基金资助项目（黔科合基础[2016]1054）；贵州省联合资金资助项目（黔科合LH字[2017]7226号）；贵州省科技计划重点项目（[2019]1416）；贵州大学2017年度学术新苗培养及创新探索专项（黔科合平台人才[2017]5788）。

Abstract

Abstract: To solve the problems of high complexity of precoding and difficulty of linear matrix inversion in downlink Massive Multi-Input Multi-Output (Massive MIMO) system, a precoding algorithm based on low-complexity Symmetric Successive Over Relaxation Preconditioned Conjugate Gradient (SSOR-PCG) was proposed. Based on preconditioned Conjugate Gradient Precoding (PCG) algorithm, a Symmetric Successive Over Relaxation (SSOR) algorithm was used to preprocess the matrix to reduce its condition number, accelerating the convergence speed and the decreasing the complexity. Simulation results demonstrate that compared with PCG algorithm, the proposed algorithm has running time of around 88.93% shortened and achieves convergence when the Signal-to-Noise Ratio (SNR) is 26 dB. Furthermore, compared to zero-forcing precoding algorithm, the proposed algorithm requires only two iterations capacity-approaching performance,the overall complexity is reduced by one order of magnitude, and the bit error rate is decreased by about 49.94%.

Key words: Massive Multi-Input Multi-Output (Massive MIMO), linear precoding, conjugate gradient, Symmetric Successive Over Relaxation (SSOR)

摘要： 针对大规模多输入多输出（Massive MIMO）系统下行链路预编码实现复杂、线性预编码矩阵求逆困难等问题，提出一种基于对称逐步超松弛预处理共轭梯度法（SSOR-PCG）的低复杂度预编码算法。该算法在共轭梯度（PCG）算法的基础上，采用对称逐步超松弛分裂（SSOR）算法对矩阵进行预处理以降低矩阵的条件数，达到提高预编码算法收敛速度、降低复杂度的目的。仿真结果表明：与PCG算法相比，所提出的SSOR-PCG预编码算法运行时间缩短约88.93%，在信噪比为26 dB时已收敛；与迫零预编码算法相比，所提算法迭代2次即可获得与迫零预编码算法相近的系统容量性能，复杂度降低约一个数量级，误码率降低约49.94%。

关键词: 大规模多输入多输出, 线性预编码, 共轭梯度, 对称逐步超松弛

CLC Number:

TP391.9

BAI He, LIU Ziyan, ZHANG Jie, WAN Peipei, MA Shanshan. Precoding based on improved conjugate gradient algorithm in massive multi-input multi-output system[J]. Journal of Computer Applications, 2019, 39(10): 3007-3012.

白鹤, 刘紫燕, 张杰, 万培佩, 马珊珊. 基于改进共轭梯度的大规模多输入多输出预编码[J]. 计算机应用, 2019, 39(10): 3007-3012.

References

[1] 唐虎, 刘紫燕, 刘世美, 等. 基于压缩感知的大规模多输入多输出空间共稀疏信道估计[J]. 计算机应用, 2018, 38(4):1106-1110. (TANG H, LIU Z Y, LIU S M, et al. Spatially common sparsity channel estimation based on compressive sensing for massive multi-input multi-output system[J]. Journal of Computer Applications, 2018, 38(4):1106-1110.)
[2] KETSEOGLOU T, AYANOGLU E. Downlink precoding for massive MIMO systems exploiting virtual channel model sparsity[J]. IEEE Transactions on Communications, 2018, 66(5):1925-1939.
[3] GAO X, DAI L, HAN S, et al. Energy-Efficient hybrid analog and digital precoding for MmWave MIMO systems with large antenna arrays[J]. IEEE Journal on Selected Areas in Communications, 2016, 34(4):998-1009.
[4] GRASSI A, KURRAS M, PIRO G, et al.Density based clustering for downlink user grouping in FDD massive MIMO[C]//Proceedings of the 24th European Wireless Conference. Piscataway:IEEE, 2018:1-6.
[5] TRIFAN R, ENESCU A. MU-MIMO precoding performance conditioned by inter-user angular separation[C]//Proceedings of the 2018 International Symposium on Electronics and Telecommunications. Piscataway:IEEE, 2018:1-4.
[6] FENG M, XU Y. Low-complexity linear precoding for pilot contamination mitigation in multi-cell massive MIMO systems[C]//Proceedings of the 2017 International Symposium on Intelligent Signal Processing and Communication Systems. Piscataway:IEEE, 2017:807-811.
[7] LU Z, NING J, ZHANG Y, et al. Richardson method based linear precoding with low complexity for massive MIMO systems[C]//Proceedings of the IEEE 81st Vehicular Technology Conference. Piscataway:IEEE, 2015:1-4.
[8] NAM J, ADHIKARY A, AHN J, et al. Joint spatial division and multiplexing:opportunistic beamforming, user grouping and simplified downlink scheduling[J]. IEEE Journal of Selected Topics in Signal Processing, 2014, 8(5):876-890.
[9] JU M, QIAN J, LI Y, et al. Comparison of multiuser MIMO systems with MF, ZF and MMSE receivers[C]//Proceedings of the 2013 IEEE Third International Conference on Information Science and Technology. Piscataway:IEEE, 2013:1260-1263.
[10] QIN X, YAN Z, HE G. A near-optimal detection scheme based on joint steepest descent and Jacobi method for uplink massive MIMO systems[J]. IEEE Communications Letters, 2016, 20(2):276-279.
[11] XIE T, HAN Q, XU H, et al. A low-complexity linear precoding scheme based on SOR method for massive MIMO systems[C]//Proceedings of the IEEE 81st Vehicular Technology Conference. Piscataway:IEEE, 2015:1-5.
[12] 曲桦, 梁静, 赵季红, 等. 基于SOR-PCG的低复杂度信号检测算法研究[J]. 电视技术, 2016, 40(8):99-102, 117. (QU H, LIANG J, ZHAO J H, et al. Study on low-complexity signal detection based on successive over relaxation-preconditioned conjugate gradient method[J]. Video Engineering, 2016, 40(8):99-102, 117.)
[13] XIE T, DAI L, GAO X, et al. Low-complexity SSOR-based precoding for massive MIMO systems[J]. IEEE Communications Letters, 2016, 20(4):744-747.
[14] 杨伟茜. Massive MIMO系统中低复杂度预编码算法研究[D]. 赣州:江西理工大学, 2018:41-48. (YANG W Q. Research on low-complexity precoding algorithm in Massive MIMO system[D]. Ganzhou:Jiangxi University of Science and Technology, 2018:41-48.)
[15] 朱庆浩. 大规模MIMO系统预编码算法的研究与优化[D]. 赣州:江西理工大学, 2018:27-30. (ZHU Q H. Research and optimization of precoding algorithm for large-scale MIMO systems[D]. Ganzhou:Jiangxi University of Science and Technology, 2018:27-30.)
[16] 张弛. 大规模MIMO系统低复杂度预编码算法研究[D]. 南京:东南大学, 2018:20-35. (ZHANG C. Research on low-complexity precoding algorithm of large-scale MIMO system[D]. Nanjing:Southeast University, 2018:20-35.)
[17] 任彦. 大规模MIMO预编码算法的研究[D]. 成都:西南交通大学, 2017:30-42. (REN Y. Research on large-scale MIMO precoding algorithm[D]. Chengdu:Southwest Jiaotong University, 2017:30-42.)
[18] 李婉婉. 大规模天线系统中的预编码及相关技术研究[D]. 成都:电子科技大学, 2018:14-16. (LI W W. Research on precoding and related technologies in large-scale antenna systems[D]. Chengdu:University of Electronic Science and Technology of China, 2018:14-16.)

Precoding based on improved conjugate gradient algorithm in massive multi-input multi-output system

基于改进共轭梯度的大规模多输入多输出预编码

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	YANG Libo, JIANG Tiegang, XU Zhiqiang. Hybrid gradient based hard thresholding pursuit algorithm [J]. Journal of Computer Applications, 2020, 40(3): 912-916.
[2]	TANG Hu, LIU Ziyan, LIU Shimei, FENG Li. Spatially common sparsity channel estimation based on compressive sensing for massive multi-input multi-output system [J]. Journal of Computer Applications, 2018, 38(4): 1106-1110.
[3]	ZHANG Yanfeng, FAN Xi'an, YIN Zhiyi, JIANG Tiegang. Backtracking-based conjugate gradient iterative hard thresholding reconstruction algorithm [J]. Journal of Computer Applications, 2018, 38(12): 3580-3583.
[4]	LIU Ziyan, TANG Hu, LIU Shimei. Multi-cell channel estimation based on compressive sensing in MASSIVE MIMO system [J]. Journal of Computer Applications, 2017, 37(9): 2474-2478.
[5]	FU Chaojiang, CHEN Hongjun. Finite element parallel computing based on minimal residual-preconditioned conjugate gradient method [J]. Journal of Computer Applications, 2015, 35(12): 3387-3391.
[6]	ZHANG Peizhou, WANG Xizhao, GU Di, ZHAO Shixin. Extreme learning machine based on conjugate gradient [J]. Journal of Computer Applications, 2015, 35(10): 2757-2760.
[7]	WANG Guanhao XU Jun. Joint reconstruction of breast dynamic contrast-enhanced magnetic resonance images with group sparsity method [J]. Journal of Computer Applications, 2014, 34(11): 3304-3308.
[8]	WEI Haoyang ZENG Guosun DING Chunling. Parallel computing and numerical analysis of laminar diffusion combustion on GPU [J]. Journal of Computer Applications, 2013, 33(09): 2428-2431.
[9]	WANG Jianchen QI Xiaohui SHAN Ganlin. Mixed optimization strategy based on eating particle swarm and conjugate gradient [J]. Journal of Computer Applications, 2013, 33(08): 2257-2260.
[10]	JIN Yuping DANG Jie. Boiler combustion efficiency optimization based on improved radial basis neural network [J]. Journal of Computer Applications, 2013, 33(06): 1771-1779.
[11]	ZHANG Jianfei SHEN Defei. GPU-based preconditioned conjugate gradient method for solving sparse linear systems [J]. Journal of Computer Applications, 2013, 33(03): 825-829.
[12]	SONG Qing-zeng GU Jun-hua. Design and implementation of conjugate gradient iterative solver on FPGA [J]. Journal of Computer Applications, 2011, 31(09): 2571-2573.
[13]	. Chaotic particle swarm optimization algorithm based on nonlinear conjugate gradient algorithm [J]. Journal of Computer Applications, 2009, 29(12): 3273-3276.
[14]	Yu Liu Hong-Chun Yuan Zheng Liang. Study on acceleration technique for ADI-FDTD algorithm based on GPU [J]. Journal of Computer Applications, 2008, 28(7): 1882-1885.
[15]	. Variable conjugate gradient algorithm and its application in prediction of the total yield of main agricultural products of China [J]. Journal of Computer Applications, 2006, 26(11): 2765-2768.