高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

大规模MIMO系统中基于二对角矩阵分解的低复杂度检测算法

曹海燕 杨敬畏 方昕 许方敏

刘永俊, 陈才扣, 王正群. 修正的最大散度差鉴别分析及人脸识别[J]. 电子与信息学报, 2008, 30(1): 190-193. doi: 10.3724/SP.J.1146.2006.00811
引用本文: 曹海燕, 杨敬畏, 方昕, 许方敏. 大规模MIMO系统中基于二对角矩阵分解的低复杂度检测算法[J]. 电子与信息学报, 2018, 40(2): 416-420. doi: 10.11999/JEIT170498
Liu Yong-jun, Chen Cai-kou, Wang Zheng-qun . Modified Maximum Scatter-difference Discriminant Analysis and Face Recognition[J]. Journal of Electronics & Information Technology, 2008, 30(1): 190-193. doi: 10.3724/SP.J.1146.2006.00811
Citation: CAO Haiyan, YANG Jingwei, FANG Xin, XU Fangmin. Low Complexity Detection Algorithm Based on Two-diagonal Matrix Decomposition in Massive MIMO Systems[J]. Journal of Electronics & Information Technology, 2018, 40(2): 416-420. doi: 10.11999/JEIT170498

大规模MIMO系统中基于二对角矩阵分解的低复杂度检测算法

doi: 10.11999/JEIT170498
基金项目: 

国家自然科学基金(61501158, 61379027),浙江省自然科学基金(LY14F010019, LQ15F01004)

Low Complexity Detection Algorithm Based on Two-diagonal Matrix Decomposition in Massive MIMO Systems

Funds: 

The National Natural Science Foundation of China (61501158, 61379027), The Natural Science Foundation of Zhejiang Province (LY14F010019, LQ15F01004)

  • 摘要: 在大规模多输入多输出(MIMO)系统的上行链路检测算法中,最小均方误差(MMSE)算法是接近最优的,但算法涉及到大矩阵求逆运算,计算复杂度仍然较高。近年提出的基于诺依曼级数近似的检测算法降低了复杂度但性能有一定的损失。为了降低复杂度的同时逼近MMSE算法性能,该文提出基于二对角矩阵分解的诺依曼级数(Neumann Series)近似,即将大矩阵分解为以两条主对角线上元素组成的矩阵与空心矩阵之和。理论分析与仿真结果表明所提算法检测性能逼近MMSE检测算法,且其复杂度从O(K3)降低到O(K2),这里K是用户的数目。
  • ANDREWA J G, BUZZI S, WAN C, et al. What will 5G be?[J]. IEEE Journal on Selected Areas in Communications, 2014, 32(6): 1065-1082. doi: 10.1109/JSAC.2014.2328098.
    MARZETTA T L. Noncooperative cellular wireless with unlimited numbers of base station antennas[J]. IEEE Transactions on Wireless Communications, 2010, 9(11): 3590-3600. doi: 10.1109/TWC.2010.092810.091092.
    NGO H Q, LARSSON E G, and MARZETTA T L. Energy and spectral efficiency of very large multiuser MIMO systems [J]. IEEE Transactions on Communications, 2013, 61(4): 1436-1449. doi: 10.1109/TCOMM.2013.020413.110848.
    RUSEK F, PERSSON D, LAU B K, et al. Scaling up MIMO: Opportunities and challenges with very large arrays[J]. IEEE Signal Processing Magazine, 2012, 30(1): 40-60. doi: 10.1109/ MSP.2011.2178495.
    VIVONE G and BRACA P. Joint probabilistic data association tracker for extended target tracking applied to X-band marine radar data[J]. IEEE Journal of Oceanic Engineering, 2016, 41(4): 1007-1019. doi: 10.1109/JOE.2015. 2503499.
    YUAN G, HAN N, and KAISER T. Massive MIMO detection based on belief propagation in spatially correlated channels[C]. International Itg Conference on Systems, Communications and Coding, Hamburg, Germany, 2017: 1-6.
    YIN B, WU M, STYDER C, et al. Implementation trade-offs for linear detection in large-scale MIMO systems[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, 2013: 2679-2683. doi: 10.1109/ ICASSP.2013.6638142.
    HOCHWALD B M, MARZETTA T L, and TAROKH V. Multiple-antenna channel hardening and its implications for rate feedback and scheduling[J]. IEEE Transactions on Information Theory, 2004, 50(9): 1893-1909. doi: 10.1109/ TIT.2004.833345.
    TANG C, LIU C, YUAN L, et al. High precision low complexity matrix inversion based on Newton iteration for data detection in the massive MIMO[J]. IEEE Communications Letters, 2016, 20(3): 490-493. doi: 10.1109/ LCOMM.2015.2514281.
    NING J, LU Z, XIE T, et al. Low complexity signal detector based on SSOR method for massive MIMO systems[C]. IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, Ghent, 2015: 1-4. doi: 10.1109/BMSB. 2015.7177185.
    GAZZAH H. Low-complexity delay-controlled blind MMSE/ ZF multichannel equalization[C]. IEEE GCC Conference and Exhibition, Dubai, 2011: 100-103. doi: 10.1109/IEEEGCC. 2011.5752472.
    WU M, YIN B, VOSOUGHI A, et al. Approximate matrix inversion for high-throughput data detection in the large-scale MIMO uplink[C]. IEEE International Symposium on Circuits and Systems, Beijing, 2013: 2155-2158.
    VORST H A V D. An iterative solution method for solving f (A) x=b, using Krylov subspace information obtained for the symmetric positive definite matrix A[J]. Journal of Computational and Applied Mathematics, 1987, 18(2): 249-263.
  • 期刊类型引用(10)

    1. 暴琳,朱志宇,孙晓燕,徐标. 面向多源异构数据的个性化搜索和推荐算法综述. 控制理论与应用. 2024(02): 189-209 . 百度学术
    2. 龚桃,杨晓霞,李怡洁. 融合用户活跃度的上下文感知兴趣点推荐算法. 应用科技. 2024(04): 91-99 . 百度学术
    3. 徐红艳,党依铭,冯勇,王嵘冰. 融合时间信息的序列商品推荐模型. 计算机技术与发展. 2023(03): 139-145 . 百度学术
    4. 邹小花,邓伦丹. 基于退火算法的软件测试数据侧信道缓存仿真. 计算机仿真. 2023(03): 385-389 . 百度学术
    5. 叶裴雷,张大斌. 高速运动目标特征关联检测模型仿真. 计算机仿真. 2023(04): 208-212 . 百度学术
    6. 李胜,刘桂云,何熊熊. 基于类别转移加权张量分解模型的兴趣点分区推荐. 电子与信息学报. 2022(01): 203-210 . 本站查看
    7. 王金威. 基于大数据分析的高校云招聘信息个性化推送研究. 安徽电子信息职业技术学院学报. 2022(04): 25-31 . 百度学术
    8. 张红霞,董燕辉,肖军弼,杨勇进. 基于行为延迟共享网络的个性化商品推荐方法. 电子与信息学报. 2021(10): 2993-3000 . 本站查看
    9. 李世宝,张益维,刘建航,崔学荣,张玉成. 基于知识图谱共同邻居排序采样的推荐模型. 电子与信息学报. 2021(12): 3522-3529 . 本站查看
    10. 叶继华,杨思渝,左家莉,王明文. 基于时空上下文信息的POI推荐模型研究. 电子与信息学报. 2021(12): 3546-3553 . 本站查看

    其他类型引用(12)

  • 加载中
计量
  • 文章访问数:  1561
  • HTML全文浏览量:  229
  • PDF下载量:  213
  • 被引次数: 22
出版历程
  • 收稿日期:  2017-05-24
  • 修回日期:  2017-10-24
  • 刊出日期:  2018-02-19

目录

    /

    返回文章
    返回