SIMO-MUSA系统下的块稀疏多用户检测算法

陈发堂; 石贝贝; 邓青

doi:doi:10.13756/j.gtxyj.2020.05.017

光通信研究, 2020 (5): 73, 网络出版: 2021-04-17

SIMO-MUSA系统下的块稀疏多用户检测算法

Block Sparse Multiuser Detection Algorithm in SIMO-MUSA System

论文大纲

陈发堂石贝贝 ^*邓青

作者单位

重庆邮电大学通信与信息工程学院, 重庆 400065

多用户共享接入压缩感知块稀疏多用户检测 MUSA compressive sensing block sparsity multi-user detection

摘要

对于上行免调度的多用户共享接入(MUSA)方案, 用户具备随机突发接入系统的特性, 用户信号具有稀疏性, 可利用压缩感知的相关算法进行多用户检测。针对活跃用户数未知的情况, 文章提出了基于验证误差的正交匹配追踪(VE-OMP)多用户检测算法。所提算法利用误差在迭代次数等于用户稀疏度时能够达到最小值来估计稀疏度, 且验证误差的思想移植性强, 可用于块稀疏压缩感知。考虑基站端配备多天线的情况, 将发送信号转为块稀疏模型, 提出验证误差的块稀疏正交匹配追踪(VE-BOMP)算法进行活跃用户数据检测。仿真结果表明, VE-OMP算法能够在稀疏度未知的情况下对多用户同时进行活跃性估计和数据检测, VE-BOMP算法可用于多天线接收系统, 其检测性能随天线数的增加而提高。

Abstract

For the uplink grant-free Multi-User Shared Access (MUSA) scheme, the user has the characteristics of access system randomly, and the user signal has sparseness, which can use the relevant algorithm of compression perception for multi-user detection. In view of the unknown number of active users, a multi-user detection algorithm based on Validation Error Orthogonal Matching Pursuit (VE-OMP) is proposed. The proposed algorithm can estimate the sparsity by using the error to reach the minimum value when the number of iterations is equal to the user’s sparsity. It is also noted that the idea of verifying the error has strong portability, which can be used for block sparse compression sensing. Considering that the base station is equipped with multiple antennas, the transmission signal is transformed into a block sparse model. A Validation Error Block Sparse Orthogonal Matching Pursuit (VE-BOMP) algorithm is also proposed to detect active user data. The simulation results show that the VE-OMP algorithm can jointly detect the activity and data of the users under the condition of unknown sparsity. The VE-BOMP algorithm can also be used in multi antenna receiving system, and its detection performance increases with the increase of the number of antennas.

参考文献

[1] Zhang Y, Guo Q, Wang Z, et al. Block Sparse Bayesian Learning based Joint User Activity Detection and Channel Estimation for Grant-Free NOMA Systems[J]. IEEE Transactions on Vehicular Technology, 2018, 67(10):9631-9640.

[2] Dai L, Wang B, Yuan Y, et al. Non-orthogonal Multiple Access for 5G: Solutions, Challenges, Opportunities, and Future Research Trends[J]. IEEE Communications Magazine, 2015, 53(9):74-81.

[3] Li T, Yan C, Li W, et al. On Uplink Non-orthogonal Multiple Access for 5G: Opportunities and Challenges[J]. China Communications, 2017, 14(12):142-152.

[4] Wang B, Dai L, Mir T, et al. Joint User Activity and Data Detection based on Structured Compressive Sensing for NOMA[J]. IEEE Communications Letters, 2016, 20(7):1473-1476.

[5] He K, Li Y, Yin C. Sparse Multi-user Detection for Non-orthogonal Multiple Access in 5G Systems[C]// 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). Valencia, Spain: IEEE, 2016: 7794930.

[6] Schepker H F, Dekorsy A . Sparse Multi-user Detection for CDMA Transmission Using Greedy Algorithms[C]// 2011 8th International Symposium on Wireless Communication Systems. Aachen, Germany: IEEE, 2011:291-295.

[7] Wang B, Dai L, Yuan Y, et al. Compressive Sensing based Multi-user Detection for Uplink Grant-Free Non-orthogonal Multiple Access[C]// Vehicular Technology Conference. Boston, MA, USA: IEEE, 2015: 7390876.

[8] Wang B, Dai L, Yuan Z, et al. Dynamic Compressive Sensing-based Multi-user Detection for Uplink Grant-Free NOMA[J]. IEEE Communications Letters, 2016, 20(11):2320-2323.

[9] 张男,龚磊,翟旭平.M2M通信系统下基于压缩感知的多用户检测技术[J].电子测量技术,2018(15):72-77.

[10] Fan F. Toeplitz-Structured Measurement Matrix Construction for Chaotic Compressive Sensing[C]// Fifth International Conference on Intelligent Control & Information Processing. Dalian, China: IEEE, 2015: 7010279.

[11] Do T T, Gan L, Nguyen N, et al. Sparsity Adaptive Matching Pursuit Algorithm for Practical Compressed Sensing[C]// 2008 42nd Asilomar Conference on Signals, Systems & Computers. Pacific Grove, CA, USA: IEEE, 2008:581-587.

[12] Du Y, Dong B, Zhu W, et al. Joint Channel Estimation and Multiuser Detection for Uplink Grant-Free NOMA[J]. IEEE Wireless Communications Letters, 2018, 7(4):682-685.

陈发堂, 石贝贝, 邓青. SIMO-MUSA系统下的块稀疏多用户检测算法[J]. 光通信研究, 2020, 46(5): 73. CHEN Fa-tang, SHI Bei-bei, DENG Qing. Block Sparse Multiuser Detection Algorithm in SIMO-MUSA System[J]. Study On Optical Communications, 2020, 46(5): 73.

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