申滨^,,
吴和彪,
赵书锋,
崔太平
重庆邮电大学通信与信息工程学院 重庆 400065
基金项目:国家重大研发计划(2017YFE0118900)，欧盟H2020项目(734798)

详细信息

作者简介:申滨：男，1978年生，教授，研究方向为认知无线电、大规模MIMO等
吴和彪：男，1994年生，硕士生，研究方向为大规模机器类系统多用户检测
赵书锋：男，1991年生，硕士生，研究方向为大规模MIMO系统信号检测
崔太平：男，1981年生，讲师，研究方向为认知无线电、车联网

通讯作者:申滨　shenbin@cqupt.edu.cn

中图分类号:TN929.5

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出版历程

收稿日期:2019-12-13
修回日期:2020-06-23
网络出版日期:2020-07-18
刊出日期:2020-12-08

Sparsity-aware Ordered Successive Interference Cancellation Based Multi-user Detection for Uplink mMTC

Bin SHEN^,,
Hebiao WU,
Shufeng ZHAO,
Taiping CUI
School of Communications and Information Engineering, Chongqing University of Posts andTelecommunications, Chongqing 400065, China
Funds:The National Key R&D Program of China (2017YFE0118900), The EU H2020 Project (734798)


摘要
摘要:在大规模机器类通信(mMTC)系统中，以用户活跃性为先验信息，接收机可以基于稀疏感知最大后验概率(S-MAP)准则来检测多用户信号。为了降低S-MAP检测的计算复杂度，基于干扰消除的思想，该文提出一种改进的活跃性感知有序正交三角分解(IA-SQRD)算法，以适用于mMTC系统上行链路多用户信号检测。IA-SQRD算法将传统的活跃性感知有序正交三角分解(A-SQRD)算法的最终解作为初始解，并额外增加迭代干扰消除操作，以进一步提高检测性能。此外，利用与改进A-SQRD算法相似的思路，该文对稀疏感知串行干扰消除(SA-SIC)、有序正交三角分解(SQRD)及数据相关的排序和正则化(DDS)算法亦进行了改进设计，分别获得了相应的改进型算法，即ISA-SIC、I-SQRD及I-DDS算法。仿真结果表明：相对于A-SQRD算法，在未显著增加计算复杂度的情况下，在系统误比特率(BER)为
\begin{document}$2.5 \times {10^{ - 2}}$\end{document}时，该文所提IA-SQRD算法可取得3 dB性能增益；并且，对于不同的活跃概率或扩频序列长度等参数配置下的mMTC系统，IA-SQRD算法相对于其它算法均表现出更优良的多用户检测性能。
关键词:多用户检测/
大规模机器类通信/
最大后验概率/
串行干扰消除
Abstract:In massive Machine-Type Communication (mMTC) systems, when the user activity is exploited as a priori information for the receiver, the Sparsity-aware Maximum A Posteriori probability (S-MAP) criterion can be used to recover the sparse multi-user vectors over the uplink mMTC systems. In order to reduce the computational complexity of S-MAP detection, based on interference cancellation mechanism, an Improved Activity-aware Sorted QR Decomposition (IA-SQRD) algorithm is proposed in this paper. The IA-SQRD algorithm utilizes the final solution of the A-SQRD algorithm as the initial solution and the iterative interference cancellation operation is performed to improve further the detection performance. Following the same philosophy in improving the A-SQRD algorithm, the conventional Sparsity-Aware Successive Interference Cancellation (SA-SIC), Sorted QR Decomposition (SQRD), and Data-Dependent Sorting and regularization (DDS) algorithms are modified to enhance the performance, respectively. Simulation results verify that compared with the A-SQRD algorithm, a 3 dB gain is achieved by the proposed IA-SQRD algorithm when the Bit Error Rate (BER) is
\begin{document}$2.5 \times {10^{ - 2}}$\end{document}, without significantly increasing the computational complexity. In addition, given different system configurations in terms of active probability and the length of spread spectrum sequence, the proposed IA-SQRD also exhibits better performance than that of the other algorithms mentioned in this paper.
Key words:Multi-user detection/
Massive Machine-Type Communication (mMTC)/
Maximum A Posteriori (MAP) probability/
Successive interference cancellation



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