Bayesian Learning Based Multiuser Detection for Grant-Free NOMA Systems
Bayesian Learning Based Multiuser Detection for Grant-Free NOMA Systems
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DOI：
10.1109/twc.2022.3148262
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发表时间：
2022
影响因子：
10.4
通讯作者：
Xiaoxu Zhang;P. Fan;Jiaqi Liu;L. Hao
中科院分区：
文献类型：
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作者：
Xiaoxu Zhang;P. Fan;Jiaqi Liu;L. Hao
免授权非正交多址接入（GF-NOMA）被认为是支持机器类型通信（MTC）大规模连接的一种有前途的技术。设计高效、高性能的多用户检测（MUD）方案是GF-NOMA的一个挑战性问题，特别是当活跃用户数未知且相对较多时。本文采用稀疏贝叶斯学习（SBL）方法解决MTC中GF-NOMA的多用户检测问题。在某个接入时隙内的MUD问题被公式化为单测量向量（SMV）模型，并通过基于SBL的方法有效地解决。为了进一步提高多用户检测的性能，我们建立了一个多测量矢量（MMV）模型，并开发基于块SBL的多用户检测方法，通过利用连续接入时隙的用户活动的时间相关性。然后，将上述算法的使用扩展到具有相对较高或准稀疏用户活动的场景，我们通过后稀疏错误恢复方法提出了新的基于SBL的MUD算法，用于SMV和MMV问题模型。仿真结果表明，所提出的基于SBL的多用户检测算法取得了显着的性能增益比传统的，特别是当活跃用户的数量是未知的，相对较高的。
Grant-Free Non-Orthogonal Multiple Access (GF-NOMA) is considered as a promising technology to support the massive connectivity of Machine-Type Communications (MTC). The design of efficient and high-performance multi-user detection (MUD) scheme is a challenging issue of GF-NOMA, especially when the number of active users is unknown and relatively high. This paper adopts Sparse Bayesian Learning (SBL) approaches to solve the MUD problem of GF-NOMA in MTC. The MUD problem within a certain access slot is formulated as a Single Measurement Vector (SMV) model and efficiently solved via SBL-based methods. To further improve the MUD performance, we set up a Multiple Measurement Vector (MMV) model and develop block SBL-based MUD methods, by exploiting the temporal correlation of user activity over successive access slots. Then to extend the usage of the aforementioned algorithms to the scenarios with relatively high, or quasi-sparse, user activity, we propose novel SBL-based MUD algorithms via post sparse error recovery methodology, for both the SMV and MMV problem models. Simulation results show that the proposed SBL-based MUD algorithms achieve substantial performance gain over traditional ones, especially when the number of active users is unknown and relatively high.