Improved Bayesian Learning Detectors for Uplink Grant-Free MIMO-NOMA
Improved Bayesian Learning Detectors for Uplink Grant-Free MIMO-NOMA
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DOI：
10.1109/lwc.2023.3317374
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发表时间：
2023-12
影响因子：
6.3
通讯作者：
Boran Yang;Xiaoxu Zhang;Li Hao;G. Karagiannidis
中科院分区：
文献类型：
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作者：
Boran Yang;Xiaoxu Zhang;Li Hao;G. Karagiannidis
无授权非正交多址（GF-NOMA）和多输入多输出（MIMO）技术是5G蜂窝物联网中大规模机器型通信（mMTC）的关键实现技术。另一方面，压缩感知（CS）被广泛接受用于多用户检测，由于mMTC的零星流量。在这封信中，我们提出了两个基于贝叶斯的CS算法用于上行链路无授权MIMO-NOMA。利用迭代期望最大化（EM）和最大比值组合技术，提出了空间增强稀疏贝叶斯学习（SE-SBL）算法，交替更新超参数值和组合观测信号。此外，我们将广义近似消息传递技术嵌入到SE-SBL中，并提出了一种低复杂度的计算方法。特别是，前面提到的贝叶斯算法不需要任何关于用户活动水平和噪声功率的先验知识。仿真结果表明，所提出的贝叶斯方法比目前的方法具有更好的性能增益。
Grant-free non-orthogonal multiple access (GF-NOMA) and multiple-input multiple-output (MIMO) techniques are key enablers for massive machine-type communications (mMTC) in 5G cellular Internet of Things. On the other hand, compressed sensing (CS) is widely accepted for multiuser detection, due to the sporadic traffic of mMTC. In this letter, we propose two Bayesian-based CS algorithms for uplink grant-free MIMO-NOMA. Exploiting the iterative expectation maximization (EM) and maximum ratio combining techniques, the spatially enhanced sparse Bayesian learning (SE-SBL) algorithm is developed to alternately update hyperparameter values and combined observation signals. Furthermore, we embed the generalized approximate message passing technique into the SE-SBL and propose a low-complexity computational approach. In particular, the aforementioned Bayesian algorithms do not require any prior knowledge of user activity level and noise power. Simulation results show that the proposed Bayesian methods exhibit a superior performance gain over the state-of-the-art.