Model-Based Deep Learning for Joint Activity Detection and Channel Estimation in Massive and Sporadic Connectivity
Model-Based Deep Learning for Joint Activity Detection and Channel Estimation in Massive and Sporadic Connectivity
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DOI：
10.1109/twc.2022.3179600
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发表时间：
2022
影响因子：
10.4
通讯作者：
Jeremy Johnston;Xiaodong Wang
中科院分区：
文献类型：
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作者：
Jeremy Johnston;Xiaodong Wang
我们提出了两种基于模型的神经网络结构，用于大规模机器类型通信中的零星用户检测和信道估计。在考虑的场景中，基站为用户分配了一组线性相关的导频序列，但由于用户活动是零星的，因此检测/估计问题适用于稀疏恢复算法。此外，我们考虑毫米波无线信道，使信道向量在已知字典中是稀疏的。我们通过展开两种迭代优化算法，应用深度展开框架来设计自定义神经网络层：(1)应用于约束凸问题的乘法器线性化交替方向方法，以及(2)基于迭代收缩阈值算法的新型去噪的矢量近似消息传递。因此，网络继承了由信号模型封装的领域知识，以及由算法通知的适当操作-与利用图像和音频固有结构的卷积网络的精神相同，除了基于优化和统计。该网络基于块衰落毫米波多址通道模型生成的合成数据进行训练，相对于迭代式网络，其复杂性和精度都有所提高，对于无小区MIMO系统来说是一个潜在的福音。
We present two model-based neural network architectures purposed for sporadic user detection and channel estimation in massive machine-type communications. In the scenario under consideration, a base station assigns the users a set of pilot sequences that is linearly dependent, but because user activity is sporadic the detection/estimation problem is amenable to sparse recovery algorithms. Further, we consider a millimeter-wave wireless channel, so that the channel vectors are sparse in a known dictionary. We apply the deep unfolding framework to design custom neural network layers by unrolling two iterative optimization algorithms: (1) linearized alternating direction method of multipliers, which we apply to a constrained convex problem, and (2) vector approximate message passing featuring a novel denoiser based on the iterative shrinkage thresholding algorithm. The networks thus inherit domain knowledge as encapsulated by the signal model, and suitable operations as informed by the algorithms—in the same spirit as convolutional networks that exploit structure inherent in images and audio, except grounded in optimization and statistics. The networks, trained on synthetic data generated from the block-fading millimeter-wave multiple access channel model, offer improved complexity and accuracy relative to their iterative counterparts, and are potentially a boon to cell-free MIMO systems.