Regularized Neural Detection for Millimeter Wave Massive Mimo Communication Systems with One-Bit Adcs
Regularized Neural Detection for Millimeter Wave Massive Mimo Communication Systems with One-Bit Adcs
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DOI：
10.1109/icassp49357.2023.10096921
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Aditya Sant;B. Rao
中科院分区：
文献类型：
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作者：
Aditya Sant;B. Rao
多用户大规模MIMO信号检测从一个比特的接收测量强烈地依赖于无线信道。为此，大多数的模型和学习为基础的方法地址检测器设计的丰富的散射，均匀瑞利衰落信道。我们的工作提出了一个比特的大规模MIMO的低分集毫米波信道的检测。我们分析了目前最先进的梯度下降（GD）为基础的联合多用户检测的一位接收信号的毫米波信道的局限性。针对这些问题，我们引入了一个新的框架，以确保公平的每用户性能，尽管联合多用户检测。这是通过以下方式实现的：（i）参数化深度学习系统，即，mmW-ROBNet，（ii）星座感知损失函数，以及（iii）分层检测训练策略。实验结果证实了这种方法的公平每用户检测。
Multi-user massive MIMO signal detection from one-bit received measurements strongly depends on the wireless channel. To this end, majority of the model and learning-based approaches address detector design for the rich-scattering, homogeneous Rayleigh fading channel. Our work proposes detection for one-bit massive MIMO for the lower diversity mmWave channel. We analyze the limitations of the current state-of-the-art gradient descent (GD)-based joint multiuser detection of one-bit received signals for the mmWave channels. Addressing these, we introduce a new framework to ensure equitable per-user performance, in spite of joint multi-user detection. This is realized by means of: (i) a parametric deep learning system, i.e., the mmW-ROBNet, (ii) a constellation-aware loss function, and (iii) a hierarchical detection training strategy. The experimental results corroborate this proposed approach for equitable per-user detection.