Regularized Neural Detection for Millimeter Wave Massive MIMO Communication Systems with One-bit ADCs

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 multi-user 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: \textit{(i)} a parametric deep learning system, i.e., the mmW-ROBNet, \textit{(ii)} a constellation-aware loss function, and \textit{(iii)} a hierarchical detection training strategy. The experimental results corroborate this proposed approach for equitable per-user detection.