2023/05/24 by Aditya Sant, Bhaskar D. Rao, Sant, Aditya +1
Computer Science · Engineering · #Advanced SAR Imaging Techniques #FOS: Electrical engineering #Machine Learning and ELM #Signal Processing (eess.SP) #Wireless Signal Modulation Classification #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2305.15543
openalex publication_date 2023/05/24 · openalex created_date 2023/05/27 · openalex updated_date 2026/07/28
Detection for one-bit massive MIMO systems presents several challenges especially for higher order constellations. Recent advances in both model-based analysis and deep learning frameworks have resulted in several robust one-bit detector designs. Our work builds on the current state-of-the-art gradient descent (GD)-based detector. We introduce two novel contributions in our detector design: (i) We augment each GD iteration with a deep learning-aided regularization step, and (ii) We introduce a novel constellation-based loss function for our regularized DNN detector. This one-bit detection strategy is applied to two different DNN architectures based on algorithm unrolling, namely, a deep unfolded neural network and a deep recurrent neural network. Being trained on multiple randomly sampled channel matrices, these networks are developed as general one-bit detectors. The numerical results show that the combination of the DNN-augmented regularized GD and constellation-based loss function improve the quality of our one-bit detector, especially for higher order M-QAM constellations.