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Title: 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 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.  more » « less
Award ID(s):
2124929 2225617
PAR ID:
10417299
Author(s) / Creator(s):
;
Date Published:
Journal Name:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023
Page Range / eLocation ID:
1 to 5
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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