DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs
Abstract
Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately, low-resolution ADCs significantly distort the received signals, and thus make data detection much more challenging. In this paper, we develop a new deep neural network (DNN) framework for efficient and low-complexity data detection in low-resolution massive MIMO systems. Based on reformulated maximum likelihood detection problems, we propose two model-driven DNN-based detectors, namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems, respectively. The proposed OBMNet and FBMNet detectors have unique and simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented. Numerical results also show that OBMNet and FBMNet significantly outperform existing detection methods.
Cite
@article{arxiv.2011.03325,
title = {DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs},
author = {Ly V. Nguyen and Duy H. N. Nguyen and A. Lee Swindlehurst},
journal= {arXiv preprint arXiv:2011.03325},
year = {2020}
}
Comments
6 pages, 8 figures, submitted for publication. arXiv admin note: text overlap with arXiv:2008.03757