Journal
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 20, Issue 11, Pages 7333-7345Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2021.3082844
Keywords
Receivers; Massive MIMO; Wireless communication; Radio frequency; Search methods; Computational complexity; Support vector machines; Massive MIMO; one-bit ADCs; linear receivers; deep neural networks; machine learning; data detection
Funding
- University Grants Program (UGP) from San Diego State University
- U.S. National Science Foundation [CCF-1703635, ECCS-1824565]
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This paper proposes a two-stage detection method for massive MIMO systems with one-bit ADCs, including linear receivers, model-driven deep neural network-based detector, and nearest-neighbor search method, which effectively reduce computational complexity and improve performance.
The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we present several linear receivers based on the Bussgang decomposition that show significant performance gains over conventional linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based detector, namely OBMNet, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.
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