Deep Learning Based Interference Exploitation in 1-Bit Massive MIMO Precoding

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Hossienzadeh, Mohsen, Aghaeinia, Hassan, Kazemi, Mohammad
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Codes; Deep learning; Digital to analog converters; Exploitation; Interference; interference exploitation; Massive MIMO; MIMO communication; one-bit DAC; Optimization; Phase shift keying; Precoding; Radio frequency; Symbols; Wireless communication systems
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3244928

In this paper, we focus on one-bit precoding approach for downlink massive multiple-input multiple-output (MIMO) systems, where we exploit the concept of constructive interference (CI) employing deep learning (DL) techniques. One of the main performance limiting factors in wireless communication systems is interference, which needs to be minimized or mitigated. By controlling the interference signals in order to add up constructively at the receiver side, there is a possibility to improve the system performance. This paper presents a DL-based one-bit precoding scheme that improves the massive MIMO performance via CI exploitation in the presence of one-bit digital to analog converters (DAC) as a hardware impairment. More precisely, for phase shift keying signaling, we first formulate the optimization problem in order to maximize the CI effects in the case of a base station equipped with one-bit DACs. Then, after solving the optimization problem and creating a large enough dataset, a DL network is trained to do the precoding. Numerical results show that the DL-based solution approaches the performance of the conventional interference exploitation one-bit precoding schemes in the massive MIMO systems while having an order of magnitude less complexity.