On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems

• URL: http://arxiv.org/abs/2107.08293v1
• Date: Sat, 17 Jul 2021 17:42:25 GMT
• Title: On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems
• Authors: Amal Feriani, Amine Mezghani, and Ekram Hossain
• Abstract summary: We consider an Intelligent Reflecting Surface (IRS)-aided multiple-input single-output (MISO) system for downlink transmission. We compare the performance of Deep Reinforcement Learning (DRL) and conventional optimization methods in finding optimal phase shifts of the IRS elements. We demonstrate numerically that DRL solutions show more robustness to noisy channels and user mobility.
• Score: 31.70191055921352
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: We consider an Intelligent Reflecting Surface (IRS)-aided multiple-input single-output (MISO) system for downlink transmission. We compare the performance of Deep Reinforcement Learning (DRL) and conventional optimization methods in finding optimal phase shifts of the IRS elements to maximize the user signal-to-noise (SNR) ratio. Furthermore, we evaluate the robustness of these methods to channel impairments and changes in the system. We demonstrate numerically that DRL solutions show more robustness to noisy channels and user mobility.

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