Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications

• URL: http://arxiv.org/abs/2005.11885v1
• Date: Mon, 25 May 2020 01:42:55 GMT
• Title: Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications
• Authors: Jiaye Lin, Yuze Zou, Xiaoru Dong, Shimin Gong, Dinh Thai Hoang, Dusit Niyato
• Abstract summary: Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. We minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.
• Score: 54.610318402371185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. In this paper, we minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. Due to uncertain channel conditions, we formulate a robust power minimization problem subject to the receiver's signal-to-noise ratio (SNR) requirement and the IRS's power budget constraint. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences. To improve the learning performance, we derive a convex approximation as a lower bound on the robust problem, which is integrated into the DRL framework and thus promoting a novel optimization-driven deep deterministic policy gradient (DDPG) approach. In particular, when the DDPG algorithm generates a part of the action (e.g., passive beamforming), we can use the model-based convex approximation to optimize the other part (e.g., active beamforming) of the action more efficiently. Our simulation results demonstrate that the optimization-driven DDPG algorithm can improve both the learning rate and reward performance significantly compared to the conventional model-free DDPG algorithm.

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