Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment

• URL: http://arxiv.org/abs/2001.09251v2
• Date: Sat, 20 Feb 2021 14:02:45 GMT
• Title: Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment
• Authors: Vishnu Raj, Nancy Nayak and Sheetal Kalyani
• Abstract summary: We introduce a method for blind beam alignment based on the RF fingerprints of user equipment obtained by the base stations. The proposed system performs blind beam alignment on a multiple base station cellular environment with multiple mobile users using deep reinforcement learning. Our results show that the proposed method can achieve a data rate of up to four times the traditional method without any overheads.
• Score: 11.17667928756077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Directional beamforming is a crucial component for realizing robust wireless communication systems using millimeter wave (mmWave) technology. Beam alignment using brute-force search of the space introduces time overhead while location aided blind beam alignment adds additional hardware requirements to the system. In this paper, we introduce a method for blind beam alignment based on the RF fingerprints of user equipment obtained by the base stations. The proposed system performs blind beam alignment on a multiple base station cellular environment with multiple mobile users using deep reinforcement learning. We present a novel neural network architecture that can handle a mix of both continuous and discrete actions and use policy gradient methods to train the model. Our results show that the proposed method can achieve a data rate of up to four times the traditional method without any overheads.

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