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Deep Reinforcement Learning Based Blind mmWave MIMO Beam Alignment
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DOI：10.1109/TWC.2022.3169900.png)
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摘要
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Directional beamforming is a crucial component for realizing robust wireless millimeter wave (mmWave) communication systems. Beam alignment using brute-force search introduces time overhead, and the location aided blind beam alignment adds additional hardware requirements to the system. In this paper, we propose a blind beam alignment method based on the radio frequency (RF) fingerprints of the user equipment obtained from the base stations. The proposed system performs blind beam alignment using deep reinforcement learning on a multiple-base station cellular environment with multiple mobile users. 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 data rate of the traditional method without any overheads.
Keyword:
Array signal processing
Deep learning
Base stations
Training
MIMO communication
Wireless communication
Millimeter wave communication
MIMO
beam alignment
deep reinforcement learning
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
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