Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning

Recently, the reconfigurable intelligent surface (RIS), benefited from the breakthrough on the fabrication of programmable meta-material, has been speculated as one of the key enabling technologies for the future six generation (6G) wireless communication systems scaled up beyond massive multiple in...

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Vydané v:IEEE journal on selected areas in communications Ročník 38; číslo 8; s. 1839 - 1850
Hlavní autori: Huang, Chongwen, Mo, Ronghong, Yuen, Chau
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Antenna arrays
Array signal processing
Beamforming
beamforming matrix
Computer simulation
Deep learning
deep reinforcement learning
Machine learning
Massive MIMO
Metamaterials
MIMO (control systems)
MIMO communication
MISO (control systems)
MISO communication
Neural networks
Optimization
Optimization techniques
phase shift matrix
Power consumption
Receivers
Reconfigurable intelligent surface
Reconfigurable intelligent surfaces
smart radio environment
Wireless communication
Wireless communication systems
Wireless communications
ISSN:0733-8716, 1558-0008
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Shrnutí:Recently, the reconfigurable intelligent surface (RIS), benefited from the breakthrough on the fabrication of programmable meta-material, has been speculated as one of the key enabling technologies for the future six generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology to achieve smart radio environments. Employed as reflecting arrays, RIS is able to assist MIMO transmissions without the need of radio frequency chains resulting in considerable reduction in power consumption. In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL). We first develop a DRL based algorithm, in which the joint design is obtained through trial-and-error interactions with the environment by observing predefined rewards, in the context of continuous state and action. Unlike the most reported works utilizing the alternating optimization techniques to alternatively obtain the transmit beamforming and phase shifts, the proposed DRL based algorithm obtains the joint design simultaneously as the output of the DRL neural network. Simulation results show that the proposed algorithm is not only able to learn from the environment and gradually improve its behavior, but also obtains the comparable performance compared with two state-of-the-art benchmarks. It is also observed that, appropriate neural network parameter settings will improve significantly the performance and convergence rate of the proposed algorithm.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2020.3000835