The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning
Open accessAbstract
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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Cites
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Cited by
Active RIS vs. Passive RIS: Which Will Prevail in 6G?
Zhang, Z., Dai, L., Chen, X., Liu, C., Yang, F., Schober, R., & Poor, H. V. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2022.3231893 · Cited by 1,150
Simultaneously Transmitting and Reflecting (STAR) RIS Aided Wireless Communications
Mu, X., Liu, Y., Guo, L., Lin, J., & Schober, R. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3118225 · Cited by 896
Edge artificial intelligence for 6g: vision, enabling technologies, and applications
Letaief, K. B., Shi, Y., Lu, J., & Lu, J. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126076 · Cited by 829
“By considering CSI variations [275], [55], [276] and stochastic task arrivals [85], [84], the dynamic”
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A Survey of Beam Management for mmWave and THz Communications Towards 6G
Xue, Q., Ji, C., Ma, S., Guo, J., Xu, Y., Chen, Q., & Zhang, W. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3361991 · Cited by 280
Metadata
- Authors
- Huang, C., Mo, R., & Yuen, C.
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 2020-08
- Volume
- 38
- Issue
- 8
- Pages
- 1839-1850
- ISSN
- 0733-8716 (ISSN-L)
- 1558-0008
- OpenAlex
- W3008014991
- Cited by
- 938
- References
- 32
- FWCI
- 48.27
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- Funders
- A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund–Pre Positioning (A19D6a0053)
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- Huang, C., Mo, R., & Yuen, C. (2020). Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/jsac.2020.3000835
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