Deep Reinforcement Learning for RIS-Aided Multiuser MISO System with Hardware Impairments

Ma, Wensheng; Zhuo, Liuchang; Li, Luchu; Wang, Kun; Ren, Hong

doi:10.3390/app12147236

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Though the deployment of RIS can greatly enhance the key performance indicators of a network that is improvement in the coverage/data rates but the vision of a greener communication has yet to be investigated. Many energy efficient 2) [119] 3) [104] 4) [105] Beamforming 1) DNN + DDPG 2) DNN 3) RNN 4) DDPG + Twin DDPG RL 5) DRL 1) Optimizes beamforming matrix in order to get the maximize sumrate capacity in RIS-assisted multi-user MIMO systems. 2) Configures RIS to get optimal beam steering using DNN.…”

Section: A a Path To 6g: Green Communicationmentioning

confidence: 99%

Going Beyond a Simple RIS: Trends and Techniques Paving the Path of Future RIS

Shafique,

Alhassoun

2024

IEEE Open J. Antennas Propag.

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To have uninterrupted wireless connectivity, higher throughput, and latency rate down to nanoseconds; future networks will rely heavily on higher frequency bands, yet these signals suffer from attenuation and can be easily blocked by objects. Hence, those signals lose strength over longer distances significantly compared to their low-frequency counterparts. To overcome these constraints, reconfigurable intelligent surfaces (RIS) can increase the wireless network capacity and coverage by consciously altering the wireless propagation environment, which makes them one of the key enablers of the 6G communication networks. The potential benefits of RIS-assisted communication systems can be effectively increased using machine learning (ML) to pave the path towards Wireless 2.0, having a vision of digitization, programmability, and intelligence. This paper reviews recent developments in RIS technology with a special emphasis on tuning techniques, hardware concepts, RIS empowered by artificial intelligence techniques, and applications for next generation of networks. Finally, a viewpoint on the difficulties and potential directions of intelligent metasurfaces are also presented.

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Section: A a Path To 6g: Green Communicationmentioning

confidence: 99%

Going Beyond a Simple RIS: Trends and Techniques Paving the Path of Future RIS

Shafique,

Alhassoun

2024

IEEE Open J. Antennas Propag.

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A Survey on Reinforcement Learning for Reconfigurable Intelligent Surfaces in Wireless Communications

Puspitasari

Lee

2023

Sensors

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A reconfigurable intelligent surface (RIS) is a development of conventional relay technology that can send a signal by reflecting the signal received from a transmitter to a receiver without additional power. RISs are a promising technology for future wireless communication due to their improvement of the quality of the received signal, energy efficiency, and power allocation. In addition, machine learning (ML) is widely used in many technologies because it can create machines that mimic human mindsets with mathematical algorithms without requiring direct human assistance. Meanwhile, it is necessary to implement a subfield of ML, reinforcement learning (RL), to automatically allow a machine to make decisions based on real-time conditions. However, few studies have provided comprehensive information related to RL algorithms—especially deep RL (DRL)—for RIS technology. Therefore, in this study, we provide an overview of RISs and an explanation of the operations and implementations of RL algorithms for optimizing the parameters of RIS technology. Optimizing the parameters of RISs can offer several benefits for communication systems, such as the maximization of the sum rate, user power allocation, and energy efficiency or the minimization of the information age. Finally, we highlight several issues to consider in implementing RL algorithms for RIS technology in wireless communications in the future and provide possible solutions.

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Deep Reinforcement Learning for RIS-Aided Multiuser MISO System with Hardware Impairments

Cited by 2 publications

References 19 publications

Going Beyond a Simple RIS: Trends and Techniques Paving the Path of Future RIS

Going Beyond a Simple RIS: Trends and Techniques Paving the Path of Future RIS

A Survey on Reinforcement Learning for Reconfigurable Intelligent Surfaces in Wireless Communications

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