Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning (2002.10072v1)

Abstract: 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.

• The paper presents a DRL-based method that jointly optimizes transmit beamforming and RIS phase shifts in multiuser MISO systems to boost performance.
• It demonstrates significant improvements in sum rate and convergence speed over conventional algorithms like WMMSE and FP.
• The study highlights the impact of hyper-parameters and opens new avenues for applying DRL in next-generation wireless network optimization.

Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning

The paper "Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning" addresses a pivotal area in wireless communication research, focusing on the integration of Reconfigurable Intelligent Surfaces (RIS) with multiuser Multiple Input Single Output (MISO) systems using Deep Reinforcement Learning (DRL) techniques. This paper is particularly relevant for advancing the capabilities of future 6G networks beyond current massive MIMO implementations.

Overview and Methodology

The authors propose an innovative approach to jointly optimize the transmit beamforming at the base station and the phase shifts at the RIS. Traditionally, such optimizations in MISO systems are tackled using alternating optimization techniques. However, this paper introduces a DRL-based framework that effectively leverages the continuous state and action spaces. Specifically, the use of Deterministic Policy Gradient (DDPG) allows the model to handle the non-convex optimization problem inherent in the multiuser interference context.

By modeling the optimization problem through DRL, the algorithm iteratively learns optimal configurations by interacting with the environment, observing rewards, and adjusting accordingly. The transformative aspect of this approach lies in its ability to jointly compute the beamforming matrix and phase shifts as part of DRL's neural network output.

Numerical Results and Analysis

The research demonstrates that the proposed DRL-based method not only achieves state-of-the-art performance but often surpasses traditional approaches in terms of convergence rate and performance efficiency. Simulation results indicate substantial gains in the sum rate as well as improved adaptability to varying system settings compared to benchmarks like the Weighted Minimum Mean Square Error (WMMSE) and Fractional Programming (FP) algorithms.

The paper further reveals key insights into the impact of hyper-parameters such as learning rates and decay rates, emphasizing their sensitivity and influence on convergence and performance. The sum rate continues to show dependence on system parameters like the number of elements in the RIS (N) and the transmission power (P_t), corroborating the theoretical expectations of MISO systems.

Implications and Future Prospects

The integration of DRL with RIS-augmented MISO systems offers considerable potential for achieving high energy efficiency and enhanced network performance in 6G scenarios. This novel methodology could be pivotal in overcoming the scalability challenges faced by conventional Massive-MIMO systems. Moreover, the DRL framework's adaptability indicates promising avenues for future research into dynamic wireless environments.

Future work could explore enhancing the robustness of DRL algorithms to cope with real-world channel state information (CSI) acquisition challenges, potentially integrating supervised learning methods for improved sample efficiency. Furthermore, extending this framework to incorporate more complex and realistic scenarios, such as heterogeneous network environments and mobile RIS components, could pave the way for significant advancements in intelligent network design.

Conclusion

This paper presents a sophisticated approach to harnessing the potential of RIS in enhancing multiuser MISO systems, marking a significant stride in wireless communication optimization. By deploying deep reinforcement learning, the research provides a robust framework for potentially revolutionizing the smart radio environment, essential for next-generation wireless technologies. The paper's insights and methodologies lay a foundation for future explorations in AI-driven network optimization and intelligent surface technology.