A Deep Evolution Policy-Based Approach for RIS-Enhanced Communication System
Author Information
Author(s): Zhao Ke, Song Zhiqun, Li Yong, Li Xingjian, Liu Lizhe, Wang Bin
Primary Institution: 54th Research Institute of China Electronics Technology Group Corporation
Hypothesis
The study investigates the design of active and passive beamforming in a RIS-aided MU-MISO system to maximize the sum rate using a deep evolution policy-based algorithm.
Conclusion
The proposed DEP-based algorithm significantly enhances performance in RIS-aided communication systems compared to traditional methods.
Supporting Evidence
- The DEP-based algorithm operates without the need for gradient descent or backpropagation.
- Simulation results indicate significant performance enhancements with the proposed algorithm.
- The study addresses the challenges of traditional optimization methods in RIS-aided systems.
Takeaway
This study shows a new way to improve how signals are sent in wireless communication by using smart technology that helps devices talk to each other better.
Methodology
The study uses a deep evolution policy-based algorithm to optimize beamforming strategies in a RIS-aided MU-MISO communication system.
Digital Object Identifier (DOI)
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