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Mobility Aware Intelligent Reflecting Surfaces Assisted Communications : A Deep Reinforcement Learning Approach

Mohammadzadeh, Amir Hossein | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58329 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Jafari Siavoshani, Mahdi
  7. Abstract:
  8. The stacked intelligent metasurface has been introduced as an innovative technology in signal processing, enabling the rapid and instantaneous control of electromagnetic waves at the speed of light. In this research, the performance of a wireless system is investigated, where a tunable reconfigurable intelligent surface facilitates communication between a base station equipped with a stacked intelligent metasurface and downstream users, in scenarios where the line-of-sight link is unavailable. One of the main motivations for using stacked intelligent metasurfaces is the reduction in energy consumption, as their presence eliminates the need for radio frequency chains. Unlike traditional communication systems, beamforming in this structure is achieved in the wave domain through the optimized electromagnetic response of the stacked intelligent metasurface. To evaluate the system's performance, a resource allocation optimization problem has been defined, with the objective of maximizing the total data rate of the system under constraints of quality of service and base station transmission power budget. Due to the complexity and nonlinear dependence of the variables, this problem is rewritten as a Markov decision process and optimized using the twin delayed deep deterministic policy gradient algorithm. Moreover, to enhance adaptability to network dynamics and terminal mobility, the algorithm is trained through meta reinforcement learning. Numerical results show that the use of a tunable reconfigurable intelligent surface, combined with a stacked intelligent metasurface, increases the total system data rate by up to 20% compared to baseline methods. Furthermore, increasing the number of metasurface layers from 3 to 4 leads to a 50% improvement in data rate, while further increases in layers lead to performance saturation. Additionally, the proposed algorithm demonstrates faster convergence and less than 10% performance degradation under channel state information (CSI) uncertainty, compared to similar algorithms
  9. Keywords:
  10. Reconfigurable Intelligent Surface (RIS) ; Twin Delayed Deep Deterministic Policy Gradient (TD3)Algorithm ; Metalearning ; Stacked Intelligent Metasurface (SIM) ; Deep Reinforcement Learning ; Mobility Aware Algorithm

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