RL-Driven Adaptive Phase Optimization for IRS-Based Systems
Version 1.0 (3,24 ko) par
Ardavan Rahimian
This code simulates an RL-based methodology to dynamically optimize phase shifts within an IRS, aiming to enhance communication quality.
This code simulates a reinforcement learning (RL) strategy for the dynamic optimization of phase shifts in an intelligent reflective surface (IRS) within a wireless communication scenario. Its main goal is the adaptive modification of IRS phase shifts to optimize the signal-to-noise ratio (SNR) at the receiving end, thus improving overall system performance. This code can serve as a foundational framework for exploring the capabilities of RL in more complex and practical IRS optimization scenarios.
Citation pour cette source
Ardavan Rahimian (2024). RL-Driven Adaptive Phase Optimization for IRS-Based Systems (https://www.mathworks.com/matlabcentral/fileexchange/136816-rl-driven-adaptive-phase-optimization-for-irs-based-systems), MATLAB Central File Exchange. Récupéré le .
Compatibilité avec les versions de MATLAB
Créé avec
R2023b
Compatible avec toutes les versions
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Version | Publié le | Notes de version | |
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1.0 |