The growing demand for improved spectral efficiency is one of the main challenges for the upcoming beyond fifth-generation wireless mobile communications networks. While massive multiple-input multiple-output (MIMO) technology has been demonstrating its potential in achieving higher spectral efficiency, the persistent problem of pilot contamination poses a significant hurdle for these systems. To address this issue, the Rate-Splitting Multiple Access (RSMA) framework has emerged as a potential solution. In this paper, we present a novel approach that leverages reinforcement learning (RL) with the Deep Deterministic Policy Gradient (DDPG) algorithm to maximize the sum spectral efficiency (SUM-SE) in a massive MIMO system implementing the RSMA framework with all users sharing a single pilot. The numerical results indicate that the proposed DDPG-based method is a competitive tool for optimizing the SUM-SE in massive MIMO scenarios employing the RSMA framework.


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    Title :

    A DDPG-Based Procedure for Mitigating Pilot Contamination in Massive MIMO RSMA Systems




    Publication date :

    2024-06-24


    Size :

    211315 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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