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@article{167292,
        author = {Shveta S and Naveena A Priyadharsini},
        title = {Q-Learning-based Power Allocation for Near and Far Users in 6G Cell-Free Networks },
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {3},
        pages = {1280-1285},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167292},
        abstract = {The introduction of 6G cell-free networks, which will offer improved user experiences and widespread coverage, has the potential to completely transform wireless communication. Power allocation is severely hampered by the different channel characteristics and the different distances of near and far customers. Conventional power allocation systems frequently provide insufficient resources to far users while favouring near user. Here, machine learning (ML) approaches are Reinforcement Learning algorithm to achieve power allocation. We compare the effectiveness of near and far user using Q-learning models in dynamically allocating power according to network conditions. A Q-Learning-based power allocation technique that maximises network performance for both near and far users is suggested as a solution to this problem. It makes use of Q-Learning to figure out the best power allocation strategy, adjusting to shifting user distributions and network conditions. The suggested algorithm guarantees fairness.},
        keywords = {Q-Learning, Far User, Near Users, Power Allocation.},
        month = {August},
        }