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@article{209910,
        author = {Dr.Rajat Dixit},
        title = {ARTIFICIAL INTELLIGENCE-DRIVEN OPTIMIZATION OF WIRELESS NETWORKS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {3781-3793},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209910},
        abstract = {Wireless networks are becoming increasingly heterogeneous as cellular systems, Wi-Fi, Internet of Things (IoT) devices, connected vehicles, and edge services share limited spectrum and infrastructure. These environments create difficult optimization problems involving resource allocation, interference, mobility, latency, reliability, and energy consumption. Conventional optimization and rule-based control remain valuable, but their computational demands and limited adaptability can become problematic when network conditions change rapidly. Artificial intelligence (AI), particularly machine learning (ML), deep learning (DL), reinforcement learning (RL), and federated learning (FL), offers data-driven methods for prediction and adaptive control. This review examines how AI techniques are used to optimize wireless-network resources, mitigate interference, and improve energy efficiency. 
It develops a conceptual taxonomy based on the network task, learning paradigm, deployment location, and performance objective; compares the strengths and limitations of representative methods; and discusses data, safety, explainability, privacy, and reproducibility challenges. The review also outlines a practical evaluation framework and research directions for 5G-Advanced and emerging 6G systems. The paper is a literature-based conceptual review and does not claim new experimental measurements. Its central argument is that AI is most useful when integrated with communication theory, constrained optimization, and reliable network-control mechanisms rather than treated as a universal replacement for established methods.},
        keywords = {Artificial intelligence; wireless networks; machine learning; resource allocation; interference management; energy efficiency; reinforcement learning; 5G; 6G; Internet of Things.},
        month = {October},
        }