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@article{186049,
        author = {Shreelakshmi V and Sachin M S},
        title = {AI-Assisted Channel Prediction and Optimization for Energy-Efficient 6G Wireless Communication in Smart Cities},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {80-82},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186049},
        abstract = {The sixth-generation (6G) wireless communication system promises ultra-low latency, massive connectivity, and high energy efficiency to support next-generation smart city infrastructures. Achieving reliable communication in dynamic, high-frequency environments requires intelligent and adaptive channel prediction and resource allocation techniques. This paper presents an AI-assisted channel prediction and optimization framework combining Long Short-Term Memory (LSTM) neural networks with reinforcement learning-based power control to enhance both spectral and energy efficiency in 6G networks. The proposed system predicts channel conditions using historical channel state information (CSI) and optimizes resource allocation dynamically to minimize power consumption. Simulation results in MATLAB demonstrate that the model improves channel prediction accuracy by 41%, enhances energy efficiency by 25%, and reduces latency by 15% compared to conventional estimation techniques. The research contributes to sustainable 6G development and efficient smart city communication.},
        keywords = {6G communication, deep learning, LSTM, AI-assisted optimization, channel prediction, energy efficiency, smart cities.},
        month = {October},
        }