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@article{199290,
        author = {Sharanya Dubasi and Varshini Marri},
        title = {Deep Learning-Based Spectrum Occupancy Prediction in 6G Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {12310-12315},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199290},
        abstract = {The rapid evolution toward sixth-generation (6G) wireless networks is expected to support ultra-high data rates, massive device connectivity, holographic communications, and intelligent services. Efficient utilization of the radio frequency spectrum becomes a critical challenge because traditional static allocation strategies cannot cope with the dynamic and heterogeneous traffic patterns of future networks. Spectrum occupancy prediction using deep learning emerges as a promising solution to proactively estimate channel usage and enable intelligent spectrum access for cognitive radios and network controllers. This project proposes a deep learning–based framework for predicting spectrum availability in 6G environments by analyzing historical sensing data, temporal traffic variations, and spatial features. Advanced neural architectures such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Convolutional Neural Networks (CNN) are employed to capture time–frequency correlations and non-linear patterns in spectrum usage. The system integrates data preprocessing, feature extraction, model training, and real-time inference to assist dynamic spectrum allocation decisions. Performance is evaluated using metrics such as prediction accuracy, precision–recall, and spectral efficiency improvement. The proposed approach aims to reduce interference, minimize spectrum wastage, and enhance quality of service for next-generation applications, thereby contributing toward intelligent, autonomous, and energy-efficient 6G wireless communication systems.},
        keywords = {},
        month = {April},
        }