Authors - Sunakshi Singh, Abhay Kumar Agrahari, Raghav Abstract - As cellular networks move toward 6G, traffic behavior becomes increasingly complex, shaped by user mobility and diverse service demands that vary across time and location. Accurate traffic prediction is therefore critical for efficient resource allocation and intelligent network operation. However, traditional statistical and conventional machine learning approaches rely on simplifying assumptions and struggle to capture the rich spatio-temporal interactions observed in large urban networks. Although recurrent models such as LSTM are effective at learning temporal patterns, they offer limited insight into how traffic evolves across geographically distributed regions. To address these limitations, this work frames cellular traffic prediction as a spatio-temporal learning problem and introduces a deep learning framework that jointly models temporal dynamics and spatial correlations using historical CDR data. The proposed approach is evaluated on real-world urban datasets and benchmarked against statistical and deep learning baselines, demonstrating superior prediction accuracy, faster convergence, and greater robustness under limited training data.
