Optimizing 5G resource allocation with attention-based CNN-BiLSTM and squeeze-and-excitation architecture

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Introduction The swift advancement of computational capabilities has rendered deep learning indispensable for tackling intricate challenges. In 5G networks, efficient resource allocation is crucial for optimizing performance and minimizing latency. Traditional machine learning models struggle to capture intricate temporal dependencies and handle imbalanced data distributions, limiting their effectiveness in real-world applications. Methods To overcome these limitations, this study presents an innovative deep learning-based framework that combines a convolutional layer with squeeze-and-excitation block, bidirectional long short-term memory, and a self-attention mechanism for resource allocation prediction. A custom weighted loss function addresses data imbalance, while Bayesian optimization fine-tunes hyperparameters. Results Experimental results demonstrate that the proposed model achieves state-of-the-art predictive accuracy, with a remarkably low Mean Absolute Error (MAE) of 0.0087, Mean Squared Error (MSE) of 0.0003, Root Mean Squared Error (RMSE) of 0.0161, Mean Squared Log Error (MSLE) of 0.0001, and Mean Absolute Percentage Error (MAPE) of 0.0194. Furthermore, it attains an R2 score of 0.9964 and an Explained Variance Score (EVS) of 0.9966, confirming its ability to capture key patterns in the dataset. Discussion Compared to conventional machine learning models and related studies, the proposed framework consistently outperforms existing approaches, highlighting the potential of deep learning in enhancing 5G networks for adaptive resource allocation in wireless systems. This approach can also support smart university environments by enabling efficient bandwidth distribution and real-time connectivity for educational and administrative services.