Congestion Minimization of LTE Networks: A Deep Learning Approach

Abstract

Reducing the number of users serviced by congested cellular towers given an offered load and a minimum level of acceptable user quality is a major challenge in the operation of LTE networks. In this paper, we utilize a supervised Deep Learning (DL) technique to predict the LTE and LTE-A loading of connected users and then dynamically predict the congestion threshold of each cellular tower under offered load. We then use the predicted congestion thresholds together with quality constraints to fine-tune cellular network operating parameters leading to minimizing overall network congestion. We propose two sets of optimization algorithms to solve our formulated congestion optimization problem. Those are, namely, a variant of Simulated Annealing (SA) algorithm to which we refer as Block Coordinated Descent Simulated Annealing (BCDSA) and Genetic Algorithm (GA). We first compare the performance of integrated DL-BCDSA and DL-GA algorithms and then show that our integrated DL-BCDSA can outperform existing state-of-the-art commercial self organizing tool already deployed in actual cellular networks.