Resource Management Through Energy Harvesting and Mobility Prediction: A Learning Approach

With the rapid expansion of wireless networks, the mathematical models associated with them have grown increasingly intricate and time-consuming to solve. This places significant demands on network infrastructure, requiring substantial resources for mathematical model evaluations. Consequently, a fresh approach has emerged for achieving Energy-Efficient (EE) operations in Small Base Stations (SBSs) within Heterogeneous Wireless Networks (HetNets), and it’s rooted in Artificial Neural Networks (ANNs). In this novel approach, the potent capabilities of Deep Learning (DL) techniques are harnessed to address the system model with reduced computational requirements. Therefore, this study utilized a learning approach that involved predicting the mobility of user equipment (UE) and employing an ANN to manage the exponential rise in the complexity of the optimization problem. ANNs are employed to establish the relationship between the inputs and outputs of the system’s mathematical model. This formulated mathematical model is then utilized to generate synthetic data, which, in turn, trains and configures the ANN. The trained ANN essentially functions as a communication system, offering computationally efficient solutions whenever the system parameters change. An extensive simulation is presented to show the effectiveness of our approaches in comparison to the optimal scheme. The algorithm proposed in this study achieved an average mean-squared error (MSE) of 1% and provided a prediction for UE spanning 10 steps.