Deep Learning-Based Network-Wide Energy Efficiency Optimization in Ultra-Dense Small Cell Networks

Abstract

In ultra-dense small cell networks (UDSCNs), where a significant number of small cell base stations (SBSs) coexist, the amount of power consumed at the SBSs can be extremely high, rendering the efficient management of power consumption for the SBSs particularly important. Herein, we propose a deep-learning-based resource allocation strategy to maximize network-wide energy efficiency in the UDSCN by optimally controlling the transmit power and user association. In this regard, a novel deep neural network (DNN) structure comprising three separate DNN units, each of which determines the activation of the SBSs, user association, and transmit power, as well as an unsupervised-learning-based training methodology are designed. Simulation results verify that the proposed scheme achieves a near-optimal performance while requiring a short computation time.