Data-Driven Radio Resource Allocation Relying on Domain Adversarial Neural Networks

Drawing upon a data-driven methodology, deep learning has emerged as an innovative approach for dynamic resource allocation in large-scale cellular networks. This paper proposes an optimization strategy relying on domain adversarial networks to reduce the number of poorly performing base stations (BSs). The approach dynamically allocates radio resources to address real-time mobile traffic needs. We calculate the interference coefficients among BSs and design a performance classifier that evaluates BS performance with respect to provided traffic-resource pairs as either poor or good. Most importantly, we use well-performing BSs as source domain data to reallocate the resources of poorly performing ones through the domain adversarial neural network. Experimental results demonstrate that the proposed domain adversarial resource allocation strategy effectively decreases the number of poorly performing BSs in the cellular network, which in turn outperforms other benchmark algorithms in terms of both the ratio of poor BSs and radio resource consumption.