A Tabu Search DSA Algorithm for Reward Maximization in Cellular Networks

In this paper, we present and analyze a Tabu Search (TS) algorithm for DSA (Dynamic Spectrum Access) in cellular networks. We study a mono-operator case where the operator is providing packet services to the end-users. The objective of the cellular operator is to maximize its reward while taking into account the trade-off between the spectrum cost and the revenues obtained from end-users. These revenue are modeled here as an increasing function of the achieved throughput. Results show that the algorithm allows the operator to increase its reward by taking advantage of the spatial heterogeneity of the traffic in the network, rather than assuming homogeneous traffic for radio resource allocation. Our TS-based DSA algorithm is efficient in terms of the required memory space and convergence speed. Results show that the algorithm is fast enough to suit a dynamic context.