Throughput maximization of cognitive radio network by conflict-free link allocation using neural network

Abstract

Interference, which can be caused by Secondary Users (SUs) to other Primary Users (PUs) or SUs, is of major concern for Cognitive Radio Networks (CRNs). We study a spectrum allocation problem, where our primary focus is on maximizing the throughput of the CRN while protecting the PUs from interference and controlling the co-channel interference among the SUs. We build a CRN, where initially, all SUs explore prospective links for communicating between them and send the sensed data to a centralized Base Station (SU BS). The SU BS aggregates the received information and discovers conflicting links among the SUs by using an interference graph. Conflict among links affects the SINR, which in essence hampers the efficiency of the CRN. We form an optimization problem, which is a binary integer linear programming problem, with an objective to determine the optimal set of conflict-free links that satisfies the interference constraints. We propose the CFLA (Conflict-free Link Assignment) algorithm, which uses a neural network for capacity-aware link distribution. Simulation results show that the algorithm has less time complexity than the evolutionary algorithms such as genetic algorithm in particular and perform steadily for large scale CRNs as well.