Evading eavesdroppers in adversarial cognitive radio networks

In this paper, we investigate the problem of secure communications between a number of secondary users (SUs) transmitting data to a common base station in the presence of primary users (PUs) and eavesdroppers in a cognitive radio network. The SUs aim at mitigating the effect of eavesdropping by changing their positions using only partial information about the locations of the eavesdroppers. Accordingly, for each SU, we propose an appropriate utility function and then maximize the social welfare of all SUs without interfering with the PUs´ radio receivers and taking into account the interference thresholds set by the PUs on each channel. Given these constraints, we formulate the problem so as to optimize the social welfare of all SUs. Depending on the possible communication links and the available information, we propose three different algorithms to solve the proposed constrained optimization: first we solve the problem centrally at the BS, second we propose a decentralized game theoretic approach, and third we consider a Lagrangian-heuristic based algorithm. Simulation results show that the proposed decentralized algorithms can achieve high near-optimal performances.