Robust Distributed Power Control in Cognitive Radio Networks
We propose a robust distributed uplink power allocation algorithm for underlay cognitive radio networks (CRNs) with a view to maximizing the social utility of secondary users (SUs) when channel gains from SUs to primary base stations, and interference caused by primary users (PUs) to the SUs' base station are uncertain. In doing so, we utilize the worst case robust optimization to keep the interference caused by SUs to each primary base station below a given threshold, and satisfy each SU's quality of service in terms of its required SINR for all realizations of uncertain parameters. We model each uncertain parameter by a bounded distance between its estimated and exact values, and formulate the robust power allocation problem via protection values for constraints. We demonstrate that the convexity of our problem is preserved, and converts into a geometric programming problem, which we solve via a distributed algorithm by using Lagrange dual decomposition. To reduce the cost of robustness, defined as the reduction in the social utility of SUs and the increase in message passing, we utilize the D-norm approach to trade off between robustness and optimality, and propose a distributed power allocation algorithm with infrequent message passing. Simulation results validate the effectiveness of our proposed approach.
cognitive radio network robust power allocation geometric programming d-norm approach infrequent message passing price of robustness uncertain channel gain underlay spectrum sharing