Increasing sum-rate in large-scale cognitive radio networks by centralized power and spectrum allocation
1 Department of Electronics and Telecommunications, Norwegian University of Science and Technology, Trondheim, Norway
2 Shool of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, South Korea
EURASIP Journal on Wireless Communications and Networking 2012, 2012:362 doi:10.1186/1687-1499-2012-362Published: 11 December 2012
We revisit the widely investigated problem of maximizing the centralized sum-rate capacity in a cognitive radio network. We consider an interference-limited multi-user multi-channel environment, with a transmit sum-power constraint over all channels as well as an aggregate average interference constraint towards multiple primary users. Until very recently only sub-optimal algorithms were proposed due to the inherent non-convexity of the problem. Yet, the problem at hand has been neglected in the large-scale setting (i.e., number of nodes and channels) as usually encountered in practical scenarios. To tackle this issue, we first propose an exact mathematical adaptation of the well-known successive convex geometric programming with condensation approximations (SCVX) to better cope with large systems while keeping the convergence proof intact. Alternatively, we also propose a novel efficient low-complexity heuristic algorithm, ELCI. ELCI is an iterative approach, where the constraints are handled alternately based on the special property of the optimal solution, with a particular power update formulation based on the KKT conditions of the problem. In order to demonstrate ELCI’s efficiency we compare it to two state-of-the-art algorithms, SCVX, and the recently proposed global optimum approach, MARL. The salient highlight of ELCI is the relatively fast and very good sub-optimal performance in large-scale CR systems.