Increasing Sum-Rate in Large-Scale Cognitive Radio Networks by Centralized Power and Spectrum Allocation
The authors revisit the widely investigated problem of maximizing the centralized sum-rate capacity in a cognitive radio network. They 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.