Cross-Layer Optimization and Receiver Localization for Cognitive Networks Using Interference Tweets

A cross-layer resource allocation scheme for underlay multi-hop cognitive radio networks is formulated, in the presence of uncertain propagation gains and locations of primary users (PUs). Secondary network design variables are optimized under long-term probability-of-interference constraints, by exploiting channel statistics and maps that pinpoint areas where PU receivers are likely to reside. These maps are tracked using a Bayesian approach, based on 1-bit messages - here refereed to as "interference tweet" - broadcasted by the PU system whenever a communication disruption occurs due to interference. Although nonconvex, the problem has zero duality gap, and it is optimally solved using a Lagrangian dual approach. Numerical experiments demonstrate the ability of the proposed scheme to localize PU receivers, as well as the performance gains enabled by this minimal primary-secondary interplay.