Joint link learning and cognitive radio sensing

Novel cooperative spectrum sensing algorithms for cognitive radios (CRs) are developed, which can blindly learn the channel gains between CRs and licensed primary users (PUs), while jointly detecting active PU transmitters at each time instant. A dictionary learning approach is taken to decompose the received signal energy samples per CR into linear combinations of channel gains and PU transmit-powers, up to scaling ambiguity. In addition to a batch baseline algorithm, an efficient online implementation that can track slow variation of channel gains is developed, as well as a distributed alternative, which requires only local message passing among neighbors in CR networks. Numerical tests verify the proposed design.