Abstract: We introduce a novel technique for access by a cognitive Secondary User (SU) to a spectrum with an incumbent Primary User (PU), which uses Type-I Hybrid ARQ. The technique allows the SU to perform selective retransmissions of previously corrupted SU data packets. The temporal redundancy introduced by the primary ARQ protocol and by the selective SU retransmission process can be exploited by the SU receiver to perform Interference Cancellation (IC) over the entire interference pattern, thus creating a “clean” channel for the decoding of the concurrent message. The chain decoding technique, initiated by a successful decoding operation of a SU or PU message, consists in the iterative application of IC, as previously corrupted messages become decodable. Based on this scheme, we design an optimal policy that maximizes the SU throughput under a constraint on the average long-term PU throughput degradation. We show that the optimal policy can be found by ﬁrst optimizing the SU access policy using a Markov Decision Process formulation, and then applying a chain decoding protocol deﬁned by ﬁve basic rules. Such an approach enables a compact state representation of the protocol, and its efﬁcient numerical optimization. Finally, we show by numerical results the throughput beneﬁt of the proposed technique.
Bio: Dr. Nicolo Michelusi received his B.Sc. and M.Sc. degrees (with honors) in Electrical Engineering from University of Padova, Italy, in 2006 and 2009, respectively, and the M.Sc. degree in Telecommunication Engineering from Technical University of Denmark in 2009, under the T.I.M.E. double degree program (www.time-association.org). He received his Ph.D. degree from University of Padova in 2013. In 2011, he was a visiting research scholar at University of Southern California, under the advisement of Prof. Urbashi Mitra. He is currently a postdoctoral research fellow at the Ming Hsieh Department of Electrical Engineering, University of Southern California, USA. His research interests are in the areas of wireless communications, cognitive networks, energy harvesting for communication, stochastic optimization.