Centralized and Distributed Cognitive Relay-Selection Schemes for SWIPT Cognitive Networks

We investigate the model of a single primary-transceiver pair with multiple secondary-transceiver pairs. The secondary pairs can act as relays for the primary transmitter enabling access to its channel resources. Each secondary user (SU) is assumed to be a radio-frequency energy-harvester node. We formulate a framework that aims at specifying the optimal SU set that operates as relay nodes for the primary user (PU) data message. The set of the SUs is selected such that the SUs total throughput is maximized under a certain quality-of-service (QoS) requirement constraint on the PU target data rate. We propose both centralized and distributed approaches for solving the formulated optimization problems. The centralized approach is based on solving a convex optimization problem at the PU. On the other hand, the distributed approach leverages a Sackelberg game where all users interact to achieve the best relay-selection scheme and PU’s transmit power. We prove the uniqueness and Nash equilibrium of the considered Stackelberg game, and develop a game-theoretic relay and PU’s transmit power selection algorithm. We also introduce a fairness optimization-based scheme (FOBS) that aims at enhancing the fairness among the SUs under our proposed centralized approach. Our simulation results show the efficiency of our proposed schemes in terms of SUs total throughput.