Pareto optimal time-frequency resource allocation for selfish wireless cooperative multicast networks

In selfish wireless cooperative multicast networks (WCMNs), a source node wants to achieve the optimal benefit (i.e., rate gain), while the relaying nodes are willing to get fairness rewards (i.e., rate gains) from the source for the cooperative relaying. In this paper, we implement these two different objectives for the source and the relays through the Pareto optimal resource allocation. Define the cooperative strategy of a node as the fraction of a data-frame that it is willing to contribute to its cooperative partners. Consider the rational decision made by one node will definitely affect its cooperative partners’ choice. Then, we can formulate this resource sharing problem as a Nash bargaining problem (NBP), and the Nash bargaining solution (NBS) to the NBP encapsulates the Pareto optimality naturally. Finally, to enable the nodes to be capable of computing the NBS cooperative strategies rapidly as the wireless channel changes, we propose a fast particle swarm optimizer (PSO) algorithm to search for the NBS. Simulation results show that the two specified objectives of the source and the relays can be implemented in the Pareto optimal sense, i.e., the source can achieve a significant performance gain in comparison with direct multicast and the relays can get a fair reward by the source according to the level of contribution it has made to improve the performance of the source.