A Q-learning game-theory-based algorithm to improve the energy efficiency of a multiple relay-aided network

This paper studies a resource allocation problem for a cooperative network with multiple wireless transmitters, multiple full-duplex amplify-and-forward relays, and one destination. A game-theoretic model is used to devise a power control algorithm among all active nodes, wherein the sources aim at maximizing their energy efficiency, and the relays aim at maximizing the network sum-rate. To this end, we formulate a low-complexity Q-learning-based algorithm to let the active players converge to the best mixed-strategy Nash equilibrium point, that combines good performance in terms of energy efficiency and overall data rate. Numerical results show that the proposed scheme outperforms Nash bargaining, max-min fairness, and max-rate optimization schemes.