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

Abstract

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

CBRS Spectrum Sharing between LTE-U and WiFi: A Multiarmed Bandit Approach

“…In this regard, discrete mechanisms such as dynamic channel selection, retaining transmission gaps, transmission duty cycle manipulation, and LBT have been proposed in the literature for harmonious coexistence with improved performance. To select resources dynamically, learn from the environment, and adaptively modify transmission parameters for performance improvement, various machine learning based techniques [13][14][15][16] have been introduced.…”