PROJECT TITLE :
Energy-Efficient Power Control for Multiple-Relay Cooperative Networks Using $Q$-Learning
In this paper, we investigate the power control drawback in a cooperative network with multiple wireless transmitters, multiple amplify-and-forward relays, and one destination. The relay communication can be either full duplex or [*fr1]-duplex, and all supply nodes interfere with each different at every intermediate relay node, and every one active nodes (transmitters and relay nodes) interfere with each other at the base station. A game-theory-primarily based power management algorithm is devised to allocate the powers among all active nodes. The supply nodes aim at maximizing their energy efficiency (in bits per Joule per Hertz), whereas the relays aim at maximizing the network total rate. We tend to show that the proposed game admits multiple pure/mixed-strategy Nash equilibrium points. A $Q$-learning-based algorithm is then formulated to let the active players converge to the best Nash equilibrium purpose that combines smart performance in terms of both energy efficiency and overall data rate. Numerical results show that the complete-duplex theme outperforms half-duplex configuration, Nash bargaining solution, the max-min fairness, and also the max-rate optimization schemes in terms of energy efficiency, and outperforms the half-duplex mode, Nash bargaining system, and also the max-min fairness theme in terms of network add rate.
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