Energy-Efficient Power Control for Multiple-Relay Cooperative Networks Using Q-Learning

In this paper, we investigate the power control problem 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 half-duplex, and all source nodes interfere with each other at every in...

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Vydáno v:IEEE transactions on wireless communications Ročník 14; číslo 3; s. 1567 - 1580
Hlavní autoři: Shams, Farshad, Bacci, Giacomo, Luise, Marco
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.03.2015
Témata:
Active control
Algorithms
Computational efficiency
Energy efficiency
Energy management
full-duplex communications
Games
Mathematical models
mixed-strategy Nash equilibria
Networks
Peer-to-peer computing
Power control
reinforcement learning algorithms
relay-assisted communications
Relays
Resource management
Transmitters
Wireless communication
full-duplex communications
reinforcement learning algorithms
Energy efficiency
power control
mixed-strategy Nash equilibria
relay-assisted communications
ISSN:1536-1276
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Shrnutí:In this paper, we investigate the power control problem 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 half-duplex, and all source nodes interfere with each other at every intermediate relay node, and all active nodes (transmitters and relay nodes) interfere with each other at the base station. A game-theory-based power control algorithm is devised to allocate the powers among all active nodes. The source nodes aim at maximizing their energy efficiency (in bits per Joule per Hertz), whereas the relays aim at maximizing the network sum rate. We 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 point that combines good performance in terms of both energy efficiency and overall data rate. Numerical results show that the full-duplex scheme outperforms half-duplex configuration, Nash bargaining solution, the max-min fairness, and the max-rate optimization schemes in terms of energy efficiency, and outperforms the half-duplex mode, Nash bargaining system, and the max-min fairness scheme in terms of network sum rate.
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ISSN:1536-1276
DOI:10.1109/TWC.2014.2370046