This paper aims at developing a power control framework to jointly optimize energy efficiency (measured in bit/joule) and delay in wireless networks. A multi-objective approach is taken dealing with both performance metrics, while ensuring a minimum quality-of-service to each user in the network. Each user in the network is modeled as a rational agent that engages in a generalized non-cooperative game. Feasibility conditions are derived for the existence of each player's best response, and used to show that if these conditions are met, the game best response dynamics will converge to a unique Nash equilibrium. Based on these results, a convergent power control algorithm is derived, which can be implemented in a fully decentralized fashion. Next, a centralized power control algorithm is proposed, which also serves as a benchmark for the proposed decentralized solution. Due to the non-convexity of the centralized problem, the tool of maximum block improvement is used, to tradeoff complexity with optimality.

Energy and Delay Efficient Power Control in Wireless Networks

Sanguinetti, Luca;
2018-01-01

Abstract

This paper aims at developing a power control framework to jointly optimize energy efficiency (measured in bit/joule) and delay in wireless networks. A multi-objective approach is taken dealing with both performance metrics, while ensuring a minimum quality-of-service to each user in the network. Each user in the network is modeled as a rational agent that engages in a generalized non-cooperative game. Feasibility conditions are derived for the existence of each player's best response, and used to show that if these conditions are met, the game best response dynamics will converge to a unique Nash equilibrium. Based on these results, a convergent power control algorithm is derived, which can be implemented in a fully decentralized fashion. Next, a centralized power control algorithm is proposed, which also serves as a benchmark for the proposed decentralized solution. Due to the non-convexity of the centralized problem, the tool of maximum block improvement is used, to tradeoff complexity with optimality.
2018
Zappone, Alessio; Sanguinetti, Luca; Debbah, Merouane
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/884534
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