Distributed power allocation with SINR constraints using trial and error learning

In this paper, we address the problem of global transmit power minimization in a self-configuring network where radio devices are subject to operate at a minimum signal to interference plus noise ratio (SINR) level. We model the network as a parallel Gaussian interference channel and we introduce a...

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Vydané v:2012 IEEE Wireless Communications and Networking Conference (WCNC) s. 1835 - 1840
Hlavní autori: Rose, L., Perlaza, S. M., Debbah, M., Le Martret, C. J.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.04.2012
Predmet:
Convergence
Games
Interference
Learning
Mood
Nash equilibrium
power control
Power demand
Signal to noise ratio
spectrum sharing
trial and error
ISBN:9781467304368, 1467304360
ISSN:1525-3511
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Shrnutí:In this paper, we address the problem of global transmit power minimization in a self-configuring network where radio devices are subject to operate at a minimum signal to interference plus noise ratio (SINR) level. We model the network as a parallel Gaussian interference channel and we introduce a fully decentralized algorithm (based on trial and error) able to statistically achieve a configuration where the performance demands are met. Contrary to existing solutions, our algorithm requires only local information and can learn stable and efficient working points by using only one bit feedback. We model the network under two different game theoretical frameworks: normal form and satisfaction form. We show that the converging points correspond to equilibrium points, namely Nash and satisfaction equilibrium. Similarly, we provide sufficient conditions for the algorithm to converge in both formulations. Moreover, we provide analytical results to estimate the algorithm's performance, as a function of the network parameters. Finally, numerical results are provided to validate our theoretical conclusions.
ISBN:9781467304368
1467304360
ISSN:1525-3511
DOI:10.1109/WCNC.2012.6214083