Distributed Power Allocation with SINR Constraints Using Trial and Error Learning
This addresses power allocation in wireless networks for improved efficiency and stability, though it is incremental as it builds on existing game-theoretic frameworks.
The paper tackles the problem of minimizing global transmit power in self-configuring networks while meeting minimum SINR requirements, introducing a decentralized trial-and-error algorithm that uses only local information and one-bit feedback to achieve stable and efficient configurations, with numerical validation of theoretical results.
In this paper, we address the problem of global transmit power minimization in a self-congiguring 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 congiguration 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. Keywords: Learning, power control, trial and error, Nash equilibrium, spectrum sharing.