Distributed Power Allocation With SINR Constraints Using Trial and Error Learning
In this paper, the authors 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. They model the network as a parallel Gaussian interference channel and they 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, their algorithm requires only local information and can learn stable and efficient working points by using only one bit feedback. They model the network under two different game theoretical frameworks: normal form and satisfaction form.