Cognitive Interference Networks with Partial and Noisy Observations: A Learning Framework

An algorithm for the optimization of secondary user´s transmission strategies in cognitive networks with imperfect network state observations is presented. The task of the secondary user is to maximize its performance while generating a bounded performance loss to the primary users´ network. The state of the primary users´ network, defined as a collection of variables describing features of the network (e.g., buffer state, ARQ state), evolves according to a Markov process whose statistics depend on the transmission strategy of the secondary user. The main contribution of this paper is an online learning algorithm that, without any a priori knowledge about the statistics of the network and state-observation map, iteratively optimizes the strategy of the secondary user based on a sample-path of noisy and partial state observations.