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.