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.

Provided by: Institute of Electrical and Electronics Engineers Topic: Mobility Date Added: Oct 2011 Format: PDF

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