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In this paper, a cross-layer framework to jointly optimize spectrum sensingand scheduling in resource constrained agile wireless networks is presented. Anetwork of secondary users (SUs) accesses portions of the spectrum left unusedby a network of licensed primary users (PUs). A central controller (CC)schedules the traffic of the SUs, based on distributed compressed measurementscollected by the SUs. Sensing and scheduling are jointly controlled to maximizethe SU throughput, with constraints on PU throughput degradation and SU cost.The sparsity in the spectrum dynamics is exploited: leveraging a prior spectrumoccupancy estimate, the CC needs to estimate only a residual uncertainty vectorvia sparse recovery techniques. The high complexity entailed by the POMDPformulation is reduced by a low-dimensional belief representation viaminimization of the Kullback-Leibler divergence. It is proved that theoptimization of sensing and scheduling can be decoupled. A partially myopicscheduling strategy is proposed for which structural properties can be provedshowing that the myopic scheme allocates SU traffic to likely idle spectralbands. Simulation results show that this framework balances optimally theresources between spectrum sensing and data transmission. This frameworkdefines sensing-scheduling schemes most informative for network control,yielding energy efficient resource utilization.

Cross-layer estimation and control for Cognitive Radio: Exploiting Sparse Network Dynamics