  In this paper, a cross-layer framework to jointly optimize spectrum sensing
and scheduling in resource constrained agile wireless networks is presented. A
network of secondary users (SUs) accesses portions of the spectrum left unused
by a network of licensed primary users (PUs). A central controller (CC)
schedules the traffic of the SUs, based on distributed compressed measurements
collected by the SUs. Sensing and scheduling are jointly controlled to maximize
the SU throughput, with constraints on PU throughput degradation and SU cost.
The sparsity in the spectrum dynamics is exploited: leveraging a prior spectrum
occupancy estimate, the CC needs to estimate only a residual uncertainty vector
via sparse recovery techniques. The high complexity entailed by the POMDP
formulation is reduced by a low-dimensional belief representation via
minimization of the Kullback-Leibler divergence. It is proved that the
optimization of sensing and scheduling can be decoupled. A partially myopic
scheduling strategy is proposed for which structural properties can be proved
showing that the myopic scheme allocates SU traffic to likely idle spectral
bands. Simulation results show that this framework balances optimally the
resources between spectrum sensing and data transmission. This framework
defines sensing-scheduling schemes most informative for network control,
yielding energy efficient resource utilization.
