Sparsity aware dynamic distributed compressive spectrum sensing and scheduling

A cross-layer framework for resource constrained dynamic distributed spectrum sensing and scheduling is presented. A network of secondary users (SUs) opportunistically communicate over portions of the spectrum estimated to be unused by other systems. A central controller (CC) schedules the traffic of the SUs, based on distributed compressed measurements collected by the SUs. Sensing and access are jointly controlled to maximize the SU throughput, with constraints on PU throughput degradation and SU cost. Sparsity in the network dynamics is exploited: leveraging a prior spectrum occupancy estimate, the CC needs to estimate only a residual uncertainty vector via sparse recovery techniques. The trade-off between achieving accurate spectrum estimates, high throughput, and low state information overhead, is optimized via dynamic programming.