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

TL;DR

This paper presents a cross-layer framework for optimizing spectrum sensing and scheduling in cognitive radio networks, exploiting spectrum sparsity to improve efficiency and reduce complexity.

Contribution

It introduces a novel joint sensing and scheduling approach that leverages sparse recovery and belief minimization to enhance resource utilization in cognitive radio networks.

Findings

Decoupled sensing and scheduling optimization simplifies implementation.

Proposed myopic scheduling effectively targets likely idle bands.

Framework achieves energy-efficient spectrum utilization.

Abstract

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.