Dynamic Sensor Selection for Reliable Spectrum Sensing via E-Optimal Criterion

TL;DR

This paper proposes a dynamic sensor selection method based on E-optimality for reliable spectrum sensing, leveraging compressive sensing and feedback to adaptively improve measurement quality.

Contribution

It introduces a novel E-optimality based sensor selection framework that dynamically adapts sensor choices to enhance spectrum sensing reliability.

Findings

E-optimality criterion improves sensor selection quality

Dynamic feedback mechanism enhances spectrum sensing reliability

Framework leverages sparsity and compressive sensing principles

Abstract

Reliable and efficient spectrum sensing through dynamic selection of a subset of spectrum sensors is studied. The problem of selecting K sensor measurements from a set of M potential sensors is considered where K << M. In addition, K may be less than the dimension of the unknown variables of estimation. Through sensor selection, we reduce the problem to an under-determined system of equations with potentially infinite number of solutions. However, the sparsity of the underlying data facilitates limiting the set of solutions to a unique solution. Sparsity enables employing the emerging compressive sensing technique, where the compressed measurements are selected from a large number of potential sensors. This paper suggests selecting sensors in a way that the reduced system of equations constructs a well-conditioned measurement matrix. Our criterion for sensor selection is based on E-optimalily, which is highly related to the restricted isometry property that provides some guarantees for sparse solution obtained by L1 minimization. Moreover, the proposed framework exploits a feedback mechanism to evolve the selected sensors dynamically over time. The evolution aims to maximize the reliability of the sensed spectrum.