Dynamic Sensor Selection For Reliable Spectrum Sensing Via E-Optimal Criterion

Keywords

Compressive Spectrum Sensing and Sparse Recovery; E-optimality; Matrix Subset Selection; Restricted Isometry Property (RIP); Sensor Selection

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.

Publication Date

11-14-2017

Publication Title

Proceedings - 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017

Number of Pages

452-460

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/MASS.2017.72

Socpus ID

85040588336 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85040588336

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