Spatial-Spectral Sensing using the Shrink & Match Algorithm in Asynchronous MIMO OFDM Signals

TL;DR

This paper introduces a novel spectrum sensing method for cognitive radio systems that combines array processing, compressed sensing, and a shrinkage algorithm to efficiently detect spectrum opportunities in asynchronous MIMO OFDM signals.

Contribution

It proposes a new spatial-spectral sensing approach using the Shrink & Match algorithm, improving detection accuracy and reducing sensing costs in cognitive radio environments.

Findings

Enhanced detection of spectrum opportunities in asynchronous MIMO OFDM signals.

Reduced sensing cost through sparse covariance modeling.

Improved spectrum exploitation capabilities for secondary users.

Abstract

Spectrum sensing (SS) in cognitive radio (CR) systems is of paramount importance to approach the capacity limits for the Secondary Users (SU), while ensuring the undisturbed transmission of Primary Users (PU). In this paper, we formulate a cognitive radio (CR)systems spectrum sensing (SS) problem in which Secondary Users (SU), with multiple receive antennae, sense a channel shared among multiple asynchronous Primary Users (PU) transmitting Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) signals. The method we propose to estimate the opportunities available to the SUs combines advances in array processing and compressed channel sensing, and leverages on both the so called "shrinkage method" as well as on an over-complete basis expansion of the PUs interference covariance matrix to detect the occupied and idle angles of arrivals and subcarriers. The covariance "shrinkage" step and the sparse modeling step that follows, allow to resolve ambiguities that arise when the observations are scarce, reducing the sensing cost for the SU, thereby increasing its spectrum exploitation capabilities compared to competing sensing methods. Simulations corroborate that exploiting the sparse representation of the covariance matrix in CR sensing resolves the spatial and frequency spectrum of the sources.