Spatial-spectral sensing using the Shrink & Match algorithm in asynchronous MIMO OFDM signals

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 these claims.