Non-Antipodal Signaling Based Robust Detection for Cooperative Spectrum Sensing in MIMO Cognitive Radio Networks

Abstract

In this letter, we present novel detection schemes for non-antipodal signaling based cooperative spectrum sensing in multiple-input multiple-output (MIMO) cognitive radio (CR) networks, which are robust against the uncertainty in channel estimates. We consider a scenario in which the secondary users (SU) cooperate by reporting the sensed data to the fusion center for soft combining towards primary user (PU) detection. We formulate this problem employing the optimal linear discriminant and model the uncertainties in the channel state information (CSI) as ellipsoidal uncertainty sets. It is then demonstrated that this problem of PU detection with uncertainty in the channel estimates for cooperative spectrum sensing in a CR system can be formulated as second order cone program (SOCP). Further, we extend this paradigm to the associated relaxed robust detector (RRD) and multicriterion robust detector (MRD) that maximally separate the hypothesis ellipsoids in low signal-to-noise power (SNR) and deep fade channel conditions. We present a closed form solution for the proposed robust detector for the above MIMO cooperative spectrum sensing scenario. Simulation results demonstrate a significant improvement in the detection performance of the proposed uncertainty aware robust detection schemes in comparison to the conventional uncertainty agnostic matched filter detector for cooperative MIMO PU detection.