Reliable Detection of Transmit-Antenna Number for MIMO Systems in Cognitive Radio-Enabled Internet of Things

Identification of transmit-antenna number is of importance in cognitive Internet of Things (IoT) with multiple-input–multiple-output (MIMO). Previous studies on transmit-antenna number detection only consider Gaussian noise and ignore impulsive interference. In the practical wireless communication, impulsive interference may exist due to low-frequency atmospheric noise, multiple access, and electromagnetic disturbance. Such interference can usually be modeled as symmetric alpha stable (
$S\alpha S$
), which cause the performance degradation of conventional algorithms based on the Gaussian model. In this article, we present a novel scheme to detect the transmit-antenna number for MIMO systems in cognitive IoT, assuming that signals are corrupted by both
$S\alpha S$
interference and Gaussian noise. We first introduce a new approach to characterize the generalized correlation matrix (GCM), and provide its bound with
$S\alpha S$
interference. Then, the discriminating feature vector is constructed by utilizing the higher order moments (HOMs) of eigenvalues of the GCM. Finally, an advanced clustering algorithm is employed to detect the transmit-antenna number, using the cluster where the minimum eigenvalue is located. The proposed algorithm avoids the need for
a priori
information about the transmitted signals, such as coding mode, modulation type, and pilot patterns. Simulation experiments demonstrate the feasibility of the proposed transmit-antenna number detection scheme in MIMO systems with Gaussian noise and
$S\alpha S$
interference.