One-bit spectrum sensing using Gustafson–Kessel fuzzy clustering for cognitive radio network

Abstract

In this article, we address the problem of one-bit spectrum sensing using a multi-antenna array for cognitive radio networks. Spectrum sensing with one-bit analog-to-digital converter (ADC) is a far-reaching technology as it requires only a simple comparator for conversion and has the advantage of low-cost and high-speed. However, spectrum sensing with one-bit quantized data is challenging because of severe loss of information due to nonlinearities involved. Existing one-bit spectrum sensing techniques utilize arcsine law to approximate original signal covariance matrix from their quantized version and then analytically model the signal features for deriving the spectrum classification rule. However, these techniques are vulnerable to the inaccuracies of the model and it may not be at all possible to derive a classification rule for analytically intractable noise scenarios. In this article, we follow a data-driven approach for one-bit spectrum sensing and propose Gustafson–Kessel fuzzy c-means (GKFCM) clustering based algorithms for detection of primary user signals in white and correlated noise. We identify the features which are conserved in the process of quantization and form decision vectors using them. Decision vectors obtained from the received one-bit quantized data are then clustered using GKFCM to obtain the model parameters and classification is performed to identify the occupancy of the channel. Simulation results reveal that proposed algorithms provide significant improvement in detection performance compared to existing one-bit spectrum sensing techniques in literature.