RF-Spectrum Opportunities for Cognitive Radio Networks Operating Over GSM Channels

Abstract

In this paper, we characterize the radio frequency spectrum opportunities available in a common global system for mobile communications (GSM) channel to support the operation of a cognitive radio network (CRN). In a first step, we describe the technical details involved to sample the channel using a software defined radio device. Adopting a simple energy-based detector, we identify the two energy regions where the GSM system is active or inactive and evaluate the spectrum sensing accuracy. Based on the output of the detector, we show that the distribution of the durations of busy and idle periods are approximated by geometric distributions. Finally, we validate a theoretical model for the distribution of the service time. The validation results indicate that the service time can be successfully represented by a discrete generalized Pareto distribution, which is confirmed by the Kolmogorov-Smirnov test. Because the throughput of the CRN is represented by the inverse of the service time, the proposed analysis provides an upper bound for the networks' throughput, indicating the maximum throughput that can be attained when a single secondary user transmits over a GSM cellular channel. The results presented in this paper are validated with real data, confirming the accuracy of the proposed service time model.

A Stochastic Method to Physical Layer Security of an Amplify-and-Forward Spectrum Sensing in Cognitive Radio Networks: Secondary User to Relay

“…Cognitive Radio Networks (CRNs) are becoming one of the most promising technologies that aim for efficient spectrum utilization and alleviating the spectrum scarcity problem caused by the demand for wireless bandwidth growing rapidly due to the increase in growth of various mobile and IoT application [12][13][14]. CRNs are found to be without difficulty wide-open to external malicious threats.…”

GSM‐RF Channel Characterization Using a Wideband Subspace Sensing Mechanism for Cognitive Radio Networks

“…However, ED is vulnerable to noise uncertainty and needs to have an accurate knowledge of the noise floor, which interrogates the accuracy of signal detection in the presence of injurious shadowing and multipath fading effects. Another relevant paper [14] has portrayed CR opportunities over GSM by examining captured samples in real time, based upon Fast Fourier Transform (FFT) algorithm to do spectrum sensing. Nonetheless, the FFT analysis is marked by tradeoffs in windowing, time domain averaging, and frequency domain averaging of sampled data obtained from random processes in order to balance the need to reduce side lobes and to ensure adequate spectral resolution [20].…”

“…In addition, they do not need a priori information of the PUs signal characteristics as in [21,22] since they are not restricted to specific modulations. Furthermore, subspace techniques are significantly robust against noise as shown in [29], unlike the methods used in [13,14,19], because they rely on the eigen-decomposition of the autocorrelation matrix which allows to remove the uncertain background noise in advance. This was the main motivation behind the recent study held in [30] that used subspace filtering to sense multiple PU signals corrupted with AWGN (Additive White Guassian Noise) and Rayleigh fading.…”

“…(1) Different from the works in [11,13,14], we are proposing realistic testbed using SUs devices enabled with SDR, configured to act independently and collect periodically the signal samples emitted by GSM PUs to lower the sensing cost that usually accompanies subspace techniques in multiple antennas system design as in [15].…”

“…Authors have adopted many assumptions to meet spectrum sensing constraints in GSM bands, which question the validity of the model for real case scenarios. Other works in the literature [13,14] have addressed spectrum sensing using Energy Detection (ED) based techniques in GSM, though they are significantly vulnerable in noisy environments. Furthermore, such techniques are limited to narrow band sensing problems, which make them unable to detect more than one frequency channel per time.…”