Abstract
In this paper, we examine a spectrum sharing opportunities over the existing Global System of Mobile Communication (GSM) networks, by identifying the unused channels at a specific time and location. For this purpose, we propose a wideband spectrum sensing mechanism to analyze the status of 51 channels at once, belonging to the 10 MHz bandwidth centered at the frequency 945 MHz, in four different areas. We propose a subspace based spectral estimation mechanism, adapted to deal with real measurements. The process begins with data collection using Secondary User (SU) device enabled with Software Defined Radio (SDR) technology, configured to operate in the GSM band. Obtained samples are used then to feed the sensing mechanism. Spectral analysis is delivered to estimate power density peaks and corresponding frequencies. Decision making phase brings together power thresholding technique and GSM control channel decoding to identify idle and busy channels. Experiments are evaluated using detection and false alarm probabilities emulated via Receiver Operating Characteristic (ROC) curves. Obtained performances show better detection accuracy and robustness against variant noise/fading effects, when using our mechanism compared to Energy Detection (ED) based ones as Welch method, and Beamforming based ones as Minimum Variance Distortionless Response (MVDR) method. Occupancy results exhibit considerable potential of secondary use in GSM based primary network.
Highlights
Nowadays, the Internet of Things (IoT) paradigm is known as being the trend that defines the global orientation of information technology actors
A subspace technique based on the eigen-decomposition of signals autocorrelation matrix has been proposed to perform wideband spectrum sensing over the Global System of Mobile Communication (GSM) system network
The Average of power density peaks obtained using Multiple Signal Classification (MUSIC) algorithm is evaluated in each channel to be compared to a decision threshold
Summary
The Internet of Things (IoT) paradigm is known as being the trend that defines the global orientation of information technology actors. Billions of things already proliferate in the IoT ecosystem, yielding to novel application domains such as Smart Cities [1, 2] as an all-embracing perspective including variant sub-applications, Industry 4.0, e-government, among others This vision appears highly promising, it divulges new types of challenges requiring low latency, low energy consumption and cost, and easy operation of massive number of embedded-systems. 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 Such techniques are limited to narrow band sensing problems, which make them unable to detect more than one frequency channel per time.
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