Wael Guibène

Cooperative Spectrum Sensing and Localization in Cognitive Radio Systems Using Compressed Sensing

We propose to fuse two main enabling features in cognitive radio systems (CRS): spectrum sensing and location awareness in a single compressed sensing based formalism. In this way, we exploit sparse characteristics of primary units to be detected, both in terms of spectrum used and location occupied. The compressed sensing approach also allows to overcome hardware limitations, in terms of the incapacity to acquire measurements and signals at the Nyquist rate when the spectrum to be scanned is large. Simulation results for realistic network topologies and different compressed sensing reconstruction algorithms testify to the performance and the feasibility of the proposed technique to enable in a single formalism the two main features of cognitive sensor networks.

Centralized collaborative compressed sensing of wideband spectrum for cognitive radios

This paper (1) presents a new centralized collaborative sensing technique for cognitive radio systems which combines algebraic tools and compressive sampling techniques. The proposed approach consists of the detection of spectrum holes using spectrum distribution discontinuities detector fed by compressed measurements. The compressed sensing algorithm is designed to take advantage from the primary signals sparsity and to keep the linearity and properties of the original signal in order to be able to apply algebraic detector on the compressed measurements. Collaboration among radios enables the cognitive network to detect hidden primary users and makes it more robust against fading and unknown channel conditions. Furthermore, as an important key point, collaboration makes it possible to sample more compressively at each radio, i.e., each radio performs sampling with a lower rate. The complexity of the proposed detector is also discussed and compared with the energy detector as reference algorithm. The comparison shows that the proposed technique outperforms energy detector in addition to its low complexity.

Spectrum sensing for cognitive radio exploiting spectrum discontinuities detection

This article presents a spectrum sensing algorithm for wideband cognitive radio exploiting sensed spectrum discontinuity properties. Some work has already been investigated by wavelet approach by Giannakis et al., but in this article we investigate an algebraic framework in order to model spectrum discontinuities. The information derived at the level of these irregularities will be exploited in order to derive a spectrum sensing algorithm. The numerical simulation show satisfying results in terms of detection performance and receiver operating characteristics curves as the detector takes into account noise annihilation in its inner structure.

Distribution discontinuities detection using algebraic technique for spectrum sensing in cognitive radio

In this paper1, we propose a new Spectrum Sensing technique for spectrum distribution discontinuities detection using algebraic detection (AD). The presented mathematical background leading to such technique is based on operational calculus and differential algebra which offers a new stand point in sensing theory. Even if this background is quite heavy, the sensing algorithm related to this technique is nevertheless very simple and remains with extremely low complexity, comparable even to the Energy Detection technique (ED). Simulations results show good performance of the proposed technique in terms of reliability and accuracy with low complexity and good robustness in noisy context.

A Complete Framework for Spectrum Sensing Based on Spectrum Change Points Detection for Wideband Signals

This paper presents a novel technique in spectrum sensing based on a new characterization of primary users signals in wideband communications. First, we have to remind that in cognitive radio networks, the very first task to be operated by a cognitive radio is sensing and identification of spectrum holes in the wireless environment. This paper summarizes the advances in the algebraic approach. Initial results have been already disseminated in few other conferences. This paper aims at finalizing and presenting the last results and the complete framework of the proposed technique based on algebraic spectrum discontinuities detection. The signal spectrum over a wide frequency band is decomposed into elementary building blocks of subbands that are well characterized by local irregularities in frequency. As a powerful mathematical tool for analyzing singularities and edges, the algebraic framework is employed to detect and estimate the local spectral irregular structure, which carries important information on the frequency locations and power spectral densities of the sensed subbands. In this context, a wideband spectrum sensing techniques was developed based on an analog decision function to multi-scale wavelet product. The proposed sensing techniques provide an effective sensing framework to identify and locate spectrum holes in the signal spectrum.

A combined spectrum sensing and terminals localization technique for cognitive radio networks

Cognitive radio is a smart wireless communication concept that is able to promote the efficiency of the spectrum usage by exploiting its free frequency bands, namely spectrum holes. Detection of spectrum holes is one of the first steps of implementing a cognitive radio system. Another step towards the feasibility and a real implementation of a cognitive radio network is the problem of location awareness. This problem arises when we do consider a realistic scenario in hybrid overlay/underlay systems, when these spectrum opportunities permit cognitive radios to transmit below the primary users tolerance threshold. In this case, the cognitive radio, have to estimate robustly the primary users locations in the network in order to adjust its transmission power function of the estimated location in the network. Adding to this the fact that in wideband radio one may not be able to acquire signals at the Nyquist sampling rate due to the current limitations in Analog-to-Digital Converter (ADC) technology, we end up with a system that should, at a sub-Nyquist rate, properly recover the bands over which the primary users transmit and estimate their location in the network. In this paper 1, we proposed to analyze all these arisen problems. During the problem formulation and when analyzing more deeply the equations related to each question apart, we will make the link between the formulation of spectrum sensing, location awareness and the hardware limitation by describing those problems in a unique compressed sensing formalism. Via the proposed framework, we made it possible to overcame a challenging postulate of fixed frequency spectrum allocation by also estimating the spectrum usage boundaries in a blind way.

Performance comparison for low complexity blind sensing techniques in cognitive radio systems

In this paper1, we will provide a straightforward classification of some spectrum sensing strategies derived at Eurecom attempting to show the diversity and advantages of these spectrum sensing techniques. Specifically, two low complexity blind sensing algorithms were developed to detect spectrum holes in the primary users bands: the distribution analysis detector (DAD) and the algebraic detector (AD), which are compared with the energy detector (ED) as reference algorithm. For performance evaluation we have chosen to thoroughly investigate the DVB-T primary user system. Simulation results show that the two proposed detectors offer high performances and detect primary users presence even at very low SNR with comparable complexity to ED.

A compressive sampling approach for spectrum sensing and terminals localization in cognitive radio networks

In this paper, we propose to analyze and combine two of the main enabling features of cognitive radio: location awareness and spectrum sensing with taking into account one of the most challenging hardware limitation that cognitive radio may suffer from: signal acquisition at a Nyquist rate. During the problem formulation and when analyzing more deeply the equations related to each question apart, we will make the link between the formulation of spectrum sensing, location awareness and the hardware limitation by describing those problems in a unique compressed sensing formalism. Via the proposed framework, and compared to what has already been proposed, we made it possible to overcame another challenging postulate of fixed frequency spectrum allocation by also estimating the spectrum usage boundaries dynamically and in a fully blind way.

Joint time-frequency spectrum sensing for Cognitive Radio

In this paper1, we propose a new concept of spectrum sensing techniques based on a joint time-frequency detection of primary users. In this new approach, we aim at detecting the presence of the PU in frequency and in time as well. The proposed technique based on an algebraic detection of the spectrum is compared to one of the most well known tool in time frequency analysis tools: the Wigner Ville Distribution. Simulation results show how reliable the proposed technique is comparing to classical energy detection in time-frequency plane.

Signal separation and classification algorithm for cognitive radio networks

In the context of spectrum sharing, many approaches were developed and many algorithms were proposed in order to model and regulate the use of spectral resources. Despite the proposed solutions and spectrum access policies, there is still a big issue in cognitive radio networks with users who may intend (or not) to violate these communication rules and force their radios to access the spectrum bands when some other users are already communicating. These users become hostile terminals in the network and the fusion center has to eliminate their interfering signals. In this context we1 propose a mixed signals separation and classification algorithm that helps eliminating hostile devices. The first step consists in locating the frequency band over which the hostile terminal is communicating and then, by some mixed signals separation technique, isolate and then eliminate its interfering signal by analyzing the obtained signals from the mixture. For the simulations, we introduced some metric for the probability of detecting and classifying the hostile terminal as such.