Espace membre

Cet espace est dédié aux étudiants, aux enseignants et au personnel administratif de l'école


Mot de passe oublié?

Evènements et manifestations


Doctorate thesis defense of Zakia Jellali

Doctorate thesis defense on January 24th 2017 at 09H00 AM ,in Amphi I, Sup’Com.

Entitled : Sparse Regularization Approach: Application to Wireless Communications

Presented by : Zakia Jellali 



Prof. Monia Turki

Professor at ENIT, Tunisia





Prof. Pascal Larzabal

Professor at ENS Cachan, France



Prof. Benoit Geller

Professsor at ENSTA ParisTech, France


Dr. Ines Kammoun Jemal

Associate Professor at ENIS, Tunisia



Dr. Leïla Najjar Atallah

Associate professor at SUP’COM, Tunisia



Pr. Sofiane Cherif

Professor at SUP'COM, Tunisia



In recent years, there has been an explosion in the data rate to be processed in varied fields. The need for high ratio compression is rapidly evolving with these increasing requirements of large amount of data acquisition and aggregation in the imaging field, big data and emerging Internet of Things context. Sparse representation have recently made possible to reduce the amount of processed data by extracting only the main components. This thesis falls within the scope of sparse representation study. Compressed Sensing (CS) theory defines a framework in which it is possible to estimate in a unique and improved way signals having a sparse structure in a given incomplete basis. Our main goal is to exploit the potentials of CS to improve performance in wireless communication systems.

The first part of this thesis deals with the recovery and the tracking issues of sparse signals in known bases. In this context, the problem of rare events detection and counting in Wireless Sensors Networks (WSN) is considered. Firstly, we envisaged small-scale WSN scenario. In this context, under the hypothesis of rare events, the number of targets per cell forms a sparse vector with discrete integer components. Thus, new approaches based on the Greedy aspect and the Orthogonal Matching Pursuit (OMP) algorithm are proposed for targets detection and counting. Secondly, we focus on large scale WSN, for which the large-scale fading severely affects the transmitted signal. Therefore, in order to improve the detection performance in WSN, we propose collaborative schemes that control the transmitted power of certain targets based on CS coherence criterion. Also, we consider the continuous sparse parameter tracking based on the assumption of slow time variation of its support. The envisaged application is the sparse Channel Impulse Response (CIR) tracking in OFDM systems. In such scenario, the slow time variation of the support and the sparse CIR structure have been exploited to improve the channel estimation accuracy. Indeed, a new scheme based on a combination of delay subspace tracking by Kalman filtering and an adaptive CIR support tracking procedure is suggested. The proposed approaches lead to performance enhancement with respect to tracking approaches not accounting for channel sparsity. In addition, we consider the problem of optimized pilot subcarriers placement in OFDM systems for spectral efficiency increase. In this context, we propose a new scheme that iteratively allows to find the near-optimal pilot pattern in a forward manner using a tree-based structure. In addition to performance improvement, the proposed forward scheme allows a noticeable computational load reduction compared to former schemes.

The second part of this thesis focuses on a more general framework where the observed signal is a priori non sparse yet compressible in a given basis to be optimized. The chosen application is that of spatially correlated 2D WSN measurements. In this framework, in addition to conventional sparsity inducing transforms, we propose a new technique based on linear prediction coding (LPC) that exploits the data spatial correlation. This technique allows to design a sparsification transformation allowing for CS application, thus guaranteeing the original signal recovery from a reduced number of sensor readings. First, a 1D reading of the network is considered. Then, a 2D scenario, which is better adapted to exploit the spatial correlation, is envisaged.


Compressed Sensing, sparsity, coherence, Greedy, small-scale WSN, large-scale WSN, power control, OFDM systems, tracking, Kalman filter, optimized pilot allocation, spatial correlation, LPC, 2D WSN readings.