IC0902 Ph.D. theses

  • Title: Contribution to learning and decision making under uncertainty for Cognitive Radio
  • Ph.D. candidate: Wassim Jouini ( This e-mail address is being protected from spambots. You need JavaScript enabled to view it )
  • Advisor: Prof. Christophe Moy ( This e-mail address is being protected from spambots. You need JavaScript enabled to view it )
  • Institution: Supeléc, France
  • Date of defense: June 15, 2012
  • Abstract:  During the last century, most of the meaningful frequency bands were licensed to emerging wireless applications. Because of the static model of frequency allocation, the growing number of spectrum demanding services led to a spectrum scarcity. However, recently, series of measurements on the spectrum utilization showed that the different frequency bands were underutilized (sometimes even unoccupied) and thus that the scarcity of the spectrum resource is virtual and only due to the static allocation of the different bands to specific wireless services. Moreover, the underutilization of the spectrum resource varies on different scales in time and space offering many opportunities to an unlicensed user or network to access the spectrum. Cognitive Radio (CR) and Opportunistic Spectrum Access (OSA) were introduced as possible solutions to alleviate the spectrum scarcity issue. In this dissertation, we aim at enabling CR equipments to exploit autonomously communication opportunities found in their vicinity. For that purpose, we suggest decision making mechanisms designed and/or adapted to answer CR related problems in general, and more specifically, OSA related scenarios. Thus, we argue that OSA scenarios can be modeled as Multi-Armed Bandit (MAB) problems. As a matter of fact, within OSA contexts, CR equipments are assumed to have no prior knowledge on their environment. Acquiring the necessary information relies on a sequential interaction between the CR equipment and its environment. Finally, the CR equipment is modeled as a cognitive agent whose purpose is to learn while providing an improving service to its user. During a preliminary phase, we discuss different solutions borrowed from the Machine Learning literature. We chose in the dissertation to focus on a simple yet efficient learningalgorithm known as UCB1 algorithm. The rest of the analysis aims at exploring the performance of UCB1 in more complex and realistic scenarios. Namely, we consider one secondary user (SU) willing to exploit communication opportunities left vacant by their incumbent users. The SU in allowed to access a frequency band if he senses it free. Consequently, he needs to learn the availability of the different bands in order to select the most available one (i.e., the optimal band). The sensing process is unfortunately prone to errors. Thus, firstly we analyze the performance of UCB1 algorithm when dealing with OSAproblems with imperfect sensing. More specifically, we show that UCB1 can efficiently cope with sensing errors. We prove its convergence to the optimal channel and quantify its loss of performance compared to the case with perfect sensing. Secondly, we combine UCB1 algorithm with collaborative and coordination mechanism to model a secondary network (i.e. several SUs). We show that within this complex scenario, a coordinated learning mechanism can lead to efficient secondary networks. These scenarios assume that a SU can efficiently detect incumbent users’ activity while having no prior knowledgeon their characteristics. Usually, energy detection is suggested as a possible approachi to handle such task. Unfortunately, energy detection in known to perform poorly when dealing with uncertainty. Consequently, we ventured in this Ph.D. to revisit the problem of energy detection limits under uncertainty. We present new results on its performances as well as its limits when the noise level is uncertain and the uncertainty is modeled by a log-normal distribution (as suggested by Alexander Sonnenschein and Philip M. Fishman in 1992). Within OSA contexts, we address a final problem where a sensor aims at quantifying the quality of a channel in fading environments. In such contexts, UCB1 algorithms seem to fail. Consequently, we designed a new algorithm called Multiplicative UCB (UCB) and prove its convergence. Moreover, we prove that MUCB algorithms are order optimal (i.e., the order of their learning rate is optimal). This last work provides a contribution that goes beyond CR and OSA. As a matter of fact, MUCB algorithms are introduced and solved within a general MAB framework.

  • Title: Resource Management in Multicarrier Based Cognitive Radio Systems
  • Ph.D. candidate: Musbah Shaat ( This e-mail address is being protected from spambots. You need JavaScript enabled to view it )
  • Advisor: Dr. Carlos F. Bader ( This e-mail address is being protected from spambots. You need JavaScript enabled to view it )
  • Institution: CTTC, Spain
  • Date of defense: March 9, 2012
  • Abstract: The ever-increasing growth of the wireless application and services affirms the importance of the effective usage of the limited radio spectrum. Existing spectrum management policies have led to significant spectrum under-utilization. Recent measurements showed that large range of the spectrum is sparsely used in both temporal and spatial manner. This conflict between the inefficient usage of the spectrum and the continuous evolution in the wireless communication calls upon the development of more flexible management policies. Cognitive radio (CR) with the dynamic spectrum access (DSA) is considered to be a key technology in making the best solution of this conflict by allowing a group of secondary users (SUs) to share the radio spectrum originally allocated to the primary user (PUs). The operation of CR should not negatively alter the performance of the PUs. Therefore, the interference control along with the highly dynamic nature of PUs activities open up new resource allocation problems in CR systems. The resource allocation algorithms should ensure an effective share of the temporarily available frequency bands and deliver the solutions in timely fashion to cope with quick changes in the network.

    In this dissertation, the resource management problem in multicarrier based CR systems is considered. The dissertation focuses on three main issues: 1) design of efficient resource allocation algorithms to allocate subcarriers and powers between SUs such that no harmful interference is introduced to PUs, 2) compare the spectral efficiency of using different multicarrier schemes in the CR physical layer, specifically, orthogonal frequency division multiplexing (OFDM) and filter bank multicarrier (FBMC) schemes, 3) investigate the impact of the different constraints values on the overall performance of the CR system.

    Three different scenarios are considered in this dissertation, namely downlink transmission, uplink transmission, and relayed transmission. For every scenario, the optimal solution is examined and efficient sub-optimal algorithms are proposed to reduce the computational burden of obtaining the optimal solution. The suboptimal algorithms are developed by separate the subcarrier and power allocation into two steps in downlink and uplink scenarios. In the relayed scenario, dual decomposition technique is used to obtain an asymptotically optimal solution, and a joint heuristic algorithm is proposed to find the suboptimal solution. Numerical simulations show that the proposed suboptimal algorithms achieve a near optimal performance and perform better than the existing algorithms designed for cognitive and non-cognitive systems. Eventually, the ability of FBMC to overcome the OFDM drawbacks and achieve more spectral efficiency is verified which recommends the consideration of FBMC in the future CR systems.

  • Download linkhttp://theses.eurasip.org/theses/422/resource-management-in-multicarrier-based/download/



  • Title: Cooperative spectrum sensing and radio environment map construction in cognitive radio networks
  • Ph.D. candidate: H. Birkan Yilmaz ( This e-mail address is being protected from spambots. You need JavaScript enabled to view it )
  • Advisor: Prof. T. Tugcu ( This e-mail address is being protected from spambots. You need JavaScript enabled to view it )
  • Institution: Dept. of Computer Engineering, Bogazici University, Turkey
  • Date of defense: May 24, 2012
  • Abstract:  In this thesis, we focus on both internal and external sensing in Cognitive Radio (CR) networks. In internal sensing, individual CRs discover spectrum opportunities via spectrum sensing whereas in external sensing, an external entity provides the spectrum occupancy and related information. For the first, we propose a novel cooperative spectrum sensing scheme, Uniform Quantization-based Cooperative Sensing (UniQCS) that uses uniform quantization and an effective fusion strategy. Numerical results demonstrate that under imperfect reporting channel and false reports, UniQCS performs better than hard decision algorithms such as Majority and M-of-N in terms of probability of detection and false alarm at the expense of a marginal increase in overhead bits. We demonstrate that the performance of UniQCS is very close to that of equal gain combiner, which constitutes the upper bound for the decision performance. 

    Due to the challenges in internal sensing, external sensing recently has gained noticeable interest. In external sensing, CRs access spectrum through geolocation databases, which keep relatively static information. Radio Environment Map (REM) is a kind of improved geolocation database and an emerging topic with the latest regulations on TV white space communications. It constructs a signal power temperature map of the CR operation area via processing spectrum measurements collected from sensors dynamically. In this thesis, transmitter LocatIon Estimation based (LIvE) REM construction technique is proposed and compared with the well-known REM construction techniques in shadow and multipath fading channels. The simulation results suggest that the LIvE REM construction outperforms the compared methods in terms of root mean square error and correct detection zone ratio.


  • Title: Dynamic Spectrum Sharing among Femtocells : Coping with Spectrum Scarcity in 4G and Beyond
  • Ph.D. candidate: Gustavo Wagner Oliveira da Costa ( This e-mail address is being protected from spambots. You need JavaScript enabled to view it )
  • Advisor: Prof. Andrea Fabio Cattoni ( This e-mail address is being protected from spambots. You need JavaScript enabled to view it )
  • Institution: Dept. of Electronic Systems, Aalborg University, Denmark
  • Date of defense: August 2012
  • AbstractThe ever-growing demand for mobile broadband is leading to an imminent spectrum scarcity. In order to cope with such situation dynamic spectrum sharing and the widespread deployment of small cells (femtocells) are promising solutions. Delivering such a view is not short of challenges. Massive deployment of femtocells in an uncoordinated way will create difficult interference scenarios.

    For these reasons, this PhD thesis investigates how each femtocell can autonomously select portions of the spectrum in order to achieve more spectral efficiency and fairness. The solutions also strive for scalability to a large number of cells, stability and limited complexity. The work represents an evolution towards future cognitive femtocells.
    The issue of inter-cell interference among femtocells is thoroughly analyzed with game theory, graph theory and system-level simulations. These analyses show that the interference should be managed, and the frequency reuse should be dynamically adapted locally. Three different methods are proposed. Two of them completely avoid the need for signaling among different cells, and the third method involves the setting of mutual agreements among neighbor cells. These methods allow attaining both high average throughput and high outage performance. Overall, they can be seen as future enablers of truly ubiquitous mobile broadband. 

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