Contribution to spectrum management in cognitive radio networks: a cognitive management framework

To overcome the current under-utilization of spectrum resources, the CR (Cognitive Radio) paradigm has gained an increasing interest to perform the so-called Dynamic Spectrum Access (DSA). In this respect, Cognitive Radio networks (CRNs) have been strengthened with cognitive management support to push forward their deployment and commercialization. This dissertation has assessed the relevance of exploiting several cognitive management functionalities in various scenarios and case studies. Specifically, this dissertation has constructed a generic cognitive management framework, based on the fittingness factor concept, to support spectrum management in CRNs. Under this framework, the dissertation has addressed two of the most promising CR applications, namely an Opportunistic Spectrum Access (OSA) to licensed bands and open sharing of license-exempt bands. In the former application, several strategies that exploit temporal statistical dependence between primary activity/inactivity durations to perform a proactive spectrum selection have been discussed. A set of guidelines to select the most relevant strategy for a given environment have been provided. In the latter application, a fittingness factor-based spectrum selection strategy has been proposed to efficiency exploit the different bands. Several formulations of the fittingness factor have been compared, and their relevance have been assessed under different settings. Drawing inspiration from these applications, a more general proactive strategy exploiting a characterization of spectrum resources at both the time and frequency domains has been developed to jointly assist spectrum selection (SS) and spectrum mobility (SM) functionalities. Several variants of the proposed strategy, each combining different choices and options of implementation, have been compared to identify which of its components have the most significant impact on performance depending on the working conditions of the CRN. To assess rationality of the proposed strategy with respect to other strategies, a cost-benefit analysis has been conducted to confront the introduced gain in terms of user satisfaction level to the incurred cost in terms of signaling amount. Finally, the dissertation has conducted an analysis of practicality aspects in terms of robustness to environment uncertainty and applicability to realistic environments. With respect to the former aspect, robustness has been assessed in front of two sources of uncertainty, namely imperfection of the acquisition process and non-stationarity of the environment, and additional functionalities have been developed, when needed, to improve robustness. With respect to the latter, the proposed framework has been applied to a Digital Home (DH) environment to validate the obtained key findings under realistic conditions.Postprint (published version

Contribution to spectrum management in cognitive radio networks: a cognitive management framework

To overcome the current under-utilization of spectrum resources, the CR (Cognitive Radio) paradigm has gained an increasing interest to perform the so-called Dynamic Spectrum Access (DSA). In this respect, Cognitive Radio networks (CRNs) have been strengthened with cognitive management support to push forward their deployment and commercialization. This dissertation has assessed the relevance of exploiting several cognitive management functionalities in various scenarios and case studies. Specifically, this dissertation has constructed a generic cognitive management framework, based on the fittingness factor concept, to support spectrum management in CRNs. Under this framework, the dissertation has addressed two of the most promising CR applications, namely an Opportunistic Spectrum Access (OSA) to licensed bands and open sharing of license-exempt bands. In the former application, several strategies that exploit temporal statistical dependence between primary activity/inactivity durations to perform a proactive spectrum selection have been discussed. A set of guidelines to select the most relevant strategy for a given environment have been provided. In the latter application, a fittingness factor-based spectrum selection strategy has been proposed to efficiency exploit the different bands. Several formulations of the fittingness factor have been compared, and their relevance have been assessed under different settings. Drawing inspiration from these applications, a more general proactive strategy exploiting a characterization of spectrum resources at both the time and frequency domains has been developed to jointly assist spectrum selection (SS) and spectrum mobility (SM) functionalities. Several variants of the proposed strategy, each combining different choices and options of implementation, have been compared to identify which of its components have the most significant impact on performance depending on the working conditions of the CRN. To assess rationality of the proposed strategy with respect to other strategies, a cost-benefit analysis has been conducted to confront the introduced gain in terms of user satisfaction level to the incurred cost in terms of signaling amount. Finally, the dissertation has conducted an analysis of practicality aspects in terms of robustness to environment uncertainty and applicability to realistic environments. With respect to the former aspect, robustness has been assessed in front of two sources of uncertainty, namely imperfection of the acquisition process and non-stationarity of the environment, and additional functionalities have been developed, when needed, to improve robustness. With respect to the latter, the proposed framework has been applied to a Digital Home (DH) environment to validate the obtained key findings under realistic conditions