Abstract-Research on context-aware communications recently led to the introduction of features and algorithms relying on the presence and rich, accurate context information, requiring however the introduction of cross-layer information exchanges. Cognitive radio (CR), in particular, is expected to benefit from context awareness, as the cognitive engine (CE) relies on the availability of multiple information sources to operate efficiently. In this context, this work delivers a detailed, yet concise classification and description of the information exchanged in a CR network between the layers of a generic protocol stack, and between each layer and the CE. For each layer, the key services provided and delivered are presented, followed by a catalogue of exchanged parameters. The analysis, supported by a set of use cases providing a quantitative assessment of the impact of crosslayer information exchanges in a CR framework, is the basis for the for the discussion of key implementation challenges and the identification of the most promising partition of functions and tasks between layers and CE.

SUMMARY Academic and industrial research initiatives have sought to make fully autonomic networks a reality. Some of these initiatives pursued a holistic approach, while others focused on setting up functionalities for specific networking domains. These efforts did not succeed in being extensively deployed, because the goals of network operators were not satisfactorily met. These goals include unification of management operations, enablement of end‐to‐end management and enhancement of the overall system performance in a trusted way, while reducing management cost. In this paper, we analyse a set of existing autonomic management architectures and frameworks with respect to a selected set of criteria. We then identify missing parts and challenges and propose a framework to unify the most promising attributes towards a novel approach of realization of autonomic networking management. We call this proposal Unified Management Framework (UMF). Copyright © 2013 John Wiley & Sons, Ltd.

This work investigates the impact of neighbour discovery on distributed learning schemes applied on optimal network selection based on the acquisition by the selecting device of context information on the capabilities and status of surrounding networks. The work introduces the problem of neighbour discovery in multiple channel and cognitive networks, and identifies the trade-offs between neighbour discovery performance and overall network performance. Next, an optimal network selection algorithm based on distributed learning is introduced, and key parameters and components relevant to its operation are presented, focusing in particular on the common control channel required to exchange the context information. Finally, the paper discusses the relation between neighbour discovery and the distributed learning process at the basis of the context information acquisition; a model for mapping the learning process on a neighbour discovery problem is proposed, and the potential impact of neighbour discovery failures on the performance of the optimal network selection scheme is discussed. © 2012 IEEE

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