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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.