In order to maintain a conflict-free environment among licensed primary users (PUs) and unlicensed secondary users (SUs) in cognitive radio networks, providing frequency and geographical information through control channels, such as the cognitive pilot channel (CPC), has been recently proposed. While existing literature focused on the type of information that these control channels need to carry, this paper investigates the problem of gathering this information cooperatively, among a network of secondary base stations (SBSs). In this regard, given a cognitive network where every SBS can only have accurate knowledge on a small number of different primary users (PUs) or channels, each SBS can cooperate with neighboring SBSs in order to improve its view of the spectrum, i.e., learn about new PUs that can subsequently be used by its served SUs. We model the problem as a hedonic coalition formation game among the SBSs and we propose an algorithm for forming the coalitions. Using the proposed algorithm, each SBS can take an individual decision to join or leave a coalition while maximizing its overall potential utility, which accounts for the tradeoff between the benefit from learning new channels through coalition members and the cost from receiving inaccurate information. Simulation results show that the proposed algorithm yields a performance advantage, in terms of the average payoff per SBS reaching up to 165% relative to the non-cooperative case for a large network with 27 SBSs.