Collaboration and Coordination in Secondary Networks for Opportunistic Spectrum Access

In this paper, we address the general case of a coordinated secondary network willing to exploit communication opportunities left vacant by a licensed primary network. Since secondary users (SU) usually have no prior knowledge on the environment, they need to learn the availability of each channel through sensing techniques, which however can be prone to detection errors. We argue that cooperation among secondary users can enable efficient learning and coordination mechanisms in order to maximize the spectrum exploitation by SUs, while minimizing the impact on the primary network. To this goal, we provide three novel contributions. First, we formulate the spectrum allocation problem through a general learning model that takes into account the observation limits of the SUs. Second, we derive fundamental limits on the optimality of the Upper Confidence Bound algorithm, in case secondary users can share the rewards and have symmetric goals and communication capabilities. Third, we introduce a general coordination mechanism under uncertainty based on the Hungarian algorithm and we show, through several simulations, that it converges to the optimal channels that maximize the secondary network overall performance without prior knowledge on its nature (symmetric or not).