1 research outputs found
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 in this paper. First, we formulate the spectrum
selection in secondary networks as an instance of the Multi-Armed Bandit (MAB)
problem, and we extend the analysis to the collaboration learning case, in
which each SU learns the spectrum occupation, and shares this information with
other SUs. We show that collaboration among SUs can mitigate the impact of
sensing errors on system performance, and improve the convergence of the
learning process to the optimal solution. Second, we integrate the learning
algorithms with two collaboration techniques based on modified versions of the
Hungarian algorithm and of the Round Robin algorithm that allows reducing the
interference among SUs. Third, we derive fundamental limits to the performance
of cooperative learning algorithms based on Upper Confidence Bound (UCB)
policies in a symmetric scenario where all SU have the same perception of the
quality of the resources. Extensive simulation results confirm the
effectiveness of our joint learning-collaboration algorithm in protecting the
operations of Primary Users (PUs), while maximizing the performance of SUs.Comment: 28 pages. Paper submitted to a journa