Carrier Aggregation as a Repeated Game: Learning Algorithms for Efficient Convergence to a Nash Equilibrium

Carrier aggregation is a key feature of next generation wireless networks to deliver high-bandwidth links. This paper studies carrier aggregation for autonomous networks operating in shared spectrum. In our model, networks decide how many and which channels to aggregate in multiple frequency bands, hence extending the distributed channel allocation framework. Moreover, our model takes into the account physical layer issues, such as the out-of-channel interference in adjacent frequency channels and the cost associated with inter-band carrier aggregation. We propose learning algorithms that converge to Nash equilibria in a reasonable number of iterations under the assumption of incomplete and imperfect information.