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

Abstract

Carrier aggregation is a key feature of next generation wireless networks to deliver high-bandwidth links. This paper studies carrier aggregation for au- tonomous networks operating in shared spectrum. In our model, networks decide how many and which chan- nels to aggregate in multiple frequency bands, hence ex- tending 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 learn- ing algorithms that converge to Nash equilibria in a reasonable number of iterations under the assumption of incomplete and imperfect information