A Distributed Stable Strategy Learning Algorithm for Multi-User Dynamic Spectrum Access

Tomer Gafni, Kobi Cohen

Abstract:

We consider the problem of multi-user dynamic
spectrum access (DSA) in cognitive radio networks. The shared
bandwidth is divided into K orthogonal channels, and M
(secondary) users aim at accessing the spectrum, where K ≥ M .
Each user is allowed to choose a single channel for transmission
at each time slot. The state of each channel is modeled by a
restless unknown Markovian process. By contrast to existing
studies that analyzed a special case of this setting, in which
each channel yields the same expected rate for all users, in
this paper we consider the more general model, where each
channel yields a different expected rate for each user. This
general model adds a significant challenge of how to efficiently
learn a channel allocation in a distributed manner so as to yield
a global system wide objective. We adopt the stable matching
utility as the system objective, which is known to yield strong
performance in multichannel wireless networks, and develop a
novel Distributed Stable Strategy Learning (DSSL) algorithm
to achieve the objective. We prove theoretically that the DSSL
algorithm converges to the stable matching allocation, and the
regret, defined as the loss in total rate with respect to the stable
matching solution, has a logarithmic order with time. Finally, we
present numerical examples that support the theoretical results
and demonstrate strong performance of the DSSL algorithm.