2 research outputs found
A Reinforcement Learning Approach for the Multichannel Rendezvous Problem
In this paper, we consider the multichannel rendezvous problem in cognitive
radio networks (CRNs) where the probability that two users hopping on the same
channel have a successful rendezvous is a function of channel states. The
channel states are modelled by two-state Markov chains that have a good state
and a bad state. These channel states are not observable by the users. For such
a multichannel rendezvous problem, we are interested in finding the optimal
policy to minimize the expected time-to-rendezvous (ETTR) among the class of
{\em dynamic blind rendezvous policies}, i.e., at the time slot each
user selects channel independently with probability , . By formulating such a multichannel rendezvous problem as an
adversarial bandit problem, we propose using a reinforcement learning approach
to learn the channel selection probabilities , . Our
experimental results show that the reinforcement learning approach is very
effective and yields comparable ETTRs when comparing to various approximation
policies in the literature.Comment: 5 pages, 9 figures. arXiv admin note: text overlap with
arXiv:1906.1042