We consider a dynamic multichannel access problem, where multiple correlated channels
follow an unknown joint Markov model and users select the channel to transmit data. The
objective is to find a policy that maximizes the expected long-term number of successful
transmissions. The problem is formulated as a partially observable Markov decision process
with unknown system dynamics. To overcome the challenges of unknown dynamics and
prohibitive computation, we apply the concept of reinforcement learning and implement a …

We consider the problem of dynamic multichannel access in a Wireless Sensor Network
(WSN) containing N correlated channels, where the states of these channels follow a joint
Markov model. A user at each time slot selects a channel to transmit a packet and receives a
reward based on the success or failure of the transmission, which is dictated by the state of
the selected channel. The objective is to find a policy that maximizes the expected long-term
reward. The problem can be formulated as a partially observable Markov decision process …