Abstract:
Spectrum today is regulated based on fixed licensees. In the past radio
operators have been allocated a frequency band for exclusive use. This
has become problem for new users and the modern explosion in wireless
services that, having arrived late find there is a scarcity in the remaining
available spectrum.
Cognitive radio (CR) presents a solution. CRs combine intelligence,
spectrum sensing and software reconfigurable radio capabilities. This allows
them to opportunistically transmit among several licensed bands for
seamless communications, switching to another channel when a licensee
is sensed in the original band without causing interference. Enabling this
is an intelligent dynamic channel selection strategy capable of finding the
best quality channel to transmit on that suffers from the least licensee interruption.
This thesis evaluates a Q-learning channel selection scheme using an
experimental approach. A cognitive radio deploying the scheme is implemented
on GNU Radio and its performance is measured among channels
with different utilizations in terms of its packet transmission success rate,
goodput and interference caused. We derive similar analytical expressions
in the general case of large-scale networks.
Our results show that using the Q-learning scheme for channel selection
significantly improves the goodput and packet transmission success
rate of the system.
