GSoC Proposal for GNU
Radio
GNU radio has been one of the best simulation software platform for
designing almost any communication system. In particular, our
research expertise exists in the field of
software defined
radio (cognitive radio). The major utility of cognitive
radios (CR) lies in developing a protocol for efficient dynamic
spectrum access. As of now, there are various blocks available in
the GNU radio companion which help in building different cognitive
radio specific systems but our interest is mainly focused over the
enhancement of Quality of Experience of CR users (secondary or
unlicensed users) through
Machine Learning based efficient
dynamic spectrum access (DSA).
In GNU radio, we intend to develop a comprehensive Learning
based (supervised learning like Artificial Neural Networks,
Support Vector Machines, Recurrent Neural Networks, and
unsupervised learning like K-means clustering) DSA library
which would help the CR research community to immediately design
gamut of systems simply by utilizing the blocks present in our
library, viz. spectrum prediction, spectrum modeling, spectrum
characterization and many more.
We have already published the efficiency of applied machine
learning in the context of cognitive radio scenarios thereby
providing better and enhanced QoE of CR users and our idea is to
extend this horizon towards GNU radio companion so as to
better appreciate and qualify the CR research with simplicity,
robustness and efficiency.
We would love to be a part of this program and contribute vitally
towards the community.