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.