Blind Signal Classification via Sparse Coding

Youngjune Gwon, Siamak Dastangoo, H. T. Kung, Carl Fossa
2016 2016 IEEE Global Communications Conference (GLOBECOM)  
Motivation • Competing Cognitive Radio Network (CCRN) models tactical radio networks under competition -Blue Force (friend) vs. Red Force (adversary) -Dynamic, open spectrum resource for opportunistic data access -Nodes are cognitive radios Ø Comm nodes and jammers -Strategic jamming attacks This paper is about signal classification at spectrum sensing level using semi-supervised machine learning approach GLOBECOM 2016 -4 YG, 12/5/2016 • Non-learning based spectrum sensing -Energy detection
more » ... Energy detection -Cyclostationary detection • Learning-based spectrum sensing -Supervised learning (requires labeled examples of all signals you want to classify) Ø Support vector machine (SVM), logistic/softmax regression, neural network -Unsupervised learning (no labeled examples required) Ø Clustering techniques (e.g., K-means, GMM): partition data mixed of unknown identities into clusters -Semi-supervised (unsupervised feature learning followed by supervised phase) Ø Sparse coding + SVM (you need some labeled examples) GLOBECOM 2016 -5 YG, 12/5/2016 • Sparse coding is an unsupervised learning method -Transforms raw data into their sparse feature representations given set of basis vectors (dictionary) • Dictionary learning -Learns basis vectors d k (dictionary atoms) required for sparse coding Background: Sparse Coding and Dictionary Learning
doi:10.1109/glocom.2016.7841634 dblp:conf/globecom/GwonDKF16 fatcat:bd3cja2lnje67hz7dm2ci2r6oy