Publications

Refine Results

(Filters Applied) Clear All

By

Blind signal classification via sparse coding

April 10, 2016
  |
Conference Paper
Author:
Youngjune L. Gwon
…
Published in:
IEEE Int. Conf. Computer Communications, IEEE INFOCOM 2016, 10-15 April 2016.
Topic:
signal processing
R&D area:
R&D group:

Summary

We propose a novel RF signal classification method based on sparse coding, an unsupervised learning method popular in computer vision. In particular, we employ a convolutional sparse coder that can extract high-level features by computing the maximal similarity between an unknown received signal against an overcomplete dictionary of matched filter templates. Such dictionary can be either generated or trained in an unsupervised fashion from signal examples labeled with no ground truths. The computed sparse code then is applied to train SVM classifiers to discriminate RF signals. As a result, the proposed approach can achieve blind signal classification that requires no prior knowledge (e.g., MCS, pulse shaping) about the signals present in an arbitrary RF channel. Since modulated RF signals undergo pulse shaping to aid the matched filter detection by a receiver for the same radio protocol, our method can exploit variability in relative similarity against the dictionary atoms as the key discriminating factor for SVM. We present an empirical validation of our approach. The results indicate that we can separate different classes of digitally modulated signals from blind sampling with 70.3% recall and 24.6% false alarm at 10 dB SNR. If a labeled dataset were available for supervised classifier training, we can enhance the classification accuracy to 87.8% recall and 14.1% false alarm.
READ LESS

Summary

We propose a novel RF signal classification method based on sparse coding, an unsupervised learning method popular in computer vision. In particular, we employ a convolutional sparse coder that can extract high-level features by computing the maximal similarity between an unknown received signal against an overcomplete dictionary of matched filter...

READ MORE
Blind signal classification via sparse coding

Competing cognitive resilient networks

March 1, 2016
  |
Journal Article
Author:
Siamak Dastangoo
…
Published in:
IEEE Trans. Cognit. Commun. and Netw., Vol. 2, No. 1, March 2016, pp. 95-109.
Topic:
networking
R&D area:
R&D group:

Summary

We introduce competing cognitive resilient network (CCRN) of mobile radios challenged to optimize data throughput and networking efficiency under dynamic spectrum access and adversarial threats (e.g., jamming). Unlike the conventional approaches, CCRN features both communicator and jamming nodes in a friendly coalition to take joint actions against hostile networking entities. In particular, this paper showcases hypothetical blue force and red force CCRNs and their competition for open spectrum resources. We present state-agnostic and stateful solution approaches based on the decision theoretic framework. The state-agnostic approach builds on multiarmed bandit to develop an optimal strategy that enables the exploratory-exploitative actions from sequential sampling of channel rewards. The stateful approach makes an explicit model of states and actions from an underlying Markov decision process and uses multiagent Q-learning to compute optimal node actions. We provide a theoretical framework for CCRN and propose new algorithms for both approaches. Simulation results indicate that the proposed algorithms outperform some of the most important algorithms known to date.
READ LESS

Summary

We introduce competing cognitive resilient network (CCRN) of mobile radios challenged to optimize data throughput and networking efficiency under dynamic spectrum access and adversarial threats (e.g., jamming). Unlike the conventional approaches, CCRN features both communicator and jamming nodes in a friendly coalition to take joint actions against hostile networking entities...

READ MORE
Competing cognitive resilient networks

Fast online learning of antijamming and jamming strategies

December 6, 2015
  |
Conference Paper
Author:
Youngjune L. Gwon
…
Published in:
2015 IEEE Global Communications Conf., 6-10 December 2015.
Topic:
communications
R&D area:
R&D group:

Summary

Competing Cognitive Radio Network (CCRN) coalesces communicator (comm) nodes and jammers to achieve maximal networking efficiency in the presence of adversarial threats. We have previously developed two contrasting approaches for CCRN based on multi-armed bandit (MAB) and Qlearning. Despite their differences, both approaches have shown to achieve optimal throughput performance. This paper addresses a harder class of problems where channel rewards are time-varying such that learning based on stochastic assumptions cannot guarantee the optimal performance. This new problem is important because an intelligent adversary will likely introduce dynamic changepoints, which can make our previous approaches ineffective. We propose a new, faster learning algorithm using online convex programming that is computationally simpler and stateless. According to our empirical results, the new algorithm can almost instantly find an optimal strategy that achieves the best steady-state channel rewards.
READ LESS

Summary

Competing Cognitive Radio Network (CCRN) coalesces communicator (comm) nodes and jammers to achieve maximal networking efficiency in the presence of adversarial threats. We have previously developed two contrasting approaches for CCRN based on multi-armed bandit (MAB) and Qlearning. Despite their differences, both approaches have shown to achieve optimal throughput performance...

READ MORE
Fast online learning of antijamming and jamming strategies

Competing Mobile Network Game: embracing antijamming and jamming strategies with reinforcement learning

October 14, 2013
  |
Journal Article
Author:
Youngjune L. Gwon
…
Published in:
2013 IEEE Conf. on Communications and Network Security (CNS), 14-16 October 2013, pp. 28-36.
Topic:
communications
R&D area:
R&D group:

Summary

We introduce Competing Mobile Network Game (CMNG), a stochastic game played by cognitive radio networks that compete for dominating an open spectrum access. Differentiated from existing approaches, we incorporate both communicator and jamming nodes to form a network for friendly coalition, integrate antijamming and jamming subgames into a stochastic framework, and apply Q-learning techniques to solve for an optimal channel access strategy. We empirically evaluate our Q-learning based strategies and find that Minimax-Q learning is more suitable for an aggressive environment than Nash-Q while Friend-or-for Q-learning can provide the best solution under distributed mobile ad hoc networking scenarios in which the centralized control can hardly be available.
READ LESS

Summary

We introduce Competing Mobile Network Game (CMNG), a stochastic game played by cognitive radio networks that compete for dominating an open spectrum access. Differentiated from existing approaches, we incorporate both communicator and jamming nodes to form a network for friendly coalition, integrate antijamming and jamming subgames into a stochastic framework...

READ MORE
Competing Mobile Network Game: embracing antijamming and jamming strategies with reinforcement learning
1-4 of 4