Zobrazeno 1 - 10
of 189
pro vyhledávání: '"Clancy, T. Charles"'
Existing communication systems exhibit inherent limitations in translating theory to practice when handling the complexity of optimization for emerging wireless applications with high degrees of freedom. Deep learning has a strong potential to overco
Externí odkaz:
http://arxiv.org/abs/2005.06068
This paper addresses a fundamental question of multi-agent knowledge distribution: what information should be sent to whom and when, with the limited resources available to each agent? Communication requirements for multi-agent systems can be rather
Externí odkaz:
http://arxiv.org/abs/1903.03086
We conduct an in depth study on the performance of deep learning based radio signal classification for radio communications signals. We consider a rigorous baseline method using higher order moments and strong boosted gradient tree classification and
Externí odkaz:
http://arxiv.org/abs/1712.04578
We introduce a novel physical layer scheme for single user Multiple-Input Multiple-Output (MIMO) communications based on unsupervised deep learning using an autoencoder. This method extends prior work on the joint optimization of physical layer repre
Externí odkaz:
http://arxiv.org/abs/1707.07980
Estimation is a critical component of synchronization in wireless and signal processing systems. There is a rich body of work on estimator derivation, optimization, and statistical characterization from analytic system models which are used pervasive
Externí odkaz:
http://arxiv.org/abs/1707.06260
Publikováno v:
2015 IEEE Globecom Workshops (GC Wkshps), San Diego, CA, 2015, pp. 1-5
In this paper, we present the results of using bags of system calls for learning the behavior of Linux containers for use in anomaly-detection based intrusion detection system. By using system calls of the containers monitored from the host kernel fo
Externí odkaz:
http://arxiv.org/abs/1611.03053
Radio emitter recognition in dense multi-user environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, and enforcing spectrum policy. Radio data is readily available and easy to obtain from an an
Externí odkaz:
http://arxiv.org/abs/1611.00303
We introduce a powerful recurrent neural network based method for novelty detection to the application of detecting radio anomalies. This approach holds promise in significantly increasing the ability of naive anomaly detection to detect small anomal
Externí odkaz:
http://arxiv.org/abs/1611.00301
We address the problem of learning efficient and adaptive ways to communicate binary information over an impaired channel. We treat the problem as reconstruction optimization through impairment layers in a channel autoencoder and introduce several ne
Externí odkaz:
http://arxiv.org/abs/1608.06409
Autor:
O'Shea, Timothy J., Clancy, T. Charles
This paper presents research in progress investigating the viability and adaptation of reinforcement learning using deep neural network based function approximation for the task of radio control and signal detection in the wireless domain. We demonst
Externí odkaz:
http://arxiv.org/abs/1605.09221