Search Results for author:
Found 12 papers, 6 papers with code
Deep Learning for Wireless Communications
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.
Over the Air Deep Learning Based Radio Signal Classification
We conduct an in depth study on the performance of deep learning based radio signal classification for radio communications signals.
Deep Learning Based MIMO Communications
We introduce a novel physical layer scheme for single user Multiple-Input Multiple-Output (MIMO) communications based on unsupervised deep learning using an autoencoder.
Information Theory Information Theory
Learning Approximate Neural Estimators for Wireless Channel State Information
Estimation is a critical component of synchronization in wireless and signal processing systems.
Applying Bag of System Calls for Anomalous Behavior Detection of Applications in Linux Containers
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.
Cryptography and Security
Semi-Supervised Radio Signal Identification
Radio emitter recognition in dense multi-user environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, and enforcing spectrum policy.
Recurrent Neural Radio Anomaly Detection
We introduce a powerful recurrent neural network based method for novelty detection to the application of detecting radio anomalies.
Learning to Communicate: Channel Auto-encoders, Domain Specific Regularizers, and Attention
We address the problem of learning efficient and adaptive ways to communicate binary information over an impaired channel.
Deep Reinforcement Learning Radio Control and Signal Detection with KeRLym, a Gym RL Agent
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.
Radio Transformer Networks: Attention Models for Learning to Synchronize in Wireless Systems
This attention model allows the network to learn a localization network capable of synchronizing and normalizing a radio signal blindly with zero knowledge of the signals structure based on optimization of the network for classification accuracy, sparse representation, and regularization.
Unsupervised Representation Learning of Structured Radio Communication Signals
We explore unsupervised representation learning of radio communication signals in raw sampled time series representation.
Convolutional Radio Modulation Recognition Networks
We study the adaptation of convolutional neural networks to the complex temporal radio signal domain.
