The advancement of fifth generation (5G) wireless communication networks has created a greater demand for wireless resource management solutions that offer high data rates, extensive coverage, minimal latency and energy-efficient performance.
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.
Channel modeling is a critical topic when considering designing, learning, or evaluating the performance of any communications system.
This paper presents a novel method for synthesizing new physical layer modulation and coding schemes for communications systems using a learning-based approach which does not require an analytic model of the impairments in the channel.
We conduct an in depth study on the performance of deep learning based radio signal classification for radio communications signals.
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
We introduce a powerful recurrent neural network based method for novelty detection to the application of detecting radio anomalies.
Radio emitter recognition in dense multi-user environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, and enforcing spectrum policy.
We investigate sequence machine learning techniques on raw radio signal time-series data.
We address the problem of learning efficient and adaptive ways to communicate binary information over an impaired channel.
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.
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.
We explore unsupervised representation learning of radio communication signals in raw sampled time series representation.
We study the adaptation of convolutional neural networks to the complex temporal radio signal domain.