Articles

illustration-of-people-devices-and-vehicles

In the Future, AIS - Not Humans - Will Design Our Wireless Signals

The era of telecommunications systems designed solely by humans is coming to an end. From here on, artificial intelligence will play a pivotal role in the design and operation of these systems. The reason is simple: rapidly escalating complexity.

Read More

IzvjKckL_400x400

Encoder-Decoder Networks for Self-Supervised Pretraining and Downstream Signal Bandwidth Regression on Digital Antenna Arrays

This work presents the first applications of self-supervised learning applied to data from digital antenna arrays. Encoder-decoder networks are pretrained on digital array data to perform a self-supervised noisy-reconstruction task called channel in-painting, in which the network infers the contents of array data that has been masked with zeros. The self-supervised step requires no human-labeled data. The encoder architecture and weights from pretraining are then transferred to a new network with a task-specific decoder, and the new network is trained on a small volume of labeled data. We show that pretraining on the unlabeled data allows the new network to perform the task of bandwidth regression on the digital array data better than an equivalent network that is trained on the same labeled data from random initialization.

 

Learn More

IzvjKckL_400x400

Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks

Channel modeling is a critical topic when considering designing, learning, or evaluating the performance of any communications system. Most prior work in designing or learning new modulation schemes has focused on using highly simplified analytic channel models such as additive white Gaussian noise (AWGN), Rayleigh fading channels or similar. 

Read More

IzvjKckL_400x400

Physical Layer Communications System Design Over-the-Air Using Adversarial Networks

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. It extends prior work published on the channel autoencoder to consider the case where the channel response is not known or can not be easily modeled in a closed form analytic expression.

Read More

IzvjKckL_400x400

Physical layer deep learning of encodings for the MIMO fading channel

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. It extends prior work published on the channel autoencoder to consider the case where the channel response is not known or can not be easily modeled in a closed form analytic expression.

Read More

download-2

An Introduction to Deep Learning for the Physical Layer

We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process.

Read More

IzvjKckL_400x400

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.

Read More

download-2

Deep architectures for modulation recognition

We survey the latest advances in machine learning with deep neural networks by applying them to the task of radio modulation recognition. Results show that radio modulation recognition is not limited by network depth and further work should focus on improving learned synchronization and equalization.

Read More

download-2

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. 

Read More

download-2

Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention

We address the problem of learning an efficient and adaptive physical layer encoding to communicate binary information over an impaired channel.

 

Read More

download-2

Radio transformer networks: Attention models for learning to synchronize in wireless systems

We introduce learned attention models into the radio machine learning domain for the task of modulation recognition by leveraging spatial transformer networks and introducing new radio domain appropriate transformations.

 

Learn More

gnu-radio

Radio Machine Learning Dataset Generation with GNU Radio

This paper surveys emerging applications of Machine Learning (ML) to the Radio Signal Processing domain.  Provides some brief background on enabling methods and discusses some of the potential advancements for the field. 

Read More

v1

Convolutional Radio Modulation Recognition Networks

We study the adaptation of convolutional neural networks to the complex-valued temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert feature based methods which are widely used today and e show significant performance improvements. 

 

Read More

download-2

Unsupervised representation learning of structured radio communication signals

We explore unsupervised representation learning of radio communication signals in raw sampled time series representation. We demonstrate that we can learn modulation basis functions using convolutional autoencoders and visually recognize their relationship to the analytic bases used in digital communications.

 

Learn More

mesbluelogo_out@2x

AI and military RF systems

Advances in artificial intelligence (AI) are enabling significant leaps in science and technology, including the fields of digital signal processing (DSP) and radio frequency (RF) systems. 

 

Learn More

Ready to Learn More?

If you are interested in learning more about DeepSig and our solutions, contact us!