A PyTorch implementation of Radio Transformer Networks from the paper "An Introduction to Deep Learning for the Physical Layer".

Overview

An Introduction to Deep Learning for the Physical Layer

An usable PyTorch implementation of the noisy autoencoder infrastructure in the paper "An Introduction to Deep Learning for the Physical Layer" by Kenta Iwasaki on behalf of Gram.AI.

Overall a fun experiment for constructing a communications system for the physical layer with transmitters/receivers in which the transmitter efficiently encodes a signal in a way such that the receiver can still, with minimal error, decode this encoded signal despite being inflicted with noise in amidst transmission.

The signal dimension for the encoded message is set to be 4, with the compressed signal representation's channel size being 2 (log_2(signal_dim)) to maximize information/bit as a basis to the principles of shannon entropy.

The signal-to-noise ratio simulated in amidst training is 7dbW. That may be changed accordingly to your preferences.

Checks for the bit error rate have been ignored for the decoder, and instead the reconstruction of the input based on categorical cross-entropy is used to validate model generalization and performance.

Training for the model is done using TorchNet.

Description

We present and discuss several novel applications of deep learning (DL) 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. We show how this idea can be extended to networks of multiple transmitters and receivers and present the concept of radio transformer networks (RTNs) as a means to incorporate expert domain knowledge in the machine learning (ML) model. Lastly, we demonstrate the application of convolutional neural networks (CNNs) on raw IQ samples for modulation classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. The paper is concluded with a discussion of open challenges and areas for future investigation.

Paper written by Tim O'Shea and Jakob Hoydis. For more information, please check out the paper here.

Requirements

  • Python 3
  • PyTorch
  • TorchNet
  • TQDM

Usage

Step 1 Start training.

$ python3 radio_transformer_networks.py

Step 2 Call model.decode_signal(x) on any noisy data on the transmitter's end.

Benchmarks

Achieves 100% within a span of ~30 epochs.

Default PyTorch Adam optimizer hyperparameters were used with no learning rate scheduling. Epochs with batch size of 256 takes half a second on a Razer Blade w/ GTX 1050.

TODO

  • Signal modulation classification using convolutional neural networks as outlined on the paper.

Contact/Support

Gram.AI is currently heavily developing a wide number of AI models to be either open-sourced or released for free to the community, hence why we cannot guarantee complete support for this work.

If any issues come up with the usage of this implementation however, or if you would like to contribute in any way, please feel free to send an e-mail to [email protected] or open a new GitHub issue on this repository.

You might also like...
The implementation of
The implementation of "Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer"

Shuffle Transformer The implementation of "Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer" Introduction Very recently, window-

Unofficial implementation of
Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)

Swin-Transformer-Tensorflow A direct translation of the official PyTorch implementation of "Swin Transformer: Hierarchical Vision Transformer using Sh

Implementation of the Transformer variant proposed in
Implementation of the Transformer variant proposed in "Transformer Quality in Linear Time"

FLASH - Pytorch Implementation of the Transformer variant proposed in the paper Transformer Quality in Linear Time Install $ pip install FLASH-pytorch

This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data.

This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data.

This is the official PyTorch implementation of the paper
This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, Alan Yuille).

TransFG: A Transformer Architecture for Fine-grained Recognition Official PyTorch code for the paper: TransFG: A Transformer Architecture for Fine-gra

Unofficial PyTorch implementation of MobileViT based on paper
Unofficial PyTorch implementation of MobileViT based on paper "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer".

MobileViT RegNet Unofficial PyTorch implementation of MobileViT based on paper MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TR

PyTorch implementation for paper StARformer: Transformer with State-Action-Reward Representations.
PyTorch implementation for paper StARformer: Transformer with State-Action-Reward Representations.

StARformer This repository contains the PyTorch implementation for our paper titled StARformer: Transformer with State-Action-Reward Representations.

Official Pytorch implementation of the paper
Official Pytorch implementation of the paper "Action-Conditioned 3D Human Motion Synthesis with Transformer VAE", ICCV 2021

ACTOR Official Pytorch implementation of the paper "Action-Conditioned 3D Human Motion Synthesis with Transformer VAE", ICCV 2021. Please visit our we

PyTorch implementation for our NeurIPS 2021 Spotlight paper
PyTorch implementation for our NeurIPS 2021 Spotlight paper "Long Short-Term Transformer for Online Action Detection".

Long Short-Term Transformer for Online Action Detection Introduction This is a PyTorch implementation for our NeurIPS 2021 Spotlight paper "Long Short

Comments
  • Some questions on this interesting project

    Some questions on this interesting project

    Hello, I am working on physical lay signal processing and new to deep learning and Python. I have read the paper and found your code here. I managed to run several tests and have some questions.

    • Your code is an implementation of the paper's Section III-A autoencoder for end to end communication system (figure 2), not exactly the Section III-C radio transformer networks (figure 8). Are you going to realize that in the future?
    • I spent some time learn to use PyTorch and then realized that half of the code is using TorchNet. Why do you use TorchNet? Can we realize it with PyTorch only? I only know how to use Matlab, running python code is really painful to me...
    • You mentioned it can achieve 100% within a span of ~30 epochs. However, I seldom achieved this result, in fact, most of the time, it went to 50% or 75% and stoped improving. Any suggestions?
    • I am interested in the encoder's output. I tried to plot it after each epoch, but because I am new to Python, I can only draw the final result. I was hoping to have something like figure 4 (a), but never succeed, even when the accuracy is 100%.
    opened by planewave 3
Owner
Gram.AI
Machine-learning models for the community done in gram-sized proportions.
Gram.AI
Monitora la qualità della ricezione dei segnali radio nelle province siciliane.

FMap-server Monitora la qualità della ricezione dei segnali radio nelle province siciliane. Conversion data Frequency - StationName maps are stored in

Triglie 5 May 24, 2021
Builds a LoRa radio frequency fingerprint identification (RFFI) system based on deep learning techiniques

This project builds a LoRa radio frequency fingerprint identification (RFFI) system based on deep learning techiniques.

null 20 Dec 30, 2022
Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image classification, in Pytorch

Transformer in Transformer Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image c

Phil Wang 272 Dec 23, 2022
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Phil Wang 12.6k Jan 9, 2023
Implementation of the "Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos" paper.

Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos Introduction Point cloud videos exhibit irregularities and lack of or

Hehe Fan 101 Dec 29, 2022
The official implementation of paper Siamese Transformer Pyramid Networks for Real-Time UAV Tracking, accepted by WACV22

SiamTPN Introduction This is the official implementation of the SiamTPN (WACV2022). The tracker intergrates pyramid feature network and transformer in

Robotics and Intelligent Systems Control @ NYUAD 28 Nov 25, 2022
nnFormer: Interleaved Transformer for Volumetric Segmentation Code for paper "nnFormer: Interleaved Transformer for Volumetric Segmentation "

nnFormer: Interleaved Transformer for Volumetric Segmentation Code for paper "nnFormer: Interleaved Transformer for Volumetric Segmentation ". Please

jsguo 610 Dec 28, 2022
Third party Pytorch implement of Image Processing Transformer (Pre-Trained Image Processing Transformer arXiv:2012.00364v2)

ImageProcessingTransformer Third party Pytorch implement of Image Processing Transformer (Pre-Trained Image Processing Transformer arXiv:2012.00364v2)

null 61 Jan 1, 2023
Transformer - Transformer in PyTorch

Transformer 完成进度 Embeddings and PositionalEncoding with example. MultiHeadAttent

Tianyang Li 1 Jan 6, 2022
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.

Swin Transformer for Object Detection This repo contains the supported code and configuration files to reproduce object detection results of Swin Tran

Swin Transformer 1.4k Dec 30, 2022