National Institute of Technology Raipur
Department of Electronics and Communication Engineering
Mid-semester Project Progress Presentation
On
“An Introduction to Deep Learning for the Physical Layer”
Project Supervisor : Dr. Saikat Majumder Sir
Acknowledgement
We would like to express a special thanks of gratitude to our Project Supervisor
Dr. Saikat Majumder Sir for giving us the opportunity to work on this project titled
“An Introduction to Deep Learning for the Physical Layer”.
20116001 Aanchal Sharma
20116028 Deepshikha Sharma
20116097 Smita Prajapati
20116905 Palak Singh
INTRODUCTION
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Deep learning revolutionizes system design in communications, offering a novel
approach beyond traditional modeling of channels and hardware imperfections for
reliable data transfer.
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Demonstrating practicality, they train neural networks to model integrated transmitterreceiver systems within a single autoencoder NN, optimizing for diverse channels and
applications without known optimal solutions.
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Expanding to multiple transmitter-receiver pairs, they employ "radio transformer
networks" (RTNs) to enhance deep learning expertise, improving symbol detection
performance in interference channels, and demonstrate CNN superiority for modulation
classification on complex IQ samples.
LITERATURE REVIEW
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This article discusses using deep learning to design communication systems. The authors
propose a new way to think about communication system design as an end-to-end
reconstruction task. They show how this idea can be extended to networks of multiple
transmitters and receivers. They also introduce the concept of radio transformer networks.
Finally, they demonstrate the application of convolutional neural networks on raw IQ samples
for modulation classification.
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It also highlights the challenges associated with implementing DL in the physical layer. Author
provides insights into potential solutions to these challenges, including data augmentation
techniques, robust training strategies, and hardware optimization for efficient inference in realtime communication systems.
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Overall, this
paper serves as an invaluable introductory guide for researchers and
practitioners seeking to leverage DL techniques for enhancing the performance and reliability
of wireless communication systems in the physical layer.
PROBLEM STATEMENT
Implementing the existing paper : AN INTRODUCTION TO DEEP LEARNING FOR
THE PHYSICAL LAYER
By Interpreting a communication system as an autoencoder, develop a
fundamental new way to think about communication system design as an endto-end reconstruction task that seeks to jointly optimize transmitter and receiver
components in a single process.
OBJECTIVES
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Extending the idea to networks of multiple transmitters and receivers.
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Presenting the concept of Radio transformer networks as a means to incorporate
expert domain knowledge in a machine learning model.
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Demonstrating the application of convolution neural networks on raw IQ samples
for modulation classification which achieves competitive accuracy with respect to
traditional schemes.
METHODOLOGY AND IMPLEMENTATION
MACHINE LEARNING APPLICATIONS FOR THE PHYSICAL LAYER
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Autoencoders for end-to-end communications systems
Autoencoders for multiple transmitters and receivers
Radio transformer networks for augmented signal processing algorithms
CNNs for classification tasks
A. Autoencoders for end-to-end communications systems
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From a DL point of view, this simple communications system can be seen as a particular type
of autoencoder.
The goal of an autoencoder is to find a low-dimensional representation of its input at some
intermediate layer which allows reconstruction at the output with minimal error.
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In our case, the purpose of the autoencoder is different. It seeks to learn representations x of the
messages s that are robust with respect to the channel impairments mapping x to y.
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The transmitter consists of a feedforward NN with multiple dense layers followed by a
normalization layer. The receiver is also implemented as a feedforward NN. Its last layer uses a
softmax activation.
IMPLEMENTATION OUTPUT
Autoencoder (2,2).
Autoencoder (4,4)
IMPLEMENTATION OUTPUT
Autoencoder (8,8).
Autoencoder (7,4)
B. Autoencoders for multiple transmitters and receivers
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Transmitter 1 wants to communicate message s1 ∈ M to Receiver 1 while Transmitter 2 wants to
communicate message s2 ∈ M to Receiver 2.
Both transmitter-receiver pairs are implemented as NNs and the only difference is that the
transmitted messages x1, x2 ∈ C n now interfere at the receivers, resulting in the noisy
observations .
From a DL point of view, the training approach consists of minimizing a weighted sum of both
losses, i.e., L˜ = αL˜1 + (1 − α)L˜2 for some α ∈ [0, 1].
There are many other possibilities to train such a system and there is no optimal approach.
However, in our experiments, the desired result is identical BLERs for both transmitter-receiver
pairs.
IMPLEMENTATION OUTPUT
Multiple Transmitters and Receivers
C. Radio transformer networks for signal processing algorithms
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One way of augmenting DL models with expert propagation domain knowledge but not signal
specific assumptions is through the use of an RTN.
An RTN consists of three parts: (i) a learned parameter estimator gω (ii) a parametric transform T
(iii) a learned discriminative network g.
The basic functioning of an RTN is best understood as a problem of phase offset estimation and
compensation.
The goal of gω is to estimate a scalar, that is close to the phase offset ϕ, which is then used by the
parametric transform t to compute the signal and then fed into the discriminative network g for
further processing, such as classification.
IMPLEMENTATION OUTPUT
D. CNNs for classification tasks
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We look at the well-known problem of modulation classification of single carrier modulation
schemes based on sampled radio frequency time-series data, i.e., IQ samples.
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We leverage a series of narrowing convolutional layers followed by dense/fully connected layers
and terminated with a dense softmax layer for our classifier.
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We compare the classification accuracy of the CNN against that of extreme gradient boosting
with 1000 estimators, as well as a single scikit-learn tree.
IMPLEMENTATION OUTPUT
RESULTS AND DISCUSSIONS
● The autoencoder achieves the same BLER as uncoded BPSK for (2,2), and it
outperforms the uncoded BPSK for (8,8) over the full range of Eb/N0.
● The trained Radio Transformer Network Model is to be implemented on
dataset to study its performance.
● Confusion Matrix is to be plotted for different modulation schemes to
determine the modulation classification accuracy of CNN
PENDING WORK
● Implementation of the Trained Radio Transformer Network Algorithm based
Model on Noisy dataset
● Plotting Confusion Matrix and Performance vs SNR Graph for CNN for
Modulation Classification
REFERENCES
● T. O’Shea and J. Hoydis, “an Introduction To Deep Learning For The Physical
Layer”
● RML2016.10b—https://radioml.com/datasets/radioml-2016-10-dataset/