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AutoEncoder-Based-Communication-System

Implementation and result of AutoEncoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/

This Repo is effictively implementation of AutoEncoder based Communication System From Research Paper "An Introduction to Deep Learning for the Physical Layer" written by Tim O'Shea and Jakob Hoydis.During My wireless Communication Lab Course,I worked on this research Paper and re-generated result of this research Paper. Idea of Deep learning Based Communication System is new and there is many advantages of Deep learning based Communication.This paper gives complete different apporach than many other paper and tries to introduce deep learning in physical layer.

Abstract of Research Paper

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

From "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/ written by Tim O'Shea and Jakob Hoydis

Requirements

  • Tensorflow
  • Keras
  • Numpy
  • Matplotlib

Note

Given Jupyter-Notebook file is dynamic to train any given (n,k) autoencodeer but for getting optimal result one has to manually tweak learning rate and epochs. Plots are generated by matlab script which for now i am not providing it.Anyone can plot result in matlab by training autoencoder and copy-pasting BER array and ploting it into matlab. All re-generated result below are generated with autoencoder_dynamic.ipynb file.

Result

Re-generated ResultResearch Paper
BER Perfomance of (7,4) AutoEncoderResearch Paper Result-1
BER Perfomance of R=1 AutoEncodersResearch Paper Result-2

Constellation diagram

(2,2) AutoEncoder's Constellation diagram

Following Constellation diagram are learned by Autoencoder after training it. (2,2) Autoencoder constellation diagram

(2,4) AutoEncoder Constellation diagram

(2,4) Autoencoder constellation diagram

About

Tensorflow Implementation and result of Auto-encoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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try {
var __m = "github.com";
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AutoEncoder-Based-Communication-System
Implementation and result of AutoEncoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
This Repo is effictively implementation of AutoEncoder based Communication System From Research Paper "An Introduction to Deep Learning
for the Physical Layer" written by Tim O'Shea and Jakob Hoydis.During My wireless Communication Lab Course,I worked on this research Paper
and re-generated result of this research Paper.
Idea of Deep learning Based Communication System is new and there is many advantages of Deep learning based Communication.This paper gives
complete different apporach than many other paper and tries to introduce deep learning in physical layer.
Abstract of Research Paper
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. We show how this idea can be extended to networks of multiple
transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the
machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation
classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. This paper is concluded
with a discussion of open challenges and areas for future investigation.
From "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/ written by Tim O'Shea and Jakob Hoydis
Requirements
  • Tensorflow
  • Keras
  • Numpy
  • Matplotlib
Note
Given Jupyter-Notebook file is dynamic to train any given (n,k) autoencodeer but for getting optimal result one has to manually tweak
learning rate and epochs.
Plots are generated by matlab script which for now i am not providing it.Anyone can plot result in matlab by training autoencoder and
copy-pasting BER array and ploting it into matlab.
All re-generated result below are generated with autoencoder_dynamic.ipynb file.
Result
Re-generated ResultResearch Paper
BER Perfomance of (7,4) AutoEncoderResearch Paper Result-1
BER Perfomance of R=1 AutoEncodersResearch Paper Result-2
Constellation diagram
(2,2) AutoEncoder's Constellation diagram
Following Constellation diagram are learned by Autoencoder after training it.
(2,2) Autoencoder constellation diagram
(2,4) AutoEncoder Constellation diagram
(2,4) Autoencoder constellation diagram
About
Tensorflow Implementation and result of Auto-encoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
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AutoEncoder-Based-Communication-System
Implementation and result of AutoEncoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
This Repo is effictively implementation of AutoEncoder based Communication System From Research Paper "An Introduction to Deep Learning
for the Physical Layer" written by Tim O'Shea and Jakob Hoydis.During My wireless Communication Lab Course,I worked on this research Paper
and re-generated result of this research Paper.
Idea of Deep learning Based Communication System is new and there is many advantages of Deep learning based Communication.This paper gives
complete different apporach than many other paper and tries to introduce deep learning in physical layer.
Abstract of Research Paper
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. We show how this idea can be extended to networks of multiple
transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the
machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation
classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. This paper is concluded
with a discussion of open challenges and areas for future investigation.
From "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/ written by Tim O'Shea and Jakob Hoydis
Requirements
  • Tensorflow
  • Keras
  • Numpy
  • Matplotlib
Note
Given Jupyter-Notebook file is dynamic to train any given (n,k) autoencodeer but for getting optimal result one has to manually tweak
learning rate and epochs.
Plots are generated by matlab script which for now i am not providing it.Anyone can plot result in matlab by training autoencoder and
copy-pasting BER array and ploting it into matlab.
All re-generated result below are generated with autoencoder_dynamic.ipynb file.
Result
Re-generated ResultResearch Paper
BER Perfomance of (7,4) AutoEncoderResearch Paper Result-1
BER Perfomance of R=1 AutoEncodersResearch Paper Result-2
Constellation diagram
(2,2) AutoEncoder's Constellation diagram
Following Constellation diagram are learned by Autoencoder after training it.
(2,2) Autoencoder constellation diagram
(2,4) AutoEncoder Constellation diagram
(2,4) Autoencoder constellation diagram
About
Tensorflow Implementation and result of Auto-encoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
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injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
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AutoEncoder-Based-Communication-System
Implementation and result of AutoEncoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
This Repo is effictively implementation of AutoEncoder based Communication System From Research Paper "An Introduction to Deep Learning
for the Physical Layer" written by Tim O'Shea and Jakob Hoydis.During My wireless Communication Lab Course,I worked on this research Paper
and re-generated result of this research Paper.
Idea of Deep learning Based Communication System is new and there is many advantages of Deep learning based Communication.This paper gives
complete different apporach than many other paper and tries to introduce deep learning in physical layer.
Abstract of Research Paper
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. We show how this idea can be extended to networks of multiple
transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the
machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation
classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. This paper is concluded
with a discussion of open challenges and areas for future investigation.
From "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/ written by Tim O'Shea and Jakob Hoydis
Requirements
  • Tensorflow
  • Keras
  • Numpy
  • Matplotlib
Note
Given Jupyter-Notebook file is dynamic to train any given (n,k) autoencodeer but for getting optimal result one has to manually tweak
learning rate and epochs.
Plots are generated by matlab script which for now i am not providing it.Anyone can plot result in matlab by training autoencoder and
copy-pasting BER array and ploting it into matlab.
All re-generated result below are generated with autoencoder_dynamic.ipynb file.
Result
Re-generated ResultResearch Paper
BER Perfomance of (7,4) AutoEncoderResearch Paper Result-1
BER Perfomance of R=1 AutoEncodersResearch Paper Result-2
Constellation diagram
(2,2) AutoEncoder's Constellation diagram
Following Constellation diagram are learned by Autoencoder after training it.
(2,2) Autoencoder constellation diagram
(2,4) AutoEncoder Constellation diagram
(2,4) Autoencoder constellation diagram
About
Tensorflow Implementation and result of Auto-encoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
Resources
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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})();
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AutoEncoder-Based-Communication-System
Implementation and result of AutoEncoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
This Repo is effictively implementation of AutoEncoder based Communication System From Research Paper "An Introduction to Deep Learning
for the Physical Layer" written by Tim O'Shea and Jakob Hoydis.During My wireless Communication Lab Course,I worked on this research Paper
and re-generated result of this research Paper.
Idea of Deep learning Based Communication System is new and there is many advantages of Deep learning based Communication.This paper gives
complete different apporach than many other paper and tries to introduce deep learning in physical layer.
Abstract of Research Paper
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. We show how this idea can be extended to networks of multiple
transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the
machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation
classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. This paper is concluded
with a discussion of open challenges and areas for future investigation.
From "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/ written by Tim O'Shea and Jakob Hoydis
Requirements
  • Tensorflow
  • Keras
  • Numpy
  • Matplotlib
Note
Given Jupyter-Notebook file is dynamic to train any given (n,k) autoencodeer but for getting optimal result one has to manually tweak
learning rate and epochs.
Plots are generated by matlab script which for now i am not providing it.Anyone can plot result in matlab by training autoencoder and
copy-pasting BER array and ploting it into matlab.
All re-generated result below are generated with autoencoder_dynamic.ipynb file.
Result
Re-generated ResultResearch Paper
BER Perfomance of (7,4) AutoEncoderResearch Paper Result-1
BER Perfomance of R=1 AutoEncodersResearch Paper Result-2
Constellation diagram
(2,2) AutoEncoder's Constellation diagram
Following Constellation diagram are learned by Autoencoder after training it.
(2,2) Autoencoder constellation diagram
(2,4) AutoEncoder Constellation diagram
(2,4) Autoencoder constellation diagram
About
Tensorflow Implementation and result of Auto-encoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
Resources
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injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
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AutoEncoder-Based-Communication-System
Implementation and result of AutoEncoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
This Repo is effictively implementation of AutoEncoder based Communication System From Research Paper "An Introduction to Deep Learning
for the Physical Layer" written by Tim O'Shea and Jakob Hoydis.During My wireless Communication Lab Course,I worked on this research Paper
and re-generated result of this research Paper.
Idea of Deep learning Based Communication System is new and there is many advantages of Deep learning based Communication.This paper gives
complete different apporach than many other paper and tries to introduce deep learning in physical layer.
Abstract of Research Paper
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. We show how this idea can be extended to networks of multiple
transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the
machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation
classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. This paper is concluded
with a discussion of open challenges and areas for future investigation.
From "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/ written by Tim O'Shea and Jakob Hoydis
Requirements
  • Tensorflow
  • Keras
  • Numpy
  • Matplotlib
Note
Given Jupyter-Notebook file is dynamic to train any given (n,k) autoencodeer but for getting optimal result one has to manually tweak
learning rate and epochs.
Plots are generated by matlab script which for now i am not providing it.Anyone can plot result in matlab by training autoencoder and
copy-pasting BER array and ploting it into matlab.
All re-generated result below are generated with autoencoder_dynamic.ipynb file.
Result
Re-generated ResultResearch Paper
BER Perfomance of (7,4) AutoEncoderResearch Paper Result-1
BER Perfomance of R=1 AutoEncodersResearch Paper Result-2
Constellation diagram
(2,2) AutoEncoder's Constellation diagram
Following Constellation diagram are learned by Autoencoder after training it.
(2,2) Autoencoder constellation diagram
(2,4) AutoEncoder Constellation diagram
(2,4) Autoencoder constellation diagram
About
Tensorflow Implementation and result of Auto-encoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
Resources
Readme
Activity
Stars
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, 'i');
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injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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AutoEncoder-Based-Communication-System
Implementation and result of AutoEncoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
This Repo is effictively implementation of AutoEncoder based Communication System From Research Paper "An Introduction to Deep Learning
for the Physical Layer" written by Tim O'Shea and Jakob Hoydis.During My wireless Communication Lab Course,I worked on this research Paper
and re-generated result of this research Paper.
Idea of Deep learning Based Communication System is new and there is many advantages of Deep learning based Communication.This paper gives
complete different apporach than many other paper and tries to introduce deep learning in physical layer.
Abstract of Research Paper
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. We show how this idea can be extended to networks of multiple
transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the
machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation
classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. This paper is concluded
with a discussion of open challenges and areas for future investigation.
From "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/ written by Tim O'Shea and Jakob Hoydis
Requirements
  • Tensorflow
  • Keras
  • Numpy
  • Matplotlib
Note
Given Jupyter-Notebook file is dynamic to train any given (n,k) autoencodeer but for getting optimal result one has to manually tweak
learning rate and epochs.
Plots are generated by matlab script which for now i am not providing it.Anyone can plot result in matlab by training autoencoder and
copy-pasting BER array and ploting it into matlab.
All re-generated result below are generated with autoencoder_dynamic.ipynb file.
Result
Re-generated ResultResearch Paper
BER Perfomance of (7,4) AutoEncoderResearch Paper Result-1
BER Perfomance of R=1 AutoEncodersResearch Paper Result-2
Constellation diagram
(2,2) AutoEncoder's Constellation diagram
Following Constellation diagram are learned by Autoencoder after training it.
(2,2) Autoencoder constellation diagram
(2,4) AutoEncoder Constellation diagram
(2,4) Autoencoder constellation diagram
About
Tensorflow Implementation and result of Auto-encoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
Resources
Readme
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AutoEncoder-Based-Communication-System
Implementation and result of AutoEncoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
This Repo is effictively implementation of AutoEncoder based Communication System From Research Paper "An Introduction to Deep Learning
for the Physical Layer" written by Tim O'Shea and Jakob Hoydis.During My wireless Communication Lab Course,I worked on this research Paper
and re-generated result of this research Paper.
Idea of Deep learning Based Communication System is new and there is many advantages of Deep learning based Communication.This paper gives
complete different apporach than many other paper and tries to introduce deep learning in physical layer.
Abstract of Research Paper
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. We show how this idea can be extended to networks of multiple
transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the
machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation
classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. This paper is concluded
with a discussion of open challenges and areas for future investigation.
From "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/ written by Tim O'Shea and Jakob Hoydis
Requirements
  • Tensorflow
  • Keras
  • Numpy
  • Matplotlib
Note
Given Jupyter-Notebook file is dynamic to train any given (n,k) autoencodeer but for getting optimal result one has to manually tweak
learning rate and epochs.
Plots are generated by matlab script which for now i am not providing it.Anyone can plot result in matlab by training autoencoder and
copy-pasting BER array and ploting it into matlab.
All re-generated result below are generated with autoencoder_dynamic.ipynb file.
Result
Re-generated ResultResearch Paper
BER Perfomance of (7,4) AutoEncoderResearch Paper Result-1
BER Perfomance of R=1 AutoEncodersResearch Paper Result-2
Constellation diagram
(2,2) AutoEncoder's Constellation diagram
Following Constellation diagram are learned by Autoencoder after training it.
(2,2) Autoencoder constellation diagram
(2,4) AutoEncoder Constellation diagram
(2,4) Autoencoder constellation diagram
About
Tensorflow Implementation and result of Auto-encoder Based Communication System From Research Paper : "An Introduction to Deep Learning for the Physical Layer" http://ieeexplore.ieee.org/document/8054694/
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