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Application Layer Coding for Delay and Feedback Constraints Scenarios

This repository is not being actively maintained but most of the functionality of [1] has been implemented. The entire code was ported in MATLAB for further experiments which would be released shortly.

The current state of the repository emulates the work of the paper [1].

The entire code is written in python with matplotlib as the only dependency.

There are two runnable scripts:

  • system.py
  • simulation.py

system.py performs one transmission of n messages from a sender to receiver over a defined a channel and other parameters. simulation.py has a set of simulation as designed in the paper for easy comparison of results.

Using system.py

  • All the different settings that can be used can be listed using the following command
python system.py -h

This will open the help menu enumerating all the options and how to use

  • The file can be run with a particular setting as
python system.py -l 8 -n 50 -c Bernouli --scheme ICC

This will set the message length as 8 and the number of messages to be 10 transmitted over a Bernouli channel with ICC coding scheme (the scheme in the paper).

  • Running with default options
python system.py

This will transmit 8 bit long 10,000 messages over a bernouli channel with erasure probability of 0.5 and feedback erasrue probability of 0.4 and packet size 4.

Using simulation.py

  • It is very straightforward to run. There are five simulations currently more can be added later.
python simulation.py

This will run all the simulations with similar settings as in the paper. It will show the results as a plot on the screen. Additionally it will also store the plots in a folder name 'plots' and the results of each run in a file in 'logs' folder. The entire thing may take a few minutes to run.

  • If you want to run a particular simulation use
python simulation.py -n 1

This will run the first simulation as mentioned in the paper.

References

[1] S. S. Borkotoky, U. Schilcher and C. Raffelsberger, "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9148646.

About

Official implementation of the paper "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints"

sites.google.com/view/vatsalyachaubey/projects/application-layer-coding

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Application Layer Coding for Delay and Feedback Constraints Scenarios
This repository is not being actively maintained but most of the functionality of [1] has been implemented. The entire code was ported in MATLAB for further experiments which would be released shortly.
The current state of the repository emulates the work of the paper [1].
The entire code is written in python with matplotlib as the only dependency.
There are two runnable scripts:
  • system.py
  • simulation.py
system.py performs one transmission of n messages from a sender to receiver over a defined a channel
and other parameters. simulation.py has a set of simulation as designed in the paper for easy comparison of results.
Using system.py
  • All the different settings that can be used can be listed using the following command
python system.py -h

This will open the help menu enumerating all the options and how to use
  • The file can be run with a particular setting as
python system.py -l 8 -n 50 -c Bernouli --scheme ICC

This will set the message length as 8 and the number of messages to be 10 transmitted over a Bernouli channel with ICC coding scheme (the scheme in the paper).
  • Running with default options
python system.py

This will transmit 8 bit long 10,000 messages over a bernouli channel with erasure probability of 0.5 and feedback erasrue probability of 0.4 and packet size 4.
Using simulation.py
  • It is very straightforward to run. There are five simulations currently more can be added later.
python simulation.py

This will run all the simulations with similar settings as in the paper. It will show the results as a plot on the screen. Additionally it will also store the plots in a folder name 'plots' and the results of each run in a file in 'logs' folder. The entire thing may take a few minutes to run.
  • If you want to run a particular simulation use
python simulation.py -n 1

This will run the first simulation as mentioned in the paper.
References
[1] S. S. Borkotoky, U. Schilcher and C. Raffelsberger, "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9148646.
About
Official implementation of the paper "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints"
sites.google.com/view/vatsalyachaubey/projects/application-layer-coding
Topics
application-layersimulationswireless-communication
Resources
Readme
MIT license
Activity
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injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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Application Layer Coding for Delay and Feedback Constraints Scenarios
This repository is not being actively maintained but most of the functionality of [1] has been implemented. The entire code was ported in MATLAB for further experiments which would be released shortly.
The current state of the repository emulates the work of the paper [1].
The entire code is written in python with matplotlib as the only dependency.
There are two runnable scripts:
  • system.py
  • simulation.py
system.py performs one transmission of n messages from a sender to receiver over a defined a channel
and other parameters. simulation.py has a set of simulation as designed in the paper for easy comparison of results.
Using system.py
  • All the different settings that can be used can be listed using the following command
python system.py -h

This will open the help menu enumerating all the options and how to use
  • The file can be run with a particular setting as
python system.py -l 8 -n 50 -c Bernouli --scheme ICC

This will set the message length as 8 and the number of messages to be 10 transmitted over a Bernouli channel with ICC coding scheme (the scheme in the paper).
  • Running with default options
python system.py

This will transmit 8 bit long 10,000 messages over a bernouli channel with erasure probability of 0.5 and feedback erasrue probability of 0.4 and packet size 4.
Using simulation.py
  • It is very straightforward to run. There are five simulations currently more can be added later.
python simulation.py

This will run all the simulations with similar settings as in the paper. It will show the results as a plot on the screen. Additionally it will also store the plots in a folder name 'plots' and the results of each run in a file in 'logs' folder. The entire thing may take a few minutes to run.
  • If you want to run a particular simulation use
python simulation.py -n 1

This will run the first simulation as mentioned in the paper.
References
[1] S. S. Borkotoky, U. Schilcher and C. Raffelsberger, "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9148646.
About
Official implementation of the paper "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints"
sites.google.com/view/vatsalyachaubey/projects/application-layer-coding
Topics
application-layersimulationswireless-communication
Resources
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Application Layer Coding for Delay and Feedback Constraints Scenarios
This repository is not being actively maintained but most of the functionality of [1] has been implemented. The entire code was ported in MATLAB for further experiments which would be released shortly.
The current state of the repository emulates the work of the paper [1].
The entire code is written in python with matplotlib as the only dependency.
There are two runnable scripts:
  • system.py
  • simulation.py
system.py performs one transmission of n messages from a sender to receiver over a defined a channel
and other parameters. simulation.py has a set of simulation as designed in the paper for easy comparison of results.
Using system.py
  • All the different settings that can be used can be listed using the following command
python system.py -h

This will open the help menu enumerating all the options and how to use
  • The file can be run with a particular setting as
python system.py -l 8 -n 50 -c Bernouli --scheme ICC

This will set the message length as 8 and the number of messages to be 10 transmitted over a Bernouli channel with ICC coding scheme (the scheme in the paper).
  • Running with default options
python system.py

This will transmit 8 bit long 10,000 messages over a bernouli channel with erasure probability of 0.5 and feedback erasrue probability of 0.4 and packet size 4.
Using simulation.py
  • It is very straightforward to run. There are five simulations currently more can be added later.
python simulation.py

This will run all the simulations with similar settings as in the paper. It will show the results as a plot on the screen. Additionally it will also store the plots in a folder name 'plots' and the results of each run in a file in 'logs' folder. The entire thing may take a few minutes to run.
  • If you want to run a particular simulation use
python simulation.py -n 1

This will run the first simulation as mentioned in the paper.
References
[1] S. S. Borkotoky, U. Schilcher and C. Raffelsberger, "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9148646.
About
Official implementation of the paper "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints"
sites.google.com/view/vatsalyachaubey/projects/application-layer-coding
Topics
application-layersimulationswireless-communication
Resources
Readme
MIT license
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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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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Application Layer Coding for Delay and Feedback Constraints Scenarios
This repository is not being actively maintained but most of the functionality of [1] has been implemented. The entire code was ported in MATLAB for further experiments which would be released shortly.
The current state of the repository emulates the work of the paper [1].
The entire code is written in python with matplotlib as the only dependency.
There are two runnable scripts:
  • system.py
  • simulation.py
system.py performs one transmission of n messages from a sender to receiver over a defined a channel
and other parameters. simulation.py has a set of simulation as designed in the paper for easy comparison of results.
Using system.py
  • All the different settings that can be used can be listed using the following command
python system.py -h

This will open the help menu enumerating all the options and how to use
  • The file can be run with a particular setting as
python system.py -l 8 -n 50 -c Bernouli --scheme ICC

This will set the message length as 8 and the number of messages to be 10 transmitted over a Bernouli channel with ICC coding scheme (the scheme in the paper).
  • Running with default options
python system.py

This will transmit 8 bit long 10,000 messages over a bernouli channel with erasure probability of 0.5 and feedback erasrue probability of 0.4 and packet size 4.
Using simulation.py
  • It is very straightforward to run. There are five simulations currently more can be added later.
python simulation.py

This will run all the simulations with similar settings as in the paper. It will show the results as a plot on the screen. Additionally it will also store the plots in a folder name 'plots' and the results of each run in a file in 'logs' folder. The entire thing may take a few minutes to run.
  • If you want to run a particular simulation use
python simulation.py -n 1

This will run the first simulation as mentioned in the paper.
References
[1] S. S. Borkotoky, U. Schilcher and C. Raffelsberger, "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9148646.
About
Official implementation of the paper "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints"
sites.google.com/view/vatsalyachaubey/projects/application-layer-coding
Topics
application-layersimulationswireless-communication
Resources
Readme
MIT license
Activity
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
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Application Layer Coding for Delay and Feedback Constraints Scenarios
This repository is not being actively maintained but most of the functionality of [1] has been implemented. The entire code was ported in MATLAB for further experiments which would be released shortly.
The current state of the repository emulates the work of the paper [1].
The entire code is written in python with matplotlib as the only dependency.
There are two runnable scripts:
  • system.py
  • simulation.py
system.py performs one transmission of n messages from a sender to receiver over a defined a channel
and other parameters. simulation.py has a set of simulation as designed in the paper for easy comparison of results.
Using system.py
  • All the different settings that can be used can be listed using the following command
python system.py -h

This will open the help menu enumerating all the options and how to use
  • The file can be run with a particular setting as
python system.py -l 8 -n 50 -c Bernouli --scheme ICC

This will set the message length as 8 and the number of messages to be 10 transmitted over a Bernouli channel with ICC coding scheme (the scheme in the paper).
  • Running with default options
python system.py

This will transmit 8 bit long 10,000 messages over a bernouli channel with erasure probability of 0.5 and feedback erasrue probability of 0.4 and packet size 4.
Using simulation.py
  • It is very straightforward to run. There are five simulations currently more can be added later.
python simulation.py

This will run all the simulations with similar settings as in the paper. It will show the results as a plot on the screen. Additionally it will also store the plots in a folder name 'plots' and the results of each run in a file in 'logs' folder. The entire thing may take a few minutes to run.
  • If you want to run a particular simulation use
python simulation.py -n 1

This will run the first simulation as mentioned in the paper.
References
[1] S. S. Borkotoky, U. Schilcher and C. Raffelsberger, "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9148646.
About
Official implementation of the paper "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints"
sites.google.com/view/vatsalyachaubey/projects/application-layer-coding
Topics
application-layersimulationswireless-communication
Resources
Readme
MIT license
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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");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
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Application Layer Coding for Delay and Feedback Constraints Scenarios
This repository is not being actively maintained but most of the functionality of [1] has been implemented. The entire code was ported in MATLAB for further experiments which would be released shortly.
The current state of the repository emulates the work of the paper [1].
The entire code is written in python with matplotlib as the only dependency.
There are two runnable scripts:
  • system.py
  • simulation.py
system.py performs one transmission of n messages from a sender to receiver over a defined a channel
and other parameters. simulation.py has a set of simulation as designed in the paper for easy comparison of results.
Using system.py
  • All the different settings that can be used can be listed using the following command
python system.py -h

This will open the help menu enumerating all the options and how to use
  • The file can be run with a particular setting as
python system.py -l 8 -n 50 -c Bernouli --scheme ICC

This will set the message length as 8 and the number of messages to be 10 transmitted over a Bernouli channel with ICC coding scheme (the scheme in the paper).
  • Running with default options
python system.py

This will transmit 8 bit long 10,000 messages over a bernouli channel with erasure probability of 0.5 and feedback erasrue probability of 0.4 and packet size 4.
Using simulation.py
  • It is very straightforward to run. There are five simulations currently more can be added later.
python simulation.py

This will run all the simulations with similar settings as in the paper. It will show the results as a plot on the screen. Additionally it will also store the plots in a folder name 'plots' and the results of each run in a file in 'logs' folder. The entire thing may take a few minutes to run.
  • If you want to run a particular simulation use
python simulation.py -n 1

This will run the first simulation as mentioned in the paper.
References
[1] S. S. Borkotoky, U. Schilcher and C. Raffelsberger, "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9148646.
About
Official implementation of the paper "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints"
sites.google.com/view/vatsalyachaubey/projects/application-layer-coding
Topics
application-layersimulationswireless-communication
Resources
Readme
MIT license
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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Application Layer Coding for Delay and Feedback Constraints Scenarios
This repository is not being actively maintained but most of the functionality of [1] has been implemented. The entire code was ported in MATLAB for further experiments which would be released shortly.
The current state of the repository emulates the work of the paper [1].
The entire code is written in python with matplotlib as the only dependency.
There are two runnable scripts:
  • system.py
  • simulation.py
system.py performs one transmission of n messages from a sender to receiver over a defined a channel
and other parameters. simulation.py has a set of simulation as designed in the paper for easy comparison of results.
Using system.py
  • All the different settings that can be used can be listed using the following command
python system.py -h

This will open the help menu enumerating all the options and how to use
  • The file can be run with a particular setting as
python system.py -l 8 -n 50 -c Bernouli --scheme ICC

This will set the message length as 8 and the number of messages to be 10 transmitted over a Bernouli channel with ICC coding scheme (the scheme in the paper).
  • Running with default options
python system.py

This will transmit 8 bit long 10,000 messages over a bernouli channel with erasure probability of 0.5 and feedback erasrue probability of 0.4 and packet size 4.
Using simulation.py
  • It is very straightforward to run. There are five simulations currently more can be added later.
python simulation.py

This will run all the simulations with similar settings as in the paper. It will show the results as a plot on the screen. Additionally it will also store the plots in a folder name 'plots' and the results of each run in a file in 'logs' folder. The entire thing may take a few minutes to run.
  • If you want to run a particular simulation use
python simulation.py -n 1

This will run the first simulation as mentioned in the paper.
References
[1] S. S. Borkotoky, U. Schilcher and C. Raffelsberger, "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9148646.
About
Official implementation of the paper "Application-Layer Coding with Intermittent Feedback Under Delay and Duty-Cycle Constraints"
sites.google.com/view/vatsalyachaubey/projects/application-layer-coding
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