Adaptive Causal Network Coding with Feedback
© 1972-2012 IEEE. We propose a novel adaptive and causal random linear network coding (AC-RLNC) algorithm with forward error correction (FEC) for a point-to-point communication channel with delayed feedback. AC-RLNC is adaptive to the channel condition, that the algorithm estimates, and is causal, a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/135397 |
| _version_ | 1826200956765732864 |
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| author | Cohen, Alejandro Malak, Derya Bracha, Vered Bar Medard, Muriel |
| author2 | Massachusetts Institute of Technology. Research Laboratory of Electronics |
| author_facet | Massachusetts Institute of Technology. Research Laboratory of Electronics Cohen, Alejandro Malak, Derya Bracha, Vered Bar Medard, Muriel |
| author_sort | Cohen, Alejandro |
| collection | MIT |
| description | © 1972-2012 IEEE. We propose a novel adaptive and causal random linear network coding (AC-RLNC) algorithm with forward error correction (FEC) for a point-to-point communication channel with delayed feedback. AC-RLNC is adaptive to the channel condition, that the algorithm estimates, and is causal, as coding depends on the particular erasure realizations, as reflected in the feedback acknowledgments. Specifically, the proposed model can learn the erasure pattern of the channel via feedback acknowledgments, and adaptively adjust its retransmission rates using a priori and posteriori algorithms. By those adjustments, AC-RLNC achieves the desired delay and throughput, and enables transmission with zero error probability. We upper bound the throughput and the mean and maximum in order delivery delay of AC-RLNC, and prove that for the point to point communication channel in the non-asymptotic regime the proposed code may achieve more than 90% of the channel capacity. To upper bound the throughput we utilize the minimum Bhattacharyya distance for the AC-RLNC code. We validate those results via simulations. We contrast the performance of AC-RLNC with the one of selective repeat (SR)-ARQ, which is causal but not adaptive, and is a posteriori. Via a study on experimentally obtained commercial traces, we demonstrate that a protocol based on AC-RLNC can, vis-à-vis SR-ARQ, double the throughput gains, and triple the gain in terms of mean in order delivery delay when the channel is bursty. Furthermore, the difference between the maximum and mean in order delivery delay is much smaller than that of SR-ARQ. Closing the delay gap along with boosting the throughput is very promising for enabling ultra-reliable low-latency communications (URLLC) applications. |
| first_indexed | 2024-09-23T11:44:22Z |
| format | Article |
| id | mit-1721.1/135397 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:44:22Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1353972023-09-27T20:39:01Z Adaptive Causal Network Coding with Feedback Cohen, Alejandro Malak, Derya Bracha, Vered Bar Medard, Muriel Massachusetts Institute of Technology. Research Laboratory of Electronics © 1972-2012 IEEE. We propose a novel adaptive and causal random linear network coding (AC-RLNC) algorithm with forward error correction (FEC) for a point-to-point communication channel with delayed feedback. AC-RLNC is adaptive to the channel condition, that the algorithm estimates, and is causal, as coding depends on the particular erasure realizations, as reflected in the feedback acknowledgments. Specifically, the proposed model can learn the erasure pattern of the channel via feedback acknowledgments, and adaptively adjust its retransmission rates using a priori and posteriori algorithms. By those adjustments, AC-RLNC achieves the desired delay and throughput, and enables transmission with zero error probability. We upper bound the throughput and the mean and maximum in order delivery delay of AC-RLNC, and prove that for the point to point communication channel in the non-asymptotic regime the proposed code may achieve more than 90% of the channel capacity. To upper bound the throughput we utilize the minimum Bhattacharyya distance for the AC-RLNC code. We validate those results via simulations. We contrast the performance of AC-RLNC with the one of selective repeat (SR)-ARQ, which is causal but not adaptive, and is a posteriori. Via a study on experimentally obtained commercial traces, we demonstrate that a protocol based on AC-RLNC can, vis-à-vis SR-ARQ, double the throughput gains, and triple the gain in terms of mean in order delivery delay when the channel is bursty. Furthermore, the difference between the maximum and mean in order delivery delay is much smaller than that of SR-ARQ. Closing the delay gap along with boosting the throughput is very promising for enabling ultra-reliable low-latency communications (URLLC) applications. 2021-10-27T20:23:17Z 2021-10-27T20:23:17Z 2020 2021-03-09T17:38:56Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/135397 en 10.1109/TCOMM.2020.2989827 IEEE Transactions on Communications Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Cohen, Alejandro Malak, Derya Bracha, Vered Bar Medard, Muriel Adaptive Causal Network Coding with Feedback |
| title | Adaptive Causal Network Coding with Feedback |
| title_full | Adaptive Causal Network Coding with Feedback |
| title_fullStr | Adaptive Causal Network Coding with Feedback |
| title_full_unstemmed | Adaptive Causal Network Coding with Feedback |
| title_short | Adaptive Causal Network Coding with Feedback |
| title_sort | adaptive causal network coding with feedback |
| url | https://hdl.handle.net/1721.1/135397 |
| work_keys_str_mv | AT cohenalejandro adaptivecausalnetworkcodingwithfeedback AT malakderya adaptivecausalnetworkcodingwithfeedback AT brachaveredbar adaptivecausalnetworkcodingwithfeedback AT medardmuriel adaptivecausalnetworkcodingwithfeedback |