Adaptive Causal Network Coding With Feedback

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 o...

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Veröffentlicht in:	IEEE transactions on communications Jg. 68; H. 7; S. 4325 - 4341
Hauptverfasser:	Cohen, Alejandro, Malak, Derya, Bracha, Vered Bar, Medard, Muriel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.07.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models adaptive Algorithms causal Channel capacity Coding Communication channels Computer simulation Decoding Delay Delays Encoding Error correction Feedback Forward error correction forward error correction (FEC) in order delivery delay Network coding Network latency Point to point communication Random linear network coding (RLNC) Throughput Upper bounds
ISSN:	0090-6778, 1558-0857
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Abstract	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.
AbstractList	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.
Author	Medard, Muriel Bracha, Vered Bar Malak, Derya Cohen, Alejandro
Author_xml	– sequence: 1 givenname: Alejandro orcidid: 0000-0002-5664-2518 surname: Cohen fullname: Cohen, Alejandro email: cohenale@mit.edu organization: Research Laboratory of Electronics, MIT, Cambridge, MA, USA – sequence: 2 givenname: Derya orcidid: 0000-0002-5991-6641 surname: Malak fullname: Malak, Derya email: malakd@rpi.edu organization: Research Laboratory of Electronics, MIT, Cambridge, MA, USA – sequence: 3 givenname: Vered Bar orcidid: 0000-0001-5219-3199 surname: Bracha fullname: Bracha, Vered Bar email: vered.bar.bracha@intel.com organization: Intel Corporation, Petach-Tikva, Israel – sequence: 4 givenname: Muriel surname: Medard fullname: Medard, Muriel email: medard@mit.edu organization: Research Laboratory of Electronics, MIT, Cambridge, MA, USA
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SubjectTerms	Adaptation models adaptive Algorithms causal Channel capacity Coding Communication channels Computer simulation Decoding Delay Delays Encoding Error correction Feedback Forward error correction forward error correction (FEC) in order delivery delay Network coding Network latency Point to point communication Random linear network coding (RLNC) Throughput Upper bounds
Title	Adaptive Causal Network Coding With Feedback
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