TY - JOUR
T1 - Adaptive Causal Network Coding with Feedback for Multipath Multi-Hop Communications
AU - Cohen, Alejandro
AU - Thiran, Guillaume
AU - Bracha, Vered Bar
AU - Medard, Muriel
N1 - Funding Information:
Manuscript received March 18, 2020; revised July 14, 2020 and September 18, 2020; accepted October 24, 2020. Date of publication October 30, 2020; date of current version February 17, 2021. This research was supported in part by the Intel Corporation and by DARPA: DFARS 252.235-7010. Patent application submitted: no. 62/853,090. This article was presented in part at the IEEE International Conference on Communications (ICC), 2020. The associate editor coordinating the review of this article and approving it for publication was M. Ardakani. (Corresponding author: Alejandro Cohen.) Alejandro Cohen and Muriel Médard are with the Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139 USA (e-mail: cohenale@mit.edu; medard@mit.edu).
Publisher Copyright:
© 1972-2012 IEEE.
PY - 2021/2/1
Y1 - 2021/2/1
N2 - We propose a novel multipath multi-hop adaptive and causal random linear network coding (AC-RLNC) algorithm with forward error correction. This algorithm generalizes our joint optimization coding solution for point-to-point communication with delayed feedback. AC-RLNC is adaptive to the estimated channel condition, and is causal, as the coding adjusts the retransmission rates using a priori and posteriori algorithms. In the multipath network, to achieve the desired throughput and delay, we propose to incorporate an adaptive packet allocation algorithm for retransmission, across the available resources of the paths. This approach is based on a discrete water filling algorithm, i.e., bit-filling, but, with two desired objectives, maximize throughput and minimize the delay. In the multipath multi-hop setting, we propose a new decentralized balancing optimization algorithm. This balancing algorithm minimizes the throughput degradation, caused by the variations in the channel quality of the paths at each hop. Furthermore, to increase the efficiency, in terms of the desired objectives, we propose a new selective recoding method at the intermediate nodes. We derive bounds on the throughput and the mean and maximum in-order delivery delay of AC-RLNC, both in the multipath and multipath multi-hop case. In the multipath case, we prove that in the non-asymptotic regime, the suggested code may achieve more than 90% of the channel capacity with zero error probability under mean and maximum in-order delay constraints, namely a mean delay smaller than three times the optimal genie-aided one and a maximum delay within eight times the optimum. In the multipath multi-hop case, the balancing procedure is proven to be optimal with regards to the achieved rate. Through simulations, we demonstrate that the performance of our adaptive and causal approach, compared to selective repeat (SR)-ARQ protocol, is capable of gains up to a factor two in throughput and a factor of more than three in mean delay and eight in maximum delay. The improvements on the throughput delay trade-off are also shown to be significant with regards to the previously developed singlepath AC-RLNC solution.
AB - We propose a novel multipath multi-hop adaptive and causal random linear network coding (AC-RLNC) algorithm with forward error correction. This algorithm generalizes our joint optimization coding solution for point-to-point communication with delayed feedback. AC-RLNC is adaptive to the estimated channel condition, and is causal, as the coding adjusts the retransmission rates using a priori and posteriori algorithms. In the multipath network, to achieve the desired throughput and delay, we propose to incorporate an adaptive packet allocation algorithm for retransmission, across the available resources of the paths. This approach is based on a discrete water filling algorithm, i.e., bit-filling, but, with two desired objectives, maximize throughput and minimize the delay. In the multipath multi-hop setting, we propose a new decentralized balancing optimization algorithm. This balancing algorithm minimizes the throughput degradation, caused by the variations in the channel quality of the paths at each hop. Furthermore, to increase the efficiency, in terms of the desired objectives, we propose a new selective recoding method at the intermediate nodes. We derive bounds on the throughput and the mean and maximum in-order delivery delay of AC-RLNC, both in the multipath and multipath multi-hop case. In the multipath case, we prove that in the non-asymptotic regime, the suggested code may achieve more than 90% of the channel capacity with zero error probability under mean and maximum in-order delay constraints, namely a mean delay smaller than three times the optimal genie-aided one and a maximum delay within eight times the optimum. In the multipath multi-hop case, the balancing procedure is proven to be optimal with regards to the achieved rate. Through simulations, we demonstrate that the performance of our adaptive and causal approach, compared to selective repeat (SR)-ARQ protocol, is capable of gains up to a factor two in throughput and a factor of more than three in mean delay and eight in maximum delay. The improvements on the throughput delay trade-off are also shown to be significant with regards to the previously developed singlepath AC-RLNC solution.
KW - Ultra-reliable low-latency communications
KW - adaptive
KW - causal
KW - coding
KW - feedback
KW - forward error correction (FEC)
KW - in order delivery delay
KW - random linear network coding (RLNC)
KW - throughput
UR - http://www.scopus.com/inward/record.url?scp=85098767016&partnerID=8YFLogxK
U2 - 10.1109/TCOMM.2020.3034941
DO - 10.1109/TCOMM.2020.3034941
M3 - Article
AN - SCOPUS:85098767016
SN - 0090-6778
VL - 69
SP - 766
EP - 785
JO - IEEE Transactions on Communications
JF - IEEE Transactions on Communications
IS - 2
M1 - 9245536
ER -