Adaptive Causal Network Coding With Feedback

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.Index Terms-Random linear network coding (RLNC), forward error correction (FEC), feedback, causal, coding, adaptive, in order delivery delay, throughput.

Reconfiguration algorithms for high precision communications in time sensitive networks: Time-aware shaper configuration with IEEE 802.1Qcc

“…As an alternative approach to coordinating the recon igurations, emerging softwarized control and virtualization paradigms can be explored [7,13,19,20,44,56,75,78]. Regarding the reliability aspects, a potential future research direction is to explore low-latency network coding mechanisms, e.g., [4,18,24,32,54,55,69], to enhance networking protocols targeting reliable low-latency communication.…”

DeepNP: Deep Learning-Based Noise Prediction for Ultra-Reliable Low-Latency Communications

“…Our design detailed in Section III builds upon the AC-RLNC scheme proposed in [10], which implements adaptive and causal network coding. In AC-RLNC, the sender decides at each time step whether to transmit a new coded linear combination or to repeat the last sent combination according to the feedback information.…”

“…For coded blocks, [6] proposed an adaptive solution, where the sender can choose the size of the next block and the number of packets information in the block for deadline-aware applications. The recently proposed adaptive and causal random linear network coding (AC-RLNC) scheme, applied to single-path, multi-path, and multihop networks [10]- [12], implements joint scheduling-coding in a manner that is both causal and adaptive. The former stems from its reactive operation which operates using sliding window applied to the delayed feedback acknowledgements, while the latter follows as its rate of retransmissions is adapted based on the estimated rate.…”

Low-Latency Network-Adaptive Error Control for Interactive Streaming

“…Note that the maximum delay constraint that appeared in Property (b) and Property (i) is a suitable delay metric for interactive communication because any packet recovered beyond a certain threshold must be discarded in order to avoid undesirable pauses (recall the 150 ms threshold recommended by ITU for interactive voice and video). On the contrary, the average delay metric is more relevant for non-interactive streaming applications such as video streaming, which has been used in [18]- [21] to study the tradeoff between throughput and average delay for non-interactive streaming where no packet is ever discarded.…”