Adaptive Causal Network Coding with Feedback for Multipath Multi-hop Communications

Cohen, Alejandro; Thiran, Guillaume; Bracha, Vered Bar; Médard, Muriel

doi:10.1109/tcomm.2020.3034941

Cited by 22 publications

(42 citation statements)

References 35 publications

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“…A recent work [25] has also shown that allowing the relay to adapt its coding strategy, as opposed to the non-adaptive scheme proposed in [1], allows for rate improvement. The multi-hop multi-path setting is also studied in [26], [27], where random linear codes are used, but adapt using a feedback signal.…”

Section: Introductionmentioning

confidence: 99%

“…This network is useful in modeling mobile applications that simultaneously use both WiFi and cellular links, heterogeneous networks [28], software-defined networking [29] and smart cities backhaul [30]. Taking the results in [24] into account, we develop a coding scheme that allows for the relay to jointly encode data arriving from different paths, which has not been studied in [23], [27]. More precisely, we develop a framework that allows us to separately design point-to-point, single-link codes for each link, but that, at the same time, allows "mixing" packets at the relay, similar to [24], but with multiple links in both hops.…”

Section: Introductionmentioning

confidence: 99%

“…An additional challenge that arises in the multi-link setting is that different links may have a different propagation delay. Handling these different delays is not considered in [2], and [27] considers only the worst propagation delay in each hop, and assumes that each hop has an equal propagation delay. In our work, we show that our framework is highly versatile and can be easily adapted to consider different propagation delays in each link with minor modifications in our algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Error-correcting codes for low latency streaming over multiple link relay networks

Facenda¹,

Domanovitz²,

Khisti³

et al. 2022

Preprint

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This paper investigates the performance of streaming codes in low-latency applications over a multi-link three-node relayed network. The source wishes to transmit a sequence of messages to the destination through a relay. Each message must be reconstructed after a fixed decoding delay. The special case with one link connecting each node has been studied by Fong et. al [1], and a multi-hop multi-link setting has been studied by Domanovitz et. al [2]. The topology with three nodes and multiple links is studied in this paper. Each link is subject to a different number of erasures due to different channel conditions. An information-theoretic upper bound is derived, and an achievable scheme is presented. The proposed scheme judiciously allocates rates for each link based on the concept of delay spectrum. The achievable scheme is compared to two baseline schemes and the scheme proposed in [2]. Experimental results show that this scheme achieves higher rates than the other schemes, and can achieve the upper bound even in non-trivial scenarios. The scheme is further extended to handle different propagation delays in each link, something not previously considered in the literature. Simulations over statistical channels show that the proposed scheme can outperform the simpler baseline under practical models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Error-correcting codes for low latency streaming over multiple link relay networks

Facenda¹,

Domanovitz²,

Khisti³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…We contrast the performance of the proposed approach with that of the channel-model-based AC-RLNC [11], where the a-posteriori decisions are made at the sender using average statistical information. We show that the proposed DeepNP can gain up to a factor of four in mean and maximum delay and a factor of two in throughput.…”

Section: Introductionmentioning

confidence: 99%

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

Cohen¹,

Solomon²,

Shlezinger³

2021

Preprint

Self Cite

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Closing the gap between high data rates and low delay in real-time streaming applications is a major challenge in advanced communication systems. While adaptive network coding schemes have the potential of balancing rate and delay in real-time, they often rely on prediction of the channel behavior. In practice, such prediction is based on delayed feedback, making it difficult to acquire causally, particularly when the underlying channel model is unknown. In this work, we propose a deep learning-based noise prediction (DeepNP) algorithm, which augments the recently proposed adaptive and causal random linear network coding scheme with a dedicated deep neural network, that learns to carry out noise prediction from data. This neural augmentation is utilized to maximize the throughput while minimizing in-order delivery delay of the network coding scheme, and operate in a channel-model-agnostic manner. We numerically show that performance can dramatically increase by the learned prediction of the channel noise rate. In particular, we demonstrate that DeepNP gains up to a factor of four in mean and maximum delay and a factor two in throughput compared with statistic-based network coding approaches.

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“…In [1], an adaptive causal network coding was proposed as a generalization of the joint optimization coding solution for point-to-point communication with delayed feedback. Intraflow random linear network coding has been used to improve the performance of a wireless network containing lossy links [2].…”

Section: Introductionmentioning

confidence: 99%

Content Distribution Network for Streaming Using Multiple Galois Fields

Hung

Kaushal

Hsiao

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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In this paper, the architecture of random linear network coding based on the hybrid coding in multiple Galois field sizes is proposed. Random linear network coding is an efficient network coding approach that enables network to generate independently and randomly linear mapping between input and output symbols over finite field. With the proposed reduction method, coded symbols and coefficients in higher degree of Galois field can be converted to symbols and coefficients in GF(2) so that hybrid coding in multiple Galois field sizes can be made possible. Therefore, peers in the heterogeneous environments can all benefit from the proposed content distribution network for streaming to better utilize the network resource.

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Adaptive Causal Network Coding with Feedback for Multipath Multi-hop Communications

Cited by 22 publications

References 35 publications

Error-correcting codes for low latency streaming over multiple link relay networks

Error-correcting codes for low latency streaming over multiple link relay networks

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

Content Distribution Network for Streaming Using Multiple Galois Fields

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