Practical Inner Codes for Batched Sparse Codes

Zhang, Zhiheng; Li, Congduan; Guang, Xuan

doi:10.1109/glocom.2017.8254528

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…The upper bound in (18) is strictly better than that in (21). For scalabilities, the first and the third cases in both theorems have the same condition and the same scalability order of L. For the second case, in Theorem 10, there is an extra condition that N = Ω(ln L).…”

Section: B General Channelsmentioning

confidence: 87%

Capacity Scalability of Line Networks with Batched Codes

Yang,

Wang,

Dong

et al. 2021

Preprint

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The capacity of line networks with buffer size constraints is an open, but practically important problem. In this paper, we consider line networks formed by discrete memoryless channels, and study the achievable rate of batched codes, which include several existing coding schemes studied in literature for line networks as special cases. A batched code has an outer code and an inner code. The outer code encodes the information messages into batches, each of which is a group of coded symbols, while the inner code performs recoding on the symbols belonging to the same batch at all nodes that forwarding a batch. Batched codes enable us to impose various block-length and buffer size constraints by tuning coding parameters. Using a technique that captures the communication bottleneck of line networks, we derive upper bounds on the achievable rates of batched codes as functions of line network length for several coding parameter sets. We also discuss specific recoding schemes that can achieve rates of the same order of the line network length for these coding parameter sets. Our results shed light on how to design large multi-hop network communications in practical scenarios using a unified coding framework instead of the traditional layered approach.

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Section: B General Channelsmentioning

confidence: 87%

Capacity Scalability of Line Networks with Batched Codes

Yang,

Wang,

Dong

et al. 2021

Preprint

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“…There are techniques other than RLNC for generating recoded packets, for example, systematic recoding [ 5 , 41 ]. It is known that the design of the inner code has impact on the achievable rates [ 41 ], for example, the advanced recoding schemes in References [ 30 , 42 , 43 , 44 ]. On the other hand, Reference [ 41 ] also suggested that burst packet loss can reduce the advantage of using BATS codes.…”

Section: Introductionmentioning

confidence: 99%

A Protocol Design Paradigm for Batched Sparse Codes

Yin

Yeung

Yang

2020

Entropy

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Internet of Things (IoT) connects billions of everyday objects to the Internet. The mobility of devices can be facilitated by means of employing multiple wireless links. However, packet loss is a common phenomenon in wireless communications, where the traditional forwarding strategy undergoes severe performance issues in a multi-hop wireless network. One solution is to apply batched sparse (BATS) codes. A fundamental difference from the traditional strategy is that BATS codes require the intermediate network nodes to perform recoding, which generates recoded packets by network coding operations. Literature showed that advanced recoding schemes and burst packet loss can enhance and diminish the performance of BATS codes respectively. However, the existing protocols for BATS codes cannot handle both of them at the same time. In this paper, we propose a paradigm of protocol design for BATS codes. Our design can be applied in different layers of the network stack and it is compatible to the existing network infrastructures. The modular nature of the protocol can support different recoding techniques and different ways to handle burst packet loss. We also give some examples to demonstrate how to use the protocol.

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Packet Efficiency of BATS Coding on Wireless Relay Network with Overhearing

Yin

et al. 2019

2019 IEEE International Symposium on Information Theory (ISIT)

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Wireless relay network is a solution to extend the reach of a wireless connection by installing a relay node between the source node and the sink node. Due to the broadcast nature of wireless transmission, the sink node has a chance to receive part of the data sent by the source node. In this paper, we apply a network coding scheme called BATS codes on a wireless relay network where the relay node has a stable power supply, so that we can aim for the best decoding time instead of minimizing the number of transmissions for saving energy. We optimize the time efficiency that maximize the average decoding rate per unit time by some heuristics, and bring out a message that it is not optimal to set an average number of recoded packets per batch at the relay node equals the number of packets per batch sent by the source node.

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Practical Inner Codes for Batched Sparse Codes

Cited by 4 publications

References 23 publications

Capacity Scalability of Line Networks with Batched Codes

Capacity Scalability of Line Networks with Batched Codes

A Protocol Design Paradigm for Batched Sparse Codes

Packet Efficiency of BATS Coding on Wireless Relay Network with Overhearing

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