Packet Efficiency of BATS Coding on Wireless Relay Network with Overhearing

Yin, Hoover H. F.; Xu, Xiaoli; Ng, Ka Hei; Guan, Yong Liang; Yeung, Raymond W.

doi:10.1109/isit.2019.8849280

Cited by 20 publications

(13 citation statements)

References 11 publications

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“…The model proposed in Ref. [96] assumed that the broadcast nature of wireless communication can enable nodes to overhear. Under this model, an adaptive re-encoding framework is adopted to increase the achievable rate under the limitation of the average channel usage times.…”

Section: Batched Sparse Codesmentioning

confidence: 99%

Intelligent cognitive spectrum collaboration: Convergence of spectrum sensing, spectrum access, and coding technology

Cai

2020

Intell. and Converged Netw.

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For a future scenario where everything is connected, cognitive technology can be used for spectrum sensing and access, and emerging coding technologies can be used to address the erasure of packets caused by dynamic spectrum access and realize cognitive spectrum collaboration among users in mass connection scenarios. Machine learning technologies are being increasingly used in the implementation of smart networks. In this paper, after an overview of several key technologies in the cognitive spectrum collaboration, a joint optimization algorithm of dynamic spectrum access and coding is proposed and implemented using reinforcement learning, and the effectiveness of the algorithm is verified by simulations, thus providing a feasible research direction for the realization of cognitive spectrum collaboration.

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Section: Batched Sparse Codesmentioning

confidence: 99%

Intelligent cognitive spectrum collaboration: Convergence of spectrum sensing, spectrum access, and coding technology

Cai

2020

Intell. and Converged Netw.

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“…RLNC is one of the solutions to the first question, that is, the recoded packets of a batch are generated by taking random linear combinations of all the received packets of this batch. Systematic recoding scheme [ 5 , 41 ] is another approach, which treats part of the received packets as recoded packets, but it is not feasible in all network topologies, for example, wireless relay networks [ 73 ]. In this section, we will further introduce causal recoding , which is a more practical recoding approach that can adapt to the dynamics of packet receiving and transmitting rates.…”

Section: Recoding Schemesmentioning

confidence: 99%

“…For non-integer

, it means that we first transmit

recoded packets, and then we transmit one more packet with probability

, which is the decimal part of

. The unified model (It was shown in Reference [ 73 ] that this model can be extended to wireless relay networks by modifying the meanings of input rank distribution and expected rank functions) is

…”

Section: Recoding Schemesmentioning

confidence: 99%

“…Here, the link between the source and destination nodes is an overhearing channel. The deployment of BATS codes in wireless relay networks was discussed in Reference [ 73 ]. There is a chance that both the relay and destination nodes receive the same packet transmitted by the source node.…”

Section: Recoding Schemesmentioning

confidence: 99%

“…We can generate more BATS packets for the batches at the source node so that this redundancy can reduce the drop of the ranks of the batches. It was demonstrated in References [ 30 , 73 ] that a suitable redundancy can improve the throughput and packet efficiency.…”

Section: Bats Protocol Modulesmentioning

confidence: 99%

See 2 more Smart Citations

A Protocol Design Paradigm for Batched Sparse Codes

Yin

Yeung

Yang

2020

Entropy

Self Cite

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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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Joint BATS Code and Periodic Scheduling in Multihop Wireless Networks

2020

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Batched sparse (BATS) code is a promising technology for reliable data transmission in error-prone environments. A BATS code consists of an outer code and an inner code. It has been shown that a well-designed inner code is critical to the performance of BATS. Though great efforts have been made in the design of inner codes, the impact of scheduling (that determines when inner codes take place at nodes in the path) on inner codes is still unclear. In this paper, we study the joint design of inner code and scheduling in multihop wireless networks. We first introduce a new network utility from which to associate inner codes with scheduling, and formulate the coding constraint and the scheduling constraint using independent sets of a graph. With the utility, the joint design problem is then transformed to a network utility maximization problem under the constraints. We next exploit the property of the proposed optimization problem and reveal the relation between the expected batch transfer matrix rank and maximal independent sets. In the light of their relationship, we propose joint coding and scheduling rules and show that a periodic scheduling can be used to achieve provable performance guarantees. However, under most realistic coding settings, the proposed coding and scheduling problem is NP-hard. In order to meet the practical needs of the implementation, we develop greedy algorithms that attempt to iteratively improve the current best solution. Numerical results show that our algorithms enables us to approach the utilization of multi-hop wireless networks with a relatively low end-to-end delay. INDEX TERMS Network coding, batched sparse code, network utility maximization, scheduling.

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Packet Efficiency of BATS Coding on Wireless Relay Network with Overhearing

Cited by 20 publications

References 11 publications

Intelligent cognitive spectrum collaboration: Convergence of spectrum sensing, spectrum access, and coding technology

Intelligent cognitive spectrum collaboration: Convergence of spectrum sensing, spectrum access, and coding technology

A Protocol Design Paradigm for Batched Sparse Codes

Joint BATS Code and Periodic Scheduling in Multihop Wireless Networks

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