A Lossy Graph Model for Delay Reduction in Generalized Instantly Decodable Network Coding

Douik, Ahmed; Sorour, Sameh; Al-Naffouri, Tareq Y.; Alouini, Mohamed‐Slim

doi:10.1109/wcl.2014.022814.140067

Cited by 19 publications

(19 citation statements)

References 15 publications

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“…The combinations between all packets in the Wants sets of all vehicles can be represented as a graph model, denoted as the IDNC graph. 28,37 The IDNC graph GðN ; EÞ is constructed by first generating a vertex n ij 2 N for each packet p j 2 W i , 1 i M. Two vertices n ij and n kl are connected by an edge in E if one of the following conditions is true:…”

Section: Applying Idnc Coding Methodsmentioning

confidence: 99%

Distributed relay selection with network coding for data dissemination in vehicular ad hoc networks

Wang

Yin

2017

International Journal of Distributed Sensor Networks

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Data dissemination in vehicular ad hoc networks has been an interesting research topic since rising applications can be run and served based on the success and efficiency of data dissemination in vehicular ad hoc networks. However, the volatile vehicle density, the highly dynamic topologies, and the packet lossy nature of the vehicular wireless communications pose real challenges for vehicular ad hoc networks to achieve high-speed content downloading and efficient resource utilization. In this work, we design a collision-free distributed relay selection method, by jointly considering the geographical locations, channel conditions, moving velocities, and packets receiving statuses of vehicles, to combat the mobility and lossy channel property of vehicular ad hoc networks. Moreover, we adopt the instantly decodable network coding for the selected relay vehicle to retransmit packets which were lost in the original transmission, resulting significant improvements in both network throughput and transmission delay. Simulation results show that the proposed strategy effectively reduces the delay of data dissemination in highway scenarios and improves the downloading rate, as well as the resource utilization of wireless channels.

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Section: Applying Idnc Coding Methodsmentioning

confidence: 99%

Distributed relay selection with network coding for data dissemination in vehicular ad hoc networks

Wang

Yin

2017

International Journal of Distributed Sensor Networks

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“…In a context of limited feedback, the authors in [125] Feedback matrix giving the lost and received packets for all receivers. 0 refers that a packet is received, whereas 1 refers that it is wanted and −1 to unwanted.…”

Section: Graph Representationmentioning

confidence: 99%

Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications

Douik

Sorour

Al-Naffouri

et al. 2017

IEEE Commun. Surv. Tutorials

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Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications Ahmed DouikFrom its introduction to its quindecennial, network coding has built a strong reputation in enhancing the packet recovery process and achieving maximum information flow in both wired and wireless networks. Traditional studies focused on optimizing the throughput of the network by proposing complex schemes that achieve optimal delay. With the shift toward distributed computing on mobile devices, throughput and complexity become both critical factors that affect the efficiency of a coding scheme. Instantly decodable network coding imposed itself as a new paradigm in network coding that trades off these two aspects. This paper presents a survey of instantly decodable network coding schemes that are proposed in the literature. The various schemes are identified, categorized and evaluated. Two categories can be distinguished namely the conventional centralized schemes and the distributed or cooperative schemes. For each scheme, the comparison is carried out in terms of reliability, performance, complexity and packet selection methodology. Although the performance is generally inversely proportional to the computation complexity, numerous successful schemes from both the performance and complexity viewpoint are identified.

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“…In the context of decoding delay reduction, the authors in [34] show that the IDNC graph fails to produce satisfactory results as it considers only packet that are surely combinable. To address the problem, they introduced the lossy IDNC graph (LG-IDNC) that represents a bigger number of possible combinations including all the combinations in the IDNC graph.…”

Section: ) Maximum Weight Clique Solutionmentioning

confidence: 99%

Decoding-Delay-Controlled Completion Time Reduction in Instantly Decodable Network Coding

Douik

Sorour

Al-Naffouri

et al. 2017

IEEE Trans. Veh. Technol.

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For several years, the completion time and the decoding delay problems in Instantly Decodable Network Coding (IDNC) were considered separately and were thought to completely act against each other. Recently, some works aimed to balance the effects of these two important IDNC metrics but none of them studied a further optimization of one by controlling the other. In this paper, we study the effect of controlling the decoding delay to reduce the completion time below its currently best known solution in persistent erasure channels. We first derive the decoding-delay-dependent expressions of the users' and overall completion times. Although using such expressions to find the optimal overall completion time is NP-hard, we design two novel heuristics that minimizes the probability of increasing the maximum of these decoding-delay-dependent completion time expressions after each transmission through a layered control of their decoding delays. We, then, extend our study to the limited feedback scenario. Simulation results show that our new algorithms achieves both a lower mean completion time and mean decoding delay compared to the best known heuristic for completion time reduction. The gap in performance becomes significant for harsh erasure scenarios.

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A Lossy Graph Model for Delay Reduction in Generalized Instantly Decodable Network Coding

Cited by 19 publications

References 15 publications

Distributed relay selection with network coding for data dissemination in vehicular ad hoc networks

Distributed relay selection with network coding for data dissemination in vehicular ad hoc networks

Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications

Decoding-Delay-Controlled Completion Time Reduction in Instantly Decodable Network Coding

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