Sparse Random Linear Network Coding For Low Latency Allcast

Numerous applications require the sharing of data from each node on a network with every other node. In the case of Connected and Autonomous Vehicles (CAVs), it will be necessary for vehicles to update each other with their positions, manoeuvring intentions, and other telemetry data, despite shadowi...

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Veröffentlicht in:2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton) S. 560 - 564
Hauptverfasser: Graham, Mark A., Ganesh, Ayalvadi, Piechocki, Robert J.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2019
Schlagworte:
Allcast
CAV
Complexity theory
Encoding
Fading channels
gossip
Network coding
Shadow mapping
Sparse RLNC
Transceivers
V2V
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Zusammenfassung:Numerous applications require the sharing of data from each node on a network with every other node. In the case of Connected and Autonomous Vehicles (CAVs), it will be necessary for vehicles to update each other with their positions, manoeuvring intentions, and other telemetry data, despite shadowing caused by other vehicles. These applications require scalable, reliable, low latency communications, over challenging broadcast channels. In this article, we consider the allcast problem, of achieving multiple simultaneous network broadcasts, over a broadcast medium. We model slow fading using random graphs, and show that an allcast method based on sparse random linear network coding can achieve reliable allcast in a constant number of transmission rounds. We compare this with an uncoded baseline, which we show requires O(log(n)) transmission rounds. We justify and compare our analysis with extensive simulations.
DOI:10.1109/ALLERTON.2019.8919833