Edge Cloud-Enabled Radio Resource Management for Co-Operative Automated Driving

Co-operative automated driving (CAD) is a key fifth generation mobile networks (5G) use case in which autonomous vehicles communicate over vehicle-to-vehicle (V2V) links requiring a wide range of rate-reliability-delay performance. One key 5G enabler for CAD sidelink radio resource management (RRM)...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications 2020-07, Vol.38 (7), p.1515-1530
Main Authors: Keshavamurthy, Prajwal, Pateromichelakis, Emmanouil, Dahlhaus, Dirk, Zhou, Chan
Format: Article
Language:English
Subjects:
Automation
Cloud computing
Clusters
Co-operative automated driving
Complexity
Delay
Delays
Edge computing
Interference
Modularity
Multiple objective analysis
Optimization
radio resource management
Reliability
Resource allocation
Resource management
Servers
V2V communications
Wireless networks
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Summary:Co-operative automated driving (CAD) is a key fifth generation mobile networks (5G) use case in which autonomous vehicles communicate over vehicle-to-vehicle (V2V) links requiring a wide range of rate-reliability-delay performance. One key 5G enabler for CAD sidelink radio resource management (RRM) in a multi-operator environment is the virtualization of RRM at the cloud server. This, however, is challenging due to an increase in control plane delay, signaling overhead and complexity. This paper introduces an edge cloud-enabled end-to-end vehicle-to-everything (V2X) architecture to support sidelink RRM in CAD scenarios. Analyzing the problem of a cloud-based sidelink resource allocation for CAD, a utility-based multi-objective optimization problem is described and is translated to three tasks: 1) a vehicle cluster formation as a solution to the clique partitioning problem ensuring vehicle reachability on the control plane, 2) an inter-cluster resource block pool (RB-pool) allocation as a solution to a max-min fairness problem and 3) an intra-cluster resource allocation. The proposed solution framework aims to achieve high modularity, low complexity and decouples cluster formation and RB-pool assignment from the intra-cluster optimum resource allocation, which may be performed on different time scales at different edge cloud entities. Simulation results in a realistic vehicular deployment show significant gains in terms of sidelink throughput and delay while maintaining high link quality.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2020.2986870