University of Oulu

Towards reliable and low-latency vehicular edge computing networks

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Author: Batewela Vidanelage, Sadeep1
Organizations: 1University of Oulu, Faculty of Information Technology and Electrical Engineering, Communications Engineering
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.2 MB)
Pages: 40
Persistent link:
Language: English
Published: Oulu : S. Batewela Vidanelage, 2019
Publish Date: 2019-08-20
Thesis type: Master's thesis (tech)
Tutor: Bennis, Mehdi
Suraweera, Himal
Reviewer: Bennis, Mehdi


To enable autonomous driving in intelligent transportation systems, vehicular communication is one of the promising approaches to ensure safe, efficient, and comfortable travel. However, to this end, there is a huge amount of application data that needs to be exchanged and processed which makes satisfying the critical requirement in vehicular communication, i.e., low latency and ultra-reliability, challenging. In particular, the processing is executed at the vehicle user equipment (VUE) locally. To alleviate the VUE’s computation capability limitations, mobile edge computing (MEC), which pushes the computational and storage resources from the network core towards the edge, has been incorporated with vehicular communication recently. To ensure low latency and high reliability, jointly allocating resources for communication and computation is a challenging problem in highly dynamics and dense environments such as urban areas. Motivated by these critical issues, we aim to minimize the higher-order statistics of the end-to-end (E2E) delay while jointly allocating the communication and computation resources in a vehicular edge computing scenario. A novel risk-sensitive distributed learning algorithm is proposed with minimum knowledge and no information exchange among VUEs, where each VUE learns the best decision policy to achieve low latency and high reliability. Compared with the average-based approach, simulation results show that our proposed approach has the better network-wide standard deviation of E2E delay and comparable average E2E delay performance.

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Copyright information: © Sadeep Batewela Vidanelage, 2019. This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited.