Optimizing Resource Allocation with High-Reliability Constraint for Multicasting Automotive Messages in 5G NR C-V2X Networks

Cellular vehicle-to-everything (C-V2X) has been continuously evolving since Release 14 of the 3rd Generation Partnership Project (3GPP) for future autonomous vehicles. Apart from automotive safety, 5G NR further bring new capabilities to C-V2X for autonomous driving, such as real-time local update,...

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Bibliographic Details
Published inarXiv.org
Main Authors Kuan-Lin, Chen, Wei-Yu, Chen, Ren-Hung, Hwang
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 29.09.2022
Subjects
5G mobile communication
Algorithms
Approximation
Automobiles
Broadcasting
Cellular communication
Driving
Error correction
Heuristic methods
Integer programming
Mathematical analysis
Messages
Multicasting
Network reliability
Optimization
Resource allocation
Roadsides
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Summary:Cellular vehicle-to-everything (C-V2X) has been continuously evolving since Release 14 of the 3rd Generation Partnership Project (3GPP) for future autonomous vehicles. Apart from automotive safety, 5G NR further bring new capabilities to C-V2X for autonomous driving, such as real-time local update, and coordinated driving. These capabilities rely on the provision of low latency and high reliability from 5G NR. Among them, a basic demand is broadcasting or multicasting environment update messages, such as cooperative perception data, with high reliability and low latency from a Road Side Unit (RSU) or a base station (BS). In other words, broadcasting multiple types of automotive messages with high reliability and low latency is one of the key issues in 5G NR C-V2X. In this work, we consider how to select Modulation and Coding Scheme (MCS), RSU/BS, Forward Error Correction (FEC) code rate, to maximize the system utility, which is a function of message delivery reliability. We formulate the optimization problem as a nonlinear integer programming problem. Since the optimization problem is NP-hard, we propose an approximation algorithm, referred to as the Hyperbolic Successive Convex Approximation (HSCA) algorithm, which uses the successive convex approximation to find the optimal solution. In our simulations, we compare the performance of HSCA with those of three algorithms respectively, including the baseline algorithm, the heuristic algorithm, and the optimal solution. Our simulation results show that HSCA outperforms the baseline and the heuristic algorithms and is very competitive to the optimal solution.
ISSN:2331-8422