论文标题

在5G NR C-V2X网络中使用高可利用性限制的优化资源分配,并具有高可利用性约束

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

论文作者

Chen, Kuan-Lin, Chen, Wei-Yu, Hwang, Ren-Hung

论文摘要

自从第三代合伙项目(3GPP)发布第14版以来,蜂窝车辆到全能(C-V2X)一直在不断发展。除了汽车安全外,5G NR还将新的功能带到C-V2X进行自动驾驶,例如当地实时更新和协调驾驶。这些功能依赖于5G NR的低潜伏期和高可靠性的提供。其中,基本需求是广播或多播环境更新消息,例如合作感知数据,可靠性高,路边单位(RSU)或基站(BS)的潜伏期很低。换句话说,在5G NR C-V2X中,广播具有高可靠性和低延迟的多种类型的汽车消息是关键问题之一。在这项工作中,我们考虑如何选择调制和编码方案(MCS),RSU/BS,正向错误校正(FEC)代码速率,以最大化系统实用程序,这是消息传递可靠性的函数。我们将优化问题作为非线性整数编程问题。由于优化问题是NP-硬化,因此我们提出了一种近似算法,称为双曲线连续的凸近近似值(HSCA)算法,该算法使用连续的凸近似值来找到最佳解决方案。在我们的模拟中,我们将HSCA的性能与三种算法的性能进行了比较,包括基线算法,启发式算法和最佳解决方案。我们的仿真结果表明,HSCA的表现优于基线和启发式算法,并且对最佳解决方案非常有竞争力。

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.

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