面向信息新鲜度保障的车联网功率控制和资源分配策略

杨鹏; 康一铭; 杨静; 唐桐; 祝志远; 吴大鹏

doi:10.11999/JEIT240698

面向信息新鲜度保障的车联网功率控制和资源分配策略

doi: 10.11999/JEIT240698

杨鹏^{1, 2, 3},
康一铭¹,
杨静¹,
唐桐¹,
祝志远¹,
吴大鹏^1, ,

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
中国信息通信研究院北京 100191
3.
信通院(西安)科技创新有限公司西安 710116

基金项目: 国家自然科学基金项目(U24A20211, 62271096, U20A20157)，重庆市自然科学基金(CSTB2023NSCQ-LZX0134, CSTB2024NSCQ-LZX0124)，重庆市高校创新研究群体项目(CXQT20017)，重邮信通青创团队支持计划(SCIE-QN-2022-04)

详细信息

作者简介:
杨鹏：男，高级工程师，研究方向为标识解析、工业软件数据集成

康一铭：男，硕士生，研究方向为车联网资源管理

唐桐：男，讲师，研究方向为视频编码传输等

祝志远：男，讲师，研究方向为可信边缘计算

吴大鹏：男，教授，研究方向为泛在无线网络、社会计算等

通讯作者:
吴大鹏　wudp@cqupt.edu.cn

中图分类号: TN929.5
计量
- 文章访问数: 44
- HTML全文浏览量: 7
- PDF下载量: 6
- 被引次数: 0
出版历程
- 收稿日期: 2024-08-06
- 修回日期: 2025-01-13
- 网络出版日期: 2025-01-17

Resource Allocation Strategy for Information Freshness Guarantee in Internet of Vehicles

YANG Peng^{1, 2, 3},
KANG Yiming¹,
YANG Jing¹,
TANG Tong¹,
ZHU Zhiyuan¹,
WU Dapeng^{1
, ,}

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
China Academy of Information and Communications Technology, Beijing 100191, China
3.
Institute of Information and Communications Technology Innovation Co., Ltd, Xi’an 710116, China

Funds: The National Natural Science Foundation of China (U24A20211, 62271096, U20A20157), The Natural Science Foundation of Chongqing (CSTB2023NSCQ-LZX0134, CSTB2024NSCQ-LZX0124), The University Innovation Research Group Project of Chongqing (CXQT20017), Youth Innovation Group Support Program of ICE Discipline of CQUPT (SCIE-QN-2022-04)

摘要

摘要: 在差异化服务共存的车联网场景中，针对基于平均信息年龄(AoI)优化无法降低极端事件发生概率的问题，该文提出一种信息新鲜度保障的用户功率控制和资源分配策略。首先，根据系统模型刻画出车辆到车辆(V2V)用户状态更新信息新鲜度约束下最大化车辆到基站(V2I)用户体验质量(QoE)的问题。然后，结合与AoI中断约束等价的队列积压约束，并引入极值理论以优化AoI尾部分布。接着，基于李雅普诺夫优化方法将原问题转化最小化李雅普诺夫漂移加惩罚函数的问题，在此基础上求解最优的用户发射功率。最后，在构建超图的基础上，提出了一种基于遗传算法改进粒子群算法(GA-PSO)的资源分配策略确定最优的用户信道复用方式。仿真结果表明，相比于基准方案，所提方案能够在降低V2V链路AoI中断的极端事件发生概率的同时，提高约7.03%的V2I链路信道容量，实现V2I用户平均QoE提升。
- 资源分配 /
- 功率控制 /
- 信息年龄 /
- 极端事件
Abstract: Objective In the Internet of Vehicles (IoV), where differentiated services coexist, the system is progressively evolving towards safety and collaborative control applications, such as autonomous driving. Current research primarily focuses on optimizing mechanisms for high reliability and low latency, with Quality of Service (QoS) parameters commonly used as benchmarks, while the timeliness of vehicle status updates receives less attention. Merely optimizing metrics like transmission delay and throughput is insufficient for ensuring that vehicles obtain status information in a timely manner. For example, in security-critical IoV applications, which require the exchange of state information between vehicles, meeting only the constraints of delay interruption probability or data transmission interruption does not fully address the high timeliness requirements of security services. To tackle this challenge and meet the stringent timeliness demands of security and collaborative applications, this paper proposes a user power control and resource allocation strategy aimed at ensuring information freshness. Methods This paper investigates user power control and resource allocation strategies to ensure information freshness. First, the problem of maximizing the Quality of Experience (QoE) for Vehicle-to-Infrastructure (V2I) users under the constraint of freshness in Vehicle-to-Vehicle (V2V) status updates is formulated based on the system model. Then, by incorporating the queue backlog constraint, equivalent to the Age of Information (AoI) violation constraint, the extreme value theory is applied to optimize the tail distribution of AoI. Furthermore, using the Lyapunov optimization method, the original problem is transformed into minimizing the Lyapunov drift plus a penalty function, based on which the optimal user transmission power is determined. Finally, a resource allocation strategy based on Genetic Algorithm Improved Particle Swarm Optimization (GA-PSO) is proposed, leveraging a hypergraph structure to determine the optimal user channel reuse mode. Results and Discussions Simulation analysis indicates the following: 1. The proposed algorithm employs a channel gain differential partitioning method to cluster V2V links, effectively reducing intra-cluster interference. By integrating GA-PSO, it accelerates the search for the optimal channel reuse pattern in three-dimensional matching, minimizing signaling overhead and avoiding local optima. Compared with benchmark algorithms, the proposed approach increases V2I channel capacity by 7.03% and significantly improves the average QoE for V2I users (Fig. 4). 2. As vehicle speed increases, the distance between vehicles also grows, leading to higher transmission power for V2V communication to maintain link reliability and timeliness. This power increase results in reduced V2I channel capacity, subsequently lowering the average QoE for V2I users. Simulation results show a nearly linear relationship between vehicle speed and average QoE for V2I users, suggesting a relatively uniform effect of speed on V2I link capacity (Fig. 5). 3. Under varying Vehicle User Equipment (VUE) densities, the extreme event control framework is used to compare the conditional Complementary Cumulative Distribution Function (CCDF) of AoI and V2V link beacon backlog. The equivalent queue constraint, derived using extreme value theory, effectively controls the occurrence of extreme AoI violations. The simulations show improved AoI tail distribution across different VUE densities (Fig. 6 and Fig. 7). 4. With decreasing vehicle speed, the CCDF tail distribution of AoI improves (Fig. 8). Reduced speed shortens the transmission distance, decreasing V2V link path loss. This lower path loss, combined with less restrictive VUE transmission power limits, increases the V2V link transmission rate. As beacon transmission rates increase, beacon backlog is reduced, and the probability of exceeding a fixed AoI threshold decreases, ensuring the freshness of V2V beacon transmissions. 5. A comparison of curves under identical beacon reach rates (Fig. 9) reveals that worst-case AoI consistently increases with rising beacon reach rates. At low beacon arrival rates, the average AoI is high. However, once the V2V beacon queue accumulates beyond a certain threshold, further increases in the update arrival rate also raise the average AoI. In summary, the proposed scheme optimizes both the AoI tail distribution and the QoE for V2I users. Conclusions This paper investigates resource allocation and power control in vehicular network communication scenarios. By simultaneously considering the constraints of transmission reliability and status update timeliness in V2V links, restricted by the Signal-to-Interference-plus-Noise Ratio (SINR) threshold and the AoI outage probability threshold, the proposed strategy ensures both link reliability and information freshness. An extreme control framework is applied to minimize the probability of extreme AoI outage events in V2V links, ensuring the timeliness of transmitted information and meeting service requirements. The Lyapunov optimization method is then used to transform the original problem, yielding the optimal transmission power for both V2I and V2V links. Additionally, a GA-PSO-based three-dimensional matching algorithm is developed to determine the optimal spectrum sharing scheme among V2I, V2V, and subchannels. Numerical results demonstrate that the proposed scheme optimizes the AoI tail distribution while enhancing the QoE for all V2I users.
- Resource allocation /
- Power control /
- Age of Information (AoI) /
- Extreme events

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图 1 系统模型

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图 2 V2I-Channel-V2V 3维匹配图

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图 4 不同算法下所有V2I用户平均QoE的CDF对比

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图 3 GA-PSO算法流程

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图 5 不同车速下所有V2I用户的平均QoE对比

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图 6 所提算法与基准算法AoI的CCDF对比

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图 7 所提算法与基准算法信标积压的CCDF对比

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图 8 不同车速下AoI 的CDF对比

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图 9 状态更新到达率与AoI的权衡

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表 1 仿真参数

参数	取值
载波频率	2 GHz
带宽	10 MHz
基站覆盖半径	500 m
基站与公路距离	35 m
车道宽度	4 m
车辆天线高度	1.5 m
车辆天线增益	3 dBi
基站接收机噪声	5 dB
车辆接收机噪声	9 dB
V2I链路最大发射功率	23 dBm
V2V链路最大发射功率	23 dBm
V2V车辆收发距离	15 m
噪声功率	–114 dBm
车速	60 km/h
时隙长度	3 ms
最大可容忍AoI	60 ms
AoI中断概率阈值	0.001
状态更新到达率	125 updates/s
每个信标数据量大小	500 Byte

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