QoS保障的车辆切片定价与接入控制策略

崔亚平; 张峰; 吴大鹏; 何鹏; 王汝言; 汪盼

doi:10.11999/JEIT251219

QoS保障的车辆切片定价与接入控制策略

doi: 10.11999/JEIT251219 cstr: 32379.14.JEIT251219

崔亚平^{1, 2, 3, ,},
张峰^{1, 2, 3},
吴大鹏^{1, 2, 3},
何鹏^{1, 2, 3},
王汝言^{1, 2, 3},
汪盼^{1, 2, 3}

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
先进网络与智能互联技术重庆市高校重点实验室重庆 400065
3.
泛在感知与智能互联重庆市重点实验室重庆 400065

基金项目: 国家自然科学基金资助项目(U24A20211, 62271096)，重庆市教委科学技术研究项目(KJQN202500603, KJQN202300621)，重庆市自然科学基金项目(CSTB2025NSCQ-LZX0144, CSTB2024NSCQ-LZX0124, CSTB2023NSCQ-LZX0134)，重庆市高校创新研究群体(CXQT20017)，重邮信通青创团队支持计划(SCIE-QN-2022-04)，四川省重点研发计划项目(2024YFHZ0093)

详细信息

作者简介:
崔亚平：男，博士，副教授，研究方向为车联网、边缘计算等

张峰：男，硕士生，研究方向为车联网

吴大鹏：男，博士，教授，研究方向为泛在网络、边缘计算等

何鹏：男，博士，副教授，研究方向为无线人体区域网络、边缘计算等

王汝言：男，博士，教授，研究方向为车联网、物联网等

汪盼：男，硕士生，研究方向为车联网等

通讯作者:
崔亚平　cuiyp@cqupt.edu.cn

中图分类号: TN92
计量
- 文章访问数: 13
- HTML全文浏览量: 2
- PDF下载量: 3
- 被引次数: 0
出版历程
- 修回日期: 2026-03-24
- 录用日期: 2026-03-24
- 网络出版日期: 2026-04-21

Slice Pricing and Access Control with QoS Guarantee for Vehicular Networks

CUI Yaping^{1, 2, 3
, ,},
ZHANG Feng^{1, 2, 3},
WU Dapeng^{1, 2, 3},
HE Peng^{1, 2, 3},
WANG Ruyan^{1, 2, 3},
WANG Pan^{1, 2, 3}

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China, Chongqing 400065, China
3.
Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing 400065, China

Funds: National Natural Science Foundation of China (U24A20211, 62271096), Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202500603, KJQN202300621), Natural Science Foundation of Chongqing (CSTB2025NSCQ-LZX0144, CSTB2024NSCQ-LZX0124, CSTB2023NSCQ-LZX0134), University Innovation Research Group of Chongqing (CxQT20017), Youth Innovation Group Support Program of ICE Discipline of CQUPT (SCIE-QN-2022-04), Sichuan Science and Technology Program (2024YFHZ0093)

摘要

摘要: 随着车辆业务需求的多样性和波动性，车辆网络切片应用面临异构资源分配复杂、QoS保障难度高等挑战。针对上述问题，本文提出了一种基于车辆QoS服务需求的双层切片定价机制，其核心创新在于突破现有研究单一资源优化或独立定价的局限。在第一阶段设计切片生成机制，通过联合调度通信、计算与缓存三维资源，解决传统仅优化频谱或计算资源的不足；第二阶段构建基于Stackelberg博弈的切片定价机制，实现“资源预分配-动态定价-切片接入”的双层耦合控制。仿真结果表明了所提出方案在提升系统利润方面的优势。
- 车联网 /
- 网络切片 /
- 接入控制 /
- Stackelberg博弈
Abstract: Objective Vehicular applications have diverse Quality of Service (QoS) needs that traditional spectrum-focused networks struggle to meet. While network slicing over Mobile Edge Computing (MEC) offers customized provisioning, current approaches often overlook the holistic generation of slices and adaptive access control. To address these limitations, this paper proposes a two-stage vehicular network slicing framework that integrates resource-aware slice generation with intelligent pricing and access control. This framework enables efficient, dynamic resource allocation and access management, benefiting both the MEC-based Network Slice Provider (MEC-NSP) and vehicles by improving service quality, utilization, and adaptability through a Stackelberg game-based interaction mechanism. Methods The proposed solution features a two-layer coupled mechanism: “resource pre-allocation” and “Stackelberg game pricing and access control”. In the first stage, a 3D resource pre-allocation mechanism jointly optimizes communication, computation, and caching resources to satisfy vehicular latency and bandwidth requirements. This allocation is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem and decoupled into uplink and downlink sub-problems, solved via branch-and-bound and interior-point methods, respectively. In the second stage, a Stackelberg game balances the MEC-NSP’s profit and vehicles’ QoS. The MEC-NSP acts as the leader, setting dynamic slice prices, while the network controller (the follower) determines the optimal slice selection probabilities. This interaction is resolved using the Iterative Slice Pricing Algorithm (ISPA), which has been proven to converge to a Nash equilibrium. Results and Discussions Simulations demonstrate that the proposed framework consistently outperforms baseline algorithms (Fixed Slice Pricing, Average Resource Allocation, and Random Selection) under various network conditions. In bandwidth-constrained scenarios, it increases MEC-NSP profit by up to 20.77% compared to the Random Selection approach. With abundant resources (150% capacity), it maintains profit gains of 3–9% over other baselines. The ISPA algorithm exhibits fast convergence to equilibrium (approx. 175 iterations). The flexible pricing mechanism effectively balances network loads, improves cache hit rates, and reduces resource bottlenecks, ensuring high QoS satisfaction. Conclusions The proposed dual-layer framework successfully integrates slice generation and pricing to address resource-aware network slicing in vehicular MEC environments. By coupling 3D resource pre-allocation with a Stackelberg game-based pricing strategy, the system significantly improves MEC-NSP profit, resource utilization, and vehicle QoS. Future work will explore blockchain-based mechanisms to facilitate trust negotiation and decentralized resource orchestration for cross-domain cooperation in multi-operator, multi-vendor environments.
- Vehicular network /
- Network slicing /
- Access control /
- Stackelberg game

HTML全文

图 1 网络模型

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图 2 Stackelberg博弈迭代过程

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图 3 带宽资源变化对利润的影响

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图 4 计算资源变化对利润的影响

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图 5 缓存资源变化对利润的影响

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算法1迭代切片定价算法 (Iterative Slices Pricing Algorithm, 　ISPA)
输入：总成本$ \text{Cost}_{v,s}^{w,f,c} $，任务数据量$ {D}_{v,s} $，任务计算量$ {C}_{v,s} $，　缓存内容$ {D}_{l,s,v} $
输出：切片选择概率$ {x}_{v,s} $，切片价格$ {T}_{s} $，MEC-NSP的效用　$ {U}_{\text{SP}} $，车辆效用$ {U}_{D} $
阶段1：初始化策略
1：初始化参与者的策略${\mathcal{X}}$和$ {\boldsymbol{\mathcal{T}}} $
2：计算$ U_{D}^{\ast } $和$ U_{\text{SP}}^{\ast } $的初始值
3：for每个切片$ s $，$s \in {\boldsymbol{\mathcal{S}}}$do
4：　初始化每个切片的价格;
5：　for每个车辆$ v $，$v \in {\boldsymbol{\mathcal{V}}}$do
6：　　根据$ {D}_{v,s} $，$ {C}_{v,s} $，$ {D}_{l,s,v} $计算车辆的利润$ {\Pr }_{v,s} $
7：　end for
8：end for
阶段2：迭代更新
9：while${\mathcal{X}}$或${\boldsymbol{\mathcal{T}}}$不是纳什均衡解do
10：　for每个切片$ s $，$s \in {\boldsymbol{\mathcal{S}}}$do
11：　　for每个车辆$ v $，$v \in {\boldsymbol{\mathcal{V}}}$do
12：　　　计算网络控制器的总利润$ {{{U}^{\prime}}}_{D} $
13：　　　if$ \exists {U}^{\prime}_{D} \gt U_{D}^{\ast } $then
14：　　　　更新网络控制器的选择策略${\mathcal{X}}$并且设置　　　　　　　$ U_{D}^{\ast }={{{U}^{\prime}}}_{D} $
15：　　end if
16：　end for
17：end for
18：for每个切片$ s $，$s \in {\boldsymbol{\mathcal{S}}}$do
19：　for每个车辆$ v $，$v \in {\boldsymbol{\mathcal{V}}}$do
20：　　计算MEC-NSP的总利润$ {{{U}^{\prime}}}_{\text{SP}} $
21：　　if $ \exists {{{U}^{\prime}}}_{\text{SP}} \gt U_{\text{SP}}^{\ast } $then
22：　　　　更新MEC-NSP的定价策略${\boldsymbol{\mathcal{T}}}$并且设置　　　　　　　$ U_{\text{SP}}^{\ast }={{{U}^{\prime}}}_{\text{SP}} $
23：　　end if
24：　end for
25： end for
26：end while
27：return Outputs

下载: 导出CSV

表 1 仿真参数设置

参数	取值	参数	取值
BS数量	2	总缓存能力	100 GB
车辆数量	15	车辆任务大小	[300，600] Kb
车辆速度	45 Km/h	任务CPU周期数	[50, 100] Mega cycles
噪声功率	–114 dBm	缓存内容数据量	[5, 125] MB
BS发射功率	3 W	最大服务时延	[0.02, 0.20] s
车辆发射功率	257～325 mW	最大下载时延	[0.5, 5.0] s
路径损耗因子	3.5	带宽资源价格	15 units/MHz
切片数量	6	计算资源价格	25 units/Gega cycles
总带宽	100 MHz	缓存资源价格	20 units/MB
总计算资源	200 GHz	-	-

下载: 导出CSV

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