非授权频谱车联网联合功率控制和资源分配的优化方案

吴巍; 杨新杰; 王帅; 马楠

doi:10.11999/JEIT260796

非授权频谱车联网联合功率控制和资源分配的优化方案

doi: 10.11999/JEIT260796 cstr: 32379.14.JEIT260796

吴巍¹,
杨新杰^1, ,,
王帅¹,
马楠²

1.
宁波大学信息科学与工程学院宁波 315211
2.
北京邮电大学信息与通信工程学院北京 100876

基金项目: 国家自然科学基金(U21A20448)

详细信息

作者简介:
吴巍：男，硕士生，研究方向为车联网、异构通信系统、非授权频谱

杨新杰：男，副教授，研究方向为下一代移动通信系统架构、移动物联网接入技术、协作中继网络性能等

王帅：男，硕士生，研究方向为车联网、异构通信系统、非授权频谱

马楠：男，教授，研究方向为移动通信理论与测试技术，包括5G/6G关键技术、无线信道建模与空口测试理论

通讯作者:
杨新杰　yangxinjie@nbu.edu.cn

中图分类号: TN929.5
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出版历程
- 收稿日期: 2026-06-15
- 修回日期: 2026-09-17
- 录用日期: 2026-09-17
- 网络出版日期: 2026-09-27

Joint Power Control and Resource Allocation for NR-V2X Over Unlicensed Spectrum

WU Wei¹,
YANG Xinjie^{1
, ,},
WANG Shuai¹,
MA Nan²

1.
Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
2.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Funds: The National Natural Science Foundation of China (U21A20448)

摘要

摘要: 该文基于频谱资源共享策略，研究了车联网(IoV)与Wi-Fi异构系统中非授权频谱的优化分配问题。不同于现有频谱共享方法，本文提出了一种面向IoV有偿接入非授权频谱的优化方案，在保障Wi-Fi性能条件下，实现IoV对非授权频谱的公平使用。所提方案考虑到Wi-Fi系统的资源出让成本与传输需求，构建了兼顾IoV收益与Wi-Fi性能的整体效用函数，并将路边单元(RSU)频谱占用支出与Wi-Fi资源控制建模为整体效用最大化问题。随后，针对该非凸且NP-hard优化问题，结合连续时隙特性并改进传统粒子群(PSO)算法，设计了一种关联PSO(aPSO)算法，以获得次优解。仿真结果表明，所提方案及算法在保障Wi-Fi性能的同时，有效提升了IoV数据传输率和数据包传输成功率，并在收敛速度与综合性能上优于传统PSO及常见频谱共享算法。
- 车联网 /
- 频谱共存 /
- 资源分配 /
- 非授权频谱 /
- 粒子群优化算法
Abstract: Objective With the rapid growth of data transmission demands in the Internet of Vehicles (IoV), the limited licensed spectrum has become a critical bottleneck for large-scale IoV deployment. Dynamic access to unlicensed spectrum offers a promising solution. However, existing spectrum sharing methods inadequately address fairness for unlicensed spectrum providers such as Wi-Fi systems, and lack a comprehensive definition of their costs and benefits. To address these issues, this paper proposes a joint power control and resource allocation scheme for IoV paid access to Wi-Fi unlicensed spectrum. A holistic utility function is designed to balance IoV throughput gains and spectrum occupancy costs against Wi-Fi performance degradation due to spectrum leasing. A fairness index and a dynamic pricing factor are introduced to ensure fair value exchange between the two systems. The resource allocation problem is formulated as a non-convex NP-hard utility maximization problem, and an associated Particle Swarm Optimization (aPSO) algorithm is developed by exploiting the correlation between consecutive time slots and a dynamic inertia weight to obtain suboptimal solutions efficiently. The proposed approach aims to enhance IoV data transmission performance while guaranteeing Wi-Fi service quality, achieving an effective balance between resource efficiency and system fairness. Methods A heterogeneous scenario consisting of an IoV system and a Wi-Fi system is considered. The IoV Roadside Unit (RSU) serves vehicular users via licensed spectrum, while multiple Wi-Fi access points operate on independent unlicensed channels. When licensed resources are insufficient, the RSU dynamically accesses multiple unlicensed channels to transmit IoV data packets. Packet arrivals follow a Poisson distribution, packet sizes follow a normal distribution, and each packet must be transmitted on a single channel within a maximum delay constraint. A holistic utility function is constructed by jointly considering IoV transmission gains and spectrum occupation costs against Wi-Fi performance degradation and compensation revenues. A fairness index and a dynamic price factor are introduced to ensure equitable spectrum sharing under Wi-Fi performance constraints. The joint optimization of RSU transmit power, channel occupancy time, and the price factor is formulated as a non-convex NP-hard utility maximization problem. To solve it, an aPSO algorithm is proposed. The algorithm exploits consecutive time-slot correlation to initialize particle positions and adopts a dynamic inertia weight to balance global exploration and local exploitation, thereby achieving an effective suboptimal solution with improved convergence performance. Results and Discussions Simulation results verify the superior performance of the proposed aPSO algorithm. Figure 3 shows that aPSO achieves the highest overall utility across different unlicensed channel numbers by jointly optimizing time occupancy and transmit power, while EPA-aPSO and ETA-aPSO require more power or time due to single-dimensional optimization. Figure 4(a) indicates that aPSO maintains the highest packet transmission success rate, even under overload conditions. In Fig. 4(b), aPSO’s energy consumption slowly decreases as channel number grows, effectively balancing energy and transmission performance, unlike ETA-aPSO's low power consumption at the cost of a low success rate. Figure 5 demonstrates that aPSO converges at the 28th iteration, about 65% faster than standard PSO, and attains a higher utility. Figure 6 shows that aPSO significantly outperforms GA and SA under $ B=10\;\text{MHz} $. Figures 7 and 8 further confirm the robustness of aPSO: its utility remains clearly superior under varying channel bandwidths and numbers, whereas EPA-aPSO and ETA-aPSO exhibit limited scalability. Conclusions This paper proposes a joint power control and resource allocation scheme with an aPSO algorithm for IoV paid access to Wi-Fi unlicensed spectrum. By constructing a holistic utility function incorporating a fairness index and a dynamic price factor, the resource allocation is formulated as a utility maximization problem and solved by the proposed aPSO algorithm. The main findings are as follows: (1) The proposed aPSO algorithm converges approximately 65% faster than standard PSO while attaining a higher overall utility; (2) The joint optimization scheme maintains the highest packet transmission success rate even under overload conditions and achieves an effective balance between RSU energy consumption and transmission performance; (3) aPSO consistently outperforms EPA-aPSO, ETA-aPSO, PSO, GA, and SA across varying channel numbers and bandwidths, verifying its robustness and effectiveness in complex resource allocation problems. However, this study considers a single RSU scenario, and future work will investigate distributed cooperative resource allocation mechanisms in multi-RSU and large-scale IoV environments.
- Internet of Vehicles (IoV) /
- Spectrum coexistence /
- Resource Optimization /
- Unlicensed spectrum /
- Particle Swarm Optimization (PSO)

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图 1 IoV系统和Wi-Fi系统组成的异构系统模型

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图 2 连续时隙内IoV系统在非授权频谱的功率和资源分配

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图 3 不同非授权信道数对算法最优解的影响

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图 4 各算法的传输成功率与RSU能耗对比

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图 5 aPSO算法与PSO算法的收敛性能对比

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图 6 不同算法的效用值($ N=4 $，$ B=10 $$ \text{MHz} $)

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图 7 不同非授权信道带宽对效用的影响($ N=4 $)

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图 8 不同非授权信道数对效用的影响($ B=10 $$ \text{MHz} $)

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1 aPSO算法

1：随机初始化粒子群速度与位置，最大迭代次数$ M $，收敛阈值　　 $ \psi $，连续收敛次数阈值$ X $
2：for每个时隙$ k $do
3：　根据式(21)确定$ \boldsymbol{X}_{{(k+1)}_{i}}^{0} $并初始化迭代次数$ m=0 $与连续　　　收敛计数$ x=0 $
4：　repeat
5：　　根据式(19)和式(20)更新粒子速度$ \boldsymbol{V}_{{k}_{i}}^{m+1} $与位置$ \boldsymbol{X}_{{k}_{i}}^{m+1} $
6：　　评估所有例子适应度值，更新$ P_{bi}^{m+1} $和$ P_{g}^{m+1} $
7：　　根据$ P_{g}^{m+1} $计算对应的适应度值$ U_{k}^{m+1} $
8：　　if $ \|U_{k}^{m+1}-U_{k}^{m}\| \lt \psi $ then
9：　　　$ x=x+1 $
10：　 else
11：　　 $ x=0 $
12：　 end if
13：　 $ m=m+1 $
14： until$ x=X $或$ m=M $
15：输出时隙$ k $的最优适应度值$ {U}_{k} $
16：end for

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表 1 关键仿真参数及设置

参数	数值
信噪比阈值$ {r}_{th} $	10 $ \text{dB} $
高斯白噪声功率谱密$ {N}_{0} $	–174 $ \text{dBm/Hz} $
RSU在非授权信道的最大总发射功率$ {P}_{\text{total}} $	5 $ \text{W} $
RSU非授权信道最大发射功率$ {P}_{\max } $	2.5 $ \text{W} $
$ {U}_{\text{IoV}} $权重$ \alpha $	0.8
$ {U}_{\text{Wi-Fi}} $权重$ \beta $	0.2
收益转换因子$ \varepsilon $	0.1
转换因子$ \phi $	0.006
冗余因子$ \xi $	5 $ \text{KB} $
$ {d}_{n} $的均值$ {\mu }_{2} $	100 $ \text{m} $
$ {d}_{n} $的标准差$ {\sigma }_{2} $	30 $ \text{m} $
学习因子$ {c}_{1} $	1.0
学习因子$ {c}_{2} $	2.0
时隙个数$ {T}_{\max } $	1000
时间转换因子$ \tau $	1 $ {\text{ms}}^{-1} $

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