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

WU Wei; YANG Xinjie; WANG Shuai; MA Nan

doi:10.11999/JEIT260796

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WU Wei, YANG Xinjie, WANG Shuai, MA Nan. Joint Power Control and Resource Allocation for NR-V2X Over Unlicensed Spectrum[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260796

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WU Wei, YANG Xinjie, WANG Shuai, MA Nan. Joint Power Control and Resource Allocation for NR-V2X Over Unlicensed Spectrum[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260796

WU Wei, YANG Xinjie, WANG Shuai, MA Nan. Joint Power Control and Resource Allocation for NR-V2X Over Unlicensed Spectrum[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260796

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

doi: 10.11999/JEIT260796 cstr: 32379.14.JEIT260796

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)

Received Date: 2026-06-15
Accepted Date: 2026-09-17
Rev Recd Date: 2026-09-17

Available Online: 2026-09-27

Abstract

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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References(23)

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