Energy Characteristic Map Based Resource Allocation Algorithm for High-density V2V Communications

QIU Gongan; LIU Yongsheng; ZHANG Guoan; LIU Min

doi:10.11999/JEIT250004

Volume 47 Issue 8

Aug. 2025

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(8): 2642-2651

QIU Gongan, LIU Yongsheng, ZHANG Guoan, LIU Min. Energy Characteristic Map Based Resource Allocation Algorithm for High-density V2V Communications[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2642-2651. doi: 10.11999/JEIT250004

Citation:

QIU Gongan, LIU Yongsheng, ZHANG Guoan, LIU Min. Energy Characteristic Map Based Resource Allocation Algorithm for High-density V2V Communications[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2642-2651. doi: 10.11999/JEIT250004

Citation:

PDF( 4463 KB)

Energy Characteristic Map Based Resource Allocation Algorithm for High-density V2V Communications

doi: 10.11999/JEIT250004 cstr: 32379.14.JEIT250004

1.
School of Information Science and Technology, Nantong University, Nantong 226019, China
2.
School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China

Funds: The National Natural Science Foundation of China (62471258)

Received Date: 2025-01-06
Rev Recd Date: 2025-04-15

Available Online: 2025-05-08

Publish Date: 2025-08-27

Abstract

Abstract

Objective In high density scenarios, the random resource selection method has limitations in handling the high access collision probability of traffic safety messages under the limited frequency resource. At the same time, the variable topology accompanied by high mobility increases the failure rate of Vehicle to Vehicle (V2V) links. However, the traffic safety messages with ultra-high reliability and ultra-low latency are very important to ensure traffic safety and road efficiency under the present scenarios. To address these challenges, integrating the energy characteristic parameters in sub-frames and sub-carriers into the resource block map has emerged as a promising approach. By incorporating the distributed V2V links and designing effective reward functions, it is possible to decrease the access collision probability and smooth the dynamics of variable topology while maintaining high resource efficiency, thereby better meeting the needs of dense traffic. This research offers an intelligent solution for resource allocation in Cellular Vehicle to Everything (C-V2X) and provides theoretical support for the coordinated access of limited frequency with diverse link quality. Methods Based on the sustainable adjacency among the neighborhood vehicles in high-density V2V communications, Energy Characteristic Map (ECM) based resource allocation algorithm is proposed using Deep Reinforcement Learning algorithm. The guidance logic of the ECM algorithm periodically renews the energy indicators of candidate resources to train the weight coefficient matrix of two-layer Deep Neural Network (DNN) based on the characteristic results within the sensing window. The algorithm is then used as the action space in double Deep Q-learning Network (DQN) agent to maximize the V2V throughput, which has a main DQN and a target DQN. The state space in the DQN model includes the energy indicators of candidate resources such as the Received Signal Strength Indicator (RSSI) in sub-frames and Signal-to-Interference plus Noise Ratio (SINR) in sub-carriers, along with dynamic factors like the relative position and speed of other vehicles. The reward function is crucial for ensuring the resource efficiency and performance of the safety messages during the resource blocks selection. It accounts for factors such as the bandwidth and SINR of V2V links rewards to optimize decision-making. Additionally, the discount factor determines the weight of future rewards, balancing the importance of immediate versus future rewards. A lower discount factor typically emphasizes immediate rewards, leading to frequently resource block reselection, while a higher discount factor enhances the robustness of occupied resource. Results and Discussions The ECM algorithm periodically renews the energy indicators of candidate resources based on the characteristic results within the sensing window, which then serves as the action space in the double DQN agent. By defining an appropriate reward function, the main DQN in double DQN agent is established to select the candidate resource with high energy indicators for V2V links. The numerical results (Eq.(11) and Eq.(15)) between the packet received ratio and the energy indicators are analyzed using the discrete-time Markov chains. Simulation results show that the end-to-end disseminating performance of safety messages under variable V2V distances, simulated on WiLabV2Xsim, are represented (Fig.6, Fig.7). The reliability, PRR, is more than 0.95 under less than 160 veh/km (the blue line), while the comparative PRR is more than 0.95 under less than 120 veh/km (the green line) and 90 veh/km (the red line), respectively (Fig.10). At the same time, the latency, TD, is less than 3 ms under less than 180 veh/km (the blue line), while the comparative TD is less than 3 ms under less than 160 veh/km (the green line) and about 80 veh/km (the red line), respectively (Fig.11). The resource utilization, RU, is more than 0.6 under less than 180 veh/km (the blue line), while the comparative RU is more than 0.6 under less than 160 veh/km (the green line) and about 80 veh/km (the red line), respectively (Fig.12), demonstrating a 10～20% improvement in resource efficiency. When the discount factor is set to 0.9 while the learning rate is set to 0.01 (Fig.8, Fig.9), the VUE selects the resource blocks that balances immediate and long-term throughput, effectively improving the robustness of the main DQN, which meets the advanced V2V service requirements such as platooning in C-V2X. Conclusions This paper addresses the challenge of resource allocation in high-density V2V communications by integrating the ECM algorithm with double DQN agent. The proposed resource selection scheme enhances the RSS algorithm by establishing distributed V2V links using high quality resource blocks to maximize throughput. The scheme is evaluated through disseminating safety messages simulations under variable density, and the results show that: (1) The proposed scheme has high reliability with more than 0.95 PRR and ultra-low latency with less than 3 ms TD under upper 160 veh/km. (2) The resource efficiency has been improved by 10～20% over the RSS method; (3) Long-term and short-term rewards are considered by selecting the discount factor of 0.9 and the learning rate of 0.01 and enhance the robustness of DQN model. However, this study has not considered different resource characteristics for the heterogeneous messages with diverse Quality of Service (QoS) providing, which should be accounted for in future work.
- C-V2X,
- Resource allocation,
- Deep reinforcement learning,
- Energy characteristic map

FullText(HTML)

References(18)

References

[1]	GU Bo, CHEN Weixiang, ALAZAB M, et al. Multiagent reinforcement learning-based semi-persistent scheduling scheme in C-V2X mode 4[J]. IEEE Transactions on Vehicular Technology, 2022, 71(11): 12044–12056. doi: 10.1109/TVT.2022.3189019.
[2]	SEIFHASHEMI F, HAJRASOULIHA A, and GHAHFAROKHI B S. Resource-aware multi-hop routing protocol for unicast cellular V2V communications[J]. IEEE Access, 2025, 13: 6584–6593. doi: 10.1109/ACCESS.2025.3526697.
[3]	GARCIA M H C, MOLINA-GALAN A, BOBAN M, et al. A tutorial on 5G NR V2X communications[J]. IEEE Communications Surveys & Tutorials, 2021, 23(3): 1972–2026. doi: 10.1109/COMST.2021.3057017.
[4]	王巨震, 江昊, 陈琪美, 等. C-V2X资源分配方法研究综述[J]. 太赫兹科学与电子信息学报, 2022, 20(1): 1–7. doi: 10.11805/tkyda2021145. WANG Juzhen, JIANG Hao, CHEN Qimei, et al. Summary of research on C-V2X resource allocation method[J]. Journal of Terahertz Science and Electronic Information Technology, 2022, 20(1): 1–7. doi: 10.11805/tkyda2021145.
[5]	JEON Y and KIM H. An explicit reservation-augmented resource allocation scheme for C-V2X sidelink mode 4[J]. IEEE Access, 2020, 8: 147241–147255. doi: 10.1109/ACCESS.2020.3015549.
[6]	李一兵, 王宁馨, 吕威. 蜂窝车联网中基于服务异构性的V2V通信资源分配算法研究[J]. 电子与信息学报, 2023, 45(1): 235–242. doi: 10.11999/JEIT211160. LI Yibing, WANG Ningxin, and LÜ Wei. Research on resource allocation algorithm based on service heterogeneity in V2V communication in C-V2X[J]. Journal of Electronics & Information Technology, 2023, 45(1): 235–242. doi: 10.11999/JEIT211160.
[7]	BANITALEBI N, AZMI P, MOKARI N, et al. Distributed learning-based resource allocation for self-organizing C-V2X communication in cellular networks[J]. IEEE Open Journal of the Communications Society, 2022, 3: 1719–1736. doi: 10.1109/OJCOMS.2022.3211340.
[8]	PARVINI M, SCHULZ P, and FETTWEIS G. Resource allocation in V2X networks: From classical optimization to machine learning-based solutions[J]. IEEE Open Journal of the Communications Society, 2024, 5: 1958–1974. doi: 10.1109/OJCOMS.2024.3380509.
[9]	陈发堂, 张若凡. 可重构智能反射面辅助的车联网资源分配算法研究[J]. 通信学报, 2023, 44(9): 70–78. doi: 10.11959/j.issn.1000−436x.2023145. CHEN Fatang and ZHANG Ruofan. Research on IoV resource allocation algorithm assisted by reconfigurable intelligent surface[J]. Journal on Communications, 2023, 44(9): 70–78. doi: 10.11959/j.issn.1000−436x.2023145.
[10]	许耀华, 王慧平, 王贵竹, 等. 基于图着色和三维匹配的车联网资源分配算法[J]. 系统工程与电子技术, 2023, 45(3): 869–875. doi: 10.12305/j.issn.1001-506X.2023.03.29. XU Yaohua, WANG Huiping, WANG Guizhu, et al. Resource allocation algorithm for internet of vehicles based on graph coloring and three-dimensional matching[J]. Systems Engineering and Electronics, 2023, 45(3): 869–875. doi: 10.12305/j.issn.1001-506X.2023.03.29.
[11]	JI Baofeng, DONG Bingyi, LI Da, et al. Optimization of resource allocation for V2X security communication based on multi-agent reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2025, 74(2): 1849–1861. doi: 10.1109/TVT.2023.3340424.
[12]	JI Maoxin, WU Qiong, FAN Pingyi, et al. Graph neural networks and deep reinforcement learning-based resource allocation for V2X communications[J]. IEEE Internet of Things Journal, 2025, 12(4): 3613–3628. doi: 10.1109/JIOT.2024.3469547.
[13]	YACHEUR B Y, AHMED T, and MOSBAH M. Efficient DRL-based selection strategy in hybrid vehicular networks[J]. IEEE Transactions on Network and Service Management, 2023, 20(3): 2400–2411. doi: 10.1109/TNSM.2023.3300653.
[14]	ZHANG Minglong, DOU Yi, MAROJEVIC V, et al. FAQ: A fuzzy-logic-assisted Q-learning model for resource allocation in 6G V2X[J]. IEEE Internet of Things Journal, 2024, 11(2): 2472–2489. doi: 10.1109/JIOT.2023.3294279.
[15]	LI Pengfei and HUANG Xinlin. Cooperative spectrum sensing approach in C-V2X based on multi-agent reinforcement learning[C]. Proceedings of 2023 17th International Conference on Telecommunications, Graz, Austria, 2023: 1–6. doi: 10.1109/ConTEL58387.2023.10199063.
[16]	WANG Junhan, HE He, CHA J, et al. Multi-agent reinforcement learning for efficient resource allocation in Internet of Vehicles[J]. Electronics, 2025, 14(1): 192. doi: 10.3390/electronics14010192.
[17]	WIJESIRI G P N B A, HAAPOLA J, and SAMARASINGHE T. A discrete-time Markov chain based comparison of the MAC layer performance of C-V2X Mode 4 and IEEE 802.11p[J]. IEEE Transactions of Communications, 2021, 69(4): 2505–2517. doi: 10.1109/TCOMM.2020.3044340.
[18]	LAGEN S, WANUGA K, ELKOTBY H, et al. New radio physical layer abstraction for system-level simulations of 5G networks[C]. Proceedings of the ICC 2020-2020 IEEE International Conference on Communications, Dublin, Ireland, 2020: 1–7. doi: 10.1109/ICC40277.2020.9149444.