Communications, Electronics and Sensing Systems logo

Articles

Vol. 1 No. 4 (2026)

Weather-Conditioned Time-Series Ensembles for Reliable 5G V2V Link Forecasting Under Mobility

Submitted
September 19, 2026
Published
October 3, 2026

Abstract

The deployment of fifth-generation vehicle-to-vehicle communication networks introduces unprecedented opportunities for cooperative autonomous driving and advanced intelligent transportation systems. However, maintaining reliable link quality under conditions of high vehicular mobility remains a formidable challenge, further exacerbated by the severe susceptibility of high-frequency signal bands to dynamic atmospheric conditions. Existing predictive models predominantly focus on spatial-temporal mobility patterns while largely marginalizing the acute transient effects of weather. This paper introduces a novel methodological framework utilizing weather-conditioned time-series ensembles to forecast vehicle-to-vehicle link reliability under diverse mobility and environmental scenarios. By integrating meteorological data streams directly into the predictive architecture, the proposed ensemble mechanism dynamically weights distinct time-series forecasting algorithms based on real-time atmospheric states. Extensive simulations and theoretical analyses demonstrate that this weather-aware approach significantly reduces forecasting errors in terms of received signal strength and packet delivery ratios compared to conventional non-conditioned models. The framework systematically combines autoregressive models, recurrent neural architectures, and stochastic gradient boosting techniques to capture both linear attenuation and non-linear scattering effects caused by precipitation and humidity. Ultimately, this research provides a robust foundation for proactive network management and resource allocation in critical vehicular communications, ensuring ultra-reliable and low-latency performance irrespective of environmental perturbations.

References

  1. Li, Q., Feng, Y., Wen, M., Wen, J., Alexandropoulos, G. C., Basar, E., & Poor, H. V. (2024). Cooperative backscatter communications with reconfigurable intelligent surfaces: An APSK approach. IEEE Transactions on Wireless Communications, 23(11), 16218-16233.
  2. Li, R., Xiao, Z., & Zeng, Y. (2023). Toward seamless sensing coverage for cellular multi-static integrated sensing and communication. IEEE Transactions on Wireless Communications, 23(6), 5363-5376.
  3. Liu, J., Nazeri, A., Zhao, C., Abuhdima, E., Comert, G., Huang, C. T., & Pisu, P. (2023). Switching strategy for connected vehicles under variant harsh weather conditions. IEEE Journal of Radio Frequency Identification, 7, 371-378.
  4. Jia, Q., Kang, K., Wang, B., Wu, Y., Zhang, Y., & Guo, F. (2025). Research on wind power prediction with secondary decomposition and multi-algorithm fusion for complex nonlinear time series. Computers and Electrical Engineering, 128, 110688.
  5. van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., et al. (2016). WaveNet: A generative model for raw audio. arXiv preprint arXiv:1609.03499.
  6. Liu, J., & Huang, C. T. (2023, February). Evaluation of different time series forecasting models for 5g v2v networks. In 2023 International Conference on Computing, Networking and Communications (ICNC) (pp. 278-282). IEEE.
  7. Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of EMNLP.
  8. Li, X., Wang, P., Li, G., & Zhang, Y. (2023). Design of a novel self-test-on-chip interface ASIC for capacitive accelerometers. IEEE Transactions on Circuits and Systems I: Regular Papers, 70(7), 2834-2843.
  9. Liu, J., Nazeri, A., Zhao, C., Abuhdima, E., Comert, G., Huang, C. T., & Pisu, P. (2022, October). Investigation of 5G and 4G V2V communication channel performance under severe weather. In 2022 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE) (pp. 12-17). IEEE.
  10. Yang, Z., Ji, W., & Wang, Z. (2024). Adaptive joint configuration optimization for collaborative inference in edge-cloud systems. Science China Information Sciences, 67(4), 149103.
  11. Meng, X., & Lin, J. (2026). AURA-Trace: Auditable Provenance-Aware Synthetic Release of Campus IoT Time Series for Privacy-Preserving AI4Science. IEEE Internet of Things Journal.
  12. Hu, Y., Yang, Z., Zhao, C., & Ji, W. (2026, May). Adaptive guidance semantically enhanced via multimodal llm for edge-cloud object detection. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 12962-12966). IEEE.
  13. Li, Q., Ye, Q., Zhang, N., Zhang, W., & Hu, F. (2025). Digital-twin-enabled industrial IoT: Vision, framework, and future directions. IEEE Wireless Communications, 32(6), 173-181.
  14. Zhang, Z., Gao, S., Su, S., Sun, L., & Chen, R. (2023). MINING: Multi-granularity network alignment based on contrastive learning. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12785-12798.
  15. Li, Y., Zhou, Y., Zhang, Y., Wang, P., Zhang, Y., Li, X., & Zhang, X. (2025). Nonvolatile Optical Switch With a Programmable pn Heterojunction.IEEE Transactions on Electron Devices,72(8), 4030-4035.
  16. Li, X., Wang, P., Li, G., Ni, L., & Zhang, Y. (2023). Design of interface circuits and lightweight PUF for TMR sensors. IEEE Sensors Journal, 23(11), 11754-11761.
  17. Lin, Y. (2024). Design of urban road fault detection system based on artificial neural network and deep learning. Frontiers in neuroscience, 18, 1369832.
  18. Wang, S., Zhang, X., Wang, P., & Li, X. (2026). Design of Magnetic Sensor Array-Based PUFs for IoT Security. IEEE Transactions on Instrumentation and Measurement, 75, 1-9.
  19. Qiu, M., Wu, L., & Wang, S. (2026). CyberDQFormer: A Knowledge Graph-Enhanced Transformer Framework for Security-Aware Data Quality Anomaly Detection and Autonomous Repair Against Network Attacks. Fundamental Scientific Reports in Multidisciplinary Areas, 2(3), 34-52.
  20. Wu, C., Xu, C., & Liu, Y. (2026). L7-COLO: A Cost-Aware Layer-7 Load Balancing Framework with Deep Traffic Prediction for Dynamic Optimization of Enterprise Cloud Services. Fundamental Scientific Reports in Multidisciplinary Areas, 2(02), 235-249.
  21. Meng, X., & Zhu, L. (2024). Augmenting cybersecurity in smart urban energy systems through IoT and blockchain technology within the Digital Twin framework. Sustainable Cities and Society, 106, 105336.
  22. Wang, Y., Wang, Z., Kim, E. H., & Fu, Z. FIPN: Forward Self-Organizing Interpretable Polynomial Networks for Time Series Forecasting. In Forty-third International Conference on Machine Learning.
  23. Guo, F., Tao, S., Zhang, R., Zhou, H., Zhang, J., & Mo, H. (2026). Physics-guided multiscale fusion network for ultra-short-term wind power forecasting. Computers and Electrical Engineering, 139, 111367.
  24. Liu, Fuqiang, Weiping Ding, Luis Miranda-Moreno, and Lijun Sun. "Error adjustment based on spatiotemporal correlation fusion for traffic forecasting." Information Fusion (2025): 103635.
  25. Yao, Y., Shu, F., Li, Z., Cheng, X., & Wu, L. (2023). Secure transmission scheme based on joint radar and communication in mobile vehicular networks. IEEE transactions on intelligent transportation systems, 24 (9), 10027-10037.
  26. Zhang, Q., Gao, W., Liu, C., Yao, Y., Yan, S., Shu, F., & Jin, S. (2025). Covert transmission for active RIS-aided full-duplex UAV integrated sensing, communication, and computation systems. IEEE Journal on Selected Areas in Communications.
  27. Liu, Fuqiang, and Sicong Jiang. "Temporally sparse attack for fooling large language models in time series forecasting." (2025).
  28. Yao, Y., Zhao, J., Li, Z., Cheng, X., & Wu, L. (2023). Jamming and eavesdropping defense scheme based on deep reinforcement learning in autonomous vehicle networks. IEEE Transactions on Information Forensics and Security, 18, 1211-1224.
  29. Zhang, Y., Pleus, A., & Althoff, M. (2026, July). Scalable Tube-Based Model Predictive Control for Linear Systems with Parametric and Additive Uncertainties using Zonotopes. In 2026 European Control Conference (ECC) (pp. 509-516). IEEE.
  30. Wei, Q., Li, R., Bai, W., & Han, Z. (2025). Multi-UAV-enabled energy-efficient data delivery for low-altitude economy: Joint coded caching, user grouping, and UAV deployment. IEEE Internet of Things Journal, 12(14), 27519-27532.
  31. Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. In Proceedings of ICLR.
  32. Yang, Z., Hu, D., Guo, Q., Zuo, L., & Ji, W. (2023). Visual E 2 C: AI-driven visual end-edge-cloud architecture for 6G in low-carbon smart cities. IEEE Wireless Communications, 30(3), 204-210.