5G Propagation Delay Prediction for Computational Task Offloading in V2X

Intelligent vehicles necessitate offloading compute-intensive tasks to proximal edge servers or cloud infrastructures, where accurate end-to-end (E2E) latency prediction becomes essential for optimal offloading destination selection. However, the inherent complexity and stochasticity of vehicle-to-everything (V2X) scenarios pose significant challenges for precise network latency forecasting. To address this issue, an interval prediction method combining variational mode decomposition (VMD) and a non-parametric estimation-based module is proposed in this paper and applied to computational offloading scenarios. This method mitigates noise in raw data and reduces stochastic fluctuations by decomposing signals into intrinsic mode functions (IMFs) via VMD, which enhances prediction accuracy. Error distributions are subsequently modeled using kernel density estimation (KDE) to generate confidence intervals, which improves prediction reliability. Additionally, a centralized architecture leveraging the network data analytics function (NWDAF) is designed to facilitate computational offloading decisions. Simulation results show the proposed method achieves 1.721