The ubiquity of mobile phone signaling data (SD) plays a pivotal role in mobility analysis; however, privacy policies and public concerns over personal location exposure severely limit the availability of public SD trajectory datasets. To address privacy concerns in human mobility analysis, synthetic trajectory generation has emerged as a promising solution. Existing trajectory generation methods primarily target GPS data, which exhibits high spatiotemporal continuity, leaving a critical gap in SD-specific trajectory synthesis. To bridge this gap, we propose SignalingTraj, a diffusion-based framework tailored for high-fidelity SD trajectory generation. Leveraging the generative capacity of diffusion models, SignalingTraj addresses the unique challenges of SD data, including low spatial resolution and irregular sampling. Specifically, we integrate spatial attributes and handover correlations of base stations (BSs) into a pre-training strategy using Variational Graph Autoencoders (VGAE), converting BSs into continuous representation vectors. Experiments on real-world SD datasets demonstrate that SignalingTraj synthesizes high-fidelity, privacy-preserving trajectories that closely mirror original data distributions. SignalingTraj outperforms existing methods in data fidelity and utility, positioning it as a robust solution for generating scalable, synthetic SD trajectories to support diverse mobility analysis applications.