City-Scale Spatio-Temporal Modeling of 5G Downlink Exposure of Users and Non-Users by Ray-Tracing in a Real Urban Environment

In 5G networks, base stations dynamically form directional beams toward users, coupling the spatial and temporal variations of electromagnetic field exposure. This interdependence introduces significant challenges to exposure modelling, as spatial and temporal components are often evaluated separately. Therefore, we propose a novel spatio-temporal method that incorporates both active users and non-users in realistic 5G exposure simulations. Pedestrian movement is modelled using an agent-based model, and ray-tracing techniques are employed to simulate electric field strengths. Unlike prior studies that focus mainly on static scenarios, or dynamic settings without accounting for precoding effects, our work integrates precoding techniques with dynamic users. In addition, this work also provides a comprehensive comparison of exposure levels for users and non-users. The proposed method is validated with increasing complexity: single-user, two-user, and multi-user scenarios ( $\mathrm {10~}~\text {to}~\mathrm {50~}$ users). In addition, different precoding techniques and antenna configurations are investigated. The results show that users experience 5.2 dB to 3.7 dB higher field strengths for $8\times 8$ antenna arrays compared to $4\times 4$ arrays, highlighting the increased directionality of larger arrays. Non-users also experience increased exposure, with median differences up to 2.4 dB. Zero-forcing precoding reduces median exposure for users by up to 9.6 dB and for non-users by 1.1 dB compared to maximum ratio transmission precoding in multi-user settings. Importantly, all exposure levels remain well below 4 % of the ICNIRP guidelines, even under maximum antenna power. These findings provide critical insights into the interaction between antenna configuration, precoding, and user dynamics, offering a novel perspective on exposure modelling in realistic 5G environments.