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<br>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 (10 to 50 users). In addition, different precoding<br>techniques and antenna configurations are investigated. The results show that users experience 5.2 dB to 3.7 dB higher field strengths for 8×8 antenna arrays compared to 4×4 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.

在第五代移动通信（5G）网络中，基站会动态向用户形成定向波束，将电磁场暴露量的空间与时间变化进行耦合。这种耦合关系使得暴露量建模面临显著挑战，因为空间与时间分量通常会被单独评估。为此，本研究提出一种新颖的时空建模方法，可在真实5G暴露量仿真中同时纳入活跃用户与非活跃用户。本研究采用基于智能体的模型（agent-based model）对行人移动进行建模，并借助射线追踪（ray-tracing）技术仿真电场强度。与此前主要聚焦静态场景，或未考虑预编码（precoding）影响的动态场景的相关研究不同，本工作将预编码技术与动态用户进行了结合。此外，本研究还对用户与非用户的暴露量水平进行了全面对比分析。所提方法通过逐步提升复杂度的场景进行验证：单用户、双用户以及多用户场景（10至50名用户）。此外，本研究还对不同预编码技术与天线配置方案展开了探究。研究结果显示，相较于4×4天线阵列，采用8×8天线阵列时用户的电场强度高出3.7 dB至5.2 dB，这体现了更大规模阵列的定向性更强的特点。非用户群体的暴露量同样有所提升，其场强中位数差值最高可达2.4 dB。在多用户场景下，相较于最大比传输（maximum ratio transmission）预编码，迫零（zero-forcing precoding）预编码可使用户的暴露量中位数降低最高达9.6 dB，非用户群体的暴露量中位数则降低1.1 dB。值得注意的是，即便在天线最大功率工况下，所有场景的暴露量水平均远低于国际非电离辐射防护委员会（ICNIRP）指南限值的4%。本研究结果为天线配置、预编码与用户动态之间的相互作用提供了关键见解，也为真实5G环境下的暴露量建模提供了全新视角。