发表机构
Federal University of Ceará (UFC); Universidade Federal do Rio Grande do Norte (UFRN); Instituto Atlântico（塞阿拉联邦大学; 北里奥格兰德联邦大学; 大西洋学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对私有室内5G测试台的gNB部署优化，采用数据驱动方法，基于RSRP测量训练传播模型并结合组合搜索框架，验证了少量gNB即可实现良好室内覆盖与小区边缘性能。
AI 中文摘要
精准的无线电规划是室内环境无线网络部署的基本要求，室内环境中信号传播会受到墙壁、隔断及其他结构障碍物的强烈影响。尽管已有标准化传播模型可用，但它们对特定部署场景特征的表征能力往往有限，这推动了测量驱动方法的应用。在此背景下，本文利用实验第五代（5G）测试台收集的测量数据，开展办公室环境下一代NodeB（gNB）部署优化的数据驱动案例研究。该研究以距离和墙壁数量为输入特征，基于参考信号接收功率（RSRP）测量数据训练传播模型，并将其与组合搜索框架集成。所提出的工作流用于评估不同优化标准下的替代部署策略，结果表明，使用少量gNB即可实现令人满意的室内覆盖，并改善小区边缘状况。
英文摘要
Accurate radio planning is a fundamental requirement for the deployment of wireless networks in indoor environments, where signal propagation is strongly affected by walls, partitions, and other structural obstacles. Despite the availability of standardized propagation models, their ability to represent the characteristics of specific deployment scenarios is often limited, motivating the use of measurement-driven approaches. In this context, this paper presents a data-driven case study of next generation NodeB (gNB) placement optimization in an office using measurements collected from an experimental fifth generation (5G) testbed. A propagation model is trained from reference signal received power (RSRP) measurements using distance and wall count as input features and integrated with a combinatorial search framework. The proposed workflow is used to evaluate alternative deployment strategies under different optimization criteria. Results indicate that satisfactory indoor coverage and improved cell-edge conditions can be achieved with a small number of gNBs.
CommentsXLIV Brazilian Symposium on Telecommunications and Signal Processing - SBrT 2026
