A Stochastic-Geometry-Based Analytical Framework for Integrated Localization and Communication Systems

Yuan Gao, Haoyu Du, Zhenwei Jiang, Haonan Hu, Jiliang Zhang, Shunqing Zhang, Jianbo Du, F. Richard Yu, Shugong Xu

Abstract: For the Internet of Things (IoT) network, the integrated localization and communication (ILAC) is expected to provide high localization and communication performance simultaneously. However, the existing research to evaluate the performance of ILAC systems fails to reveal the fundamental performance of ILAC systems in practical IoT network topology analytically. In this article, we develop a unified analytical ILAC framework using stochastic geometry. We then validate the theoretical results obtained from the proposed analytical framework with the simulation results via extensive Monte Carlo simulations. We further analyze the communication coverage and localization coverage probability with respect to the network density, time–frequency–power-domain resource allocation, and communication throughout and localization threshold. Finally, based on the ILAC simulation results, we reveal design guidance for ILAC systems. Specifically, we observe the fundamental tradeoff between localization and communication performance attributed to the time–frequency–power-domain resource allocation. Network density positively affects the ILAC performance, while power control is much less effective due to the dense network topology. The major observations are that time-domain (TD) resource allocation is preferred in dense networks with low localization CRB thresholds, while frequency-domain (FD) resource allocation dominates in sparse networks with large localization CRB thresholds.

External IDs:doi:10.1109/jiot.2025.3586979