Localization in 5G and Beyond: A Multi-Objective Approach for Accuracy, Latency, and Resilience

The integration of localization capabilities within the cellular architecture through dedicated 5G network functions has notably enhanced cellular positioning accuracy and enabled new location-based services. However, this architectural shift requires placing measurement acquisition and computation at the network edge and core, resulting in distributed computational resources and increased latency and security risks. As a result, minimizing latency and ensuring resilience against security threats, in addition to achieving high accuracy, become critical performance indicators in location-based services. This paper examines both 3GPP-standardized and O-RAN-based 5G architectures, detailing the key functions, interfaces, and parameters influencing the localization process, from measurement acquisition to position estimation. We define performance indicators for evaluating localization services and develop a system model that quantifies costs related to latency, accuracy, computation, and resilience against security threats. By jointly considering these factors, we formulate a multi-objective optimization problem that guides the selection of an optimal system configuration to simultaneously satisfy multiple localization requirements. We validate our approach through a case study of an end-to-end 5G system using both simulations and experimental data. Specifically, we evaluate various algorithms and implementations across standardized channels and scenarios. Furthermore, we conduct experimental measurements using Software-Defined Radios (SDRs) and open-source 5G platforms to assess operational latency with commercial-off-the-shelf (COTS) devices.