Beamforming-Based Location Management Under an O-RAN Architecture Using Spark Streaming

A streaming-based Indoor Positioning System (IPS) is developed for beam-based cellular networks that apply directional beam patterns for communications and sensing. The proposed IPS is designed to provide a real-time positioning service by leveraging the computing framework and the streaming modules of Apache Spark streaming. Under the Spark Streaming framework, a beam and model-based location tracking method is designed and optimized for positioning precision and resource utilization. Experiments show that the average positioning error of the proposed user tracking method is about 0.4 meters, which achieves 56.2% improvements over static locating methods. More importantly, a static timing analysis is carried out to evaluate the throughput and bottlenecks of the proposed IPS system. Based on the analysis, our proposed architecture is capable and promising for low-latency IPS provisioning under the O-RAN framework.