Passive Network Monitoring for Dynamic Topology Inference in 5G Networks

Dr. Ravindra Kumar Sharma

Wireless Network Topology, Blind Inference, Topology Discovery, Passive Monitoring, Signal Strength (RSSI), Time of Arrival (ToA), Machine Learning, Graph Algorithms, Network Inference

Wireless networks are pivotal in modern communication systems, providing the foundation for applications ranging from mobile connectivity to IoT ecosystems. A critical aspect of managing and optimizing these networks is understanding their underlying topology. Traditional topology inference methods often rely on direct access to node-specific configurations, routing information, or active probing techniques. However, such approaches may be infeasible in scenarios where access is restricted, the network is dynamic, or resource constraints prevent active interventions. This paper explores the concept of blind wireless network topology inference, where the network structure is deduced using limited observable metrics without direct interaction with nodes or prior knowledge of configurations.

We propose a novel methodology that leverages passive data collection, signal processing, and machine learning to infer the topological structure of wireless networks. Our approach utilizes metrics such as Received Signal Strength Indicator (RSSI), Time of Arrival (ToA), and spectrum occupancy patterns to construct a probabilistic graph representation of the network. By employing graph-based learning techniques and clustering algorithms, the proposed method achieves high accuracy in identifying network links and node positions, even in the presence of noise and interference.

The paper presents an in-depth evaluation of the methodology using simulated and real-world datasets, demonstrating its scalability and robustness across various network scenarios, including ad hoc, sensor, and cellular networks. Results indicate that our approach outperforms existing methods in terms of inference accuracy, computational efficiency, and adaptability to dynamic environments.

This work not only addresses the challenges of blind topology inference but also provides a scalable framework applicable to emerging technologies such as 5G/6G, autonomous IoT networks, and cognitive radio systems. The findings highlight the potential for passive and data-driven approaches to enhance network monitoring, security, and optimization, paving the way for more resilient and intelligent wireless communication systems.