Parallel Inspection Route Optimization With Priorities for 5G Base Station Networks

5G base station networks generate numerous alarms daily. With the increasing demand for digital services, it is vital to inspect and rectify anomalies to uphold user satisfaction. This study explores the potential of unmanned aerial vehicle (UAV) empowered opportunistic inspection based on alarm dat...

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Bibliographic Details
Published in:IEEE transactions on automation science and engineering Vol. 22; pp. 10860 - 10870
Main Authors: Dai, Xiangqi, Liang, Zhenglin
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
5G mobile communication
Automation
Autonomous aerial vehicles
Base stations
Clustering algorithms
Communication system operations and management
Heuristic algorithms
Inspection
optimization methods
parallel algorithms
Routing
Transformers
Traveling salesman problems
unmanned aerial vehicle
ISSN:1545-5955, 1558-3783
Online Access:Get full text
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Summary:5G base station networks generate numerous alarms daily. With the increasing demand for digital services, it is vital to inspect and rectify anomalies to uphold user satisfaction. This study explores the potential of unmanned aerial vehicle (UAV) empowered opportunistic inspection based on alarm data. We formulate the inspection routing problem as a prioritized traveling salesman problem (PTSP) encompassing two categories of base stations. Priority is assigned to stations generating more alarms, while others are subject to opportunistic inspection. To expedite large-scale opportunistic inspection routes, we introduce a novel transformer-based parallelizable routing algorithm (TPRA). TPRA is an intelligent optimization that orchestrates multiple parallelized constrained reinforcement learning algorithms. Through balancing spectral clustering, the large-scale graph is segmented into manageable subgraphs. For each subgraph, the prioritized inspection routing problem is formulated as a constrained Markov decision process and optimized by transformer-based reinforcement learning in parallel. The optimized subgraphs are then merged using an adaptive large neighborhood search approach. Through parallel computing, our approach achieves as much as 75% reduction in computation time, while concurrently generating shorter routes. The approach is implemented in real-world cases to validate its efficacy. Note to Practitioners-The rapid expansion of 5G infrastructure underscores the critical need for advanced technology and maintenance strategies. Base stations are often placed at high altitudes to ensure line-of-sight connectivity, which poses difficulties for maintenance, particularly in challenging terrains. UAVs offer a promising solution for faster and safer inspection and rectification. The designed approach utilizes reinforcement learning in parallel to optimize UAV inspection routes in an opportunistic manner. This method strategically prioritizes inspection routes based on the real-time base station alarm data, ensuring a swift and effective response to potential issues. Trained in simulated scenarios, the model requires few adjustments for real-world deployment, making it readily implementable in 5G networks. Beyond the potential of the 5G network, the approach also unlocks new value across various types of service in the low-altitude economy.
ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2025.3530425