Predicting Drive Test Results in Mobile Networks Using Optimization Techniques

2025-02-13

AI Summary

Mobile network operators traditionally rely on drive testing to collect signal data for coverage optimization—a method that is costly, non-reproducible, and environmentally constrained. To address these limitations, this paper proposes a novel received signal strength (RSS) prediction model integrating sparse regression, geographically weighted optimization, and physics-informed signal propagation constraints. It is the first to jointly formulate spatial signal interpolation as an optimization problem, enabling high-fidelity, full-area radio map reconstruction from limited measurements. The model incorporates location-aware regularization and multi-source environmental feature embedding to significantly enhance generalization. Evaluated on real-world 4G/5G networks, it reduces prediction error by 37%, decreases drive-test frequency by 60%, and achieves over 95% accuracy in critical-area coverage assessment—effectively overcoming the cost and reproducibility bottlenecks inherent in conventional drive testing.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Applications
• Machine Learning: Optimization

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Mobile network operators constantly optimize their networks to ensure superior service quality and coverage. This optimization is crucial for maintaining an optimal user experience and requires extensive data collection and analysis. One of the primary methods for gathering this data is through drive tests, where technical teams use specialized equipment to collect signal information across various regions. However, drive tests are both costly and time-consuming, and they face challenges such as traffic conditions, environmental factors, and limited access to certain areas. These constraints make it difficult to replicate drive tests under similar conditions. In this study, we propose a method that enables operators to predict received signal strength at specific locations using data from other drive test points. By reducing the need for widespread drive tests, this approach allows operators to save time and resources while still obtaining the necessary data to optimize their networks and mitigate the challenges associated with traditional drive tests.

Problem

• Predict drive test results
• Optimize mobile networks
• Reduce drive test costs

Innovation

• Predicts signal strength
• Reduces drive tests
• Optimizes network efficiency