Predicting Drive Test Results in Mobile Networks Using Optimization Techniques

TL;DR

This paper introduces an optimization-based method for predicting mobile network drive test results, aiming to reduce costs and improve efficiency in network data collection and analysis.

Contribution

It presents a novel approach that leverages existing drive test data to accurately predict signal strength at untested locations, minimizing the need for extensive drive testing.

Findings

Effective prediction of signal strength at untested locations.

Significant reduction in drive test requirements.

Improved efficiency in network optimization processes.

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.