Autonomous Drone RF Data Collection for Wireless Network Optimization
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Solution Overview
Problem
Current wireless network optimization methods rely on field test engineers gathering RF data, which is time-consuming, typically taking several days or weeks to result in changes to the network, and lacks real-time problem-solving capabilities.
Innovation Solution
A closed-loop system utilizing an autonomous drone controlled by a self-organizing network (SON) to collect RF data, identify issues, and automatically adjust wireless network parameters in real-time, with the drone re-measuring data to confirm problem resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If field test engineers manually gather RF data along predetermined paths, then network optimization can be achieved, but the process takes several days or weeks and lacks real-time problem-solving capabilities
Solution Approach 1:
The system enables self-service through autonomous drones that automatically collect RF data without human intervention. The drones navigate predetermined paths, collect network performance data, and transmit it to the optimization system, which automatically generates and implements optimization actions, eliminating the need for manual field testing and engineer intervention.
Solution Approach 2:
The patent replaces the mechanical system of manual field testing with an autonomous aerial vehicle system. Drones equipped with RF measurement equipment substitute for human engineers driving or carrying test equipment, enabling automated data collection along optimized flight paths and real-time network optimization.
2Productivity
If manual RF data collection methods are used, then network problems can be identified, but the process lacks real-time feedback and iterative optimization capabilities
Solution Approach 1:
The system implements continuous feedback loops where drones collect RF data, the optimization system analyzes the data to identify network problems, generates optimization actions, and the drones re-measure to verify problem resolution. This closed-loop feedback mechanism enables real-time iterative optimization, with each cycle providing feedback on the effectiveness of previous optimization actions.
Solution Approach 2:
The patent establishes continuous optimization cycles where drones repeatedly traverse measurement paths, collecting RF data at regular intervals. The system continuously processes this data, generates optimization actions, and verifies their effectiveness, maintaining an ongoing optimization process rather than discrete manual interventions.
Data Source
AI summary
Embodiments herein use a real-time closed-loop system to optimize a wireless network. The system includes a drone controlled by a self-organizing network (SON) to retrieve RF data corresponding to the wireless network. In one embodiment, the SON provides the drone with a predetermined path through the coverage area of the wireless network. As the drone traverses the path, a RF scanner mounted on the drone collects RF data. The drone transmits this data to the SON which processes the RF data to identify problems in the wireless network (e.g., cell tower interference). The SON generates one or more actions for correcting the identified problem and transmits these actions to a wireless network controller. Once the wireless network controller performs the action, the SON instructs the drone to re-traverse the portion of the path to determine if the problem has been resolved.