AI RF Coverage Optimization for Congestion and Latency
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Solution Overview
Problem
Network performance and coverage monitoring in complex, geographically dispersed environments is hindered by network complexity and the inability to understand the environment's topography, leading to issues like network congestion, latency, and performance degradation.
Innovation Solution
A network coverage optimization engine that utilizes artificial intelligence to analyze network data, user device feedback, and location data to identify geographical coverage areas with sub-threshold RF performance, adjusting electrical tilt, power levels, and handover elements to optimize network configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network monitoring is performed in complex, geographically dispersed environments using traditional methods, then network coverage can be monitored, but network performance degradation occurs due to inability to understand environment topography and network complexity
Solution Approach 1:
The patent introduces an intermediary system comprising a drone equipped with sensors and a processing unit that acts as a mediator between the network infrastructure and the geographical environment. This drone-based intermediary collects environmental data (topography, vegetation, structures) and network performance data, then processes this information to generate optimized network configurations, resolving the contradiction by bridging the gap between complex network monitoring requirements and environmental understanding capabilities
Solution Approach 2:
The patent replaces traditional static, ground-based network monitoring mechanisms with a mobile, aerial drone-based system. This substitution enables dynamic data collection from multiple perspectives and locations, allowing the system to understand environment topography and network complexity that was previously inaccessible to fixed monitoring infrastructure, thereby improving reliability without being constrained by device complexity
2Productivity
If traditional network monitoring methods are used, then basic coverage can be tracked, but network congestion and latency occur due to lack of real-time optimization
Solution Approach 1:
The patent implements a closed-loop feedback system where the drone continuously collects network performance data and environmental information, processes this data to identify optimization opportunities, generates optimized network configurations, and enables implementation of these configurations. This continuous feedback loop allows real-time optimization of network parameters, improving productivity while reducing latency through dynamic adjustments based on current network conditions
Solution Approach 2:
The system performs preliminary analysis and optimization planning before network performance degradation becomes severe. The drone proactively collects data, identifies potential congestion areas, and prepares optimized configurations in advance, allowing the network to be adjusted before latency and congestion significantly impact productivity
3Reliability
If manual network optimization is performed, then some performance adjustments can be made, but performance degradation persists due to inability to continuously iterate and adapt to changing environments
Solution Approach 1:
The patent enables the network optimization system to serve itself through automated data collection, processing, and configuration generation. The drone autonomously navigates to relevant locations, collects network and environmental data, processes this information using machine learning algorithms, and generates optimized network configurations without requiring continuous manual intervention. This self-service capability maintains reliable RF performance while simplifying operation by replacing complex manual optimization processes with automated systems
Data Source
AI summary
At a high level, the technology disclosed herein relates to methods, systems, media, etc., for a network coverage optimization engine. In embodiments, network coverage can be optimized by applying particular updates, changes, etc., to a network configuration for one or more coverage areas provided by one or more base stations (e.g., a macro base station, another type of outdoor base station, an indoor cell, a distributed antenna system). For example, the network configuration can be determined based on one or more particular radio frequency (RF) performance metrics for a coverage area being below a threshold. In embodiments, one or more machine learning models may be implemented to predict signal coverage changes upon applying the determined network configuration based on the collected data for the coverage area (e.g., network data, user device network feedback, user device location data, environmental profiles for the coverage area, etc.).