Network Signal Coverage Mapping for Remote Equipment Installation
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Solution Overview
Problem
Conventional network equipment deployment is time-consuming and prone to errors due to manual processes lacking physical access to customer locations, leading to inaccurate installation plans.
Innovation Solution
Utilizing custom-trained neural networks to process network signals for generating installation plans, including equipment forecasts and simulations to optimize network equipment placement based on visual maps and coverage analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for network equipment deployment, then device complexity is reduced, but productivity decreases and measurement precision deteriorates
Solution Approach 1:
The patent replaces manual mechanical processes with an automated neural network system that processes network signals to generate installation plans. The neural network analyzes signal strength, coverage maps, and building layouts to automatically determine optimal equipment placement, eliminating the need for manual site visits and plan creation while improving both productivity and measurement precision.
Solution Approach 2:
The system creates a virtual copy of the physical environment by processing network signals to generate a digital representation (coverage map) of the building and signal distribution. This virtual model allows the neural network to simulate and optimize equipment placement without physical intervention, enabling rapid deployment planning while maintaining high accuracy through signal-based modeling.
2Measurement precision
If manual processes are used for installation planning, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent replaces manual visual inspection and estimation with automated neural network analysis that processes network signals to generate precise coverage maps. The system calculates exact signal strength distributions, identifies precise coverage gaps, and determines optimal equipment locations based on quantitative signal analysis rather than manual estimation, significantly improving measurement precision.
Solution Approach 2:
The neural network acts as an intermediary between raw network signals and installation plans. It processes signal data, generates coverage maps, and translates this information into optimized equipment placement recommendations. This intermediary processing layer enables precise measurement and planning without requiring direct manual intervention in the complex signal analysis process.
3Measurement precision
If manual site visits are required for accurate planning, then measurement precision improves, but loss of time increases
Solution Approach 1:
The system creates a virtual copy of the building and signal environment by processing network signals to generate a digital coverage map. This virtual representation allows the system to assess the site remotely with high accuracy, eliminating the need for physical site visits while maintaining measurement precision. The neural network analyzes signal strength, coverage patterns, and building layouts to produce accurate installation plans without time-consuming on-site presence.
Solution Approach 2:
The patent substitutes manual physical inspection with automated neural network analysis that processes network signals to generate precise coverage maps. The system can remotely assess building layouts, signal distribution, and coverage gaps through digital signal processing, achieving accurate site assessment without the time loss associated with manual site visits and travel.
Data Source
AI summary
One or more computing devices, systems, and/or methods for network equipment solution generation utilizing network signals are provided. Network signals collected by devices within a location are evaluated by a neural network to generate contextually aware pixel values forming a vision map of the location. The contextually aware pixel values and/or the vision map are evaluated to generate a coverage map corresponding to regions of low signal coverage and obstacles proximate the regions. A simulation of network equipment operating at the location is performed using the coverage map to generate a simulation result. The simulation result is used to generate an installation plan to install network equipment at the location.