Automated Multi-Hop Wireless Network Planning Using LiDAR Point Clouds
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Solution Overview
Problem
Deploying multi-hop wireless networks is hindered by the manual and inefficient process of identifying suitable node locations and designing network topology, which lacks automation and precision in site selection and link optimization.
Innovation Solution
A fully-automated network planning system that utilizes LiDAR data to create point clouds, detect potential node sites, and analyze lines of sight, while modeling the network as a network flow problem to optimize node placement and link configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for identifying node locations and designing network topology, then flexibility and adaptability are maintained, but productivity and deployment efficiency deteriorate
Solution Approach 1:
The patent replaces manual mechanical processes with an automated computer-based system that uses LiDAR data processing, point cloud analysis, and algorithmic network flow optimization to identify node locations and design network topology, thereby increasing productivity while maintaining deployment flexibility through software-configurable parameters
Solution Approach 2:
The system enables self-service automation where the network planning software autonomously processes LiDAR data, identifies suitable pole locations, analyzes line-of-sight conditions, and generates optimized network designs without requiring manual site surveying or iterative manual planning, thus dramatically improving deployment efficiency
2Measurement precision
If automated systems are introduced to identify node locations and optimize network design, then productivity and precision are improved, but device complexity increases
Solution Approach 1:
The patent segments the complex network planning task into distinct modular components: LiDAR data acquisition, point cloud generation, pole detection algorithms, line-of-sight analysis, and network flow optimization. Each module handles a specific aspect of the planning process, improving measurement precision while managing system complexity through functional decomposition
Solution Approach 2:
The system introduces point cloud data as an intermediary representation between raw LiDAR measurements and final node location identification. This intermediary layer enables precise geometric analysis and automated feature detection, achieving high site selection precision while abstracting the complexity of raw data processing
3Manufacturing precision
If comprehensive LiDAR data processing and analysis are performed, then manufacturing precision and network design accuracy are improved, but loss of time and computational resources worsen
Solution Approach 1:
The system performs preliminary processing of LiDAR data to generate point clouds and pre-identify potential pole locations before conducting detailed network optimization analysis. This preliminary action reduces the complexity of subsequent optimization tasks, achieving high network design accuracy while minimizing total processing time through staged computation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach streamlines the deployment process by systematically identifying optimal node locations and designing efficient network topologies, enhancing the scalability and reliability of multi-hop wireless networks.
Implementation Method 1
access a point cloud comprising a plurality of points, wherein each point corresponds to an object located in a region in a three-dimensional space
Data Source
AI summary
In one embodiment, a method includes accessing a point cloud comprising several points, wherein each point corresponds to a location on a surface of an object located in three-dimensional space; determining whether each point in the point cloud is part of a linear structure, a planar structure, or a volumetric structure; identifying a plurality of point clusters, wherein each point cluster comprises one or more points that are located within a grid segment on a two-dimensional grid derived from the three-dimensional space; determining, for each point cluster, whether the point cluster represents a vertical-linear structure or a portion of a vertical-linear structure; identifying one or more point-cluster pairs, wherein each point-cluster pair includes two point clusters corresponding to one or more vertical-linear structures within a threshold distance in the three-dimensional space; and determining, for each point-cluster pair, whether a line-of-sight exists between each point-cluster in the point-cluster pair.