CPE Deployment Parameter Optimization via Housing Clustering
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Solution Overview
Problem
Existing methods for determining Wi-Fi performance in housing environments are inefficient and resource-intensive, lacking effective means to provide optimized deployment parameters for Customer Premises Equipment (CPE) to enhance performance.
Innovation Solution
A method and system utilizing a housing database and simulation database connected to a processor, which clusters housing information based on Wi-Fi performance parameters, associates these clusters with housing types, and determines deployment parameters for specific CPE configurations, including number, type, and placement, using machine learning to optimize CPE deployment without excessive resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed housing information is collected and processed for each individual housing environment to determine optimized deployment parameters, then Wi-Fi performance prediction accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent segments housing information into clusters of housing parameter sets based on similarity in Wi-Fi performance characteristics. Instead of processing detailed information for every individual housing environment, the system divides the problem into manageable clusters, where each cluster represents a group of similar housing types. This segmentation reduces the computational burden while maintaining prediction accuracy for individual cases within each cluster.
Solution Approach 2:
The patent creates simplified representations (copies) of housing environments through clustered housing parameter sets. Rather than storing and processing complete detailed information for every housing environment, the system creates condensed cluster representations that capture the essential Wi-Fi performance characteristics. These cluster copies are then used for prediction, significantly reducing resource consumption while preserving the ability to provide accurate deployment parameters.
2Productivity
If clustering algorithms are applied to group housing parameter sets, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal clustering framework that can handle multiple housing parameters and types through a single standardized process. The clustering system is designed to be multi-functional, accommodating various housing characteristics (size, layout, materials, etc.) within a unified algorithmic structure. This universality improves processing efficiency by using one robust system rather than multiple specialized systems, while the modular design keeps complexity manageable.
Solution Approach 2:
The patent transforms detailed housing parameters into clustered representations by changing the parameter structure from individual detailed values to aggregated cluster characteristics. This parameter transformation reduces the dimensionality of the data while preserving the essential information needed for Wi-Fi performance prediction. The clustering process changes parameters from granular individual measurements to grouped statistical representations, improving efficiency while controlling complexity through standardized transformation rules.
Data Source
AI summary
A method and system for determining deployment parameters of a set of customer premises equipment (CPE) in a housing environment. A housing database (2) and a simulation database (3) connected to a processor (4) are present, the housing database (2) storing housing information, and the simulation database (3) storing simulation data with Wi-Fi performance parameters for a subset of housing types. The processor (4) clusters the stored housing information based on the Wi-Fi performance parameters, associates each of the clustered housing parameter sets with one of the subset of housing types, and for a specific one of the housing types determines deployment parameters based on data obtained from the simulation database (3). The matching of a specific housing environment with one of the subset of housing types allows to quickly and efficiently optimize deployment parameters, such as number, type and placement of CPE.