Spatial-Temporal Pattern Analysis for Network Resource Allocation
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Solution Overview
Problem
Current communication networks lack efficient methods to predict and analyze spatial-temporal patterns of user and device interactions, which hinders optimal resource allocation, network planning, and service offerings.
Innovation Solution
An Information Management Component (IMC) utilizing artificial intelligence and machine learning techniques to analyze data from various sources, including signal measurements and external data, to determine and provide spatial-temporal patterns, enabling enhanced resource allocation, network planning, and service optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network analysis methods are used, then system complexity is low, but the ability to predict and analyze spatial-temporal patterns is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/network analysis methods with artificial intelligence and machine learning systems. The IMC uses AI/ML algorithms to automatically analyze network data, predict user behaviors, and identify spatial-temporal patterns, thereby achieving high measurement precision while managing system complexity through automated intelligent processing.
2Productivity
If AI-based analysis is implemented, then resource allocation efficiency is improved, but computational requirements increase
Solution Approach 1:
The IMC performs preliminary AI-based analysis of network data to predict future user behaviors and network conditions. By conducting computations in advance to identify patterns and trends, the system optimizes real-time resource allocation without requiring excessive computational energy during peak operational periods, as the heavy lifting is done proactively.
3Measurement precision
If comprehensive data analysis is performed, then network planning accuracy is improved, but data processing time increases
Solution Approach 1:
The IMC continuously analyzes network data in real-time, maintaining an ongoing process of data collection, pattern recognition, and predictive analysis. This continuous operation allows the system to accumulate insights over time, improving network planning accuracy through comprehensive analysis while distributing processing load continuously rather than in intensive batches, thereby reducing overall data processing time.
Data Source
AI summary
Spatial-temporal informative patterns for users and devices associated with data networks can be predicted or determined. An information management component (IMC) can analyze respective groups of items of data stored in respective formats in respective databases. Some items of data can comprise respective signal measurement data representative of respective signal measurements associated with respective devices associated with a communication network. Based on the analysis results, IMC can determine a spatial-temporal pattern(s) associated with the respective groups of items of data, wherein the spatial-temporal pattern(s) can relate to a subject of interest. The IMC can utilize artificial intelligence and/or machine learning algorithms and models to facilitate determining the spatial-temporal pattern(s). In response to a query relating to the subject of interest, the IMC can provide information relating to the subject of interest and responsive to the query based on the spatial-temporal pattern(s).