Autonomous Spatial Temporal Access Point for Dynamic Cellular Network Coverage
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Solution Overview
Problem
Existing technologies for providing temporary mobile network capacity are often expensive, require significant manual labor, and are tailored mainly for large crowds, making them inefficient for smaller or dynamic coverage needs.
Innovation Solution
A method and system that utilize an autonomous device, such as an Autonomous Spatial Temporal Access Point (ASTA), which predicts network coverage or capacity needs using live data features and machine learning classifiers to dynamically activate and reposition itself within a cellular network, improving network access quality without interfering with existing infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If temporary base stations are deployed to increase network capacity, then network capacity is improved, but cost and manual labor requirements increase
Solution Approach 1:
The system automatically detects network capacity needs and deploys temporary base stations without human intervention. The network device monitors traffic patterns, predicts capacity requirements, and autonomously activates and positions temporary base stations, eliminating the need for manual deployment while reducing costs.
Solution Approach 2:
The system performs preliminary analysis of network traffic patterns and predicts future capacity needs before deployment is required. By using machine learning classifiers to analyze historical data and predict upcoming demand, the system prepares and deploys temporary base stations proactively, avoiding last-minute manual intervention.
2Quantity of substance
If temporary base stations are deployed to increase network capacity, then network capacity is improved, but device complexity increases
Solution Approach 1:
The system continuously monitors network performance metrics and traffic patterns, feeding this information back to the prediction algorithm. The machine learning classifier adjusts its predictions based on real-time feedback from the network, automatically optimizing the deployment and configuration of temporary base stations without requiring complex manual configuration.
3Stability of the object's composition
If fixed base stations are used, then network coverage is stable, but adaptability to changing demand decreases
Solution Approach 1:
The system transitions from static fixed base stations to dynamic temporary base stations that can be automatically deployed and positioned based on real-time network demand. The machine learning classifier continuously analyzes traffic patterns and adjusts the location and activation of temporary base stations, enabling the network to adapt dynamically while maintaining stable coverage through coordinated operation with fixed infrastructure.
Data Source
AI summary
It is presented a method for controlling an autonomous device (4) of a cellular network (5). The method is performed by a network device (20) and comprises the steps of: predicting (43) a need to increase coverage of the cellular network or a need to increase capacity of the cellular network, comprising the steps of: inputting live data features into a trained classifier (80); and outputting a launch class from the classifier (80); and activating (45) an autonomous device (4) of the cellular network, to improve the cellular network. Corresponding network devices, computer program and computer program product are also presented.