Cellular Band Scanning for Faster Network Attachment at Destinations
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Solution Overview
Problem
End user devices experience latency when connecting to a cellular network at destination locations, such as airports, due to the need to scan for uplink and downlink frequency numbers, impacting the user experience and perception of an 'always on' connection.
Innovation Solution
Utilizing an AI chipset and machine learning to update a master database with destination location information, enabling targeted scanning of bands and RSSI data for rapid cellular network attachment, facilitated by an AI engine and short-range communication services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the end user device scans for uplink and downlink frequency numbers to attach to a cellular network, then the device can establish a connection, but latency is experienced impacting user experience
Solution Approach 1:
The system pre-determines frequency band data for destination locations using machine learning models trained on historical traveler data. When a device arrives at a destination, the AI chipset retrieves pre-computed frequency information, eliminating the need for time-consuming scans and enabling immediate network attachment.
Solution Approach 2:
An AI chipset acts as an intermediary between the device and cellular network. It uses machine learning models to predict and provide frequency band data, serving as a mediator that bridges the gap between the device's connection needs and the network's frequency requirements, thereby reducing latency.
2Measurement precision
If the device performs a full scan of all frequency bands, then all available cells are detected, but the scanning process is slow and impacts connection speed
Solution Approach 1:
Instead of uniformly scanning all frequency bands across all locations, the system applies local quality by using location-specific machine learning models that provide tailored frequency band data for each destination. This allows the device to scan only relevant frequencies for the specific location, maintaining detection accuracy while improving connection speed.
Solution Approach 2:
The system changes the parameter of frequency scanning from a broad, comprehensive scan to a targeted, parameter-specific scan based on AI-predicted frequency band data. By modifying the scanning parameters according to pre-determined location-specific information, the device achieves both accurate cell detection and faster connection establishment.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, determining, by an end user device, a destination location for the end user device; determining, by the end user device, frequency band data for the destination location according to connection data stored in a memory of the end user device; responsive to or after exiting an airplane mode of the end user device, scanning, by the end user device, bands of a cellular network providing coverage to the destination location, where the scanning is according to the frequency band data; and attaching, by the end user device, to a cell of the cellular network based on the scanning of the bands. Other embodiments are disclosed.