Auto-Provisioning Satellite Modem Location Adaptation
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Solution Overview
Problem
Existing electronic devices, such as reconfigurable holographic satellite antennas, require manual provisioning and commissioning, which is challenging in remote locations without reliable communication services, and often necessitates location-specific settings that are unknown at the manufacturing stage, making pre-provisioning difficult or impossible.
Innovation Solution
A method for automatic provisioning and commissioning of electronic devices using first provisioning data based on machine learning from similar devices, allowing them to establish a communication link and then downloading location-specific second provisioning data after deployment, enabling optimal performance at the end-use location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual provisioning and commissioning is performed, then location-specific configuration can be achieved, but technician intervention is required which is problematic in remote locations
Solution Approach 1:
The device automatically performs provisioning and commissioning by detecting its own location and downloading appropriate configuration data without technician intervention. The system self-configures by obtaining location information through GPS or cell tower triangulation and autonomously downloads location-specific provisioning data from a server.
Solution Approach 2:
Multiple sets of provisioning data corresponding to different locations are prepared in advance and stored on servers. When the device is deployed, it automatically selects and downloads the appropriate pre-prepared configuration data based on its detected location, eliminating the need for manual configuration.
2Loss of time
If pre-provisioning is performed at manufacturing, then deployment time is reduced, but location-specific optimization is lost since final location is unknown
Solution Approach 1:
Provisioning data is segmented into multiple location-specific sets, each optimized for particular geographic areas. The device downloads only the relevant location-specific segment after deployment, achieving both fast initial deployment and subsequent location-specific optimization without requiring pre-knowledge of the final location.
Solution Approach 2:
Generic provisioning data is downloaded in advance at manufacturing to enable immediate basic operation. After deployment, the system performs preliminary location detection and then downloads additional location-specific optimization data, combining the benefits of rapid deployment with location-specific performance.
3Adaptability or versatility
If generic provisioning data is used for all locations, then device can operate anywhere, but optimal performance at specific locations is not achieved
Solution Approach 1:
The system provides different provisioning data with local quality characteristics optimized for specific locations. Each location has its own customized provisioning parameters that are downloaded after the device detects its location, ensuring optimal performance for that specific geographic area while maintaining universal operation capability.
Solution Approach 2:
The provisioning data is dynamic rather than static - the device initially operates with generic data and then dynamically updates to location-specific optimized data based on its deployed location. This dynamic adaptation ensures both universal operation and location-specific performance optimization.
Data Source
AI summary
System and methods are disclosed for automatically provisioning and commissioning an electronic device (“device”), such as a satellite modem coupled to a reconfigurable satellite antenna. The device is provisioned at a first location, e.g., at a manufacturing facility, using first provisioning data and first commissioning data that is sufficient for the device to establish a communication link with a network and a server having an activated user account. The first provisioning data and first commissioning data is based at least in part upon machine learning over a plurality of devices of a similar type. The device is then set-up and powered-on at a second location, e.g., an end-use location. The device connects to the network, logs in to the user account, and provides a serial number and the second location to the server. The server downloads second provisioning data this based at least in part upon the second location to the device.