Self-learning CBRS Geofences via Device Data Aggregation
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Solution Overview
Problem
Current CBRS networks face limitations due to network operators not updating geofence information, reluctance to share geofencing data, and potential inaccuracies, which affect user experience and usability.
Innovation Solution
Implementing self-learning geofences where user devices gather and process CBRS data points, uploading them to a central server for aggregation and geofence generation, enabling user devices to obtain accurate geofence information even without network-provided data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network operators provide geofencing information to user devices, then user devices can connect to CBRS networks, but the geofence information may be outdated, inaccurate, or not updated when CBRS deployment changes
Solution Approach 1:
User devices autonomously discover CBRS networks by gathering data points (signal strength, location, network identifiers) without requiring network operators to push geofence updates. The device independently builds and maintains its own geofence database through self-service discovery mechanisms
Solution Approach 2:
User devices continuously report discovered CBRS network data points back to a central server, which aggregates feedback from multiple devices to update and refine the collective geofence information, creating a closed-loop system that adapts to network changes
2Adaptability or versatility
If network operators share geofencing information widely, then more user devices can access CBRS networks, but network operators may be reluctant to share due to proprietary concerns
Solution Approach 1:
A central server acts as an intermediary between network operators and user devices. Operators share data with the server rather than directly with devices, reducing their operational burden while enabling widespread device access through the intermediary's distribution network
3Measurement precision
If user devices gather and process CBRS data points autonomously, then geofence information accuracy improves, but device processing complexity and energy consumption increase
Solution Approach 1:
The geofence discovery and processing system is segmented into client-side and server-side functions. Devices handle lightweight data gathering and reporting, while complex aggregation, validation, and geofence generation are performed on the server, distributing computational complexity across the system architecture
Data Source
AI summary
Disclosed are methods, systems, apparatus, and computer programs for self-learning geofences is disclosed. In one aspect, a method involves gathering a plurality of data points associated with one or more Citizens Broadband Radio Service (CBRS) deployers; determining respective identifiers of the one or more CBRS deployers associated with the plurality of data points; clustering, based on the respective identifiers of the one or more CBRS deployers, the plurality of data points into one or more clusters, where each cluster is associated with one of the one or more CBRS deployers, and where each cluster is associated with a geofence of a network of the one or more CBRS deployers; identifying an opportunity for uploading the one or more clusters to a central server; and uploading the one or more clusters to the central server during the identified opportunity.