AI Embedding System for Mobile Network Bandwidth Prediction
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Solution Overview
Problem
Mobile Virtual Network Operators (MVNOs) lack sufficient insight into user equipment (UE) usage patterns due to limited data access from Mobile Network Operators (MNOs, hindering their ability to accurately predict and manage bandwidth usage.
Innovation Solution
A system that uses artificial intelligence (AI) to predict bandwidth usage by analyzing data such as previous usage records, geolocation, and plan details, creating embeddings that encode interactions between mobile devices and wireless networks, allowing MVNOs to request necessary bandwidth from MNOs effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If MNOs provide limited data to MVNOs, then data security and control are improved, but MVNO insight into UE usage patterns deteriorates
Solution Approach 1:
An AI embedding system acts as an intermediary between MNO and MVNO. The system receives raw data from MNO, transforms it into compressed embeddings that preserve usage pattern insights, and provides these embeddings to MVNO. This intermediary transformation maintains data control for MNO while delivering sufficient insight to MVNO for bandwidth prediction.
2Reliability
If MVNOs request more bandwidth, then service quality is improved, but cost increases
Solution Approach 1:
The system performs preliminary bandwidth prediction using AI embeddings before actual bandwidth allocation. By analyzing historical usage patterns encoded in embeddings, the system predicts future bandwidth needs and enables MVNO to request appropriate bandwidth in advance, avoiding both over-provisioning (wasted cost) and under-provisioning (degraded service quality).
3Measurement precision
If AI models are trained on raw data, then prediction accuracy is improved, but data processing complexity and storage requirements increase
Solution Approach 1:
The system extracts essential usage pattern information from raw data and condenses it into compact AI embeddings. This extraction process removes redundant data while preserving the critical features needed for accurate bandwidth prediction, thereby reducing storage requirements and processing complexity while maintaining prediction accuracy.
4Measurement precision
If MVNOs have full data access, then bandwidth prediction accuracy is improved, but data security and privacy concerns worsen
Solution Approach 1:
The AI embedding system serves as a privacy-preserving intermediary. It processes raw user data on the MNO side, transforming it into aggregated embeddings that contain usage pattern information necessary for accurate bandwidth prediction, while inherently removing personally identifiable information and sensitive details, thus protecting user privacy.
Data Source
AI summary
The system obtains an encoder configured to receive a first data representing interaction between the first UE and the first network. The encoder produces a first embedding representing the first data representing the first interaction between the first UE and the first network. A memory footprint of the first embedding is smaller than a memory footprint of the first data. The system obtains a second data representing a second interaction between a second UE and a second network. The system provides the second data to the encoder configured to produce a second embedding indicating the second data representing the second interaction between the second UE and the second network. The system obtains the second embedding from the encoder.