AI Bandwidth Prediction for MVNO Plan Selection
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Solution Overview
Problem
Mobile Virtual Network Operators (MVNOs) lack sufficient insight into UE usage patterns due to limited data access from Mobile Network Operators (MNOs, hindering accurate prediction and management of bandwidth usage.
Innovation Solution
A system utilizing artificial intelligence (AI) to predict bandwidth usage by analyzing data such as previous usage, geolocation, and plan details, and creating embeddings to optimize bandwidth allocation across multiple UEs, enabling efficient plan selection and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MVNO requests bandwidth from MNO based on limited data access, then bandwidth allocation can be made, but prediction accuracy deteriorates due to lack of UE usage pattern insights
Solution Approach 1:
The patent introduces an AI-based prediction system as an intermediary between MNO and MVNO. This intermediary processes the limited data that MVNO has access to and generates accurate bandwidth predictions, effectively mediating the information asymmetry between the two operators without requiring full data transparency.
Solution Approach 2:
The system creates a virtual model (embedding) of UE usage patterns based on limited observable data. This copy or representation of the actual usage patterns allows MVNO to make informed bandwidth predictions without having direct access to the complete underlying usage information that MNO possesses.
2Measurement precision
If MVNO analyzes detailed UE usage data to improve bandwidth prediction, then prediction accuracy improves, but data processing complexity and computational resources increase
Solution Approach 1:
The patent transforms complex UE usage data into simplified embeddings that capture essential usage patterns. By changing the representation parameters from raw detailed data to compressed embedding vectors, the system maintains prediction accuracy while significantly reducing processing complexity and computational requirements.
Solution Approach 2:
The AI system extracts only the most relevant features from UE usage data to create embeddings. This extraction process removes unnecessary detailed information while retaining the critical patterns needed for accurate bandwidth prediction, thereby reducing data processing complexity.
3Productivity
If MVNO allocates bandwidth based on accurate predictions, then network resource efficiency improves, but the complexity of plan selection and management increases
Solution Approach 1:
The system performs preliminary AI-based bandwidth predictions and plan recommendations before actual bandwidth allocation decisions are made. This preliminary action provides MVNO with pre-computed insights and optimal plan selections, simplifying the subsequent allocation process while maintaining high resource efficiency.
Solution Approach 2:
The patent implements a feedback mechanism where AI predictions about bandwidth usage inform plan selections, which in turn affect actual usage patterns. This closed-loop feedback system continuously optimizes network resource allocation while providing structured guidance that simplifies management complexity through data-driven decision-making.
Data Source
AI summary
The system obtains data associated with UE, representing interaction between the UE and a network. The data includes: previous bandwidth usage associated with the UE, previous CDR associated with the UE, anticipated geolocation of the UE, a plan associated with the UE, one or more media events, a number of lines associated with the UE, a length of time the UE has been associated with the network, and a unique identifier associated with the UE. The system obtains multiple plans associated with the network, where a plan indicates the bandwidth usage associated with the UE within a predetermined period. Based on the data, the system predicts the bandwidth usage associated with the UE within the predetermined period to obtain a predicted bandwidth usage and determines the plan among the multiple plans accommodating the predicted bandwidth usage. The system requests the bandwidth usage associated with the plan from the network.