Wireless Network Campaign Groups for Bandwidth Prediction
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Solution Overview
Problem
Introducing new features in wireless telecommunication networks can alter interaction patterns, affecting bandwidth consumption, which existing systems struggle to predict and manage effectively.
Innovation Solution
A system and method to predict and manage bandwidth consumption by comparing the behavior of two groups of mobile devices, one exposed to an information campaign and one not exposed, using artificial intelligence to analyze parameters and simulate user interactions across multiple channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If new features are introduced in communication channels, then network functionality and user engagement are improved, but bandwidth consumption becomes unpredictable and harder to manage
Solution Approach 1:
The system performs preliminary actions by creating simulation groups (treatment and control groups) before actually deploying the information campaign to the entire network. Bandwidth consumption patterns are predicted through simulated interactions in these groups, allowing network operators to prepare appropriate bandwidth allocation and management strategies in advance, thus preventing unpredictable bandwidth consumption while maintaining network functionality improvements
2Productivity
If information campaigns are deployed across multiple interaction channels, then user engagement and desired transactions increase, but bandwidth consumption increases and becomes difficult to predict
Solution Approach 1:
The system creates copies of the target user population by forming simulation groups that mirror the characteristics of the broader user base. Instead of directly measuring bandwidth consumption from actual information campaign deployment across all users, the system uses these simulated copies to predict bandwidth consumption patterns. This allows accurate prediction of bandwidth requirements while maintaining high user engagement, as the prediction is derived from representative simulations rather than actual full-scale deployment
3Reliability
If existing systems attempt to manage bandwidth consumption, then resource allocation is maintained, but they fail to predict bandwidth consumption caused by changing interaction patterns
Solution Approach 1:
The system introduces an intermediary simulation layer between actual network operations and bandwidth consumption measurement. Instead of directly measuring bandwidth consumption from real user interactions with information campaigns, the system uses simulated interactions in controlled groups as an intermediary to predict bandwidth patterns. This intermediary approach enables precise prediction of bandwidth consumption while maintaining reliable resource allocation, as the simulation acts as a buffer that captures interaction patterns without actually consuming production network bandwidth
Data Source
AI summary
The system creates a first group including a first multiplicity of UEs and a second group including a second multiplicity of UEs. The system provides an information campaign to the first group and prevents the second group from being exposed to the information campaign. The system obtains a first parameters across multiple interaction channels associated with a network and a second parameters across the multiple interaction channels associated with the network. The system determines a difference between the first multiplicity of indications associated with the first group and the second multiplicity of indications associated with the second group. Based on the difference, the system predicts bandwidth consumption in multiple interaction channels of the network upon releasing the information campaign to other UEs operating on the network.