Dynamic QoS Adjustment via AI User Simulation
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Solution Overview
Problem
Current cellular network congestion management through throttling is inefficient, as it often prioritizes higher-paying users, failing to provide sufficient quality of service for those requiring higher quality, and does not dynamically adjust based on real-time conditions or user preferences.
Innovation Solution
A system that uses artificial intelligence and machine learning to determine user profiles, simulate user experiences, and adjust quality of service parameters such as bandwidth and class identifiers dynamically based on conditions like location, time, and network activity, ensuring optimal service allocation across all users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If throttling is applied based on mobile network plan tiers, then network congestion is mitigated for high-paying users, but quality of service is insufficient for users requiring higher quality service
Solution Approach 1:
The patent implements dynamic QoS parameter adjustment based on simulated user experiences and AI-generated conditions. Instead of static tier-based throttling, the system continuously monitors network conditions and user needs, adjusting bandwidth, priority, and other QoS parameters in real-time to optimize service delivery for all users regardless of payment tier.
Solution Approach 2:
The system changes QoS parameters (bandwidth, priority, latency tolerance) dynamically based on simulated user experiences and AI-determined conditions. This allows the network to adapt service characteristics to match actual user needs and network conditions rather than relying on fixed pricing tiers.
2Productivity
If static throttling is applied based on network plan tiers, then network congestion management is simplified, but efficiency is reduced due to inability to respond to real-time conditions
Solution Approach 1:
The system uses AI and machine learning to automatically determine user profiles, simulate user experiences, and adjust QoS parameters without human intervention. The network manages itself by continuously learning from simulated experiences and adapting to changing conditions, improving efficiency while the added complexity is handled autonomously by the AI system.
Solution Approach 2:
The system implements continuous feedback loops where AI simulations of user experiences inform QoS parameter adjustments. The network monitors actual performance, compares it with simulated expectations, and dynamically adjusts parameters to optimize efficiency based on real-time conditions and user needs.
3Ease of operation
If tier-based throttling is used, then network resource allocation is simplified, but user experience is degraded for users requiring higher quality service
Solution Approach 1:
The patent extracts the complexity of QoS management from static pricing tiers and relocates it to dynamic AI-driven simulations. By separating resource allocation simplicity from service quality optimization, the system maintains easy operational management while dramatically improving user experience through personalized, condition-based QoS adjustments.
Data Source
AI summary
Dynamic, continuous quality of service adjustment (e.g., using a computerized tool) is enabled. For example, a system can comprise: a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: determining a condition associated with a user profile, wherein the user profile is associated with a mobile device, determining a quality of service metric representative of a quality of service of a data transmission, via a radio access network, between the mobile device and network equipment, and based on the condition, modifying a quality of service parameter, wherein modifying the quality of service parameter comprises modifying the quality of service.