Wireless Network Personalization via Zone of Tolerance Modeling
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Solution Overview
Problem
Current wireless networks are over-provisioned, leading to high costs for users with unnecessary high-quality services and inefficiencies in resource allocation, failing to adapt to emerging bandwidth-hungry applications, and lacking flexibility in balancing cost and performance optimization.
Innovation Solution
A personalized wireless network system using a Zone-of-Tolerance (ZoT) based model, deep network modeling, and synthetic dataset design to micro-manage resources and tailor services to individual user needs, integrating user feedback and context data for real-time optimization of user satisfaction and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wireless networks are over-provisioned to provide high QoS levels for all users, then user satisfaction is improved, but resource utilization deteriorates and costs increase
Solution Approach 1:
The patent applies local quality by transitioning from uniform network provisioning to personalized quality levels. The system determines individual user satisfaction thresholds and provides customized QoS parameters (bandwidth, latency, jitter) tailored to each user's specific needs and context, rather than applying a one-size-fits-all approach. This enables high satisfaction for users who need it while reducing resource allocation for users with lower requirements.
Solution Approach 2:
The patent implements dynamics by making network provisioning adaptive and context-aware. The system continuously monitors user behavior, application requirements, and environmental factors to dynamically adjust QoS levels in real-time. This allows the network to respond to changing user needs and application demands, optimizing resource allocation based on actual usage patterns rather than static over-provisioning.
2Ease of manufacture
If uniform QoS provisioning is applied to all users, then implementation simplicity is maintained, but flexibility in balancing cost and performance deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-establishing user profiles and satisfaction thresholds before actual network service delivery. The system collects and analyzes user data, application requirements, and context information in advance to determine personalized QoS parameters. This preparatory work enables automated, context-aware resource allocation without requiring complex real-time decision-making during service delivery.
Solution Approach 2:
The patent implements feedback mechanisms to continuously monitor user satisfaction levels and adjust network provisioning accordingly. The system collects user feedback, application performance data, and context information to refine QoS parameters dynamically. This closed-loop approach enables the network to adapt to user needs while maintaining implementation feasibility through automated control.
3Productivity
If network resources are allocated based on average user requirements, then resource allocation simplicity is maintained, but user-specific optimization deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the user base into distinct segments based on their QoS requirements, behavior patterns, and context factors. The system creates personalized user profiles that capture individual preferences and needs, enabling differentiated resource allocation. This segmentation allows the network to move from average-based provisioning to precision targeting of QoS parameters for each user segment and individual user.
Solution Approach 2:
The patent implements parameter changes by adjusting multiple QoS parameters (bandwidth, latency, jitter, packet loss) based on user-specific requirements and context. The system dynamically modifies these parameters in response to user behavior, application demands, and environmental factors, enabling precise control over service quality for each user rather than applying uniform average parameters.
Data Source
AI summary
The subject application relates to telecommunication networks and more particularly, to a method and system for managing and allocating wireless network resources to optimize User satisfaction. One aspect of the invention is directed to a system comprising a wireless base station; a user device; and a wireless network connecting said wireless base-station to said user device; said wireless base station being operable: to employ a ‘zone of tolerance’ to model user satisfaction; and to respond to a request from said user device to access network resources, by allocating network resources based on said ‘zone of tolerance’ model. Other aspects of the invention are also shown and described including a system and method of allocating network resources based on an AI-Enabled and Big Data-Driven Multi-Objective Optimization Process.