Adaptive Mobile Network Traffic Control via Dynamic Thresholds
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Solution Overview
Problem
Current mobile network traffic control solutions are reactive and passive, failing to proactively address network congestion, particularly in managing mobile-terminated (MT) traffic, and are limited by static load thresholds and a narrow focus on mobile-originated (MO) traffic, leading to inefficient resource utilization and suboptimal connection quality.
Innovation Solution
A traffic control platform that forms a closed feedback loop between the base station and a gateway device, using dynamic and multi-level thresholds to detect congestion early and implement proactive corrective actions such as throttling, buffering, and notifications to manage both MO and MT traffic, optimizing network resource utilization and reducing congestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive traffic control measures are used, then network congestion is addressed after occurrence, but network downtime increases and connection quality deteriorates
Solution Approach 1:
The system performs preliminary actions by detecting early signs of network congestion through monitoring traffic patterns and predicting future congestion states before they actually occur. This allows the system to take preventive measures in advance, such as proactively throttling MT traffic or redistributing load, thereby avoiding actual congestion events and reducing network downtime while maintaining reliability
2Adaptability or versatility
If static load thresholds are used, then detection simplicity is maintained, but adaptability to diverse network traffic modes deteriorates
Solution Approach 1:
The system implements dynamic thresholds that automatically adjust based on network conditions, traffic patterns, and historical data. Instead of using fixed static thresholds, the system continuously learns from observed traffic behavior and adapts its detection parameters to match different traffic modes (MO, MT, IoT, etc.), thereby maintaining high adaptability without requiring complex manual configuration
Solution Approach 2:
The system performs self-service by automatically configuring and adjusting its own detection thresholds based on observed network behavior. The congestion detection mechanism learns from historical data and autonomously optimizes its parameters, eliminating the need for external manual configuration and reducing operational complexity while maintaining high adaptability to diverse traffic patterns
3Productivity
If focus is limited to mobile-originated traffic, then control simplicity is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The system implements a universal congestion detection and control mechanism that handles multiple traffic types (MO, MT, IoT, and other network traffic) through a single unified platform. The congestion detection device can identify and respond to congestion caused by any traffic type, and the system can selectively apply appropriate control measures for each traffic category, thereby improving overall resource utilization efficiency without proportionally increasing control complexity through multiple specialized systems
Data Source
AI summary
A device receive property data associated with a coverage area of a mobile network, wherein the property data includes identification information and location information associated with the coverage area; receive load data associated with the coverage area; determine a load threshold associated with the coverage area; determine whether the load data satisfies the load threshold; identify, based on determining that the load data satisfies the load threshold, impacted user equipment associated with the coverage area, wherein the impacted user equipment is further identified based on the property data; identify an application network device associated with the coverage area and the impacted user equipment, wherein the application network device is further identified based on the property data; determine a corrective action based on the load data, the load threshold, and the application network device; and perform the corrective action in connection with the application network device.