Self-Tuning Wireless Network Framework for Bottleneck Prevention
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Solution Overview
Problem
Existing network technologies face challenges in converging voice and data networks into a single wireless network that meets diverse performance requirements, such as latency and throughput needs, while ensuring seamless routing and minimal data loss, especially in mobile and isolated network environments.
Innovation Solution
A self-tuning network framework that uses machine learning to predict and prevent bottlenecks by adjusting routing paths and network properties, supported by a cloud server that learns and tunes network characteristics, and operates across multiple wireless protocols and hardware, enabling disruption tolerance and efficient traffic management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single wireless network supports both voice and data applications, then network convergence is achieved, but diverse performance requirements (latency for voice, throughput for data) become harder to meet
Solution Approach 1:
The patent segments network traffic into different types (voice, data, video) and applies distinct quality of service parameters to each type. Different routing paths are selected based on traffic type, with voice traffic prioritized for low latency and data traffic optimized for throughput, thereby resolving the contradiction between network convergence and performance requirement fulfillment.
Solution Approach 2:
The patent implements dynamic routing that adapts to changing network conditions and traffic patterns. The routing decisions are made dynamically based on current network state, allowing the system to optimize performance for different applications in real-time while maintaining convergence capabilities.
2Productivity
If routing paths are frequently changed to meet diverse application needs, then application-specific performance is optimized, but network stability and seamless transitions become challenging
Solution Approach 1:
The patent performs preliminary actions by pre-establishing multiple routing paths and pre-configuring quality of service parameters before traffic needs to be routed. This allows rapid switching between paths without disrupting ongoing communications, thereby optimizing application performance while maintaining network stability during transitions.
Solution Approach 2:
The patent implements beforehand cushioning by creating redundant routing paths and buffering mechanisms that prepare the network in advance for potential failures or performance bottlenecks. This ensures that when routing changes are needed, the network can transition smoothly without causing disruptions to active traffic.
3Extent of automation
If machine learning is used to predict and avoid network bottlenecks, then proactive network optimization is achieved, but system complexity increases
Solution Approach 1:
The patent implements self-service by enabling the network to automatically monitor its own state, detect bottlenecks, and adjust routing without external intervention. The machine learning components are integrated directly into network devices, allowing them to learn from their own operations and make autonomous optimization decisions, thereby achieving proactive optimization while managing complexity through distributed intelligence.
Data Source
AI summary
A multilayer architecture for supporting variable networks is discussed. The architecture includes an application layer in communication with a simulation and network management layer and a coupling layer. The coupling layer interfaces with physical connections within a network device and the remaining layers; and wherein the simulation and network management layer sets one or more network parameters within the application layer.