Autonomous Reasoning Framework for Wireless Mesh Network Optimization
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Solution Overview
Problem
Wireless Mesh Networks (WMNs) face significant performance degradation due to high density of deployment, unmanaged dynamic environments, and non-Wi-Fi interference, requiring advanced network optimization methods to maintain satisfactory performance.
Innovation Solution
An autonomous argumentative-based reasoning framework is proposed, which includes sensing, perception, learning, reasoning, and decision-making functions to detect communication performance problems and select optimization strategies with the highest likelihood to solve issues with minimal cost, using key performance indicators and optimization algorithms to dynamically adjust network settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If high density of Access Points is deployed to improve network coverage, then network coverage area is improved, but network performance degradation occurs due to interference and congestion
Solution Approach 1:
The system dynamically adjusts network parameters such as channel assignments, transmission power levels, and routing paths based on real-time network conditions. This allows the network to adapt to changing interference patterns and traffic loads, maintaining performance while preserving coverage area.
Solution Approach 2:
The optimization framework changes key network parameters including channel indices, bandwidth allocations, and power settings to resolve interference and congestion issues arising from high AP density, thereby maintaining both coverage and performance.
2Reliability
If multiple optimization strategies are applied simultaneously to solve various communication performance problems, then network performance is improved, but the ping-pong effect occurs where strategies conflict and cancel each other out
Solution Approach 1:
The system performs preliminary analysis and prioritization of communication performance problems before applying optimization strategies. By identifying and resolving critical issues first (such as severe interference or congestion), the system prevents conflicting adjustments and maintains stability.
Solution Approach 2:
The framework implements continuous monitoring of network performance metrics and feedback loops that detect when optimization strategies are causing instability. This allows the system to adjust or retract strategies that are producing the ping-pong effect, maintaining overall network stability.
3Measurement precision
If autonomous reasoning framework is implemented to intelligently select optimization strategies, then decision-making accuracy is improved, but system complexity increases
Solution Approach 1:
The autonomous reasoning framework is segmented into distinct functional modules: sensing module for data collection, perception module for metric determination, learning module for pattern recognition, reasoning module for problem identification, and decision module for strategy selection. This modular architecture improves detection accuracy while managing complexity through clear separation of concerns.
Solution Approach 2:
The system introduces an intermediary optimization manager that acts as a mediator between raw network data and optimization strategy execution. This intermediary layer processes and interprets data, applying reasoning algorithms to bridge the gap between complex inputs and actionable decisions.
Data Source
AI summary
Embodiments of the present disclosure relate to methods, apparatuses and computer program products for network optimization in a wireless network. A method includes determining a plurality of communication performance metrics associated with a plurality of access points; determining, based on the plurality of communication performance metrics, a plurality of communication performance problems to be solved for the plurality of access points; determining, from a predetermined set of optimization strategies, an optimization strategy for an access point of the plurality of access points based on the plurality of communication performance problems; and causing the access point to apply the determined optimization strategy.