Wi-Fi Router Collision Avoidance Using Channel and Power Control
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Solution Overview
Problem
WI-FI routers in different networks experience transmission collisions and signal interference due to operating in unlicensed frequency bands without limits on transmitters, leading to reduced network performance in residential and office environments.
Innovation Solution
A WI-FI collision avoidance system collects usage data from routers, generates collision avoidance plans using machine learning models, and provides these plans to routers to adjust channel and RF power settings, with optional direct router negotiations and blockchain-based plan storage to minimize interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple WI-FI routers operate in unlicensed frequency bands without limits on transmitters, then network coverage and accessibility are improved, but transmission collisions and signal interference increase
Solution Approach 1:
The system dynamically changes transmission parameters including channel selection and RF power levels based on real-time collision detection and machine learning predictions. Routers adjust their operating parameters to avoid frequency channels experiencing high collision rates, thereby maintaining network coverage while reducing signal interference.
Solution Approach 2:
The system implements continuous feedback loops where routers monitor transmission collisions, report usage data to the machine learning system, and receive updated collision avoidance plans. This closed-loop feedback enables adaptive adjustment of transmission parameters to minimize interference while preserving network accessibility.
2Reliability
If a WI-FI collision avoidance system collects usage data and generates collision avoidance plans, then transmission collisions are reduced, but system complexity increases
Solution Approach 1:
The patent introduces a centralized machine learning system as an intermediary that collects usage data from multiple routers, processes this information, and generates collision avoidance plans. This intermediary handles the computational complexity centrally, allowing individual routers to maintain simpler implementations while still benefiting from sophisticated collision avoidance capabilities.
Solution Approach 2:
Routers autonomously implement collision avoidance plans by automatically adjusting their channel and power settings based on received instructions. The system enables routers to self-manage their transmission parameters without requiring complex coordination protocols, reducing implementation complexity at the device level.
3Productivity
If machine learning models are used to generate collision avoidance plans, then network performance is enhanced, but computational resources and processing time increase
Solution Approach 1:
The machine learning system generates collision avoidance plans in advance based on historical usage data and predicted collision patterns. By performing preliminary analysis and plan generation, the system reduces real-time processing requirements and enables routers to implement pre-determined channel and power settings, thereby enhancing network performance without significant processing delays.
Data Source
AI summary
A device may include a processor configured to collect first usage data from a first WI-FI router in a first customer premises equipment (CPE) network; collect second usage data from a second WI-FI router in a second CPE network; and determine that a collision avoidance plan between the first WI-FI router and the second WI-FI router is needed. The processor may be further configured to generate a first collision avoidance plan for the first WI-FI router and a second collision avoidance plan for the second WI-FI router based on the first usage data and the second usage data. The processor may be further configured to provide the first generated collision avoidance plan to the first WI-FI router and the second collision avoidance plan to the second WI-FI router.