Client RF Feedback for Dynamic Wireless Channel Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless networking technologies fail to account for varying interference and attenuation parameters experienced by client devices, leading to unsatisfactory performance due to undetected or unknown interference sources.
Innovation Solution
Implementing a system that utilizes client device feedback in conjunction with machine learning algorithms to dynamically select optimal operating bands, channels, and channel widths based on real-time interference and attenuation conditions, thereby improving network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automatic channel selection algorithms are used to select operating channels, then channel selection is automated, but the algorithms do not account for radio frequency conditions proximal to each particular client device resulting in unsatisfactory performance
Solution Approach 1:
The system implements feedback by having client devices report their observed interference and attenuation parameters back to the access point. This feedback loop enables the access point to adjust channel selection based on actual client experience rather than relying solely on pre-configured algorithms, thereby improving wireless performance quality while maintaining automation.
Solution Approach 2:
The invention changes the parameters used for channel selection from generic algorithmic predictions to actual measured radio frequency conditions observed by client devices. By incorporating real-time interference and attenuation parameter measurements from multiple client devices, the system dynamically adjusts channel selection to match actual local conditions, resolving the contradiction between automated selection and performance quality.
2Device complexity
If a gateway utilizes a wireless channel based on its proximal RF environment, then channel selection is simplified, but the channel may not be optimal for client devices located at different positions resulting in poor network performance
Solution Approach 1:
The system segments the channel selection process into two parts: the access point maintains simplified channel management based on its own RF environment, while client devices independently measure and report their local interference and attenuation conditions. This segmentation allows each device to optimize for its specific location without overcomplicating the overall system architecture, thereby improving throughput while maintaining simplicity.
Solution Approach 2:
Client devices perform self-measurement of interference and attenuation parameters in their local environment and autonomously provide this information to the access point. This self-service approach eliminates the need for complex centralized measurement systems while enabling optimal channel selection for each client's specific location, thus improving network throughput without increasing system complexity.
3Reliability
If client device feedback is collected and analyzed to dynamically select operating bands and channels, then wireless performance is improved, but system complexity increases due to feedback mechanisms and machine learning algorithms
Solution Approach 1:
Client devices autonomously measure and report their own interference and attenuation conditions without requiring complex centralized processing. The access point simply collects these self-provided feedback messages and uses straightforward channel selection logic, avoiding the need for complex machine learning algorithms while still achieving improved wireless performance quality.
Solution Approach 2:
The system uses simple, lightweight feedback messages from client devices rather than complex persistent learning models. The access point processes these transient feedback messages using basic algorithms, avoiding the computational overhead and architectural complexity of machine learning systems while achieving comparable or superior performance optimization.
Data Source
AI summary
Aspects of the present disclosure improve wireless networking performance by reducing or limiting performance issues caused by undetected or unknown interference sources and/or attenuation sources. Feedback from client devices can be utilized to reduce or limit wireless network performance issues caused by varying interference parameters and/or attenuation parameters observed by each client device. The feedback, associated with wireless communication conditions of corresponding client devices, in conjunction with a machine learning algorithm can be used to enable a dynamic selection of one or more particular operating bands, channels, and/or channel widths to avoid or limit signal interference and/or signal attenuation observed by each client device. Feedback from a plurality of client devices can be used to recommend one or more channels and/or one or more channel widths of one or more operating bands to use that reduces latency, CCI, and/or ACI and improves speed, throughput, and/or QoS.