Access Point Proximity Graphing for Wi-Fi Roaming Optimization
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Solution Overview
Problem
Conventional wireless roaming techniques in Wi-Fi networks rely on loudest neighboring access points (APs) for client device handoffs, which may not align with the best APs for client devices due to local constraints, leading to sub-optimal associations and increased scanning and re-association times.
Innovation Solution
A process that determines client device roaming behavior and generates a proximity list of neighboring APs with the shortest roaming distances, providing this list to client devices for optimized roaming, using machine learning techniques to account for client behavior and detect anomalies in AP relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional methods use loudest neighboring APs to build the candidate list, then the AP can provide a simple neighbor list based on signal strength, but the list does not correspond to the best candidates for roaming from the client perspective
Solution Approach 1:
The patent introduces an intermediary process that collects actual roaming behavior data from multiple client devices and uses this empirical data to generate the candidate neighbor list, rather than relying directly on AP signal strength measurements. This intermediary data collection and analysis layer mediates between the simple signal strength measurement and the complex roaming optimization need.
Solution Approach 2:
The system implements feedback by continuously monitoring actual client roaming behavior and using this feedback to refine and update the candidate neighbor list. The roaming history and performance data feed back into the system to improve future roaming recommendations, creating a closed-loop optimization process.
2Reliability
If client devices perform full spectrum scanning to find the best AP, then they can identify optimal roaming targets, but the scanning and re-association time is increased
Solution Approach 1:
The system performs preliminary action by pre-calculating and providing the candidate neighbor list before the client device needs to roam. The AP prepares the optimized neighbor list in advance based on collected roaming behavior data, so when roaming is needed, the client device already has a curated list of likely candidates to choose from, eliminating the need for exhaustive full-spectrum scanning.
Solution Approach 2:
The patent segments the full spectrum scanning process into a targeted search of pre-identified candidate channels. Instead of scanning all possible channels, the client device only needs to scan the specific channels indicated in the candidate neighbor list provided by the AP, dividing the large search space into a manageable subset of relevant options.
3Measurement precision
If the system collects and analyzes roaming behavior data from multiple clients, then it can generate an accurate proximity list, but the data processing complexity and computational requirements increase
Solution Approach 1:
The system implements a centralized controller that performs multiple functions: collecting roaming data from multiple APs and clients, analyzing roaming patterns, calculating proximity relationships, and generating candidate neighbor lists for distribution to multiple APs and clients. This multi-functional controller consolidates complexity into a single entity rather than requiring each AP or client to perform complex analysis independently.
Data Source
AI summary
In one embodiment, a process determines how often client devices roam from a given access point (AP) to each particular neighbor AP of a plurality of neighbor APs of the given AP, and correspondingly determines a roaming distance from the given AP to each particular neighbor AP, the roaming distance being shorter for neighbor APs roamed to more often, and longer for neighbor APs roamed to less often, within a given interval. Successful but temporary roams to the plurality of neighbor APs may also be detected and removed from consideration in the roaming distance to that particular neighbor AP. The process then generates a proximity list of one or more of the neighboring APs having the shortest roaming distances, and feeds the proximity list to the given AP to cause the given AP to provide the proximity list to client devices for optimized client roaming.