Adaptive Preferred Scan Channels for Low-Interference Wi-Fi
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Solution Overview
Problem
Current Radio Resource Management (RRM) techniques in wireless networks fail to consider switch and backhaul capacity limitations, leading to inefficient bandwidth allocation and resource utilization, especially in high-frequency bands like 6 GHz, which can cause interference and reduce overall network performance.
Innovation Solution
Implementing a wireless controller that uses machine learning techniques to bias preferred scan channels (PSCs) based on network operation parameters, adding channels for STAs to optimize channel selection and reduce scanning time and resources, while considering legacy device compatibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If wider bandwidths (e.g., 160 MHz) are allocated to increase data rates, then bandwidth capacity is improved, but interference on channels increases due to higher frequency reuse
Solution Approach 1:
The system dynamically adjusts the set of preferred scan channels based on real-time network conditions, including the current bandwidth allocation. When bandwidth is increased to 160 MHz, the system automatically modifies which channels are marked as preferred, making the channel selection adaptive to the current bandwidth configuration rather than static.
Solution Approach 2:
The invention changes the parameters used for channel selection by incorporating bandwidth information into the preferred scan channel determination process. The system evaluates channel suitability based on multiple parameters including bandwidth, interference levels, and switch capacity, rather than relying on fixed channel assignments.
2Loss of time
If more channels are added to preferred scan channels (PSCs) to reduce scanning time, then scanning efficiency is improved, but resource allocation complexity increases
Solution Approach 1:
The system performs preliminary analysis of network conditions, switch capacity, and bandwidth allocations before determining the preferred scan channels. By pre-calculating the optimal set of preferred channels based on current network state, the system reduces the scanning time for stations without requiring complex real-time decisions during the scanning process.
Solution Approach 2:
The invention replaces manual or static configuration of preferred scan channels with an automated machine learning-based system. The ML model automatically determines the optimal set of preferred channels by analyzing network parameters, eliminating the need for manual configuration and reducing operational complexity.
3Productivity
If machine learning techniques are implemented to bias preferred scan channels, then channel selection optimization is improved, but system complexity increases
Solution Approach 1:
The system introduces a machine learning model as an intermediary component that sits between the network parameter collection layer and the channel selection decision layer. This ML intermediary processes raw network parameters and switch capacity information to generate optimized preferred channel recommendations, simplifying the overall decision-making architecture while improving efficiency.
Solution Approach 2:
The machine learning model continuously learns from network operations and automatically adjusts preferred scan channel recommendations based on observed patterns and performance metrics. This self-learning capability allows the system to improve channel selection efficiency over time without requiring manual reconfiguration or complex external control mechanisms.
4Power
If bandwidth allocation does not consider switch and backhaul capacity limits, then bandwidth capacity is maximized, but resource allocation efficiency decreases
Solution Approach 1:
The system implements a feedback mechanism where information about switch capacity and backhaul limitations is continuously monitored and fed into the machine learning model. The ML model uses this feedback to adjust bandwidth allocations and preferred channel recommendations, ensuring that bandwidth assignments are realistic and achievable given the underlying network infrastructure constraints.
Solution Approach 2:
The bandwidth allocation system is made dynamic by continuously updating preferred scan channel recommendations based on current switch and backhaul capacity utilization. When switch or backhaul capacity becomes constrained, the system automatically adjusts its recommendations to prevent over-allocation, maintaining efficiency while still maximizing usable bandwidth.
Data Source
AI summary
Disclosed are systems, apparatuses, methods, and computer-readable media for adaptive preferred scan channels in wireless networks. A method includes: retrieving previous client device information connected to at least one access point (AP), analyzing the previous client device information for at least one property associated with wireless network usage of the at least one AP; and determining whether to configure preferred scan channels with additional channels based on the at least one property associated with the wireless network. Adding preferred scan channels to the default configuration can benefit wireless network performance.