Wireless MAC Mode Selection via Machine Learning
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Solution Overview
Problem
Wireless access points face challenges in selecting optimal media access control (MAC) modes due to rapidly changing traffic patterns and client configurations, making it difficult to maintain network performance across varying conditions.
Innovation Solution
A network monitoring service uses machine learning to train a model that determines optimized MAC mode parameters for wireless access points based on input network characteristics, allowing for dynamic adjustment of MAC modes to improve network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the access point manually selects a MAC mode, then the configuration is simple, but the network performance degrades when traffic patterns change
Solution Approach 1:
The patent implements dynamic MAC mode selection by training a machine learning model that continuously monitors network characteristics (traffic patterns, client types, channel conditions) and automatically adjusts MAC mode parameters in real-time. This transforms the static, manual configuration into a dynamic system that adapts to changing network conditions, resolving the contradiction between operational simplicity and performance adaptability.
Solution Approach 2:
The system employs self-service through autonomous machine learning models that automatically select optimal MAC modes without requiring manual intervention from network administrators. The trained models independently analyze network characteristics and adjust parameters, eliminating the need for complex manual configuration while maintaining high adaptability to changing conditions.
2Adaptability or versatility
If the access point frequently changes MAC modes to adapt to changing conditions, then network performance is maintained, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that bridge the gap between network conditions and MAC mode selection. These models process complex network characteristics and translate them into optimal MAC mode decisions, reducing the apparent complexity at the access point while maintaining high adaptability. The intermediary handles the computational burden of analyzing multiple parameters and making intelligent decisions.
Solution Approach 2:
The system applies preliminary action by pre-training machine learning models offline using historical network data before deployment. This preliminary training phase captures complex patterns and relationships, allowing the model to make rapid, accurate decisions during real-time operation without requiring complex runtime computations. The heavy lifting is done in advance, simplifying the real-time selection process.
3Speed
If machine learning models are trained offline, then real-time processing is fast, but training time and data requirements increase
Solution Approach 1:
The patent implements preliminary action by performing comprehensive model training offline before deployment using historical network data. This preliminary phase captures complex patterns, traffic characteristics, and optimal MAC mode selections across various scenarios. Once trained, the model requires only rapid inference during real-time operation, achieving fast real-time processing while accepting the time investment during the offline training phase.
Data Source
AI summary
In one embodiment, a network monitoring service trains, using a training dataset from one or more wireless networks, a machine learning model to output an optimized set of media access control (MAC) mode parameters for a wireless access point given an input set of network characteristics. The service receives a plurality of network characteristics associated with a particular wireless access point in a particular wireless network. The service determines, using the received network characteristics as input to the machine learning-based model, a set of MAC mode parameters for the particular wireless access point. The service controls the particular wireless access point to communicate with one or more clients in the particular wireless network based on the determined set of MAC mode parameters.