Manufacturing Wi-Fi Access Point Control for Congestion and Interference
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Solution Overview
Problem
Wireless access points in manufacturing environments are often inaccessible due to issues like power outages, equipment malfunctions, noise pollution, and network congestion, leading to disrupted RF signal transmission and reception.
Innovation Solution
A central controller generates state vectors based on network data from wireless access points, identifies actions using a Markov decision process, determines rewards for these actions, and selectively adjusts operational characteristics to enhance network connectivity and throughput, including self-organizing, RF adjustment, and load balancing routines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If wireless access points are deployed in manufacturing environments, then wireless communication capability is improved, but reliability deteriorates due to power outages, equipment malfunctions, noise pollution, and network congestion
Solution Approach 1:
The system employs self-organizing networks where access points automatically configure themselves and adapt to environmental changes without manual intervention. The reinforcement learning model enables the network to self-optimize by learning from observed states and rewards, automatically adjusting operational characteristics to maintain reliability despite power outages, equipment malfunctions, noise pollution, or network congestion
Solution Approach 2:
The patent implements dynamic adaptation through reinforcement learning, where the system continuously observes network states, evaluates actions based on rewards, and adjusts operational characteristics in real-time. This dynamic approach allows the wireless network to respond to changing conditions such as power outages, equipment failures, and interference, transforming the static network configuration into an adaptive system that maintains reliability under varying conditions
2Productivity
If manual control of wireless access points is used, then device complexity is reduced, but productivity deteriorates due to inability to rapidly adapt to environmental changes
Solution Approach 1:
The system implements feedback loops where the reinforcement learning model continuously observes network states, evaluates the outcomes of actions through rewards, and adjusts operational characteristics accordingly. This feedback mechanism enables automatic adaptation to environmental changes without manual intervention, allowing the network to respond dynamically to power outages, equipment malfunctions, noise pollution, and network congestion while maintaining manageable complexity through automated decision-making
3Reliability
If reinforcement learning is implemented to dynamically adjust operational characteristics, then reliability is improved, but device complexity increases
Solution Approach 1:
The reinforcement learning model operates autonomously, observing network states, selecting actions, and adjusting operational characteristics without external control. This self-service capability enables the system to maintain reliable connectivity by automatically adapting to environmental changes such as power outages, equipment failures, and interference, while the modular architecture keeps implementation complexity manageable through standardized interfaces and pre-defined action spaces
Data Source
AI summary
A method for controlling one or more operational characteristics of a plurality of wireless access points of a manufacturing environment includes generating a plurality of state vectors based on network data associated with the plurality of wireless access points and identifying a set of actions from among a plurality of actions and associated with the plurality of state vectors. The method includes determining a reward for each action from among the set of actions, selecting a target action from among the set of actions based on the reward associated with each action from among the set of actions, and selectively adjusting the one or more operational characteristics of the plurality of wireless access points based on the target action.