Neural Network Channel Access Prediction for Wireless Collision Avoidance
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Solution Overview
Problem
Current channel access methods in wireless networks, such as CSMA/CA, result in low system throughput and high latency due to collisions on shared channels, as each station lacks the capability to predict other stations' access behaviors.
Innovation Solution
A channel access method that employs a neural network trained by an access point using action information from multiple stations to improve each station's prediction capability for accessing the channel, reducing collisions and enhancing system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CSMA/CA mechanism is used for channel access, then collision avoidance is achieved through randomization, but system throughput is low and latency is high
Solution Approach 1:
The patent implements a feedback mechanism where the access point collects action information from multiple stations, trains a neural network model, and distributes the trained model back to stations. This feedback loop enables stations to learn from collective experience and improve their channel access predictions, resolving the contradiction between collision avoidance and system throughput by transforming random guessing into informed decision-making.
Solution Approach 2:
Stations autonomously update their own neural network models using the training results received from the access point. This self-service mechanism allows each station to independently improve its prediction capability without requiring centralized control of the entire system, enabling scalable deployment while maintaining both collision avoidance and high throughput.
2Reliability
If CSMA/CA mechanism is used for channel access, then collision avoidance is achieved through randomization, but latency is high
Solution Approach 1:
The neural network model performs preliminary analysis of channel conditions and predicts the likelihood of successful transmission before the station actually attempts to access the channel. This preliminary action allows stations to make informed decisions about whether to transmit, avoiding unnecessary waiting and retransmissions, thereby reducing latency while maintaining collision avoidance.
3Device complexity
If neural network training uses only local action information, then processing is simple, but prediction capability is insufficient
Solution Approach 1:
The access point merges action information from multiple stations into a unified training dataset. By combining data from multiple sources, the neural network learns from diverse scenarios and patterns, significantly improving prediction capability while the access point handles the computational complexity centrally, keeping individual station processing simple.
Data Source
AI summary
In the method, an access point AP receives action information separately reported by N stations STAs, where N pieces of action information are used to determine a training result of a first neural network of each STA, and N is a positive integer. The AP determines the training result of the first neural network of each STA based on the N pieces of action information, and sends the training result of the first neural network of each STA to the corresponding STA. The training result of the first neural network of each STA is determined based on the action information reported by the N STAs, instead of only the action information of the STA. This can improve a prediction capability of the first neural network, help improve a capability of each STA to predict channel access behavior of another STA, and improve a system throughput and reduce a communication latency.