Neural Network Channel Access for Collision-Aware Shared Wireless Links
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Solution Overview
Problem
In wireless networks, simultaneous packet transmission by multiple nodes leads to communication collisions, reducing channel throughput and increasing node communication delay, particularly in distributed scenarios where no central node performs unified scheduling.
Innovation Solution
A node uses a target neural network to predict channel access success by inputting network status information from a target period, including busy/idle channel state and interfering node data, to determine the probability of successful packet transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If nodes randomly access the shared channel or use traditional prediction methods, then the channel access decision process is simple, but the probability of successfully accessing the channel is low and channel throughput is low
Solution Approach 1:
The system performs preliminary actions by collecting network status information from multiple time points before the current moment and using a neural network to predict channel access outcomes in advance. This allows nodes to make informed access decisions based on predicted success probabilities rather than random access or simple traditional methods, thereby improving channel throughput while managing complexity through structured prediction.
Solution Approach 2:
The system implements feedback by continuously monitoring network status information including busy/idle states of the shared channel, successfully sending nodes, and sending failure nodes. This feedback loop feeds into the neural network prediction model, enabling dynamic adjustment of access decisions based on real-time channel conditions, thus improving throughput while maintaining manageable complexity through automated feedback processing.
2Loss of time
If nodes use traditional channel access methods, then the decision process is simple, but communication delay increases due to collisions
Solution Approach 1:
The system performs preliminary prediction of channel access success probability before actual transmission attempts. By using a neural network to analyze historical network status information and predict outcomes in advance, nodes can avoid transmissions likely to fail, thereby reducing communication delay caused by collisions while managing complexity through structured prediction rather than trial-and-error approaches.
Solution Approach 2:
The system uses feedback from monitoring successful and failed transmissions to continuously improve prediction accuracy. By incorporating this feedback into the neural network model, the system learns from past collisions and adjusts future access decisions, reducing communication delay over time while maintaining manageable complexity through automated learning rather than manual optimization.
3Reliability
If nodes collect and analyze extensive network status information, then channel access success rate improves, but the processing complexity increases
Solution Approach 1:
The system applies universality by using a single neural network model to perform multiple functions: predicting channel access success probability, analyzing network status information from multiple time points, and determining optimal access decisions. This multi-functional approach improves channel access success rate while managing complexity by consolidating multiple processing tasks into one unified model rather than requiring separate specialized processors for each function.
Solution Approach 2:
The system changes parameters by transforming raw network status information (busy/idle states, successful/failed transmissions at different time points) into meaningful prediction inputs for the neural network. By changing the parameter representation from raw observational data to structured prediction features, the system improves access success rate while managing processing complexity through effective parameter transformation and feature engineering.
Data Source
AI summary
The present disclosure relates to a channel access method and a channel access apparatus. In an example method, a first node obtains network status information in a target period. The network status information includes a first time segment and a second time segment. The first node inputs the network status information into a target neural network, to obtain a first prediction value and a second prediction value. The first node sends a to-be-sent packet to a second node through the shared channel in response to determining that the first prediction value is greater than the second prediction value.