AI-Driven Channel Access Protocol for Wireless Medium
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Solution Overview
Problem
Existing wireless communication networks face performance issues due to improper setting of the contention window, leading to collisions and reduced network efficiency.
Innovation Solution
The implementation of artificial intelligence to predict an idle time for a wireless medium and use a machine learning model to determine optimal minimum and maximum contention window values, which are then wirelessly transmitted to devices for use during future communication periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the contention window is set improperly, then collisions occur on the network, but adjusting the contention window requires accurate prediction of wireless medium idle time
Solution Approach 1:
The system uses autonomous machine learning models that automatically learn and predict wireless medium idle times without manual configuration. The models continuously adapt to changing network conditions, performing self-optimization of contention window parameters based on observed patterns in the wireless environment.
Solution Approach 2:
The system implements feedback loops where actual collision data and transmission outcomes are fed back into the machine learning models. This feedback enables the models to continuously refine their predictions of idle times and adjust contention window settings dynamically, improving collision avoidance over time.
2Productivity
If the contention window range is optimized to reduce collisions, then network performance improves, but this requires dynamic adjustment based on future idle time predictions
Solution Approach 1:
The machine learning models perform preliminary predictions of wireless medium idle times before actual transmission attempts. By forecasting future idle periods in advance, the system can proactively optimize contention window settings, allowing devices to prepare their transmission schedules and avoid collisions before they occur.
Solution Approach 2:
The system dynamically adjusts contention window parameters based on real-time predictions from the machine learning models. The contention window range is not fixed but adapts continuously to changing network conditions, traffic patterns, and predicted idle times, enabling optimal performance under varying circumstances.
Data Source
AI summary
The present disclosure describes a network that uses artificial intelligence to set a channel access protocol. A network device includes one or more memories and one or more processors communicatively coupled to the one or more memories. The one or more processors, individually or collectively predict, using a first machine learning model, an idle time for a wireless medium for a future period of time, predict, using a second machine learning model and based on the predicted idle time, a minimum contention window value and a maximum contention window value for the wireless medium, and wirelessly transmit an instruction to use the minimum contention window value and the maximum contention window value when communicating over the wireless medium during the period of time.