ML Model Adaptation Coordination in Wireless Networks
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Solution Overview
Problem
In wireless communication networks, especially in 5G and beyond, machine learning (ML) models used in user devices face challenges in simultaneous inference and adaptation due to resource constraints, leading to performance degradation and inefficiencies in adapting to changing radio conditions.
Innovation Solution
A method where a user device and a network node agree on a set of ML functionality adaptation parameters, including adaptation cycles and validity periods, allowing the device to perform partial adaptation during each cycle while using the ML model in inference mode between cycles, ensuring consistent inputs and minimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the user device performs ML model adaptation continuously, then the model accuracy improves, but the device performance degrades and resource consumption increases
Solution Approach 1:
The patent implements periodic adaptation cycles where the ML model is adapted at regular intervals rather than continuously. The network node configures adaptation cycle parameters that define specific time windows for adaptation, allowing the device to alternate between adaptation mode and inference mode, thus maintaining model accuracy while preserving device performance.
Solution Approach 2:
The adaptation process is segmented into discrete adaptation cycles with defined start and end times. Each cycle is a separate, manageable unit that can be configured independently. This segmentation allows the system to perform adaptation in controlled bursts rather than continuously, reducing overall resource consumption while maintaining effectiveness.
2Adaptability or versatility
If the user device performs ML model adaptation frequently, then the model adapts better to changing radio conditions, but the network and device performance are negatively impacted
Solution Approach 1:
The system uses periodically spaced adaptation cycles configured by the network node. These cycles occur at optimized intervals that balance the need for adaptability with performance requirements. The periodic structure ensures the model adapts to changing radio conditions without excessive frequency that would degrade performance.
Solution Approach 2:
The adaptation cycle parameters are dynamically configurable by the network node based on current network conditions, device state, and service requirements. This dynamic adjustment allows the system to optimize the frequency and duration of adaptation cycles in real-time, achieving better adaptability while minimizing performance impact.
3Manufacturing precision
If the user device pauses RAN-related function for ML adaptation, then the adaptation can be performed thoroughly, but the service continuity is interrupted
Solution Approach 1:
The adaptation is performed in periodic cycles with controlled duration, allowing thorough adaptation within each cycle while limiting the time impact on service. The cyclic structure ensures that adaptation is completed within defined time windows, after which normal RAN functions resume.
Solution Approach 2:
The network node pre-configures adaptation cycle parameters including duration and timing before adaptation begins. This preliminary configuration allows the device to perform thorough adaptation within the predetermined time frame, ensuring that service interruption is minimized and predictable.
Data Source
AI summary
A method includes confirming, by a user device with a network node, a set of machine learning (ML) functionality adaptation parameters for the user device to perform adaptation of a ML functionality associated with at least one ML model that is used by the user device to perform a radio access network (RAN)-related function. The set of ML functionality adaptation parameters indicate at least one adaptation cycle during which the user device is to perform the ML functionality adaptation and a validity period for which the set of ML functionality adaptation parameters are valid. The method also includes performing, by the user device, adaptation of the ML functionality during the at least one adaptation cycle.