Transfer Learning for WTRU Channel State Model Adaptation
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Solution Overview
Problem
Existing wireless communication systems face challenges in effectively leveraging artificial intelligence and machine learning models across different Wireless Transmit/Receive Units (WTRUs) due to compatibility issues and suboptimal model suitability, leading to inefficiencies in channel state information enhancement.
Innovation Solution
Implementing a method where a WTRU receives AI/ML model configuration information, determines unsuitability, trains a local AI/ML model, and transfers trained parameters to a network node, utilizing a convergence threshold to enhance model suitability and compatibility across WTRUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If pre-trained AI/ML models are deployed from network node to WTRU, then model training time is reduced, but model suitability and compatibility for specific WTRU conditions deteriorates
Solution Approach 1:
The system performs preliminary training of AI/ML models at the network node using aggregated channel state information from multiple WTRUs. This pre-trained model is then transferred to individual WTRUs, which perform only fine-tuning with their local data. This preliminary action at the network level reduces the training time for individual WTRUs while the subsequent fine-tuning ensures model suitability for specific WTRU conditions.
Solution Approach 2:
The system implements a two-stage training approach where the network node performs global training using aggregated data from multiple WTRUs, creating a base model with general characteristics. Each WTRU then performs local fine-tuning using its own channel state information, adapting the model to local conditions. This combination of global pre-training and local fine-tuning resolves the contradiction between training time reduction and model suitability.
2Adaptability or versatility
If WTRU trains local AI/ML model independently, then model suitability for specific conditions is improved, but training resources and time consumption increases
Solution Approach 1:
The system merges the training process into two components: global pre-training at the network node using aggregated data from multiple WTRUs, and local fine-tuning at each WTRU using its own data. This combination allows WTRUs to benefit from the computational power of the network node for the resource-intensive global training, while performing only lightweight fine-tuning locally, thus reducing overall training resource consumption while maintaining model suitability.
3Measurement precision
If AI/ML model parameters are transferred to multiple WTRUs, then channel state information enhancement is improved, but model compatibility across different WTRU conditions deteriorates
Solution Approach 1:
The system transfers pre-trained model parameters to multiple WTRUs and then allows each WTRU to modify these parameters through fine-tuning using its own channel state information. This parameter adjustment process enables the model to maintain the general enhancements from pre-training while adapting to specific WTRU conditions, thus resolving the contradiction between channel state information enhancement and model compatibility.
4Productivity
If transfer learning is implemented across WTRUs, then training efficiency is improved, but model performance for specific WTRU conditions may deteriorate
Solution Approach 1:
The network node performs preliminary training of AI/ML models using aggregated channel state information from multiple WTRUs, creating a robust base model. This pre-trained model is then transferred to individual WTRUs for fine-tuning. The preliminary action of global pre-training improves training efficiency by leveraging shared patterns across multiple WTRUs, while the subsequent local fine-tuning ensures reliable performance for specific WTRU conditions.
Data Source
AI summary
Methods and apparatus for leveraging transfer learning of one Wireless Transmit/Receive Unit (WTRU) to benefit another WTRU are provided. One method may include the WTRU receiving AI/ML model configuration information indicating one or more AI/ML models available from the network node, a profile associated with the AI/ML models, and a training convergence threshold. Based at least on the profile(s), the WTRU determining that the one or more AI/ML models are not suitable for use by the WTRU, and sending first information indicating that the one or more AI/ML models are not suitable for the WTRU and/or that the WTRU will be training a local AI/ML model. The method may then include training the local AI/ML model according to the convergence threshold, receiving a request to transfer AI/ML model parameters, and sending an indication of the AI/ML model parameters associated with the trained local AI/ML model to the network node.