Distributed ML Model Selection for Wireless UE Power Management
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Solution Overview
Problem
Wireless communication systems face challenges in maintaining accurate signal transmission and reception while minimizing power consumption and battery strain in user equipment devices, as increasing functionality demands improved power management and efficient communication protocols.
Innovation Solution
The implementation of enhanced distributed machine learning model maintenance in wireless communication systems, where user equipment devices receive reference signals, perform measurements, and transmit compressed results to a server for model identification and selection, allowing for efficient communication using compatible encoder-decoder pairs and minimizing power consumption through metadata-driven model ID selection and continuous training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed machine learning model maintenance is implemented, then communication efficiency is improved, but device complexity increases
Solution Approach 1:
The system divides the machine learning model maintenance into distributed components: UEs perform local measurements and compress results, servers handle model training and selection, and network nodes coordinate communication. This segmentation allows communication efficiency to improve through parallel processing while managing device complexity by distributing tasks across multiple entities.
Solution Approach 2:
Servers act as intermediaries between UEs and the network, receiving compressed measurement results, training models, and returning model identifiers. This intermediary layer simplifies the interaction complexity for individual UEs while enabling efficient distributed model maintenance across the network.
2Measurement precision
If more functionality is added to UE devices, then communication accuracy is improved, but power consumption increases
Solution Approach 1:
The system performs measurements and model training only when necessary - UEs transmit compressed measurement results to servers, which then determine if model updates are needed. This partial action approach maintains communication accuracy through selective measurements and training while reducing power consumption by avoiding continuous full-scale operations.
Solution Approach 2:
Servers automatically train models and select optimal model identifiers based on received measurement results, eliminating the need for UEs to continuously perform complex processing. This self-service mechanism maintains measurement precision while significantly reducing UE power consumption.
3Manufacturing precision
If continuous model training is performed, then model accuracy is improved, but energy consumption increases
Solution Approach 1:
The system performs model training periodically based on accumulated measurement results rather than continuously. Servers receive compressed measurements from UEs, train models when sufficient data is available, and update model identifiers as needed. This periodic training maintains model accuracy while significantly reducing energy consumption compared to continuous training.
Solution Approach 2:
Measurement results are compressed and stored before model training occurs. This preliminary compression and accumulation of data allows servers to perform training more efficiently when ready, improving model accuracy while reducing the immediate energy burden on UEs.
Data Source
AI summary
A user equipment (UE) may receive, from a network node one or more reference signals and perform one or more measurements using the one or more reference signals. The UE may compress the one or more measurements into one or more measurement results and transmit the one or more measurement results to a server. The UE may request, from the server, at least one of one or more identifiers (IDs) or one or more models associated with the one or more IDs. The UE may receive, from the server, the at least one of the one or more IDs or one or more models, wherein the one or more IDs are provided based on the one or more measurement results. The UE may transmit, to the network node, an ordered list of the one or more IDs, receive a response from the network node indicating selection of an ID of the one or more IDs, and communicate with the network node using the ID.