Multi-Block Machine Learning Model Capability Indication
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Solution Overview
Problem
Current wireless communication systems face inefficiencies in updating machine learning applications for user equipment (UE) due to the large size of end-to-end models, which consume resources and do not account for UE capabilities, leading to either conservative or overly complex models.
Innovation Solution
A multi-block machine learning application is used, comprising a backbone block and a task-specific block, where the UE transmits capability information to a base station, allowing the base station to configure the appropriate model configuration based on the UE's capabilities, optimizing the machine learning model for efficient resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If end-to-end machine learning models are used for wireless communication, then communication performance can be improved, but the large model size consumes excessive system resources
Solution Approach 1:
The patent divides the end-to-end machine learning model into two separate blocks: a backbone block and a task-specific block. The backbone block contains shared features and knowledge that can be reused across different tasks, while the task-specific block handles particular communication functions. This segmentation reduces the overall model size needed at the UE while maintaining performance through the reusable backbone.
Solution Approach 2:
The backbone block is designed to be universal and task-agnostic, serving multiple different task-specific blocks for various communication tasks such as channel estimation, signal detection, and modulation classification. This multi-functionality allows a single backbone model to support diverse applications without requiring separate large models for each task.
2Measurement precision
If large machine learning models are deployed, then model accuracy can be improved, but UE resource consumption increases
Solution Approach 1:
By segmenting the model into backbone and task-specific components, the computationally intensive backbone can be optimized once and reused, while lighter task-specific blocks handle individual functions. This reduces the total computational load on UE resources compared to deploying multiple full-sized models.
Solution Approach 2:
The patent enables dynamic configuration of model parameters including the backbone block configuration and task-specific block configuration based on UE capabilities and communication conditions. This allows the system to adapt model complexity to match available UE resources, maintaining accuracy when resources permit while reducing consumption when resources are limited.
3Ease of manufacture
If machine learning models are configured without considering UE capabilities, then deployment simplicity is maintained, but model suitability decreases leading to either conservative or overly complex configurations
Solution Approach 1:
The patent implements a capability indication mechanism where the UE reports its capabilities (processing power, memory, supported operations) to the base station. The base station uses this feedback to appropriately configure the backbone block and task-specific block parameters, ensuring the model is neither too conservative nor too complex for the specific UE.
Solution Approach 2:
The model configuration is made dynamic and adaptive rather than static. The backbone block configuration and task-specific block configuration can be adjusted based on real-time UE capability reports and communication conditions, allowing the system to optimize for each UE's specific capabilities while maintaining a standardized deployment framework.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. A user equipment (UE) may indicate a support for an end-to-end multi-block machine learning application, a first UE capability for a backbone block of the multi-block machine learning application that makes up one or more front-end layers (e.g., one or more backbone layers), a second UE capability for a task-specific block of the multi-block machine learning application that makes up the end layer (s) of the end-to-end model (e.g., one or more task-specific layers), or a combination thereof. In some examples, the UE may transmit separate indications for the first UE capability and for the second UE capability. Additionally or alternatively, the UE may transmit a general machine learning capability indication, where a base station then determines the first UE capability for the base stage and the second UE capability for the task-specific stage from the general machine learning capability.