Wireless ML Model Coordination via Functionality Indication
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Solution Overview
Problem
In wireless communication systems, coordinating machine learning (ML) model operations between user equipment (UE) and network devices for compatibility is challenging, especially when different ML models are used for generating and decoding channel state information (CSI), requiring a technique to ensure seamless communication while maintaining model confidentiality.
Innovation Solution
The system provides functionality-based assistance information and indications between wireless devices to coordinate ML model usage, allowing UE and network devices to detect changes in operations and parameters, and adjust accordingly, ensuring compatibility without disclosing specific ML models used.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ML models are used for generating and decoding CSI information, then communication efficiency is improved, but model compatibility coordination becomes complex
Solution Approach 1:
The system performs preliminary actions by having each device indicate its supported ML model operations and parameters to the other device before actual CSI generation and decoding operations begin. This advance coordination allows both devices to ensure compatibility without requiring complex real-time negotiation, thus improving communication efficiency while managing coordination complexity.
Solution Approach 2:
The system implements feedback mechanisms where devices exchange information about their supported ML model operations and parameters. Each device provides feedback about its capabilities, and based on this feedback, both devices can adjust their operations to ensure compatibility. This feedback loop enables efficient ML-based communication while keeping the coordination process manageable through structured information exchange.
2Reliability
If ML model operations are coordinated between UE and network device, then compatibility is ensured, but information exchange overhead increases
Solution Approach 1:
The system extracts and indicates only the essential information needed for compatibility coordination - specifically, the supported ML model operations and parameters - without disclosing the actual ML model identities or proprietary details. This selective information extraction ensures compatibility is achieved while minimizing unnecessary information exchange overhead.
Solution Approach 2:
The system manages information exchange by changing the parameters of what is communicated - instead of exchanging complete ML model specifications or identities, the system exchanges simplified parameters indicating supported operations and parameter types. This parameter-based approach ensures compatibility coordination while reducing information overhead.
3Reliability
If specific ML models are disclosed for coordination, then compatibility is achieved, but model confidentiality is compromised
Solution Approach 1:
The system introduces an intermediary approach where instead of directly disclosing specific ML model identities, devices exchange information about supported operations and parameters through a standardized indication mechanism. This intermediary layer of abstraction maintains compatibility coordination while protecting the actual ML model identities and proprietary information from being disclosed.
Data Source
AI summary
An apparatus, method and computer-readable media are disclosed for performing wireless communications. For example, a process for wireless communications is provided. The process can include receiving a first set of operations supported by one or more machine learning models of a network entity, receiving a first set of parameters associated with the first set of operations, wherein the first set of parameters are supported by the one or more machine learning models of the network entity, selecting a machine learning model for performing a first operation of the first set of operations based on the first set of parameters, detecting a change in at least one of: the first operation, or a parameter associated with the first operation, and transmitting an indication to change the first operation based on the detected change.