AI Protocol Layer Decoupling for Wireless Communication Flexibility
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Solution Overview
Problem
The flexibility of AI functions in wireless communication systems is limited by the existing RRC protocol layer procedure, leading to insufficient and limited AI data for training, resulting in low accuracy and high development and deployment costs.
Innovation Solution
Introducing an independent AI protocol layer above the RRC protocol layer, allowing for flexible configuration of AI parameters and data collection methods, enabling the separation of AI functions from communication functions to improve data quality and reduce protocol layer complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI functions are integrated into the existing RRC protocol layer procedure, then the system structure remains simple, but the flexibility of AI function configuration is poor and adaptability is limited
Solution Approach 1:
The patent segments the protocol architecture by introducing an independent AI protocol layer above the RRC protocol layer. This separation allows AI functions to be configured and managed independently from communication functions, improving flexibility without fundamentally altering the existing RRC protocol structure. The AI protocol layer can be added or modified without impacting the underlying communication protocols.
Solution Approach 2:
The patent adds a new dimensional layer (AI protocol layer) above the existing protocol stack. This vertical expansion allows AI-specific parameters and configurations to be handled at the appropriate layer, resolving the contradiction by providing flexibility in the AI dimension while preserving the simplicity of the communication protocol layers below.
2Quantity of substance
If MDT protocol is used for AI data collection, then the implementation is straightforward using existing procedures, but the quantity and type of obtained AI data are insufficient
Solution Approach 1:
The patent segments data collection requirements by introducing AI-specific protocol elements separate from MDT. This allows tailored configuration of AI data collection parameters (such as AI model identifiers, training data types, and collection frequencies) that are specifically designed for AI training needs rather than general MDT requirements.
Solution Approach 2:
The AI protocol layer is designed to be multi-functional, supporting various AI data collection scenarios including model training, validation, and optimization. It can handle diverse data types and configurations while building upon existing protocol infrastructure, thus increasing data quantity and variety without proportionally increasing implementation complexity.
3Adaptability or versatility
If existing RRC protocol layer procedure is modified to support new AI functions, then AI functionality can be updated, but the technical difficulty is great and development and deployment costs remain high
Solution Approach 1:
The patent segments AI function management from the core RRC protocol by placing AI-specific configurations and procedures in the upper AI protocol layer. This allows AI functions to be updated, added, or removed through AI protocol modifications without touching the stable, well-tested RRC protocol layer below, significantly reducing technical difficulty and deployment costs.
Solution Approach 2:
The AI protocol layer acts as an intermediary between AI applications and the underlying communication protocols. This mediator layer absorbs the complexity of AI function updates and translations into RRC-compatible messages, shielding the core protocol from modification needs and reducing both technical difficulty and costs.
Data Source
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AI summary
This application provides a communication method and apparatus. An independent AI protocol layer is introduced to decouple an AI function from an existing RRC protocol layer procedure, to resolve a problem of poor flexibility caused by implementing the AI function through the RRC procedure. The communication method and apparatus may be applied to a 5G system, a 4G system, and a V2X system. The method includes: An AI protocol layer of a first access network device generates an AI parameter, and sends the AI parameter to a terminal device. Then, the first access network device receives, from the terminal device, AI data obtained based on the AI parameter, to complete network optimization. The AI protocol layer of the first access network device is an upper layer of an RRC protocol layer of the first access network device, an AI protocol layer of the terminal device is an upper layer of an RRC protocol layer of the terminal device, and the AI parameter is used to indicate the AI data that needs to be obtained and an AI data obtaining manner.