Wireless Communication AI Capability Signaling for Flexible Deployment
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Solution Overview
Problem
Existing communications systems do not consider AI/ML features in their capability definitions, necessitating a reorganization and definition of AI/ML capabilities for refined application of AI/ML functions.
Innovation Solution
A wireless communication method and device that transmits and receives AI/ML related capability information, including M pieces of information to indicate various AI/ML capabilities, enabling flexible interaction and deployment of AI/ML functions between devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI/ML features are integrated into communications systems, then the functionality and capabilities of the system are improved, but the complexity of capability definition and system configuration increases
Solution Approach 1:
The patent segments AI/ML capabilities into distinct, identifiable components (functional entity deployment, data collection, data reporting, data measurement, model training, model reasoning, model handover, model activation/deactivation, performance monitoring, model transmission, model update). This segmentation allows each capability to be independently defined, configured, and managed, reducing the overall complexity of capability definition while maintaining comprehensive AI/ML functionality.
Solution Approach 2:
The patent introduces dynamic capability configuration where devices can selectively enable or disable specific AI/ML capabilities based on operational needs. The capability information can be dynamically updated and reconfigured, allowing the system to adapt its AI/ML functionality without requiring complete system redesign, thus managing complexity through flexibility.
2Measurement precision
If comprehensive AI/ML capability information is transmitted between devices, then the accuracy of capability matching is improved, but the amount of data transmission and processing increases
Solution Approach 1:
The patent applies local quality by transmitting only the specific AI/ML capability information relevant to each device's actual capabilities and operational context. Rather than transmitting all possible capability data, the system selectively communicates only the necessary capability parameters (such as supported AI/ML models, computational resources, data processing abilities), reducing data volume while maintaining accurate capability matching.
Solution Approach 2:
The patent employs parameter changes by representing capabilities through standardized, compact parameter formats. Capability information is encoded using efficient data structures and parameter representations that minimize transmission overhead while preserving the essential information needed for accurate capability matching and device selection.
Data Source
AI summary
Embodiments of this application provide a communications device. The communications device is a first device, the first device includes a processor and a memory, the memory is configured to store a computer program, and the processor is configured to invoke the computer program stored from the memory and run the computer program, to cause the first device to perform transmitting a first message to a second device, where the first message includes first artificial intelligence AI/machine learning ML related capability information, the first AI/ML related capability information is associated with the first device, the first AI/ML related capability information comprises M pieces of information, and M is a positive integer.