UE-Side AI/ML Applicability Reporting for NR Network Signaling
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Solution Overview
Problem
Current mobile communication systems face challenges in efficiently managing and reporting UE-side functionality, particularly in 5G and beyond networks, which are expected to operate in licensed and unlicensed spectrums, requiring enhanced techniques for seamless wireless connectivity and functionality management.
Innovation Solution
Implementing UE-side applicable functionality reporting and management techniques, including AI-assisted communication architectures, proactive and reactive signaling frameworks, and UE-network collaborative decision-making based on UE assistance information, to optimize network operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If UE-side applicable functionality reporting and management techniques are implemented, then network performance and resource utilization are improved, but device complexity and signaling overhead increase
Solution Approach 1:
The UE functionality reporting is segmented into different types (capability information, applicability information, additional condition information) that can be reported separately and selectively. This allows the network to receive only the necessary information for specific scenarios, improving network performance while avoiding unnecessary complexity in both UE and network devices.
Solution Approach 2:
The UE performs preliminary assessment of its own functionality applicability to the current network environment and pre-packs relevant capability and condition information. This preliminary action enables more efficient network decision-making without requiring complex real-time analysis, thus improving network performance while keeping device complexity manageable through structured pre-processing.
2Reliability
If proactive and reactive signaling frameworks are implemented, then connectivity and functionality management are improved, but signaling overhead and latency increase
Solution Approach 1:
The signaling framework employs periodic reporting mechanisms where the UE reports its capability and applicability information at predetermined intervals or triggered by specific events. This periodic action ensures reliable connectivity management while controlling latency by avoiding continuous signaling, thus balancing reliability and time loss.
Solution Approach 2:
The framework implements feedback mechanisms where the network responds to UE reports with specific requests for additional information or configuration adjustments. This targeted feedback approach improves connectivity management efficiency by exchanging only necessary information, reducing overall signaling overhead and latency compared to comprehensive continuous signaling.
3Productivity
If UE-network collaborative decision-making is implemented, then resource utilization is optimized, but device complexity and processing requirements increase
Solution Approach 1:
The UE autonomously performs self-assessment of its capabilities and applicability to different network functions, and self-packages the relevant information for network decision-making. This self-service approach optimizes resource utilization by providing the network with accurate UE capability information while avoiding the need for complex network-side assessment mechanisms, thus balancing resource optimization with manageable processing requirements.
Solution Approach 2:
The patent introduces standardized information structures (capability information, applicability information, additional condition information) as intermediaries between UE and network. These structured intermediaries simplify the collaborative decision-making process by organizing complex UE capabilities into manageable formats, enabling efficient resource utilization without overwhelming processing requirements on either side.
Data Source
AI summary
A UE is configured for applicable functionality reporting in an NR network. The processing circuitry of the UE is to decode a first RRC signaling including a UE capability inquiry. The processing circuitry is to encode a UE capability information message indicating an at least one artificial intelligence/machine learning (AI/ML) functionality supported by the UE. The processing circuitry is to decode a second RRC signaling to configure reporting conditions for reporting of UE assistance information. The processing circuitry is to encode the UE assistance information for transmission based on the reporting conditions. The UE assistance information including at least one UE applicable functionality indicating whether the at least one AI/ML functionalities is applicable for inferencing by the UE.