UE AI Capability Updates for Adaptive Wireless Configuration
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Solution Overview
Problem
Existing wireless communication systems lack efficient mechanisms for updating and configuring artificial intelligence (AI) or machine learning (ML) capabilities in user equipment (UE) and base stations, leading to suboptimal performance and resource utilization.
Innovation Solution
Implementing methods and apparatuses for transmitting and receiving updates to previously reported sets of UE AI or ML capabilities, along with configurations for performance optimization, using processors and memory-based systems in both UE and base stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If UE AI/ML capabilities are updated dynamically, then operational efficiency is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic capability updates by allowing UEs to autonomously determine when to report capability changes based on predefined triggers (e.g., resource availability thresholds, timing requirements). This dynamic approach replaces static capability reporting, enabling the system to adapt to changing conditions without manual intervention, thus improving operational efficiency while managing complexity through automated decision-making
Solution Approach 2:
The patent establishes a feedback mechanism where UEs monitor their own resource availability and capability status, then automatically report updates to the network when changes occur. This closed-loop feedback system enables the network to receive timely capability information and adjust configurations accordingly, improving operational efficiency while the standardized feedback protocol manages the complexity of capability tracking
2Reliability
If capability updates are transmitted frequently, then configuration accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent applies partial action by implementing selective capability updates rather than continuous reporting. UEs are configured with thresholds and triggers that determine when an update is necessary (e.g., only when resource availability changes significantly or timing requirements are violated). This approach ensures configuration accuracy is maintained by updating only when needed, while avoiding excessive updates that would waste power and communication resources
Solution Approach 2:
The patent utilizes parameter changes in capability reporting by allowing UEs to update specific capability parameters (e.g., buffer size, processing speed, supported AI/ML operations) based on their current resource state. Rather than transmitting complete capability sets frequently, the system updates individual parameters when they change, maintaining configuration accuracy while reducing the energy and communication overhead associated with full capability reports
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit an update to a previously reported set of UE artificial intelligence (AI) or machine learning (ML) capabilities. The UE may receive a configuration associated with performance of UE AI or ML operations based at least in part on the update. Numerous other aspects are described.