Grouping User Equipments for Multicast AI Configuration
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Solution Overview
Problem
The existing communication systems face inefficiencies due to high network loads caused by specific negotiations of artificial intelligence models between base stations and user equipments, leading to reduced communication system efficiency.
Innovation Solution
The method involves user equipments reporting artificial intelligence parameters for universal communications identifier capabilities, allowing the base station to group them and multicast artificial intelligence configurations, reducing the need for UE-specific negotiations and thereby lowering network loads through group-wise transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user equipments negotiate artificial intelligence models specifically with the base station, then the AI configuration can be customized for each UE, but the network load increases significantly
Solution Approach 1:
The patent groups multiple user equipments with similar AI capabilities into clusters, and negotiates AI models at the group level rather than individually for each UE. This merging approach reduces the number of negotiation transactions while maintaining appropriate AI configuration for each device group.
Solution Approach 2:
The patent creates universal AI models that can serve multiple user equipments with similar capabilities. Instead of customizing AI models for each individual UE, the system develops multi-functional AI configurations that work across groups of devices, reducing overall system complexity and network load.
2Manufacturing precision
If user equipments negotiate artificial intelligence models specifically with the base station, then each UE can have optimized AI parameters, but the communication efficiency decreases
Solution Approach 1:
The patent combines multiple UEs into negotiation groups, allowing AI parameter optimization at the group level. This approach maintains sufficient AI parameter precision for each device while dramatically improving communication efficiency by reducing the number of individual negotiation exchanges required.
3Measurement precision
If user equipments report individual AI parameters to the base station, then the base station can configure AI models precisely for each UE, but the overhead on the air interface increases
Solution Approach 1:
The patent merges AI parameter reporting from multiple UEs into group-level reports. Instead of each UE individually reporting its AI capabilities to the base station, grouped UEs provide consolidated capability information, maintaining accurate assessment while reducing air interface overhead.
Solution Approach 2:
The patent develops universal AI capability descriptors that represent groups of UEs with similar characteristics. These multi-functional descriptors capture the essential AI capabilities of multiple devices without requiring separate detailed reports from each individual UE.
Data Source
AI summary
The present disclosure relates to a method of artificial intelligence (AI) setting within a communication system with a base station and several user equipments (UEs). The user equipments each report an artificial intelligence parameter for universal communications identifier (UCI) capabilities to the base station. The user equipments are grouped by the base station based on the artificial intelligence parameter for the universal communications identifier capabilities. The base station multicasts artificial intelligence configuration to each group of user equipments separately. Further, a communication system is described.