AI/ML-Driven Dynamic TDD Configuration for Radio Conditions
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Solution Overview
Problem
Current mobile radio systems face challenges in dynamically adapting TDD patterns and slot formats to meet diverse application requirements in real-time, leading to inefficiencies and interference due to static configurations defined by 3GPP standards.
Innovation Solution
Implementing an AI/ML model to dynamically select and modify TDD patterns and slot formats in response to changing application behavior and radio conditions, using supervised learning with historical network data to optimize configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static TDD configurations defined by 3GPP standards are used, then device complexity is reduced and ease of operation is improved, but adaptability to diverse application requirements deteriorates and spectrum efficiency is reduced
Solution Approach 1:
The patent implements dynamic TDD configuration where the network element can change TDD patterns and slot formats in real-time based on detected application requirements and radio conditions, transforming the static configuration system into a dynamic one that adapts to varying demands
Solution Approach 2:
The system employs feedback mechanisms by detecting application behavior and radio conditions, then using this information to select and adjust optimal TDD configurations, creating a closed-loop control system that continuously optimizes performance
2Productivity
If static TDD configurations are used, then implementation is simpler, but spectrum efficiency deteriorates due to inability to adapt to changing conditions
Solution Approach 1:
The patent introduces an AI/ML model as an intermediary component that bridges the gap between simple static configurations and complex dynamic adaptation, using machine learning to automatically determine optimal TDD patterns based on input conditions without requiring complex manual configuration
3Object-affected harmful factors
If static TDD configurations are used, then system operation is simpler, but interference increases due to lack of dynamic adaptation to radio conditions
Solution Approach 1:
The system detects radio conditions and application behavior in real-time, using this feedback to dynamically adjust TDD configurations, thereby reducing interference caused by mismatched uplink/downlink allocations and improving overall system performance
Data Source
AI summary
Aspects of the subject disclosure may include, for example, receiving a registration from a user equipment (UE) accessing a radio communication network, selecting an initial time division duplex (TDD) configuration for the UE, communicating information about the initial TDD configuration to the UE, communicating with the UE according to the initial TDD configuration, detecting a changed condition for the UE, selecting a new TDD configuration for the UE, wherein the new TDD configuration is based on the changed condition for the UE, and communicating the new TDD configuration to the UE for further communication with the radio communication network. Other embodiments are disclosed.