Predictive Beam Configurations with AI/ML-Based TCI State Selection
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Solution Overview
Problem
Existing wireless communication systems, such as 5G NR, face challenges in optimizing beam configurations to accommodate varying use cases and increasing user/network traffic demands, necessitating improved flexibility and configurability for enhanced performance.
Innovation Solution
A user equipment (UE) receives predictive beam configurations from a base station (BS) using artificial intelligence/machine learning (AI/ML) mechanisms, allowing it to determine and transmit reference signal reception power (RSRP) to select optimal beams for data reception, and optionally predict reference signal resources for generating configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional beam configuration methods are used, then system simplicity is maintained, but beam management efficiency and adaptability to dynamic network conditions deteriorate
Solution Approach 1:
The system performs preliminary beam configuration and prediction before actual data transmission occurs. The base station predicts future beam requirements and pre-configures the UE with multiple predicted TCI states, allowing the UE to be ready for upcoming transmissions without real-time configuration delays. This resolves the contradiction by preparing configurations in advance, improving efficiency while managing complexity through pre-computation.
Solution Approach 2:
The UE autonomously detects beams and selects from predicted configurations without requiring continuous base station intervention. The UE independently monitors reference signals, determines which predicted TCI states are applicable, and autonomously configures its reception parameters. This self-service approach improves beam management efficiency while reducing the complexity of centralized control.
2Adaptability or versatility
If real-time beam configuration is performed, then adaptability to current conditions is improved, but signaling overhead and latency increase
Solution Approach 1:
The base station performs beam configuration predictions in advance based on historical data and network conditions. Multiple predicted TCI states are prepared before the actual transmission time, allowing the system to adapt to future conditions without real-time signaling delays. This resolves the latency-adaptability contradiction by shifting configuration actions to the prediction phase rather than the execution phase.
Solution Approach 2:
The system dynamically updates predictions and configurations based on changing network conditions while maintaining a pool of pre-computed options. The UE can switch between different predicted TCI states as conditions change, providing adaptability without requiring complete reconfiguration each time. This dynamic approach balances adaptability with reduced latency by leveraging pre-computed configurations.
3Adaptability or versatility
If multiple predicted TCI states are configured, then beam selection flexibility is improved, but configuration complexity and processing requirements increase
Solution Approach 1:
The configuration is segmented into multiple independent predicted TCI states, each associated with specific reference signals and transmission conditions. The UE processes these as discrete, manageable units rather than a single complex configuration. This segmentation improves flexibility by allowing selective application of individual predicted states while reducing overall processing complexity through modular handling.
Solution Approach 2:
The system creates multiple copies of configuration parameters corresponding to different predicted scenarios. Each predicted TCI state is a replicated configuration template that can be independently applied. This copying approach enables flexibility by having multiple pre-prepared templates while simplifying processing by treating each template as a standard, pre-validated configuration unit.
4Measurement precision
If AI/ML mechanisms are used for prediction, then beam management accuracy is improved, but computational requirements and system complexity increase
Solution Approach 1:
The AI/ML prediction mechanism acts as an intermediary layer between raw network conditions and beam configuration decisions. Rather than requiring complex real-time optimization, the system uses AI/ML to pre-compute predictive models that translate historical data into actionable configuration predictions. This intermediary approach improves accuracy through sophisticated analysis while managing complexity by performing computations in advance rather than in real-time.
Data Source
AI summary
A user equipment (UE) that includes one or more non-transitory computer-readable media that stores computer-executable instructions for receiving predictive beam configurations from a base station (BS) and a processor is provided. The processor is configured to receive, from the BS, a configuration that configures the UE with several predicted transmission configuration indication (TCI) states divided into a plurality of subsets of predicted TCI states. Each subset of the predicted TCI states is associated with a different reference signal resource. The processor is configured to detect a first beam associated with a first predicted TCI state in a first subset of predicted TCI states. The processor is configured to receive downlink (DL) data from the BS through the detected first beam.