Hybrid AI and Non-AI Beam Selection With Quality Feedback
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Solution Overview
Problem
Existing beam selection methods in wireless communication systems, particularly those using artificial intelligence (AI) based algorithms, may fail to provide the best signal quality due to unpredictable factors, necessitating a fallback mechanism to alternate non-AI based algorithms like beam sweeping for improved performance.
Innovation Solution
Implement a fallback method where the system switches to a non-AI based beam selection algorithm, such as beam sweeping, when AI-based beam selection fails to meet certain quality thresholds, indicated by signal quality measurements from the user equipment (UE).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If AI-based beam selection algorithm is used, then beam selection speed is improved, but signal quality reliability deteriorates
Solution Approach 1:
The system implements a feedback mechanism where the UE reports signal quality measurements for both AI-based and non-AI-based beam selections. The base station uses this feedback to determine whether to switch between AI-based and non-AI-based algorithms, ensuring reliable signal quality while maintaining fast beam selection through adaptive algorithm selection.
2Productivity
If AI-based beam selection algorithm is used, then computational efficiency is improved, but adaptability to unpredictable factors deteriorates
Solution Approach 1:
The system dynamically switches between AI-based and non-AI-based beam selection algorithms based on real-time signal quality conditions. The base station can adaptively choose the appropriate algorithm depending on network conditions, combining the computational efficiency of AI-based methods with the reliability of non-AI-based methods when needed.
Solution Approach 2:
The system changes the operational parameters by switching between different algorithm types (AI-based vs. non-AI-based) based on signal quality thresholds. This parameter change allows the system to maintain high computational efficiency while adapting to unpredictable factors through algorithm selection.
3Reliability
If fallback mechanism to non-AI algorithm is implemented, then signal quality reliability is improved, but system complexity increases
Solution Approach 1:
The system segments the beam selection process into two distinct paths: AI-based algorithm execution and non-AI-based fallback algorithm execution. This segmentation allows the system to maintain simplicity in each individual path while providing the option to switch between them, managing complexity through modular design.
Data Source
AI summary
A user equipment (UE) includes a transceiver and a processor. The processor is configured to receive, from a base station and via the transceiver, a first indication of a transmission configuration indicator (TCI) state and a second indication of whether a beam selected for downlink (DL) transmission to the UE was selected based on an artificial intelligence (AI) based algorithm. The beam is identified by the TCI state. The processor is also configured to determine a quality of one or more DL transmissions transmitted over the beam, and to transmit, to the base station and via the transceiver, a third indication of the quality of the one or more DL transmissions over the beam.