Autonomous Beam Prediction for Slot Aggregation in Wireless
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Solution Overview
Problem
In wireless communications, especially in scenarios with high user equipment (UE) mobility or tight latency demands, selecting beams for slot aggregation configurations based on channel state reports becomes impractical due to latency and overhead issues, particularly in non-line of sight (NLOS) conditions where multipath causes random fading.
Innovation Solution
The implementation of autonomous selection and updating of beams from a set of candidate beams for a slot aggregation configuration, where user equipment (UE) performs a beam prediction process using reference signals to identify a subset of beams for communication with a network entity, allowing for slot aggregation and reporting back to the network entity for coordinated communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If beam selection is based on channel state reports, then communication reliability is improved, but latency and overhead increase significantly
Solution Approach 1:
The network entity performs beam prediction in advance using historical channel state information and machine learning models, so that beam decisions are made before actual communication begins. This preliminary prediction action eliminates the need for real-time channel state reports, reducing latency while maintaining reliable beam selection.
Solution Approach 2:
The network entity autonomously performs beam prediction using its own stored historical data and machine learning capabilities, without requiring UE to perform measurements and reports. This self-service approach eliminates the feedback loop of channel state reporting, significantly reducing latency while maintaining communication reliability.
2Loss of time
If beam prediction is performed without channel state reports, then latency is reduced, but measurement precision requirements increase
Solution Approach 1:
The network entity uses historical channel state information that has already been collected and stored in advance. This preliminary data collection allows the machine learning model to make accurate predictions without requiring precise real-time measurements, thus reducing latency while maintaining prediction accuracy through historical data analysis.
Solution Approach 2:
The system uses historical channel state information as feedback to train and refine the machine learning model for beam prediction. This feedback mechanism allows the system to learn from past performance and improve prediction accuracy over time, compensating for the lack of real-time measurements.
3Productivity
If slot aggregation is implemented with beam prediction, then communication efficiency is improved, but device complexity increases
Solution Approach 1:
The network entity performs beam prediction autonomously using its own machine learning capabilities and historical data, without requiring complex coordination with the UE. This self-service approach simplifies the overall system complexity by centralizing the prediction function at the network side, while still enabling efficient slot aggregation.
Solution Approach 2:
The machine learning model acts as an intermediary between historical channel state information and beam selection decisions. This intermediary component simplifies the complexity by providing a systematic way to process historical data and generate predictions, making the beam management process more manageable while improving communication efficiency.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. Described techniques provide for autonomous selection and updating of beams from a set of candidate beams for a slot aggregation configuration. A user equipment (UE) may receive a set of reference signals associated with the set of candidate beams for communications with a network entity. The UE may perform a beam prediction process based on measurements of the reference signals. The UE may identify, based on the beam prediction process, a subset of beams of the set of candidate beams for a slot aggregation configuration for the communications with the network entity. The UE may communicate with the network entity according to the slot aggregation configuration.