QoS-Specific Beam Prediction With Machine Learning Models
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Solution Overview
Problem
Existing beam management and prediction techniques in wireless communication systems result in significant overhead, power consumption, and inaccuracies, particularly in non-line-of-sight regions and with multipath signaling.
Innovation Solution
Implementing machine learning-based beam prediction procedures at user equipment (UE) and network entities, where the UE identifies quality of service (QoS) types and performs beam predictions using machine learning models, reducing overhead and power consumption while improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional beam management and prediction techniques are used, then beam prediction can be performed, but overhead and power consumption are significant
Solution Approach 1:
The patent changes the approach from traditional signal-based beam prediction to machine learning-based prediction by transforming input parameters (channel state information, mobility patterns, historical data) into predictive models. This parameter transformation enables more accurate beam prediction with reduced overhead by leveraging ML algorithms that can process multiple parameters simultaneously and identify patterns that traditional methods miss.
Solution Approach 2:
The patent replaces traditional mechanical beam management procedures (signal transmission, measurement, reporting cycles) with intelligent software-based machine learning models. The ML models predict beam directions by processing input data through neural networks or other algorithms, substituting the physical signal exchange mechanism with a computational prediction mechanism that reduces overhead signaling while maintaining or improving accuracy.
2Productivity
If traditional beam management and prediction techniques are used, then beam prediction can be performed, but overhead is significant
Solution Approach 1:
The patent performs preliminary actions by training machine learning models offline or in advance using historical data and channel characteristics. Once trained, these models can quickly predict beam directions without requiring extensive real-time signaling overhead. The preliminary model preparation enables fast, accurate predictions with minimal runtime overhead compared to traditional methods that require continuous signal exchange.
Solution Approach 2:
The patent creates universal machine learning models that can handle multiple beam prediction scenarios and conditions with a single model architecture. These models process various input types (channel state, mobility information, historical predictions) and provide beam predictions across different service types and channel conditions, reducing the need for multiple specialized procedures and associated overhead signaling.
3Measurement precision
If QoS-specific machine learning models are used, then beam prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the machine learning functionality into distinct modules: data collection module, model training module, prediction execution module, and result reporting module. Each module has a specific function, allowing the complex ML system to be managed through modular components. This segmentation enables QoS-specific models to be implemented without overwhelming device complexity by organizing functionality into manageable, independent units that can be configured based on service requirements.
Data Source
AI summary
Certain aspects relate to quality of service (QoS) based beam prediction. For example, an apparatus may obtain, from a network entity, a set of machine learning module configurations associated with a set of quality of service (QoS) types. The apparatus may identify a QoS type, from the set of QoS types, for data scheduled to be communicated with the network entity. The apparatus may select a machine learning module configuration from the set of machine learning module configurations based on the QoS type. The apparatus may output, for transmission to the network entity, a report associated with the one or more beam prediction procedures.