Machine-Learning Spatial Beam Prediction to Reduce CSI-RS Overhead
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Solution Overview
Problem
The increase in CSI-RS measurements and feedback overhead with high-dimensional MIMO arrays limits low latency communication due to prolonged beam sweeping and selection processes in beam management.
Innovation Solution
Implement spatial beam prediction using machine learning models trained on multiple assistance information parameters, configuring subspaces and selecting subsets based on predefined probabilities to reduce unnecessary measurements and enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If beam sweeping and beam measurements are performed with high-dimensional MIMO arrays to enable multi-TRP and multi-panel configurations, then beam management capability is improved, but CSI-RS measurements and feedback overhead radically increase
Solution Approach 1:
The patent applies preliminary action by pre-configuring multiple subspaces corresponding to different assistance information parameters before beam selection is needed. During operation, the system selects a subset of pre-configured subspaces based on current conditions, avoiding the need to measure all possible beams exhaustively while maintaining comprehensive beam management capability across multi-TRP and multi-panel configurations.
Solution Approach 2:
The patent segments the beam management task by dividing the full beam space into multiple subspaces, each associated with specific assistance information parameters. Instead of treating all beams uniformly, the system segments measurements and feedback into manageable subsets based on which subspaces are currently relevant, thereby reducing overall overhead while maintaining adaptability.
2Measurement precision
If beam sweeping is performed to establish the best beam with high-dimensional MIMO arrays, then beam selection accuracy is improved, but time required for beam sweeping and establishment increases
Solution Approach 1:
The system performs preliminary configuration of subspaces and their associated assistance information parameters before actual beam selection is needed. During beam sweeping, the system leverages these pre-configured subspaces to guide measurements, allowing accurate beam selection without performing exhaustive sweeping across all possible beams, thus reducing time requirements.
Solution Approach 2:
The patent introduces subspace selection as an intermediary mechanism between the full beam space and the measurement process. By selecting a subset of subspaces based on assistance information parameters, the system creates a filtered view of available beams that maintains selection accuracy while significantly reducing the time needed for beam sweeping and establishment.
3Measurement precision
If multiple assistance information parameters are used for spatial beam prediction, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the assistance information parameters into distinct subspaces, each representing a specific parameter or combination of parameters. This segmentation allows the system to manage multiple parameters in an organized manner, selecting only the relevant subspaces for each prediction task rather than processing all parameters simultaneously, thereby reducing effective complexity while maintaining accuracy.
Solution Approach 2:
The system dynamically selects which subspaces to activate based on current operational conditions and available assistance information. Rather than maintaining all possible subspace configurations actively at once, the system dynamically adjusts the active subspace set, reducing processing complexity while preserving the ability to achieve high prediction accuracy when needed.
Data Source
AI summary
Example embodiments provide improved spatial beam predictions based on multiple assistance information parameters. An apparatus may be configured to configure a plurality of subspaces and corresponding machine learning models based on a dataset comprising multiple assistance information parameters, select a subset of the subspaces for inference by the machine learning models based on available assistance information, and determine a preferred spatial beam prediction based on the inferences. Apparatuses, methods, and computer programs are disclosed.