Neural Network Beam Management With Model Transfer for Massive MIMO
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Solution Overview
Problem
Existing wireless communication systems face challenges in managing beam management due to excessive overhead in CSI feedback, especially with massive MIMO systems, and the logistics of training site-specific and band-specific AI/ML models for beam prediction are not efficiently addressed.
Innovation Solution
Implementing a beam management framework using neural networks (NNs) for UE-side and NW-side training and inference, with optimized model transfer and activation protocols to reduce feedback overhead and enhance beam prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional CSI feedback methods are used in massive MIMO systems, then beam management can be performed, but feedback overhead becomes excessive
Solution Approach 1:
The patent extracts the essential beam prediction functionality from traditional CSI feedback by implementing a neural network model that predicts beam indices directly. Instead of feeding back comprehensive channel state information, the system extracts only the necessary beam identification data through AI/ML inference, significantly reducing feedback overhead while maintaining beam management capability
Solution Approach 2:
The patent creates a simplified copy of the channel state information processing function by training a neural network model at the UE side. This model copies the essential pattern recognition capability needed for beam prediction but operates with much lower complexity and feedback requirements than traditional CSI feedback mechanisms
2Measurement precision
If site-specific and band-specific AI/ML models are trained for beam prediction, then prediction accuracy improves, but model training and transfer logistics become complex
Solution Approach 1:
The patent segments the AI/ML model training and deployment process into distinct phases: network-side model training for different sites and bands, model serialization to compact form, and UE-side model loading and inference. This segmentation allows site-specific and band-specific models to be trained independently with high accuracy while simplifying the overall deployment logistics through automated model management
Solution Approach 2:
The patent creates a universal model loading and inference framework at the UE that can handle multiple site-specific and band-specific models. The same neural network engine and inference mechanism work across different frequencies and locations, providing multi-functionality that simplifies the complexity of managing diverse models while maintaining high prediction accuracy for each specific scenario
3Adaptability or versatility
If UE mobility is supported in beam management, then system versatility improves, but beam prediction accuracy may deteriorate due to changing conditions
Solution Approach 1:
The patent implements dynamic beam prediction by training neural network models that incorporate temporal patterns and mobility characteristics. The UE continuously performs inference on current channel conditions using the loaded model, allowing the system to adapt to changing UE position and maintain accurate beam prediction despite mobility. The model can be updated or retrained as needed to capture new mobility patterns
Data Source
AI summary
Methods and apparatus are provided for beam management with machine learning. A user equipment (UE) receives, from a wireless network, one or more physical downlink shared channel (PDSCH) including data for a neural network (NN) model for beam management. The UE verifies an integrity of the NN model received from the wireless network and determines a UE capability to support the NN model received from the wireless network. In response to verifying the integrity and determining the UE capability to support the NN model, the UE transmits a NN model transfer complete acknowledgement to the wireless network.