Beam Management Model Download for Low-Latency Handover
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing beam management due to excessive feedback overhead and the need for frequent model updates or switching when user equipment (UE) moves between cells, especially with site-specific and band-specific artificial intelligence (AI) models, which can strain storage and logistics.
Innovation Solution
Implementing a method for neural network (NN) model transfer and activation during handover, where the network trains and downloads NN models to the UE in anticipation of handover, allowing for efficient model switching and reduced latency by using multiple NN engines and conditional handover to prepare for target cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If site-specific and band-specific AI models are used for beam management, then beam prediction accuracy is improved, but storage requirements and model switching complexity increase
Solution Approach 1:
The patent segments beam management models into cell-specific models and site-specific models. Cell-specific models are shared across multiple cells and provide general beam management capabilities, while site-specific models are downloaded only when needed for specific target cells. This segmentation reduces the overall storage burden and simplifies model switching by separating universally applicable models from location-specific models.
Solution Approach 2:
The system performs preliminary actions by downloading and storing site-specific NN models in the UE's buffer memory before handover occurs. The network anticipates potential handovers and prepares the appropriate models in advance, so that when handover actually occurs, the UE already has the necessary models ready for immediate activation, eliminating model switching complexity during the critical handover moment.
2Loss of time
If NN models are downloaded to UE before handover, then model activation latency is reduced, but signaling overhead increases
Solution Approach 1:
The UE autonomously determines whether to download additional NN models based on its own buffer memory status and the predicted handover target cell. The UE sends a capability message to the network indicating its model storage capacity and receives guidance on which models to download, but the actual decision-making and download execution is performed by the UE itself, reducing the need for extensive network signaling and control.
Solution Approach 2:
The system performs preliminary model downloads before handover occurs. The network provides configuration information about potential target cells, and the UE proactively downloads the corresponding site-specific NN models to its buffer memory in advance. This preliminary action ensures that when handover occurs, the models are already available locally, eliminating activation latency without requiring real-time signaling during the handover process.
3Reliability
If multiple NN engines are used for parallel model processing, then beam management continuity is improved, but device complexity increases
Solution Approach 1:
The patent divides the NN processing functionality into two separate engines: a first NN engine that processes cell-specific models and a second NN engine that processes site-specific models. This segmentation allows both models to be processed in parallel without interference, ensuring beam management continuity during handover. The separation of concerns reduces the complexity within each individual engine while maintaining the benefits of parallel processing.
Solution Approach 2:
The system merges the outputs of the first NN engine (cell-specific model processing) and the second NN engine (site-specific model processing) to generate the final beam management decisions. By combining the results from both engines, the system achieves reliable beam management continuity that leverages both general cell-level patterns and specific site-level characteristics, while distributing the computational complexity across two specialized engines rather than one complex engine.
Data Source
AI summary
Methods and apparatus are provided for beam management and model download during handover. A user equipment (UE) performs beam management in a first cell of a wireless network using a first neural network (NN) model activated for a NN engine of the UE. The UE downloads, from the wireless network, a second NN model configured for a second cell predicted for the UE. The UE stores the second NN model in a memory of the UE and performs a handover of the UE from the first cell to a second cell of the wireless network. The UE receives, from the wireless network in response to the handover, a signal to activate the second NN model. In response to the signal, the UE activates the second NN model for the NN engine of the UE.