Neural Network Training via Distributed 5G Node Segmentation
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Solution Overview
Problem
Training neural networks and generating outputs using neural networks consumes significant computing resources and time, necessitating more efficient methods for improving these processes.
Innovation Solution
The implementation of a system that trains neural networks, specifically CSI autoencoders, in a 5G NR network using UE, base station, and OAM node collaboration, enabling distributed processing and efficient resource utilization through techniques like federated and split training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained using centralized computing resources, then model training accuracy can be improved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent segments the neural network training process into distributed tasks across multiple computing nodes in the 5G network. Different nodes (UE devices, base stations, OAM nodes) perform localized training operations, data collection, and model updates independently, then coordinate through standardized interfaces. This segmentation parallelizes the training workload, reducing overall processing time while maintaining model accuracy through collaborative learning.
2Reliability
If more computing resources are allocated to neural network training, then training quality improves, but resource consumption and operational costs increase
Solution Approach 1:
The patent creates a universal training management framework where existing 5G network components (UE devices, base stations, OAM nodes) perform multiple functions: data collection, localized model training, performance monitoring, and coordinated updates. This multi-functionality allows the network to leverage already-deployed resources for AI training purposes, improving training quality without proportionally increasing resource consumption or operational costs.
3Productivity
If distributed training is implemented across multiple network nodes, then resource utilization efficiency improves, but system complexity increases
Solution Approach 1:
The patent introduces standardized interfaces and coordination protocols as intermediaries between distributed training nodes. These intermediaries manage communication, synchronize model updates, and coordinate resource allocation across the network, thereby simplifying the overall system architecture despite the distributed nature of training operations. The standardized interfaces abstract the complexity of inter-node coordination, making the system more manageable while maintaining high resource utilization efficiency.
Data Source
AI summary
Apparatuses, systems, and techniques to indicate capabilities of a neural network. In at least one embodiment, a processor includes one or more circuits to indicate one or more capabilities of a neural network.