CSI Quantization for Multi-Vendor Sequential Decoder Training
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Solution Overview
Problem
Training two-sided machine learning models across multiple vendors in wireless communication systems is challenging, particularly in 5G NR, due to the need for interoperability and compatibility between different network entities.
Innovation Solution
Implementing a configuration for base station-driven multi-vendor sequential training, where a shared decoder at the base station operates with multiple UE encoders, utilizing quantization methods to facilitate efficient communication and training across diverse network entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If two-sided machine learning models are trained across multiple vendors, then interoperability and compatibility between network entities improve, but training complexity and coordination difficulty increase
Solution Approach 1:
The training process is segmented into two separate phases: first training the encoder at the UE side with local data, then training the decoder at the network side using encoded data from multiple UEs. This segmentation allows each vendor to train their components independently without requiring complex coordination for joint training, thereby reducing training complexity while maintaining interoperability.
Solution Approach 2:
The encoder is trained in advance at the UE side before the decoder training begins at the network side. This preliminary action allows the network side to receive pre-trained encoders from multiple UE vendors, enabling the decoder training to focus solely on learning to decode various encoder formats without the added complexity of simultaneous two-sided training coordination.
2Productivity
If quantization methods are implemented for compression, then communication efficiency improves, but quantization error and information loss increase
Solution Approach 1:
The system dynamically adjusts the quantization parameter (QP) based on channel conditions and traffic requirements. When channel conditions are good or traffic is low, a higher QP (coarser quantization) is used to maximize compression efficiency. When channel conditions deteriorate or traffic increases, the QP is reduced to minimize quantization error, thereby adapting the balance between communication efficiency and information loss preservation.
Data Source
AI summary
Method and apparatus for quantization of base station driven multi-vendor sequential training. The apparatus generates an encoder output by inputting an input CSI to a reference encoder. The apparatus quantizes the encoder output by inputting the encoder output to a quantizer to generate a quantizer output. The apparatus trains a decoder of the network entity based at least on the quantizer output to generate a training dataset. The apparatus outputs a training dataset indication comprising the training dataset to a UE, the training dataset indication comprising at least the input CSI. The apparatus communicates with the UE using the trained decoder.