ML-Based CSI Encoder Training Across Network Decoders
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Solution Overview
Problem
Existing AI-based CSI reporting systems face challenges in multi-vendor environments due to the need for different vendors to implement and train separate encoders and decoders, leading to high computational and resource costs, as well as potential overfitting to synthetic data.
Innovation Solution
A method for training a single CSI autoencoder (AE) encoder that can work across multiple network-side decoders, utilizing training assistance information from multiple nodes to optimize its performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate encoders and decoders are implemented for each vendor, then compatibility with specific network decoders is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent implements a universal encoder design that can work with multiple different network decoders from different vendors. Instead of creating separate encoders for each decoder type, the encoder is designed with multi-functionality to serve multiple decoder implementations, thereby reducing overall system complexity while maintaining compatibility.
Solution Approach 2:
The encoder is divided into distinct functional modules or layers that can be independently configured or selected based on the target decoder. This segmentation allows the encoder to adapt to different decoder implementations without requiring complete redesign, resolving the contradiction between compatibility and complexity.
2Reliability
If multiple trained encoders are deployed for different decoders, then compatibility is improved, but memory requirements and resource overhead increase
Solution Approach 1:
The patent implements a universal encoder design that can work with multiple different network decoders from different vendors. Instead of creating separate encoders for each decoder type, the encoder is designed with multi-functionality to serve multiple decoder implementations, thereby reducing overall system complexity while maintaining compatibility.
Solution Approach 2:
Multiple encoder configurations or models that would traditionally be stored separately are merged into a single unified encoder structure. This consolidation reduces memory requirements by eliminating redundant storage while maintaining the ability to adapt to different decoder types through shared parameters or configurable components.
3Manufacturing precision
If vendor-specific encoders are trained separately, then training precision for specific decoders is improved, but training time and computational resources increase
Solution Approach 1:
The patent employs pre-training or preliminary training phases where the encoder is trained on general characteristics that are common across multiple decoders. This preliminary action reduces the need for extensive re-training when adapting to specific decoders, thereby reducing overall training time while maintaining adequate accuracy through subsequent fine-tuning or adaptation stages.
Data Source
AI summary
A method, performed by a first node having an AE-encoder, for training the AE-encoder to provide encoded CSI. The method includes providing first AE-encoder data to a second node comprising a first NN-based AE-decoder and having access to channel data representing a communications channel between a first communications node and a second communications node. Then the first node provides second AE-encoder data to a third node comprising a second NN-based AE-decoder and having access to the channel data, and then receives first training assistance information and second training assistance information. The first node determines whether or not to continue the training by updating encoder parameters of the AE-encoder based on the received first and second training assistance information.