Encoder-Decoder Sequential Training for Wireless Spectral Efficiency
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently training encoder and decoder models for improved spectral efficiency and integration with multiple-access technologies, particularly in 5G and NR networks, which affect the performance of devices like UEs and network nodes.
Innovation Solution
A hybrid sequential training method is employed for encoder and decoder models, involving gradient-based weight selection and activation-based output functions, enabling efficient training and communication between devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If encoder and decoder models are trained separately using sequential training, then training complexity is reduced and implementation is simplified, but training efficiency and convergence speed deteriorate
Solution Approach 1:
The training process is segmented into two distinct phases: a first training phase for the encoder model using a first dataset, and a second training phase for the decoder model using a second dataset. This segmentation allows each model to be trained independently with appropriate datasets while maintaining overall training efficiency through the structured sequence of operations.
Solution Approach 2:
The encoder model is trained and finalized before the decoder model training begins. The encoder's output is used as input for decoder training, establishing a preliminary action that optimizes the training sequence and improves overall training efficiency by building models in a logical dependency order.
2Productivity
If encoder and decoder models are trained jointly using concurrent training, then training efficiency and convergence speed are improved, but training complexity and computational resource requirements increase
Solution Approach 1:
The training process is divided into sequential phases where the encoder is trained first, then the decoder is trained using the encoder's output. This segmentation reduces the complexity of managing simultaneous training operations while maintaining efficiency through the optimized sequence of model development.
Solution Approach 2:
The training methodology dynamically adapts by using the encoder's output as input for decoder training, allowing the system to optimize training efficiency through a flexible, adaptive sequence rather than rigid concurrent processing, thereby reducing computational resource requirements.
3Device complexity
If a single model architecture is used for both encoder and decoder, then device complexity is reduced, but adaptability to different communication scenarios deteriorates
Solution Approach 1:
The model architecture is segmented into distinct encoder and decoder components, each optimized for its specific function in the communication scenario. This segmentation enables better adaptability to different communication conditions while managing complexity through modular design.
Solution Approach 2:
Each model component (encoder and decoder) is designed with specific local qualities optimized for its particular function. The encoder is optimized for encoding operations while the decoder is optimized for decoding operations, enhancing overall adaptability to different communication scenarios through specialized local characteristics.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first device may receive, from a second device, a function associated with a trained first model, the function being configured to output one or more gradients associated with the trained first model. The first device may train a second model based on selecting one or more weights associated with the second model using the one or more gradients, the one or more gradients being obtained based on inputting one or more activations and one or more inputs into the function. Numerous other aspects are described.