Size-Based Autoencoder Selection for Variable-Length Wireless Messages
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Solution Overview
Problem
Wireless communications systems face inefficiencies due to autoencoders being optimized for specific message sizes, leading to poor modulation techniques, large signaling overhead, and unreliable demodulation in non-coherent transmissions without channel estimation.
Innovation Solution
Implement size-based neural network (NN) selection for autoencoder-based communication by configuring UEs and base stations with a set of NN-based encoders, determining the appropriate encoder based on message size parameters, and using these encoders for modulation and demodulation in both coherent and non-coherent transmissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an autoencoder is optimized for a specific message size, then encoding efficiency is improved for that size, but performance deteriorates for other message sizes
Solution Approach 1:
The system dynamically selects from multiple pre-trained autoencoders based on the actual message size. Each autoencoder is optimized for a specific size range, and the selection is made adaptively according to the incoming message characteristics, allowing the system to maintain optimal encoding efficiency across varying message sizes without requiring a single universal autoencoder
Solution Approach 2:
The invention changes the parameter of message size to select the appropriate autoencoder. By categorizing messages into different size ranges and mapping each range to a specific pre-trained autoencoder, the system achieves both specialized optimization for each size category and overall adaptability across all message sizes
2Adaptability or versatility
If multiple autoencoders are configured for different message sizes, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system segments the message size space into discrete categories, with each category associated with a specific pre-trained autoencoder. This segmentation allows the system to handle diverse message sizes using a manageable number of specialized models rather than requiring a single complex universal model or an unlimited number of models for every possible size
Solution Approach 2:
Multiple autoencoders are pre-trained offline for different message size categories and stored in the system. During operation, the system only needs to perform size classification and select the appropriate pre-trained model, avoiding the complexity of real-time training or adaptation while maintaining support for various message sizes
3Loss of energy
If autoencoder is used for non-coherent transmission without reference signals, then signaling overhead is reduced, but demodulation reliability deteriorates
Solution Approach 1:
The autoencoder performs self-service by learning to extract channel state information and perform demodulation directly from the received signal without requiring external reference signals. The neural network is trained to handle the non-coherent detection task, enabling the system to operate with reduced signaling overhead while maintaining demodulation capability through the learned representations in the autoencoder
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. In some wireless communications systems, devices may implement multiple autoencoders for communications. A wireless device may select an autoencoder to use for communications based on a size parameter for a message. For example, a user equipment (UE) may receive a grant from a base station indicating a size parameter for communicating a message. The UE and base station may determine, from a set of neural network (NN)-based encoders configured at the UE, an NN-based encoder corresponding to the size parameter. The UE may communicate the message with the base station according to the grant and based on the determined NN-encoder. In some examples, the UE and base station may determine a number of resource segments from a set of resources allocated for communication and may determine respective NN-based encoders for the different resource segments.