Neural Network Encoder for Wireless Communication Reliability
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Solution Overview
Problem
Current wireless communication systems face challenges in optimizing performance at the physical layer, particularly in integrating AI for signal processing and resource management, due to limitations in training data and complexity of signals, which affects channel coding, decoding, and resource allocation.
Innovation Solution
The proposal involves designing neural network-based autoencoders with optimized encoder and decoder structures to improve distance characteristics of codewords and system performance by signaling neural network parameters, using techniques such as convolutional neural networks and recursive systematic convolutional codes to reduce complexity and enhance encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural networks are applied to physical layer signal processing, then communication performance is improved, but system complexity increases
Solution Approach 1:
The system divides the communication function into separate components: a neural network encoder that generates codewords with improved distance characteristics, and a traditional decoder. This segmentation allows the neural network to be applied only where it provides the most benefit (encoding) while keeping the decoding process relatively simple and traditional.
Solution Approach 2:
The patent introduces an intermediary mechanism where the neural network encoder outputs codewords with enhanced distance characteristics, which then serve as input to traditional decoding algorithms. This intermediary approach allows neural network capabilities to be integrated without completely replacing traditional communication frameworks.
2Reliability
If deep learning is used for channel coding and decoding, then error correction performance is improved, but training data requirements and computational complexity increase
Solution Approach 1:
The patent extracts the neural network component from the complete communication system, applying it only to the encoding function rather than both encoding and decoding. This extraction reduces the overall computational complexity while still providing improved error correction through the neural network's ability to generate codewords with better distance characteristics.
Solution Approach 2:
Instead of using neural networks for both encoding and decoding (the conventional approach), the patent inverts the application by using neural networks only for encoding and traditional methods for decoding. This inversion reduces computational complexity while maintaining improved performance.
3Reliability
If neural network parameters are signaled for optimization, then system performance is improved, but overhead and complexity increase
Solution Approach 1:
The patent applies partial action by signaling only the necessary neural network parameters required for optimization rather than all possible parameters. This selective signaling approach improves system performance through optimized codeword generation while minimizing the overhead and complexity associated with parameter transmission.
Data Source
AI summary
According to the present specification, it is possible to design a transmitter and a receiver configured in a neural network through end-to-end optimization. In addition, it is possible to design a neural network encoder capable of improving the distance characteristic of a codeword. Furthermore, proposed is a method for signaling information about neural network parameters of a neural network encoder and a neural network decoder.