Neural Network Encoder Structure for Wireless Codeword Distance
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Solution Overview
Problem
Current wireless communication systems face challenges in optimizing performance at the physical layer, particularly in integrating AI-driven signal processing and communication mechanisms, which require extensive training data and struggle with complex wireless signals.
Innovation Solution
The proposal involves designing neural network encoder and decoder structures for wireless communication systems, enabling end-to-end optimization and improving codeword distance characteristics through signaling information on neural network parameters, thereby enhancing system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural networks are applied to physical layer transmission, then system performance can be optimized, but extensive training data is required and complex wireless signals are difficult to process
Solution Approach 1:
The patent segments the neural network into separate encoder and decoder components, each optimized for specific functions. The encoder neural network processes transmission signals while the decoder neural network processes received signals, allowing targeted optimization without requiring the entire system to be trained on extensive datasets simultaneously.
Solution Approach 2:
The patent employs preliminary training actions where the encoder and decoder neural networks are trained separately using simulated communication scenarios before deployment. This preliminary training reduces the need for extensive real-world training data by pre-learning signal characteristics and degradation patterns.
2Reliability
If neural network encoder structure is designed for end-to-end optimization, then performance improves, but device complexity increases
Solution Approach 1:
The complex neural network is divided into distinct encoder and decoder modules with specific functions. The encoder neural network includes multiple layers for feature extraction and encoding, while the decoder neural network has corresponding layers for decoding and reconstruction, making the overall complex system manageable and implementable.
Solution Approach 2:
Different parts of the neural network are optimized for different local functions. The encoder focuses on transforming input signals into encoded representations, while the decoder focuses on reconstructing original signals from received signals, allowing each component to be optimized independently for its specific task.
Data Source
AI summary
This specification proposes a neural network encoder structure and encoding method usable in a wireless communication system.