Neural Polar Channel Coding for Reliable Low-Complexity Wireless Links
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Solution Overview
Problem
Current wireless communication systems face challenges in efficiently performing channel coding for reliable signal transmission and reception, particularly in environments requiring high communication capacity and low latency, where existing methods struggle to balance encoding complexity and decoding efficiency.
Innovation Solution
The implementation of a method and apparatus that combines polar codes with neural network-based autoencoders for channel coding, enabling encoding and decoding of signals in user equipment (UE) and base stations, utilizing a neural polar code that applies polar code transformation and neural network-based autoencoding to optimize signal transmission and reception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If polar code transformation is applied from initial layer to first layer, then encoding reliability is improved, but encoding complexity increases
Solution Approach 1:
The encoding process is divided into two distinct stages: polar code transformation applied from the initial layer to the first layer, and neural network-based autoencoder applied from the first layer to the output layer. This segmentation allows each method to operate on specific portions of the data, balancing reliability improvement with complexity management.
Solution Approach 2:
The polar code transformation is applied partially - only from the initial layer to the first layer, not throughout the entire encoding process. This partial application provides the reliability benefits of polar codes where most needed while avoiding the complexity burden in subsequent layers where neural network autoencoding takes over.
2Productivity
If neural network-based autoencoder is used for channel coding, then decoding efficiency is improved, but training complexity increases
Solution Approach 1:
The system segments the channel coding task between traditional polar code transformation (for structured reliability) and neural network autoencoder (for efficient decoding). The neural network component is trained offline, separating training complexity from operational decoding efficiency, allowing fast decoding during actual communication without repeating the training burden.
Solution Approach 2:
The neural network autoencoder is trained in advance before actual communication occurs. This preliminary training action prepares the decoding model ahead of time, so that during actual operation, only inference is needed, achieving high decoding efficiency without repeating the complex training process.
3Reliability
If adjacent bits are encoded through autoencoder, then encoding performance is improved, but processing time increases
Solution Approach 1:
The bit processing is segmented by spatial adjacency - adjacent bits are routed through the neural network autoencoder to exploit local correlations and improve performance, while non-adjacent bits undergo standard polar code transformation. This segmentation optimizes performance for bit groups that benefit most from neural processing.
Solution Approach 2:
Different encoding approaches are applied to different portions of the data based on local characteristics. Adjacent bits, which have local correlations, receive the enhanced treatment of autoencoder processing, while other bits use the more efficient standard polar code transformation, optimizing overall performance versus time trade-offs.
Data Source
AI summary
The present disclosure a method of operating user equipment (UE) in a wireless communication system, the method comprising: identifying layer information that is applied to a neural polar code; generating, based on the identified layer information, transmission data by encoding data that is input into the neural polar code; and transmitting the transmission data to a base station, wherein, based on polar code transformation, the neural polar code generates the transmission data by performing encoding, based on the polar code transformation, from an initial layer of the data to a first layer according to the identified layer information and by performing encoding through a neural network-based autoencoder after the first layer until the transmission data is generated.