Graph Neural Network Decoder for Channel Decoding Scalability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current channel decoding techniques face challenges in scalability, complexity, and efficiency, particularly for short to intermediate codeword lengths, and lack universal decoding architectures that can handle Polar, LDPC, and BCH codes effectively, especially in non-binary formats.
Innovation Solution
A graph neural network (GNN)-based decoder is employed to embed received codewords into a higher-dimensional space and decode linear block codes of arbitrary block lengths, using trainable functions for message passing over graph structures, which simplifies processing and reduces computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If belief propagation decoding is used for short to intermediate codeword lengths, then decoding complexity is kept low, but decoding performance shows a nonnegligible gap compared to optimal maximum-likelihood decoding
Solution Approach 1:
The patent embeds received codewords into a higher-dimensional space before decoding. This dimensional transformation allows the GNN decoder to achieve performance closer to optimal maximum-likelihood decoding while maintaining reasonable complexity, resolving the trade-off between reliability and complexity for short to intermediate codeword lengths.
Solution Approach 2:
The patent replaces traditional belief propagation decoding with a graph neural network-based decoder. This substitution uses learned message-passing mechanisms instead of fixed iterative algorithms, achieving significantly improved decoding performance while keeping the computational framework comparable to belief propagation.
2Reliability
If successive-cancellation list decoding is used for Polar codes, then decoding performance achieves the best results, but computational complexity becomes very high
Solution Approach 1:
The patent segments the decoding problem by representing codes as graph structures where variable nodes and check nodes are separated. The GNN processes messages between these segmented nodes in parallel, achieving near-SCL performance without the exponential complexity growth associated with list decoding approaches.
Solution Approach 2:
By embedding codewords into higher-dimensional space and using GNN's multi-layer message passing, the patent achieves performance close to SCL decoding while maintaining complexity comparable to belief propagation, effectively resolving the complexity-performance trade-off for Polar codes.
3Adaptability or versatility
If conventional channel decoders are used for non-binary codes, then decoding can be performed, but computational complexity and engineering complexity become very high
Solution Approach 1:
The patent creates a universal GNN-based decoder that can handle binary and non-binary codes through the same architecture. By representing codes as graphs and using embedding layers, the system achieves high adaptability to different code types and block lengths without requiring separate specialized decoders, reducing both computational and engineering complexity.
Solution Approach 2:
The patent uses trainable embedding parameters and message-passing functions that adapt to different code characteristics. For non-binary codes, the embedding dimension and message space are adjusted, allowing the same GNN framework to efficiently decode various code types without the high complexity of conventional approaches.
4Reliability
If deep learning-based channel decoding techniques are used, then decoding performance improves, but scalability to arbitrary block lengths is limited due to the curse of dimensionality
Solution Approach 1:
The patent embeds codewords of arbitrary block lengths into a higher-dimensional space where the GNN can process them effectively. This embedding approach overcomes the curse of dimensionality by transforming the problem into a space where scalable message passing can occur, enabling the decoder to handle arbitrary block lengths while maintaining good performance.
Solution Approach 2:
The patent uses a dynamic graph neural network architecture where the graph structure and message-passing parameters can adapt to different block lengths. This dynamic approach allows the same decoder to efficiently handle variable-length codes without being constrained by fixed-dimensional training limitations.
Data Source
AI summary
Various embodiments and implementations of graph-neural-network (GNN)-based decoding applications are disclosed. The GNN-based decoding schemes are broadly applicable to different coding schemes, and capable of operating on both binary and non-binary codewords, in different implementations. Advantageously, the inventive GNN-based decoding is scalable, even with arbitrary block lengths, and not subject to typical limits with respect to dimensionality. Decoding performance of the inventive GNN-based techniques demonstrably matches or outpaces BCH and LDPC (both regular and 5G NR) decoding algorithms, while exhibiting improvements with respect to number of iterations required and scalability of the GNN-based approach. These inventive concepts are implemented, according to various embodiments, as methods, systems, and computer program products.