TransCoder: A Neural-Enhancement Framework for Channel Codes

2025-11-27

AI Summary

To address the high computational complexity of neural decoders, which hinders their deployment on resource-constrained wireless devices, this paper proposes TransCoder—a Transformer-based neural-enhanced channel coding framework. Its core innovation is a block-wise attention mechanism enabling lightweight iterative decoding, while supporting flexible deployment at the transmitter, receiver, or both ends. TransCoder maintains full compatibility with classical codes—including LDPC, BCH, Polar, and Turbo—without modifying encoder structures. Compared to fully neural decoders, TransCoder drastically reduces computational overhead, achieving complexity comparable to conventional decoders. In long-code and low-rate regimes, it achieves 1–2 orders-of-magnitude lower block error rates (BLER) than baseline schemes. Extensive experiments validate its robust performance and efficiency across diverse channel conditions. By bridging neural enhancement with practical implementation constraints, TransCoder provides a viable, deployable pathway toward neural-augmented wireless communication systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Reliable communication over noisy channels requires the design of specialized error-correcting codes (ECCs) tailored to specific system requirements. Recently, neural network-based decoders have emerged as promising tools for enhancing ECC reliability, yet their high computational complexity prevents their potential practical deployment. In this paper, we take a different approach and design a neural transmission scheme that employs the transformer architecture in order to improve the reliability of existing ECCs. We call this approach TransCoder, alluding both to its function and architecture. TransCoder operates as a code-adaptive neural module aimed at performance enhancement that can be implemented flexibly at either the transmitter, receiver, or both. The framework employs an iterative decoding procedure, where both noisy information from the channel and updates from the conventional ECC decoder are processed by a neural decoder block, utilizing a block attention mechanism for efficiency. Through extensive simulations with various conventional codes (LDPC, BCH, Polar, and Turbo) and across a wide range of channel conditions, we demonstrate that TransCoder significantly improves block error rate (BLER) performance while maintaining computational complexity comparable to traditional decoders. Notably, our approach is particularly effective for longer codes (block length >64) and at lower code rates, scenarios in which existing neural decoders often struggle (despite their formidable computational complexity). The results establish TransCoder as a promising practical solution for reliable communication among resource-constrained wireless devices.

Problem

• Enhancing reliability of existing error-correcting codes with neural networks
• Reducing computational complexity for practical deployment of neural decoders
• Improving performance for longer codes and lower code rates efficiently

Innovation

• Transformer architecture enhances existing error-correcting codes
• Iterative decoding with block attention improves reliability efficiently
• Code-adaptive neural module works flexibly at transmitter or receiver