Anastasiia Kurmukova | Searcharxiv

arXiv2025

TransCoder: A Neural-Enhancement Framework for Channel Codes

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.

cs.IT↗

arXiv2024

Friendly Attacks to Improve Channel Coding Reliability

This paper introduces a novel approach called "friendly attack" aimed at enhancing the performance of error correction channel codes. Inspired by the concept of adversarial attacks, our method leverages the idea of introducing slight perturbations to the neural network input, resulting in a substantial impact on the network's performance. By introducing small perturbations to fixed-point modulated codewords before transmission, we effectively improve the decoder's performance without violating the input power constraint. The perturbation design is accomplished by a modified iterative fast gradient method. This study investigates various decoder architectures suitable for computing gradients to obtain the desired perturbations. Specifically, we consider belief propagation (BP) for LDPC codes; the error correcting code transformer, BP and neural BP (NBP) for polar codes, and neural BCJR for convolutional codes. We demonstrate that the proposed friendly attack method can improve the reliability across different channels, modulations, codes, and decoders. This method allows us to increase the reliability of communication with a legacy receiver by simply modifying the transmitted codeword appropriately.

cs.IT↗