Neural Network-based Information-Theoretic Transceivers for High-Order Modulation Schemes

• URL: http://arxiv.org/abs/2506.00368v1
• Date: Sat, 31 May 2025 03:22:26 GMT
• Title: Neural Network-based Information-Theoretic Transceivers for High-Order Modulation Schemes
• Authors: Ngoc Long Pham, Tri Nhu Do,
• Abstract summary: Neural network (NN)-based end-to-end (E2E) communication systems have been investigated as potential tools for developing artificial intelligence (Al)-native E2E systems.<n>We propose an NN-based bitwise receiver that improves computational efficiency while maintaining performance comparable to baseline demappers.<n>We introduce a novel symbol-wise autoencoder (AE)-based E2E system that jointly optimize the transmitter and receiver at the physical layer.
• Score: 2.295863158976069
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural network (NN)-based end-to-end (E2E) communication systems, in which each system component may consist of a portion of a neural network, have been investigated as potential tools for developing artificial intelligence (Al)-native E2E systems. In this paper, we propose an NN-based bitwise receiver that improves computational efficiency while maintaining performance comparable to baseline demappers. Building on this foundation, we introduce a novel symbol-wise autoencoder (AE)-based E2E system that jointly optimizes the transmitter and receiver at the physical layer. We evaluate the proposed NN-based receiver using bit-error rate (BER) analysis to confirm that the numerical BER achieved by NN-based receivers or transceivers is accurate. Results demonstrate that the AE-based system outperforms baseline architectures, particularly for higher-order modulation schemes. We further show that the training signal-to-noise ratio (SNR) significantly affects the performance of the systems when inference is conducted at different SNR levels.

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