Neural Network-based Information-Theoretic Transceivers for High-Order Modulation Schemes
This work addresses efficiency and performance in AI-native communication systems, representing an incremental improvement over existing methods.
The paper tackles the problem of improving computational efficiency in neural network-based end-to-end communication systems, proposing a bitwise receiver and a symbol-wise autoencoder system that outperforms baseline architectures for higher-order modulation schemes.
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.