用神经网络设计高效通信接收机，提升高阶调制性能
Neural Network-based Information-Theoretic Transceivers for High-Order Modulation Schemes
- 用神经网络构建逐比特接收机，兼顾效率与性能
- 基于自编码器的端到端系统在高阶调制下性能更优
- 训练时信噪比影响推理表现，需匹配使用场景
基于神经网络（NN）的端到端（E2E）通信系统，其中每个系统组件可由神经网络部分构成，被视为发展人工智能原生端到端系统的重要工具。本文提出一种基于神经网络的逐比特接收机，在保持性能接近基线解映射器的同时提升了计算效率。在此基础上，引入一种新的符号级自编码器（AE）- 基于的端到端系统，联合优化物理层收发机。通过误码率（BER）分析评估所提神经网络接收机，证实其数值误码率结果准确。结果表明，该自编码器系统在高阶调制方案中显著优于基线架构。此外，我们进一步证明训练时的信噪比（SNR）对推理阶段不同信噪比下的系统性能有显著影响。
原文摘要 · Abstract (English)
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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