AI 中文总结
研究针对FTN信号信道上的BPSK信号，提出基于变换器的接收机架构，构建端到端通信链并评估。与BCJR检测器对比，开发两阶段训练策略缩小误码率差距，分析其计算复杂度等，发现变换器可自主识别ISI记忆结构。
AI 中文摘要
本研究提出了一种基于新型仅编码器变换器的接收机架构，用于在快于奈奎斯特（FTN）信号信道上传输的BPSK信号，该信道引入了压缩因子为τ = 0.8的符号间干扰（ISI）。构建并评估了一个完整的端到端通信链，包括BPSK调制、RRC脉冲整形以及匹配滤波产生的ISI系数。在0至8 dB的Eb/N0范围内，将所提出的变换器接收机与最优BCJR检测器进行基准测试。为了系统地缩小与BCJR的误码率差距，开发了一种结合多信噪比预训练和每信噪比课程微调的两阶段训练策略。分析了变换器接收机与基于GRU的接收机相比的计算复杂度和推理延迟。注意力图可视化显示，变换器无需任何先验信道知识就能自主识别FTN引起的ISI记忆结构；随着信噪比增加，注意力权重显著集中在中心令牌及其最近邻周围。
英文摘要
In this study, a novel encoder-only Transformer-based receiver architecture is presented for BPSK signals transmitted over Faster-than-Nyquist (FTN) signaling channels that introduce intentional inter-symbol interference (ISI) with a compression factor of $τ=0.8$. A complete end-to-end communication chain encompassing BPSK modulation, RRC pulse shaping, and the ISI coefficients arising from matched filtering was constructed and evaluated. The proposed Transformer receiver was benchmarked against the optimal BCJR detector over an $E_b/N_0$ range of 0-8 dB. To systematically close the BER gap to the BCJR, a two-stage training strategy combining multi-SNR pretraining and per-SNR curriculum fine-tuning was developed. The computational complexity and inference latency of the Transformer receiver were analyzed in comparison with a GRU based receiver. Attention map visualizations revealed that the Transformer autonomously identifies the FTN-induced ISI memory structure without requiring any prior channel knowledge; as the SNR increases, the attention weights become significantly concentrated around the center token and its nearest neighbors.
CommentsAccepted and presented at the 2026 IEEE Signal Processing and Communications Applications Conference (SIU)
