AI 中文总结
针对高移动性通信场景频谱效率问题，提出AFDM-FTN波形，推导其输入输出关系并分析干扰，开发基于BEM的信道估计器，提出MLMP算法及BEM-MLMP-JCED方案，仿真表明该系统能提升SE、降低复杂度并保持相当误码率。
AI 中文摘要
本文提出一种仿射频分复用（AFDM）辅助的快于奈奎斯特（FTN）波形，即AFDM-FTN，以提高高移动性通信场景中的频谱效率（SE）。首先推导AFDM-FTN的输入输出关系并分析其中FTN引起的干扰模式。为应对信道估计挑战，开发了基于基扩展模型（BEM）的低复杂度信道估计器。利用AFDM信道矩阵和FTN系数矩阵的内在特性，提出多层消息传递（MLMP）算法。在此基础上进一步开发低复杂度联合信道估计和数据检测方案（BEM-MLMP-JCED）。最后分析了信道估计下限、BEM-MLMP-JCED的均方误差（MSE）性能和计算复杂度。仿真结果表明，所提带有BEM-MLMP-JCED的AFDM-FTN系统在提供增强的SE和降低复杂度的同时，实现了与传统AFDM相当的误码率。
英文摘要
This paper proposes an affine frequency division multiplexing (AFDM)-aided faster-than-Nyquist (FTN) waveform, termed AFDM-FTN, to enhance spectral efficiency (SE) in high-mobility communication scenarios. We first derive the AFDM-FTN input-output relationship and analyze the FTN-induced interference pattern in AFDM-FTN. To address the channel estimation challenges, a low-complexity channel estimator based on the basis expansion model (BEM) is developed. By exploiting the intrinsic characteristics of the AFDM channel matrix and the FTN coefficient matrix, a multi-layer message passing (MLMP) algorithm is proposed that leverages the sparsity of the time-domain (TD) channel and the FTN coefficient matrix, where belief messages are iteratively propagated across the TD channel, FTN, and transform layers. Building upon the BEM-assisted channel estimation and MLMP, a low-complexity joint channel estimation and data detection scheme (BEM-MLMP-JCED) is further developed to iteratively refine channel estimation with the aid of transmitted data. Finally, the channel estimation lower bound, the mean square error (MSE) performance of the BEM-MLMP-JCED, and the computational complexity are analyzed. Simulation results demonstrate that the proposed AFDM-FTN system with BEM-MLMP-JCED achieves comparable BER to conventional AFDM while providing enhanced SE and reduced complexity compared to benchmark receivers.
