Zhang Mi, Xia Xiaochen, Xu Kui, Yang Xiaoqin, Xie Wei, Li Yunkun, Liu Yang
School of Communication Engineering, Army Engineering University of PLA, Nanjing 210007, China.
Unit 31105 of PLA, Nanjing 210042, China.
Entropy (Basel). 2023 May 6;25(5):761. doi: 10.3390/e25050761.
Orthogonal time-frequency space (OTFS) modulation has been advocated as a promising waveform for achieving integrated sensing and communication (ISAC) due to its superiority in high-mobility adaptability and spectral efficiency. In OTFS modulation-based ISAC systems, accurate channel acquisition is critical for both communication reception and sensing parameter estimation. However, the existence of the fractional Doppler frequency shift spreads the effective channels of the OTFS signal significantly, making efficient channel acquisition very challenging. In this paper, we first derive the sparse structure of the channel in the delay Doppler (DD) domain according to the input and output relationship of OTFS signals. On this basis, a new structured Bayesian learning approach is proposed for accurate channel estimation, which includes a novel structured prior model for the delay-Doppler channel and a successive majorization-minimization (SMM) algorithm for efficient posterior channel estimate computation. Simulation results show that the proposed approach significantly outperforms the reference schemes, especially in the low signal-to-noise ratio (SNR) region.
正交时频空间（OTFS）调制因其在高移动性适应性和频谱效率方面的优势，被倡导为一种实现集成感知与通信（ISAC）的有前景的波形。在基于OTFS调制的ISAC系统中，精确的信道获取对于通信接收和感知参数估计都至关重要。然而，分数多普勒频移的存在显著扩展了OTFS信号的有效信道，使得高效的信道获取极具挑战性。在本文中，我们首先根据OTFS信号的输入输出关系推导了延迟多普勒（DD）域中信道的稀疏结构。在此基础上，提出了一种新的结构化贝叶斯学习方法用于精确的信道估计，该方法包括一个针对延迟多普勒信道的新颖结构化先验模型和一个用于高效后验信道估计计算的逐次优化最小化（SMM）算法。仿真结果表明，所提出的方法显著优于参考方案，尤其是在低信噪比（SNR）区域。
