6G New Time-delay Doppler Communication Paradigm: Technical Advantages, Design Challenges, Applications and Prospects of OTFS
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摘要: 在未来的通信网络中，被广泛期待的第6代移动通信系统(The Sixth Generation of Mobile Communications System, 6G)技术将面临诸多挑战，其中包括在高速移动场景下的超高可靠通信问题。正交时频空间(Orthogonal Time Frequency Space, OTFS)调制技术克服了传统通信系统在高速移动环境下多径和多普勒效应的影响，为实现 6G 超高可靠通信提供了新的可能性。该文首先介绍了OTFS的基本原理、数学模型、干扰与优势分析。然后，归纳分析了OTFS技术在同步、信道估计、信号检测技术上的研究现状。接着，从车联网、无人机、卫星通信、海洋通信4个典型应用场景分析了OTFS的应用趋势。最后，从降低多维匹配滤波器、相位解调和信道估计、硬件实现的复杂度和提高对时频资源的高度利用4个角度探讨了未来研究OTFS需要克服的困难和挑战。Abstract: In the future communication network, the sixth generation mobile communication system technology(6G), which is widely expected, will face many challenges, including the issue of ultra-reliable communication in high-speed mobile scenarios. Orthogonal Time Frequency Space (OTFS) modulation technology overcomes the multi-path and Doppler effects of traditional communication systems in high-speed mobile environments, and provides a new possibility for realizing 6G ultra-reliable communication. This paper first introduces the basic principle, mathematical model, interference and advantage analysis of OTFS. Then, the research status of OTFS technology in synchronization, channel estimation and signal detection is summarized and analyzed. Subsequently, the application trend of OTFS is analyzed from four typical application scenarios of vehicle networking, unmanned aerial vehicle, satellite communication and marine communication. Finally, the difficulties and challenges to be overcome in future OTFS research are discussed from four aspects: reducing multi-dimensional matching filter, phase demodulation and channel estimation, hardware implementation complexity and improving the high utilization of time-frequency resources.
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表 2 基于AI的OTFS信道估计算法
文献 网络结构 算法名称 基准算法 算法效果 [31] SBL 基于SBL的BCE算法 EP辅助信道估计算法[41]、基于最小二乘的BCE 在信道路径未知情况下，所提算法仍然可以对路径的数量和位置进行准确估计，有效提高了接收机性能。对算法进行复杂度分析和仿真分析，所提算法在复杂度、误码率和NMSE性能方面比基准算法更具优越性。 [32] SBL SBL-EM OMP-CE, GP-CE[41] 通过详尽的模拟分析，验证了所提出的信道估计方案在信道恢复误差、导频成本和导频功率等方面的优越性。 [33] SBL 基于SBL的1维、2维离网信道估计算法 并网OMP[42]、离网NOMP[43]、传统脉冲[44]、传统脉冲+ DC窗[44] 所提出的离网信道CS方案相对于并网方法，在虚拟采样分辨率较低的情况下，也能显著提高OTFS系统的CE精度。基于1维离网SBL的CE方案通过解耦时延和多普勒估计，实现了优异的性能，而所提出的2维离网方案计算复杂度更低。进一步采用数据辅助CE可以提升对未知数据符号干扰的鲁棒性，从而在无保护空间的情况下进一步提升CE性能。 [34] SBL 基于2维离网分解和SBL组合的信道估计 传统OTFS信道估计算法[45]、基于SBL的1维、2维离网信道估计算
法[33]、基于双分数模型的算法[33]仿真结果表明，所提方案比现有的基于双分数模型的方法具有更高的精度和更低的计算成本。 [35] SBL BSBL-BR PP-GP[43], SBL-EM[32], BSBL w/o BR[46], BOMP[47] 不同于传统的BSBL方法，BSBL-BR采用迭代更新非稀疏块的大小来提高信道估计精度。仿真结果表明，所提方法比基准算法具有更加优异的估计误差和噪声鲁棒性。 [36] ResNet 基于修正ResNet的联合信道估计和信号检测方案 嵌入式导频信道估计[41]+MMSE信号检测算法[48]、嵌入式导频信道估计[41]+MP信号检测算法[21] 与基准算法相比，所提方法具有更强的鲁棒性和良好的泛化能力，其算法复杂度得到降低，误码率性能也增加了约2dB。 [37] CNN 基于DCNN 的信道估计 OMP、基于导频的信道估计算法 与基准算法比较，此方法复杂度更低，对不同的车速具有较好的鲁棒性，满足了高精度信道估计。 [38] DRDN 一种数据驱动的DRDN信道估计方法 最小二乘估计器、线性最小均方
误差估计器、OMP、
基于阈值的算法在复杂噪声环境下，与基准算法相比，此算法在OTFS系统信道估计中具有更强的盲去噪能力。 [39] DRSN 基于DRSN的信道估计 最小二乘估计器、线性最小均方
误差估计器、OMP、
基于阈值的算法仿真结果表明，即使在没有先验知识的情况下，所提的算法性能依然可以接近于经典算法。相比基准算法，该算法在复杂的噪声场景下性能显著更优。 [40] RNN 基于RNN的信道估计 独有导频架构的信道估计算法[49]、嵌入式导频架构的信道估计算法[41] 与基准算法相比，所提出的方法有着更好的最小均方误差性能和误码率性能。 
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表 3 基于DL的OTFS信号检测算法
文献 算法类型 算法名称 基准算法 数据驱动/
模型驱动算法效果 [15] DNN 基于DNN的UWA OTFS通信信号检测算法 线性最小均方误差、迫零算法、匹配滤波器 数据驱动 仿真表明所提算法比基准算法的误码率更低且具有非线性映射的优势有利于对DD域中的数据进行拟合。除此外，所提方法对多径和多普勒效应具有很好的鲁棒性。 [52] DNN 用于OTFS信号检测的低复杂度延迟多普勒符号DNN算法 全DNN、最大似然 数据驱动 在误码率、复杂度方面，所提算法均优于基准算法。 [53] DNN 基于DNN的RIS辅助OTFS系统信号检测算法 最小均方误差、迫零算法、MP 数据驱动 仿真表明，此算法能够比基准算法以更低的信噪比提供无限低的误码率。 [54] MatNet OTFS-MatNet 最小均方误差、DR-DNN[60]、符号DNN[52] 数据驱动 所提算法在误码率性能改进和减少训练样本方面与基准算法相比具有显著的优势。 [55] CNN 基于2D CNN的OTFS系统信号检测算法 最优最大后验、线性最小均方误差、MP 数据驱动 所提算法的性能优于MP检测器，与最优最大后验检测器的性能近乎相同，但所提算法的时间复杂度很低。 [56] CNN 存在HI时的基于 DL 的 OTFS 信号检测 最小均方误差[61]、MP[21] 数据驱动 仿真表明所提算法比带有和不带有HI的传统MP、最小均方误差检测器都有更好的误码率性能。 [57] NN ViterbiNet 最小均方误差、MRC[62]、MP[21]、
FC-DNN[60]模型驱动 仿真验证了所提算法的鲁棒性。因为ViterbiNet是模型驱动的网络，因此仅仅要求小尺寸的神经网络和少量的训练数据也能够达到不错的性能。 [58] GNN 一种高效的GNN辅助检测算法 最小均方误差[63]、
MP[21]、GAMP[64]、GAMP-NET[65]模型驱动 仿真表明所提算法比基准算法的误码率性能更优。 [59] EP-Net 用于OTFS信号检测的EP辅助模型驱动深度学习算法 最小均方误差[49]、MP[67]、JMPA[64]、GAMP[64]、OAMP[66]、OAMP-Net[66]、EP[67] 模型驱动 仿真表明所提算法比传统的EP算法
具有更加显著的性能。
下载: 导出CSV
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