Orthogonal time frequency space (OTFS) modulation is a newly proposed modulation technique for providing a solution to high mobility doubly dispersive channel problems. In several recent research works, it is shown that OTFS has better performance over the existing conventional multicarrier modulations. OTFS modulate information symbols in a two-dimensional (2D) delay-Doppler domain rather than in time frequency domain, which can exploit the full channel diversity over time and frequency. This unique ability of OTFS can provide to design an advanced signal detection method. In this letter, we present a deep learning-based signal detection for OTFS systems. Since the input-output relation of OTFS is in 2D delay-Doppler domain, we propose a two-dimensional convolutional neural network (2D-CNN) based detector. We also employ data augmentation technique based on the widely used message-passing (MP) algorithm to improve learning ability of the proposed method. Simulation results show that the proposed method has an improved performance over the MP detector and achieves nearly the same performance as an optimal maximum a posteriori (MAP) detector with a very low time complexity.

中文翻译：

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用于 OTFS 系统的基于二维卷积神经网络的信号检测


正交时频空间（OTFS）调制是一种新提出的调制技术，为高移动性双色散信道问题提供解决方案。最近的几项研究工作表明，OTFS 比现有的传统多载波调制具有更好的性能。 OTFS 在二维 (2D) 延迟多普勒域而不是时频域中调制信息符号，这可以利用时间和频率上的完整信道分集。 OTFS 的这种独特能力可以用来设计先进的信号检测方法。在这封信中，我们提出了一种针对 OTFS 系统的基于深度学习的信号检测。由于 OTFS 的输入输出关系位于 2D 延迟多普勒域，因此我们提出了一种基于二维卷积神经网络（2D-CNN）的检测器。我们还采用基于广泛使用的消息传递（MP）算法的数据增强技术来提高所提出方法的学习能力。仿真结果表明，所提出的方法比 MP 检测器具有改进的性能，并且以非常低的时间复杂度实现了与最优最大后验（MAP）检测器几乎相同的性能。