Low-complexity Delay-Doppler Symbol DNN for OTFS Signal Detection

Ashwitha Naikoti, A. Chockalingam
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引用次数: 10

Abstract

In this paper, we consider the problem of low-complexity detection of orthogonal time frequency space (OTFS) modulation signals using deep neural networks (DNN). We consider a DNN architecture in which each symbol multiplexed in the delay-Doppler grid is associated with a separate DNN. The considered symbol-level DNN has fewer parameters to learn compared to a full DNN that takes into account all symbols in an OTFS frame jointly, and therefore has less complexity. Under the assumption of static multipath channel with i.i.d. Gaussian noise, our simulation results show that the performance of the symbol-DNN detection is quite close to that of the full-DNN detection as well as the maximum-likelihood (ML) detection. Further, when the noise model deviates from the standard i.i.d. Gaussian model (e.g., non-Gaussian noise with t-distribution), because of its ability to learn the distribution, the symbol-DNN detection is found to perform better than the ML detection. A similar performance advantage is observed in multiple-input multiple-output OTFS (MIMO-OTFS) where the noise across multiple received antennas are correlated.
用于OTFS信号检测的低复杂度延迟多普勒符号DNN
研究了基于深度神经网络的正交时频空间(OTFS)调制信号的低复杂度检测问题。我们考虑了一种深度神经网络架构,其中延迟多普勒网格中的每个符号复用与单独的深度神经网络相关联。与考虑OTFS帧中所有符号的完整DNN相比,所考虑的符号级DNN需要学习的参数更少,因此复杂性更低。仿真结果表明,在静态多径信道和高斯噪声条件下,符号-深度神经网络检测的性能与全深度神经网络检测和最大似然(ML)检测非常接近。此外,当噪声模型偏离标准的i.i.d高斯模型(例如,具有t分布的非高斯噪声)时,由于其学习分布的能力,发现符号- dnn检测比ML检测表现更好。在多输入多输出OTFS (MIMO-OTFS)中观察到类似的性能优势,其中多个接收天线之间的噪声是相关的。
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