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基于 GPU 的具有色散失真信号的联合检测和参数估计的计算高效实现。

Computationally Efficient Implementation of Joint Detection and Parameters Estimation of Signals with Dispersive Distortions on a GPU.

机构信息

Science and Research Department, Moscow Technical University of Communications and Informatics, Moscow 111024, Russia.

出版信息

Sensors (Basel). 2022 Apr 19;22(9):3105. doi: 10.3390/s22093105.

DOI:10.3390/s22093105
PMID:35590795
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9099799/
Abstract

The detector is an integral part of the device for receiving and processing radio signals. Signals that have passed through the ionospheric channel acquire an unknown Doppler shift and are subject to dispersion distortions. It is necessary to carry out joint detection and parameter estimation to improve reception quality and detection accuracy. Modern hardware base developing makes it possible to implement a device for joint detection and evaluation of signals based on standard processors (CPU) and graphic processors (GPU). The article discusses the implementation of a signal detector that allows for real-time operation. A comparison of implementations of algorithms for estimating the Doppler frequency shift through multiplication by a complex exponent and the fast Fourier transform (FFT) is performed. A comparison of computational costs and execution speed on the CPU and GPU is considered.

摘要

探测器是接收和处理无线电信号的设备的一个组成部分。经过电离层信道的信号会获得未知的多普勒频移，并受到色散失真的影响。有必要进行联合检测和参数估计，以提高接收质量和检测精度。现代硬件基础的发展使得基于标准处理器 (CPU) 和图形处理器 (GPU) 实现联合检测和信号评估设备成为可能。本文讨论了实时操作信号检测器的实现。通过复数指数乘法和快速傅里叶变换 (FFT) 执行了估计多普勒频移算法的实现比较。还考虑了在 CPU 和 GPU 上的计算成本和执行速度的比较。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/41aa/9099799/a19168a3b07b/sensors-22-03105-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/41aa/9099799/e33e43a54f56/sensors-22-03105-g011.jpg

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本文引用的文献

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MIMO Radar Parallel Simulation System Based on CPU/GPU Architecture.基于 CPU/GPU 架构的 MIMO 雷达并行仿真系统。
Sensors (Basel). 2022 Jan 5;22(1):396. doi: 10.3390/s22010396.
2
Analysis of the Ordinary and Extraordinary Ionospheric Modes for NVIS Digital Communications Channels.非视距数字通信信道的普通和非凡电离层模式分析。
Sensors (Basel). 2021 Mar 22;21(6):2210. doi: 10.3390/s21062210.
3
A Deep-Learning Method for Radar Micro-Doppler Spectrogram Restoration.一种雷达微多普勒能谱图恢复的深度学习方法。
Sensors (Basel). 2020 Sep 3;20(17):5007. doi: 10.3390/s20175007.