Implementing Digital Downconversion on a GPU

Kandaurov, N. A.; Lipatkin, V. I.; Varlamov, V. O.

doi:10.1109/synchroinfo51390.2021.9488398

Cited by 11 publications

(1 citation statement)

References 13 publications

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“…The modern technology level makes it possible to consider the possibility of developing a computationally efficient implementation of various algorithms on GPUs. For example, such GPU implementation allows you to build systems for parallel simulation of MIMO radars [33] and build digital downconverter [34]. Additionally, GPUs are very often used in deep learning [35].…”

Section: Related Workmentioning

confidence: 99%

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

Lipatkin

Lobov

Kandaurov

2022

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%