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Artificial neural network based estimation of sparse multipath channels in OFDM systems

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Date
2021-01
Author
Şenol, Habib
Abdur Rehman Bin, Tahir
Özmen, Atilla
Abstract
In order to increase the transceiver performance in frequency selective fading channel environment, orthogonal frequency division multiplexing (OFDM) system is used to combat inter-symbol-interference. In this work, a channel estimation scheme for an OFDM system in the presence of sparse multipath channel is studied using the artificial neural networks (ANN). By means of ANN's learning capability, it is shown that how to model and obtain a channel estimate and how it allows the proposed technique to give a better system throughput. The performance of proposed method is compared with the Matching Pursuit (MP) and Orthogonal MP (OMP) algorithms that are commonly used in compressed sensing literature in order to estimate delay locations and tap coefficients of a sparse multipath channel. In this work, we propose a performance- efficient ANN based sparse channel estimator with lower computational cost than that of MP and OMP based channel estimators. Even though there is a slight performance lost in a few simulation scenarios in which we have lower computational complexity advantage, in most scenarios, our computer simulations corroborate that our low complexity ANN based channel estimator has better mean squared error and the corresponding symbol error rate performances comparing with MP and OMP algorithms.

Source

TELECOMMUNICATION SYSTEMS

URI

https://hdl.handle.net/20.500.12469/3987

Collections

  • Araştırma Çıktıları / Scopus [1565]
  • Araştırma Çıktıları / WOS [1518]

Keywords

Artificial neural networks
Sparse channel
Channel estimation
Compressed sensing
Matching pursuit
OFDM

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Contact Us | Send Feedback
Theme by 
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