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Sparse channels estimation applied in software defined radio

Abstract : Communication channels are used to transmit information signals. However, these channels can cause several distortions on the signal to be transmitted, such as attenuation, multipath loss and Doppler shift, among others. For a better message recovery, the receiver can estimate the channel and bring more reliability to the communications systems. Several communications systems, for example high-definition television, mmWave system, wideband HF and ultra-wideband have sparse channels. This characteristic can be used to improve the performance of the estimator and reduce the size of the training sequence so decreasing the consumption power and bandwidth. This thesis handles the channel estimation problem by investigating methods that exploit the sparsity of the channel. The study of Compressive Sensing and its sparse recovery algorithms led to the proposition of a new algorithm called Matching Pursuit based on Least Square (MPLS). The use of neural networks (NN) to sparse signals estimation was also explored. The work focused on NN inspired by sparse recovery algorithms such as Learned Iterative Shrinkage-Thresholding Algorithm (LISTA). This resulted in two approaches that improve LISTA performance as well as to a new neural network suitable to estimate sparse signals.
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Submitted on : Friday, January 1, 2021 - 1:06:57 AM
Last modification on : Wednesday, June 15, 2022 - 8:42:28 PM
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  • HAL Id : tel-03091975, version 1


Elaine Crespo Marques. Sparse channels estimation applied in software defined radio. Signal and Image processing. Institut Polytechnique de Paris, 2019. English. ⟨NNT : 2019IPPAT004⟩. ⟨tel-03091975⟩



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