Estimation of Very Large MIMO Channels Using Compressed Sensing

Araújo, Daniel Costa; Almeida, André Lima Férrer de; Mota, João César Moura; Hui, Dennis

Use este identificador para citar ou linkar para este item: http://repositorio.ufc.br/handle/riufc/60180

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Campo DC	Valor	Idioma
dc.contributor.author	Araújo, Daniel Costa	-
dc.contributor.author	Almeida, André Lima Férrer de	-
dc.contributor.author	Mota, João César Moura	-
dc.contributor.author	Hui, Dennis	-
dc.date.accessioned	2021-08-31T15:33:16Z	-
dc.date.available	2021-08-31T15:33:16Z	-
dc.date.issued	2013	-
dc.identifier.citation	ARAÚJO, Daniel Costa; ALMEIDA, André Lima Férrer de; MOTA, João Cesar Moura; HUI, Dennis. Estimation of very large MIMO channels using compressed sensing . In: SIMPÓSIO BRASILEIRO DE TELECOMUNICAÇÕES - SBrT, XXXI., 01-04 dez. 2013, Fortaleza, CE. Anais[...], Fortaleza, CE., 2013.	pt_BR
dc.identifier.uri	http://www.repositorio.ufc.br/handle/riufc/60180	-
dc.language.iso	pt_BR	pt_BR
dc.publisher	https://www.sbrt.org.br/sbrt2013	pt_BR
dc.subject	Very-large MIMO channels	pt_BR
dc.subject	Compressed sensing	pt_BR
dc.subject	Sparsity	pt_BR
dc.subject	Channel estimation	pt_BR
dc.subject	Matching pursuit	pt_BR
dc.title	Estimation of Very Large MIMO Channels Using Compressed Sensing	pt_BR
dc.type	Artigo de Evento	pt_BR
dc.description.abstract-ptbr	In this paper, we propose an efficient pilot-assisted technique for the estimation of very-large MIMO (multiple-input multiple-output) channels exploiting the inherent sparsity of the channel. We first obtain an appropriate sparse decomposition model from a virtual channel representation of the very-large MIMO channel. Based on this model, we capitalize on a fundamental result of the compressed sensing (CS) to show that the channel matrix can be accurately estimated from very short training sequences compared to the number of used transmit antennas. We compare the normalized mean square error (NMSE) obtained using the proposed CS-based channel estimator, the least-square (LS) estimator and the Cramer-Rao lower bound (CRLB). The simulation results show that the proposed estimator obtains good performance, being 5 dB from the CRLB.	pt_BR
dc.title.en	Estimation of Very Large MIMO Channels Using Compressed Sensing	pt_BR
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