Купить СНПЧ А7 Архангельск, оперативня доставка

crosscheckdeposited

Processamento Tensorial de Sinais Aplicado às Comunicações

DOI: http://dx.doi.org/10.12721/2237-5112/rtic.v5n2p14-18

http://www.rtic.com.br

downloadpdf

Lucas N. Ribeiro1, João C. M. Mota1 & André L. F. de Almeida1

 

Resumo: A álgebra multilinear surgiu no contexto de processamento de sinais como a ferramenta adequada para o processamento de sinais multidimensionais, bastante comum em Telecomunicações. Ferramentas multilineares como tensores e suas decomposições permitem o desenvolvimento de receptores capazes de explorar as múltiplas diversidades presentes em sistemas de comunicação. Neste trabalho apresentamos a aplicação do processamento tensorial de sinais em problemas de comunicação relevantes para o desenvolvimento das novas gerações de sistemas de telefonia móvel.

Palavras-chave: PARAFAC, álgebra multilinear, MIMO

 

1 Grupo de Pesquisa em Telecomunicações Sem Fio (GTEL). Universidade Federal do Ceará. Emails: fnogueira@gtel.ufc.br, mota@gtel.ufc.br, andreg@gtel.ufc.br

 

Literatura Citada

L. De Lathauwer, Signal processing based on multilinear algebra. Katholieke Universiteit Leuven, 1997.

A. Cichocki, D. P. Mandic, A. H. Phan, C. F. Caiafa, G. Zhou, Q. Zhao, and L. De Lathauwer, “Tensor decompositions for signal processing applications,” IEEE Signal Processing Magazine, vol. 32, no. 2, pp. 145–163, 2015.

P. Comon, “Tensors: a brief introduction,” IEEE Signal Processing Magazine, vol. 31, no. 3, pp. 44–53, 2014.

T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” SIAM review, vol. 51, no. 3, pp. 455–500, 2009.

N. K. M. Faber, R. Bro, and P. K. Hopke, “Recent developments in candecomp/parafac algorithms: a critical review,” Chemometrics and Intelligent Laboratory Systems, vol. 65, no. 1, pp. 119–137, 2003.

D. Nion and N. D. Sidiropoulos, “Adaptive algorithms to track the parafac decomposition of a third-order tensor,” IEEE Transactions on Signal Processing, vol. 57, no. 6, pp. 2299–2310, 2009.

A.-H. Phan, P. Tichavsky, and A. Cichocki, “Deflation method for CANDECOMP/PARAFAC tensor decomposition,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014. IEEE, 2014, pp. 6736–6740.

A. L. F. De Almeida and A. Y. Kibangou, “Distributed large-scale tensor decomposition,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014. IEEE, 2014, pp. 26–30.

G. Favier and A. L. de Almeida, “Overview of constrained parafac models,” EURASIP Journal on Advances in Signal Processing, vol. 2014, no. 1, pp. 1–25, 2014.

N. D. Sidiropoulos, G. B. Giannakis, and R. Bro, “Blind PARAFAC receivers for DS-CDMA systems,” IEEE Transactions on Signal Processing, vol. 48, no. 3, pp. 810–823, 2000.

N. D. Sidiropoulos and R. S. Budampati, “Khatri-Rao space-time codes,” IEEE Transactions on Signal Processing, vol. 50, no. 10, pp. 2396–2407, 2002.

A. L. de Almeida, G. Favier, and L. R. Ximenes, “Space-time-frequency (STF) MIMO communication systems with blind receiver based on a generalized paratuck2 model,” IEEE Transactions on Signal Processing, vol. 61, no. 8, pp. 1895–1909, 2013.

G. Favier and A. L. F. de Almeida, “Tensor space-time-frequency coding with semi-blind receivers for MIMO wireless communication systems,” IEEE Transactions on Signal Processing, vol. 62, no. 22, pp. 5987–6002, Nov 2014.

C.-X. Wang, X. Hong, X. Ge, X. Cheng, G. Zhang, and J. Thompson, “Cooperative MIMO channel models: A survey,” IEEE Communications Magazine, vol. 48, no. 2, pp. 80–87, 2010.

X. Yu and Y. Jing, “SVD-based channel estimation for MIMO relay networks,” in IEEE Vehicular Technology Conference (VTC Fall), 2012. IEEE, 2012, pp. 1–5.

P. Lioliou, M. Viberg, and M. Coldrey, “Efficient channel estimation techniques for amplify and forward relaying systems,” IEEE Transactions on Communications, vol. 60, no. 11, pp. 3150–3155, 2012.

F. Roemer and M. Haardt, “Tensor-based channel estimation and iterative refinements for two-way relaying with multiple antennas and spatial reuse,” IEEE Transactions on Signal Processing, vol. 58, no. 11, pp. 5720–5735, 2010.

L. R. Ximenes, G. Favier, A. L. de Almeida, and Y. C. Silva, “PARAFAC-PARATUCK semi-blind receivers for two-hop cooperative MIMO relay systems,” IEEE Transactions on Signal Processing, vol. 62, no. 14, pp. 3604–3615, 2014.

X. Han, A. L. de Almeida, and Z. Yang, “Channel estimation for MIMO multi-relay systems using a tensor approach,” EURASIP Journal on Advances in Signal Processing, vol. 2014, no. 1, pp. 1–14, 2014.

J. Li and P. Stoica, “MIMO radar with colocated antennas,” IEEE Signal Processing Magazine, vol. 24, no. 5, pp. 106–114, 2007.

L. Xu, J. Li, and P. Stoica, “Adaptive techniques for MIMO radar,” in Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006. IEEE, 2006, pp. 258–262.

D. Nion and N. D. Sidiropoulos, “Tensor algebra and multidimensional harmonic retrieval in signal processing for MIMO radar,” IEEE Transactions on Signal Processing, vol. 58, no. 11, pp. 5693–5705, 2010.

J. Da Costa, F. Roemer, M. Haardt, and R. T. de Sousa Jr, “Multidimensional model order selection,” EURASIP Journal on Advances in Signal Processing, vol. 26, pp. 1–13, 2011.

M. S. Pedersen, J. Larsen, U. Kjems, and L. C. Parra, “A survey of convolutive blind source separation methods,” Multichannel Speech Processing Handbook, pp. 1065–1084, 2007.

J.-F. Cardoso and A. Souloumiac, “Blind beamforming for non-gaussian signals,” in IEE Proceedings F (Radar and Signal Processing), vol. 140, no. 6. IET, 1993, pp. 362–370.

C. E. R. Fernandes, G. Favier, and J. C. M. Mota, “Blind channel identification algorithms based on the PARAFAC decomposition of cumulant tensors: the single and multiuser cases,” Signal Processing, vol. 88, no. 6, pp. 1382–1401, 2008.

L. De Lathauwer, J. Castaing, and J.-F. Cardoso, “Fourth-order cumulant-based blind identification of underdetermined mixtures,” IEEE Transactions on Signal Processing, vol. 55, no. 6, pp. 2965–2973, 2007.

P. Comon and M. Rajih, “Blind identification of under-determined mixtures based on the characteristic function,” Signal Processing, vol. 86, no. 9, pp. 2271–2281, 2006.

X. Luciani, A. L. De Almeida, and P. Comon, “Blind identification of underdetermined mixtures based on the characteristic function: the complex case,” IEEE Transactions on Signal Processing, vol. 59, no. 2, pp. 540–553, 2011.

C. A. R. Fernandes, J. C. M. Mota, and G. Favier, “MIMO volterra modeling for nonlinear communication channels,” Learning and Nonlinear Models, vol. 2, no. 8, pp. 71–92, 2010.

S. Benedetto, E. Biglieri, and R. Daffara, “Modeling and performance evaluation of nonlinear satellite links - A Volterra series approach,” IEEE Transactions on Aerospace and Electronic Systems, no. 4, pp. 494–507, 1979.

C. A. Fernandes, G. Favier, and J. C. M. Mota, “Blind identification of multiuser nonlinear channels using tensor decomposition and precoding,” Signal Processing, vol. 89, no. 12, pp. 2644–2656, 2009.