@article {IOPORT.05830259,
    author       = {Fernandes, Carlos A.R. and Favier, G\'erard and Mota, Jo\~ao C.M.},
    title        = {PARAFAC-based channel estimation and data recovery in nonlinear MIMO spread spectrum communication systems.},
    year         = {2011},
    journal      = {Signal Processing},
    volume       = {91},
    number       = {2},
    issn         = {0165-1684},
    pages        = {311-322},
    publisher    = {Elsevier Science, Amsterdam},
    doi          = {10.1016/j.sigpro.2010.07.010},
    abstract     = {Summary: A new tensorial modeling is first proposed for nonlinear multiple-input multiple-output (MIMO) direct sequence spread spectrum communication systems. The channel is modeled as an instantaneous MIMO Volterra system. Then, a direct data approach for joint blind channel estimation and data recovery is developed using the parallel factor (PARAFAC) decomposition of a third-order tensor composed of received signals, exploiting space, time and code diversities. A blind channel estimation method based on the PARAFAC decomposition of a fifth-order tensor composed of covariances of the received signals is also proposed, considering phase shift keying (PSK) modulated transmitted signals. The proposed estimation algorithms are evaluated by simulating a nonlinear uplink MIMO radio over fiber (ROF) communication system.},
    identifier   = {05830259},
}