E.G. Gladyshev. "Periodically random sequences". Soviet Mathematics, 2, 1961. Home/Search Document Not in Database Summary Related Articles Check |
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Blind Channel Identification Based On Cyclic Statistics. - Luc Deneire (Correct)
....considerable interest. We propose to use these statistics in subspace fitting and linear prediction for (possibly multiuser and multiple antennas) channel identification. We base our identification schemes on the cyclic statistics, using the stationary multivariate representation introduced by [6] and [9] 10] This leads to the use of all cyclic statistics. The methods proposed, compared to classic approaches, have equivalent performance for the subspace fitting and enhanced performance for linear prediction. 1 Introduction Major impairments of most wireless communication channels, ....
....and linear prediction introduced with non cyclic statistics [14] which are suboptimal, but do not need complex numerical searches as the covariance matching method. In this paper, we introduce a new multichannel channel model derived from the stationary multivariate representation introduced by [6]. This representation allows us to derive the subspace fitting and linear prediction methods using the cyclic statistics. Algebraic considerations show that the cyclic subspace fitting has, in theory, the same performance as the non cyclic subspace fitting, although the cyclic approach is ....
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E.G. Gladyshev. "Periodically random sequences". Soviet Mathematics, 2, 1961.
Linear Prediction And Subspace Fitting Blind Channel.. - Luc Deneire (1997) (Correct)
....considerable interest. We propose to use these statistics in subspace fitting and linear prediction for (possibly multiuser and multiple antennas) channel identification. We base our identification schemes on the cyclic statistics, using the stationary multivariate representation introduced by [2] and [4] 5] This leads to the use of all cyclic statistics. The methods proposed appear to have good performance. 1. PROBLEM POSITION We consider a communication system with p emitters and a receiver constituted of an array of M antennas. The signals received are oversampled by a factor m ....
....TK (HN ) is the convolution matrix of HN = h(0) T h(1) T Delta Delta Delta h(N Gamma 1) T ] T and D fk;pg DFT = blockdiag[I p je Gamma 2 k m I p j Delta Delta Delta je Gamma 2 (N Gamma1)k m I p ] 3. GLADYSHEV S THEOREM AND MIAMEE PROCESS Gladyshev s theorem [2] states that : Theorem 1 Function R xx (n; is the correlation function of some PCS (Periodically Correlated Sequence) iff the matrix valued function : R( h R fkk 0 g xx ( i m Gamma1 k;k 0 =0 where R fkk 0 g xx ( R fk Gammak 0 g xx ( e 2 k =m is the ....
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E.G. Gladyshev. Periodically random sequences. Soviet Mathematics, 2, 1961.
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