S. M. Kay. Fundamentals of Statistical Signal Processing: Estimation Theory. PrenticeHall, Englewood Cli#s, New Jersey, 1993. ISBN 0-13-042268-1. Home/Search Document Not in Database Summary Related Articles Check |
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Transmit Signal Design for Optimal Estimation of Correlated.. - Kotecha, Sayeed (2002) (Correct)
....has to be estimated, typically using pilot symbols. In a rich scattering environment, the assumption of i.i.d. channels is valid and multi input multi output (MIMO) channel estimation can be straightforwardly done using, for example, least squares or minimum mean square error (MMSE) techniques [4] [5]. However, this idealized assumption does not necessarily hold and hence a study of correlated channels is of interest. In this work, we investigate transmit signal design for optimal estimation of correlated MIMO Rayleigh fading channels, assuming that the receiver and the transmitter have ....
....the true block length for correlated channels by assigning zero power to some of the transmit virtual angles. III. MMSE AND MAP ESTIMATION The model (16) is linear in h V and Gaussian. Hence, it can be shown that the linear MMSE estimate, the MMSE estimate and the MAP estimate are identical [5]. In this paper, we assume The number of unknowns in hV would be smaller in correlated channels. If the prior variance of a given element of hV is zero, then it implies that the element is itself zero. that the covariance matrix R V = E(h V h V ) or equivalently R = E(hh ) is known. The ....
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S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory. Upper Saddle River, NJ 07458: Prentice-Hall PTR, 1998.
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....peaks. However, for parametric methods, the notion of resolution is not intuitive and we will give a specific definition of this notion later in this thesis. Another goal of this thesis is to compare the performance of each method. We will naturally compare them to the Cramr Rao Lower Bound (CRLB) [9], which is a lower bound on the covariance matrix of any unbiased estimator. Throughout this thesis, we will consider Uniform Linear Arrays at both the transmitter and the receiver sites. We will assume propagation in the horizontal plane in a multipath environment. The number of multipath rays, ....
S. Kay. Fundamentals of statistical signal processing: Estimation theory. Prentice Hall International Editions, Englewood Cliffs, NJ, 1993.
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....data such as in the case of the received composite signal in multi user communication systems. A numerical solution is needed in this case. Newton Raphson and scoring methods have been used to iteratively compute an ML estimate. However, these two approaches suffer from convergence problems [34]. A third iterative method, the expectation maximization (EM) algorithm, provides guaranteed convergence to a local maximum under mild conditions [8] 23] Feder and Weinstein apply the expectation maximization (EM) algorithm to parameter estimation of superimposed signals [17] Bit sequence ....
.... of the spatial temporal channel matrix, H k;p , for k = 1; Delta Delta Delta ; K and p = 1; Delta Delta Delta ; P , see Appendix B for a detailed derivation) k;p (i) k (i) 4:40) Since H k;p = C k;p F k;p with known C k;p , we will use a least squares (LS) criterion [34] to obtain the unknown matrix F k;p . The LS estimate can be found by minimizing 75 . ....
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S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice-Hill, Inc., 1993.
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