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Download PDF by Michael L. Honig: Advances in Multiuser Detection (Wiley Series in

By Michael L. Honig

ISBN-10: 0471779717

ISBN-13: 9780471779711

A well timed Exploration of Multiuser Detection in instant NetworksDuring the prior decade, the layout and improvement of present and rising instant structures have encouraged many vital advances in multiuser detection. This e-book fills an enormous want via delivering a complete assessment of an important fresh advancements that experience happened during this energetic study sector. every one bankruptcy is contributed through famous specialists and is intended to function a self-contained therapy of the subject. insurance includes:Linear and selection suggestions methodsIterative multiuser detection and decodingMultiuser detection within the presence of channel impairmentsPerformance research with random signatures and channelsJoint detection tools for MIMO channelsInterference avoidance tools on the transmitterTransmitter precoding equipment for the MIMO downlinkThis ebook is a perfect access element for exploring ongoing examine in multiuser detection and for studying concerning the field's current unsolved difficulties and matters. it's a worthy source for researchers, engineers, and graduate scholars who're serious about the realm of electronic communications.

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Extra info for Advances in Multiuser Detection (Wiley Series in Telecommunications and Signal Processing)

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Hence the D needed to achieve full-rank performance15 does 15 More precisely, “full-rank” performance refers to a target MSE, which can be arbitary close but not equal to the full-rank MSE. 24 OVERVIEW OF MULTIUSER DETECTION not depend on the dimension of the signal subspace, unlike the eigen-space methods. The extension of this analysis to non-uniform power distributions over the transmitted symbols is presented in [48,50]. Related results, which apply to iterative methods for computing the linear MMSE filter, are discussed in [78].

Computing Sk for eigen-space methods is generally more complex than for the other reduced-rank methods described here. This may be especially challenging in a scenario where the mixing matrix M is time-varying due to fading and changing interference. Eigen-space methods will be discussed further in Chapter 3 with more elaborate channel models that explicitly account for frequency-selective fading and multiple antennas. 3 Krylov Subspace Methods Given the covariance matrix R and the signature mk, the associated D-dimensional Krylov subspace is defined as:13 Kk ¼ span{mk , Rmk , R2 mk , .

4 Adaptive Rank Selection In practice, the parameters of the reducedrank filter can be estimated with an adaptive algorithm. For example, we can directly with the Krylov space by replacing R with the sample estimate the matrix Sk associated P ^ ¼ N y(i)yy (i). An estimate for the signature mk can also covariance matrix R i¼0 be obtained given a training sequence. For the MSWF, the filters w0 , . . , wDÀ1 and the combining coefficients a0 , . . , see [35]). With limited training or data samples for filter estimation, the choice of filter rank becomes important.

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Advances in Multiuser Detection (Wiley Series in Telecommunications and Signal Processing) by Michael L. Honig

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