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Download e-book for iPad: Advances in Multiuser Detection (Wiley Series in by Michael L. Honig

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 stimulated many vital advances in multiuser detection. This booklet fills a tremendous desire through delivering a accomplished evaluation of an important fresh advancements that experience happened during this energetic examine quarter. each one bankruptcy is contributed by way of famous specialists and is intended to function a self-contained therapy of the subject. insurance includes:Linear and determination 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 equipment on the transmitterTransmitter precoding tools for the MIMO downlinkThis ebook is a perfect access element for exploring ongoing learn in multiuser detection and for studying concerning the field's latest unsolved difficulties and matters. it's a helpful source for researchers, engineers, and graduate scholars who're excited about the realm of electronic communications.

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

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The MAP detector suffers from the same drawbacks as the ML detector, namely, the complexity grows exponentially with the size of b, and it requires knowledge of M. However, in some applications where the system size is relatively small, the complexity may be manageable. 3 Sphere Decoder Because of the high complexity of the optimal detector, it is generally desirable to incur some performance loss in order to simplify the receiver. That trade off motivates the linear and decision feedback detectors to be discussed.

AD, and off~ D ¼ VyDRVD [26]. Let R ance matrix R diagonal elements d2, . . , dD. That is, dn is the (n, n – 1)st, or (n – 1, n)th element ~ D. 45) (for derivations see [43] or [12]). The reduced-rank filter is c ¼ VD~cD, where ~cD is the D Â1 vector of combining coefficients, and vD,n denotes the nth element of vD. This algorithm can be used to increment the filter rank to any desired rank starting with the rank-one (matched) filter. 45) are an efficient way to compute the reduced-rank filter. , selects the desired filter rank).

26), and (ii) the rank needed to achieve near-MMSE (full-rank) performance is typically quite small. In fact, full-rank performance can often be achieved with a D much smaller than the dimension of the signal space. This type of reduced-rank filter has taken a few different forms in the literature14, and is closely related to the Lancosz and conjugate gradient algorithms for solving sets of linear equations. An overview of those techniques is given in [12]. Here we emphasize the connection to the Multi-Stage Wiener Filter (MSWF), presented in [26].

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

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