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 previous decade, the layout and improvement of present and rising instant platforms have stimulated many very important advances in multiuser detection. This ebook fills a huge want by way of delivering a entire evaluate of an important fresh advancements that experience happened during this energetic examine zone. each one bankruptcy is contributed by way of famous specialists and is intended to function a self-contained therapy of the subject. assurance 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 equipment 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 priceless source for researchers, engineers, and graduate scholars who're enthusiastic about the world 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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This is further discussed later in this chapter and in Chapter 2. 2 Optimal (Maximum a Posteriori ) Detection The Maximum a Posteriori (MAP) detector selects: ^ ¼ arg max Pr(b transmitted j y received), b b (1:9) which minimizes the probability of error. This is the same as the ML estimate if the symbols are equally likely. However, when combined with error control coding and iterative soft decoding, the decoder can pass reliability information to the multiuser detector in the form of the a priori distribution, or likelihood ratio for each transmitted symbol.

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).

This also reduces the complexity associated with filter estimation. Of course, this generally comes at the cost of an increase in MSE. , is full column rank), as D increases, the MSE decreases, and when D ¼ N the MSE becomes the MMSE. That is because ck is no longer constrained to lie in a lower dimensional subspace. Of course, the performance (MSE) depends on the choice of Sk. A few different methods for constructing Sk have been proposed in the literature, and offer different tradeoffs between performance and complexity.

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

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