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

By Michael L. Honig

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 incredible want via delivering a accomplished review of an important fresh advancements that experience happened during this lively study sector. each one bankruptcy is contributed by way of famous specialists and is intended to function a self-contained remedy 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 publication is a perfect access element for exploring ongoing learn in multiuser detection and for studying concerning the field's current unsolved difficulties and matters. it's a precious source for researchers, engineers, and graduate scholars who're eager about the world of electronic communications.

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The resulting MMSE is: E dfd,kk ¼ 1 À myk RÀ1 U mk , (1:59) E[edfd eydfd ] is the error covariance matrix, which has the same form as where E dfd ¼ the MMSE for a linear receiver, except that R is replaced by R U. In the absence of error propagation, interference from all users in set D is therefore eliminated, and user k is affected only by the users in set U. 17 An alternative interpretation for the DFD filters can be obtained by minimizing the sum MSE: K h i X E dfd ], jdfd ¼ E jedfd,k j2 ¼ tr[E (1:60) k¼1 with respect to F and B.

An overview of those techniques is given in [12]. Here we emphasize the connection to the Multi-Stage Wiener Filter (MSWF), presented in [26]. We also present a rank-recursive algorithm, which recursively generates filters of increasing rank. That is useful for determining an appropriate filter rank in an adaptive mode. A further discussion of iterative methods for linear interference suppression is given in Chapter 2. 2. We observe that w2 in this block diagram can be viewed as an estimation filter for d1 given the input y1.

This is especially problematic at low SNRs when the detection (DFD) and decoding for error control are separated. 18 The effects of error propagation can be mitigated by using successive cancellation and demodulation rather than parallel cancellation. , user) is less than the capacity, treating the undecoded streams as 18 Computing an exact expression for the error probability associated with a particular transmitted stream, accounting for error propagation, is generally difficult. However, it is possible to determine the probability ^ is incorrect [59,83].

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