Multiuser Detection (MUD)
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01-03-2011, 10:32 AM



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Multiuser Detection (MUD)
Abstract
Introduction

Multiuser Detection (MUD) techniques are one of the most important recent advances in communications technology. MUD deals with the optimal detection of mutually interfering digital streams of information that occur in various communication systems based on TDMA, FDMA, CDMA etc.In this project and implimentation, the specific case of CDMA MUD based on pseudo-random signature sequences will be considered. Specifically, the MMSE optimization criterion will be considered in detail and the convergence charaterestics of various iterative techniques will be studied. A gradient project and implimentationion algorithm for implementing generalized MMSE multiuser detection is also presented.
Motivation for MUD.
The conventional wisdom for demodulating mutually interfering digital streams of information was the matched-filter followed by a detector where, each matched filter was matched to its corresponding signature sequence. In this techique, the cross-talk between users is neglected as additive white gaussian noise. In reality, the cross-talk between users is higly structured with the structure being described in some sense by the cross-correlation matrix of the signature sequences. Thus a detector that takes into account, the structure of the cross-talk can provide better performance than one that ignores it as noise. This is the basic motivation behind the multiuser detection techniques - the exploitation of the information contained in the cross-talk to provide optimal detection.
1.1. Overview of the project and implimentation .
• The second section of the report describes the mutliple access model used for the project and implimentation.
• The third section of the report desribes Optimal Multiuser Detection and the problems
associates with it.
• The fourth section describes the Generalized Multiuser Detector and an implementation of it using the gradient project and implimentationion method.
• The fifth and sixth sections present the results and code for the Gradient Projection algorithm respectively.
• the seventh section describes the problem of Linear Multiuser Detection and various solution methods.
• The Eight and Ninth sections present the results and code for Linear MMSE MUD respectively.
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