Elements of information theory
Elements of information theory
Multiuser Detection
Statistical Multisource-Multitarget Information Fusion
Statistical Multisource-Multitarget Information Fusion
Sequential estimation of multipath MIMO-OFDM channels
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
On the sphere-decoding algorithm I. Expected complexity
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Signal Processing
Online activity detection in a multiuser environment using the matrix CUSUM algorithm
IEEE Transactions on Information Theory
Closest point search in lattices
IEEE Transactions on Information Theory
On maximum-likelihood detection and the search for the closest lattice point
IEEE Transactions on Information Theory
Multiuser Detection in a Dynamic Environment– Part I: User Identification and Data Detection
IEEE Transactions on Information Theory
Identification of active users in synchronous CDMA multiuser detection
IEEE Journal on Selected Areas in Communications
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
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Optimum multiuser detection (MUD) with an unknown number of active users requires simultaneous estimation of the active-user set, their unknown parameters and their transmitted data. Recent advances in MUD have shown that optimum receivers for this system model can be obtained using random-set theory (RST), a widely used tool in radar signal processing. Despite the capability of generalizing standard MUD to the case of unknown number of users, RST offers a way to perform detection of user log in and out by means of Bayesian recursions (BR). While previous works have concentrated on the definition of optimal detectors, design of efficient receivers was not addressed. Indeed, implementation of optimum detectors may be limited by their complexity, which grows exponentially with the number of potential users. The aim of this paper is to show that this computational burden can be drastically reduced, with little or no loss of performance, by applying a suitable version of the sphere detection (SD) algorithm. If users' continuous parameters are known, SD algorithm allows exact implementation of the optimal detector under one-shot scenarios at polynomial complexity for moderate signal-to-noise ratio (SNR), while requiring a suitable approximation of BRs in a dynamic environment. The approach is also extended for cases wherein continuous parameters are unknown. The developed detectors are compared against their optimal counterparts and their effectiveness is shown through numerical simulations.