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32
Algebraic Methods for Deterministic Blind Beamforming
, 1998
"... Deterministic blind beamforming algorithms try to separate superpositions of source signals impinging on a phased antenna array by using deterministic properties of the signals or the channels such as their constant modulus or directionsofarrival. Progress in this area has been abundant over the p ..."
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Cited by 30 (5 self)
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Deterministic blind beamforming algorithms try to separate superpositions of source signals impinging on a phased antenna array by using deterministic properties of the signals or the channels such as their constant modulus or directionsofarrival. Progress in this area has been abundant over the past ten years and has resulted in several powerful algorithms. Unlike optimal or adaptive methods, the algebraic methods discussed in this review act on a fixed block of data and give closedform expressions for beamformers by focusing on algebraic structures. This typically leads to subspace estimation and generalized eigenvalue problems. After introducing a simple and widely used multipath channel model, the paper provides an anthology of properties that are available, and generic algorithms that exploit them.
A globally convergent approach for blind MIMO adaptive deconvolution
 IEEE Trans. Signal Processing,vol.49,no.6,pp
"... Abstract—We discuss the blind deconvolution of multiple input/multiple output (MIMO) linear convolutional mixtures and propose a set of hierarchical criteria motivated by the maximum entropy principle. The proposed criteria are based on the constant–modulus (CM) criterion in order to guarantee that ..."
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Cited by 19 (0 self)
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Abstract—We discuss the blind deconvolution of multiple input/multiple output (MIMO) linear convolutional mixtures and propose a set of hierarchical criteria motivated by the maximum entropy principle. The proposed criteria are based on the constant–modulus (CM) criterion in order to guarantee that all minima achieve perfectly restoration of different sources. The approach is moreover robust to errors in channel order estimation. Practical implementation is addressed by a stochastic adaptive algorithm with a low computational cost. Complete convergence proofs, based on the characterization of all extrema, are provided. The efficiency of the proposed method is illustrated by numerical simulations. Index Terms—Blind adaptive source separation, constant modulus criterion, multiple input/multiple output convolutional systems. I.
Nonlinear Blind Source Separation Using a Radial Basis Function Network
 IEEE Trans. on Neural Networks
, 2001
"... This paper proposes a novel neuralnetwork approach to blind source separation in nonlinear mixture. The approach utilizes a radial basis function (RBF) neuralnetwork to approximate the inverse of the nonlinear mixing mapping which is assumed to exist and able to be approximated using an RBF networ ..."
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Cited by 16 (1 self)
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This paper proposes a novel neuralnetwork approach to blind source separation in nonlinear mixture. The approach utilizes a radial basis function (RBF) neuralnetwork to approximate the inverse of the nonlinear mixing mapping which is assumed to exist and able to be approximated using an RBF network. A contrast function which consists of the mutual information and partial moments of the outputs of the separation system, is defined to separate the nonlinear mixture. The minimization of the contrast function results in the independence of the outputs with desirable moments such that the original sources are separated properly. Two learning algorithms for the parametric RBF network are developed by using the stochastic gradient descent method and an unsupervised clustering method. By virtue of the RBF neural network, this proposed approach takes advantage of high learning convergence rate of weights in the hidden layer and output layer, natural unsupervised learning characteristics, modular structure, and universal approximation capability. Simulation results are presented to demonstrate the feasibility, robustness, and computability of the proposed method. Index TermsBlind source separation, nonlinear mixtures, radial basis function (RBF) neural networks, statistical independence, unsupervised learning.
Adaptive blind source separation and equalization for multipleinput/multipleoutput systems
 IEEE Transactions on Information Theory
"... Abstract—In this paper, we investigate adaptive blind source separation and equalization for multipleinput/multipleoutput (MIMO) systems. We first analyze the convergence of the constant modulus algorithm (CMA) used in MIMO systems (MIMOCMA). Our analysis reveals that the MIMOCMA equalizer is ab ..."
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Cited by 12 (2 self)
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Abstract—In this paper, we investigate adaptive blind source separation and equalization for multipleinput/multipleoutput (MIMO) systems. We first analyze the convergence of the constant modulus algorithm (CMA) used in MIMO systems (MIMOCMA). Our analysis reveals that the MIMOCMA equalizer is able to recover one of the input signals, remove the intersymbol interference (ISI), and suppress the other input signals. Furthermore, for the MIMO finite impulse response (FIR) systems satisfying certain conditions, the MIMOCMA FIR equalizers are able to perfectly recover one of the system inputs regardless of the initial settings. We then propose a novel algorithm for blind source separation and equalization for MIMO systems. Our theoretical analysis proves that the new blind algorithm is able to recover all system inputs simultaneously regardless of the initial settings. Finally, computer simulation examples are presented to confirm our analysis and illustrate the effectiveness of blind source separation and equalization for MIMO systems. Index Terms—Blind equalization, convergence, multipleinput/ multipleoutput system, source separation. I.
Asymptotic Properties of the Algebraic Constant Modulus Algorithm
, 2001
"... The algebraic constant modulus algorithm (ACMA) is a noniterative blind source separation algorithm. It computes jointly beamforming vectors for all constant modulus sources as the solution of a joint diagonalization problem. In this paper, we analyze its asymptotic properties and show that (un ..."
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Cited by 9 (4 self)
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The algebraic constant modulus algorithm (ACMA) is a noniterative blind source separation algorithm. It computes jointly beamforming vectors for all constant modulus sources as the solution of a joint diagonalization problem. In this paper, we analyze its asymptotic properties and show that (unlike CMA) it verges to the Wiener beamformer when the number of samples the signaltonoise ratio (SNR) goes to infinity. We also sketch its connection to the related JADE algorithm and derive a version of ACMA that converges to a zeroforcing beamformer. This gives impr oved performance in applications that use the estimated mixing matrix, such as in direction finding.
SpaceTime Modems for Wireless Personal Communications
 IEEE Personal Communications
, 1998
"... This article reviews spacetime modem technology for mobile radio applications. We begin with motivations for the use of spacetime modems and then briefly discuss the challenges posed by wireless propagation. Next, we develop a signal model for the wireless environment. Channel estimation, equaliza ..."
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Cited by 7 (0 self)
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This article reviews spacetime modem technology for mobile radio applications. We begin with motivations for the use of spacetime modems and then briefly discuss the challenges posed by wireless propagation. Next, we develop a signal model for the wireless environment. Channel estimation, equalization, and filtering techniques for spacetime modems in the forward and reverse links are then discussed. Finally we review applications of spacetime modems to cellular systems and discuss industry trends.
Blind multiuser detection using linear prediction
 IEEE J. Sel. Areas Commun
, 1998
"... Abstract — We propose a blind multiuser detection technique for array processing and code division multiple access (CDMA) systems that does not require knowledge of the array geometry or transmitter signature sequences. The technique has two key elements: an adaptive algorithm for separating the sig ..."
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Cited by 7 (1 self)
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Abstract — We propose a blind multiuser detection technique for array processing and code division multiple access (CDMA) systems that does not require knowledge of the array geometry or transmitter signature sequences. The technique has two key elements: an adaptive algorithm for separating the signal subspace from the noise subspace and an adaptive whitener based on linear prediction. The proposed algorithm offers low complexity, fast convergence, compatibility with shaped signal constellations, nearWiener steadystate performance, and optimal near–far resistance. Index Terms — Adaptive subspace separation, array processing, blind source separation, cochannel demodulation, subspace tracking. I.
Multipacket reception in wireless local area networks
 Proc. IEEE ICC'06
, 2006
"... protocols assume that only one packet can be received at a given time. However, with the advent of sophisticated signal processing and antenna array techniques, it is possible to achieve multipacket reception (MPR) in the physical layer (PHY). In this paper, we propose a PHY methodology and the corr ..."
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Cited by 6 (3 self)
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protocols assume that only one packet can be received at a given time. However, with the advent of sophisticated signal processing and antenna array techniques, it is possible to achieve multipacket reception (MPR) in the physical layer (PHY). In this paper, we propose a PHY methodology and the corresponding MAC protocol for MPR in wireless local area networks (WLANs). The proposed MAC protocol closely follows the 802.11 DCF (Distributed Coordination Function) scheme and enables MPR in a distributed manner. For the proposed MPR system, a closedform expression of the average throughput is derived. Based on the expression, an optimal transmission probability that maximizes the throughput can be attained. In addition, two enhancement schemes are presented to further improve the performance of the MPR protocol. Numerical results show that the proposed MPR system can considerably increase the spectrum efficiency compared to the WLANs with conventional collision models. Index Terms – WLAN, 802.11, MPR, crosslayer design. I.
Blind Signal Separation In Communications: Making Use Of Known Signal Distributions
 Proc. IEEE DSP Workshop
, 1998
"... We apply maximum likelihood blind source separation [7] to complex valued signals mixed with complex valued nonstationary matrices. This case arises in radio communications with baseband signals. We incorporate known source signal distributions in the adaptation, thus making the algorithms less "bli ..."
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Cited by 4 (0 self)
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We apply maximum likelihood blind source separation [7] to complex valued signals mixed with complex valued nonstationary matrices. This case arises in radio communications with baseband signals. We incorporate known source signal distributions in the adaptation, thus making the algorithms less "blind". This results in drastic reduction of the amount of data needed for successful convergence. Adaptation to rapidly changing signal mixing conditions, such as to fading in mobile communications, becomes now feasible as demonstrated by simulations in noisy conditions. 1. INTRODUCTION In SDMA (spatial division multiple access) the purpose is to separate radio signals of interfering users (either intentional or accidental) from each others on the basis of the spatial characteristics of the signals using smart antennas, array processing, and beamforming [6, 11]. Unsupervised methods either rely on information about the antenna array manifold, or properties of the signals. Former approaches mi...
Kammeyer “Comparison of blind source separation methods based on iterative algorithms
 5th International ITG Conference on Source and Channel Coding
, 2004
"... In this paper some approaches to detect signal streams of a multi layer transmission system are presented. We will focus on blind algorithms for the separation of the data stream and improve their performance in an iterative way in order to gain nearly the same performance as with a known channel ma ..."
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Cited by 4 (0 self)
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In this paper some approaches to detect signal streams of a multi layer transmission system are presented. We will focus on blind algorithms for the separation of the data stream and improve their performance in an iterative way in order to gain nearly the same performance as with a known channel matrix. The overall algorithm will remain blind and does not need any training data. 1