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23
Blind joint maximum likelihood channel estimation and data detection for singleinput multipleoutput systems
 in Proc. 6th IEE Int. Conf. 3G & Beyond
"... Abstract — Blind and semiblind adaptive schemes are proposed for joint maximum likelihood (ML) channel estimation and data detection for multipleinput multipleoutput (MIMO) systems. The joint ML optimisation over channel and data is decomposed into an iterative twolevel optimisation loop. An effi ..."
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Cited by 24 (14 self)
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Abstract — Blind and semiblind adaptive schemes are proposed for joint maximum likelihood (ML) channel estimation and data detection for multipleinput multipleoutput (MIMO) systems. The joint ML optimisation over channel and data is decomposed into an iterative twolevel optimisation loop. An efficient global optimisation search algorithm called the repeated weighted boosting search is employed at the upper level to identify the unknown MIMO channel model while an enhanced ML sphere detector called the optimised hierarchy reduced search algorithm aided ML detector is used at the lower level to perform the ML detection of the transmitted data. A simulation example is included to demonstrate the effectiveness of these two schemes. I.
Particle swarm optimization aided orthogonal forward regression for unified data modelling
 IEEE TRANS. EVOLUTION. COMPUT
, 2010
"... We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave ..."
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Cited by 8 (4 self)
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We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leaveoneout (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in twoclass classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunablenode RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixednode RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.
Joint channel estimation and multiuser detection for SDMA/OFDM based on dual repeated weighted boosting search
 IEEE Transactions on Vehicular Technology
, 2011
"... Abstract—A joint channel estimation and multiuser detection (JCEMUD) scheme is proposed for multiuser multipleinput–multipleoutput (MIMO) spacedivision multipleaccess/ orthogonal frequencydivisionmultiplexing (SDMA/OFDM) systems. We design a dual repeated weighted boosting search (DRWBS) sch ..."
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Cited by 8 (3 self)
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Abstract—A joint channel estimation and multiuser detection (JCEMUD) scheme is proposed for multiuser multipleinput–multipleoutput (MIMO) spacedivision multipleaccess/ orthogonal frequencydivisionmultiplexing (SDMA/OFDM) systems. We design a dual repeated weighted boosting search (DRWBS) scheme for JCEMUD, which is capable of providing “soft ” outputs, which are directly fed to the forward error correction (FEC) decoder. The proposed DRWBSJCEMUD scheme iteratively estimates the channel impulse responses and detects the users ’ transmitted signals while exploiting the error correction capability of an FEC decoder to iteratively exchange information between the detector and the estimator. Furthermore, the proposed DRWBSJCEMUD scheme is capable of providing the loglikelihood ratios of the coded bits at low computational complexity (comparable with the singleuser scenario), which can directly be fed to the FEC decoder. The simulation results demonstrate that the proposed DRWBSJCEMUD scheme is capable of attaining a mean square error performance close to that of the ideal scenario of the leastsquare channel estimator associated with 100 % pilot overhead and narrows the discrepancy with respect to the optimal maximumlikelihood (ML) MUD associated with perfect channel knowledge. As an example, at Eb/N0 = 10 dB, a factorof0.756 complexity reduction was achieved at the cost of a 1dB performance penalty, in comparison with the MLMUD. Index Terms—Joint channel estimation and multiuser detection (JCEMUD), orthogonal frequencydivision multiplexing (OFDM), repeated weighted boosting search (RWBS), spacedivision multiple access (SDMA). I.
Construction of Tunable Radial Basis Function Networks Using Orthogonal Forward Selection
"... Abstract—An orthogonal forward selection (OFS) algorithm based on leaveoneout (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines an RBF node, namely, its center vector and diagonal covariance ..."
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Cited by 6 (4 self)
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Abstract—An orthogonal forward selection (OFS) algorithm based on leaveoneout (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines an RBF node, namely, its center vector and diagonal covariance matrix, by minimizing the LOO statistics. For regression application, the LOO criterion is chosen to be the LOO meansquare error, while the LOO misclassification rate is adopted in twoclass classification application. This OFSLOO algorithm is computationally efficient, and it is capable of constructing parsimonious RBF networks that generalize well. Moreover, the proposed algorithm is fully automatic, and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF network construction procedure is demonstrated using examples taken from both regression and classification applications. Index Terms—Classification, leaveoneout (LOO) statistics, orthogonal forward selection (OFS), radial basis function (RBF) network, regression, tunable node. I.
PARTICLE SWARM OPTIMISATION AIDED SEMIBLIND JOINT MAXIMUM LIKELIHOOD CHANNEL ESTIMATION AND DATA DETECTION FOR MIMO SYSTEMS
"... A novel scheme of semiblind joint maximum likelihood (ML) channel estimation and data detection is proposed for multipleinput multipleoutput (MIMO) systems by decomposing the joint ML optimisation over channel and data into an iterative twolevel optimisation loop. Particle swarm optimisation (PSO ..."
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Cited by 3 (3 self)
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A novel scheme of semiblind joint maximum likelihood (ML) channel estimation and data detection is proposed for multipleinput multipleoutput (MIMO) systems by decomposing the joint ML optimisation over channel and data into an iterative twolevel optimisation loop. Particle swarm optimisation (PSO) is invoked at the upper level to identify the unknown MIMO channel while an enhanced ML sphere detector is used at the lower level to detect the transmitted data. The scheme is semiblind as a minimum pilot overhead is employed to aid the initialisation of the PSO based channel estimator. Index Terms — Multipleinput multipleoutput, joint maximum likelihood estimation, particle swarm optimisation 1.
Hybrid Wavelet Model Construction Using Orthogonal Forward Selection with Boosting Search
"... This paper considers sparse regression modeling using a generalized kernel model in which each kernel regressor has its individually tuned center vector and diagonal covariance matrix. An orthogonal least squares forward selection procedure is employed to select the regressors one by one using a gui ..."
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Cited by 3 (0 self)
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This paper considers sparse regression modeling using a generalized kernel model in which each kernel regressor has its individually tuned center vector and diagonal covariance matrix. An orthogonal least squares forward selection procedure is employed to select the regressors one by one using a guided random search algorithm. In order to prevent the possible overfitting, a practical method to select termination threshold is used. A novel hybrid wavelet is constructed to make the model sparser. The experimental results show that this generalized model outperforms traditional methods in terms of precision and sparseness. And the models with wavelet and hybrid kernel have a much faster convergence rate as compared to that with conventional RBF kernel. 1.
Orthogonal forward selection for constructing the radial basis function network with tunable nodes
 in Proc. Int. Conf. Intell. Comput
"... Abstract. An orthogonal forward selection (OFS) algorithm based on the leaveoneout (LOO) criterion is proposed for the construction of radial basis function (RBF) networks with tunable nodes. This OFSLOO algorithm is computationally efficient and is capable of identifying parsimonious RBF networ ..."
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Cited by 3 (2 self)
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Abstract. An orthogonal forward selection (OFS) algorithm based on the leaveoneout (LOO) criterion is proposed for the construction of radial basis function (RBF) networks with tunable nodes. This OFSLOO algorithm is computationally efficient and is capable of identifying parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process. 1
Identification of Nonlinear Systems Using Generalized Kernel Models
"... Abstract—Nonlinear system identification is considered using a generalized kernel regression model. Unlike the standard kernel model, which employs a fixed common variance for all the kernel regressors, each kernel regressor in the generalized kernel model has an individually tuned diagonal covarian ..."
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Cited by 2 (2 self)
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Abstract—Nonlinear system identification is considered using a generalized kernel regression model. Unlike the standard kernel model, which employs a fixed common variance for all the kernel regressors, each kernel regressor in the generalized kernel model has an individually tuned diagonal covariance matrix that is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. An efficient construction algorithm based on orthogonal forward regression with leaveoneout test statistic and local regularization is then used to select a parsimonious generalized kernel regression model from the resulting full regression matrix. The proposed modeling algorithm is fully automatic and the user is not required to specify any criterion to terminate the construction procedure. Experimental results involving two real data sets demonstrate the effectiveness of the proposed nonlinear system identification approach. Keywords—Nonlinear system identification, neural networks, regression, kernel model, orthogonal least squares, cross validation, leaveoneout
Joint timing and channel estimation for bandlimited longcodebased MCDSCDMA: a lowcomplexity nearoptimal algorithm and the CRLB, under review
"... for bandlimited longcodeaided MultiCarrier DirectSequence ..."
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Cited by 1 (0 self)
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for bandlimited longcodeaided MultiCarrier DirectSequence