Application of SAGE algorithm in PET image reconstruction using modified ordered subsets

A new method that uses a modified ordered subsets （MOS） algorithm to improve the convergence rate of space-alternating generalized expectation-maximization （SAGE） algorithm for positron emission tomography （PET） image reconstruction is proposed.In the MOS-SAGE algorithm,the number of projections and the access order of the subsets are modified in order to improve the quality of the reconstructed images and accelerate the convergence speed.The number of projections in a subset increases as follows：2,4,8,16,32 and 64.This sequence means that the high frequency component is recovered first and the low frequency component is recovered in the succeeding iteration steps.In addition,the neighboring subsets are separated as much as possible so that the correlation of projections can be decreased and the convergences can be speeded up.The application of the proposed method to simulated and real images shows that the MOS-SAGE algorithm has better performance than the SAGE algorithm and the OSEM algorithm in convergence and image quality.

LMMSE-based SAGE channel estimation and data detection joint algorithm for MIMO-OFDM system

A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.

Measurement.Based Spatial-Consistent Channel Modeling Involving Clusters of Scatterers

In this paper,the conventional method of establishing spatial channel models(SCMs)based on measurements is extended by including clusters-of-scatterers(CoSs)that exist along propagation paths.The channel models resulted utilizing this new method are applicable for generating channel realizations of reasonable spatial consistency,which is required for designing techniques and systems of the fifth generation wireless communications.The scatterers’locations are estimated from channel measurement data obtained using large-scale antenna arrays through the Space-Alternating Generalized Expectation-Maximization(SAGE)algorithm derived under a spherical wavefront assumption.The stochastic properties of CoSs extracted from real measurement data in an indoor environment are presented.

SAGE-based algorithm for DOA estimation and array calibration in the presence of sensor location errors

The direction of arrival(DOA) estimation problem in the presence of sensor location errors is studied and an algorithm based on space alternating generalized expectation-maximization(SAGE) is presented. First, the narrowband case is considered.Based on the small perturbation assumption, this paper proposes an augmentation scheme so as to estimate DOA and perturbation parameters. The E-step and M-step of the SAGE algorithm in this case are derived. Then, the algorithm is extended to the wideband case. The wideband SAGE algorithm is derived in frequency domain by jointing all frequency bins. Simulation results show that the algorithm achieves good convergence and high parameter estimation precision.

DOA estimation and mutual coupling calibration with the SAGE algorithm

In this paper, a novel algorithm is presented for direction of arrival（DOA） estimation and array self-calibration in the presence of unknown mutual coupling. In order to highlight the relationship between the array output and mutual coupling coefficients, we present a novel model of the array output with the unknown mutual coupling coefficients. Based on this model, we use the space alternating generalized expectation-maximization（SAGE） algorithm to jointly estimate the DOA parameters and the mutual coupling coefficients. Unlike many existing counterparts, our method requires neither calibration sources nor initial calibration information. At the same time,our proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. By numerical experiments we demonstrate that our proposed method outperforms the existing method for DOA estimation and mutual coupling calibration.