Accurate images reconstructed from limited computed tomography (CT) data are desired when reducing the X-ray radiation exposure imposed on patients. The total variation (TV), known as the l-norm of the image gradient magnitudes, is popular in CT reconstruction from incomplete projection data. However, as the projection data collected are from a sparse-view of the limited scanning angular range, the results reconstructed by a TV-based method suffer from blocky artifact and gradual changed artifacts near the edges, which in turn make the reconstruction images degraded. Different from the TV, the ℓ-norm of an image gradient counts the number of its non-zero coefficients of the image gradient. Since the regularization based on the ℓ-norm of the image gradient will not penalize the large gradient magnitudes, the edge can be effectively retained. In this work, an edge-preserving image reconstruction method based on l-regularized gradient prior was investigated for limited-angle computed tomography from sparse projections. To solve the optimization model effectively, the variable splitting and the alternating direction method (ADM) were utilized. Experiments demonstrated that the ADM-like method used for the non-convex optimization problem has better performance than other classical iterative reconstruction algorithms in terms of edge preservation and artifact reduction.
In this paper, the investigation of TomlinsonHarashima precoding (THP) in downlink multiuser multipleinput multiple-output (MIMO) systems with imperfect channel state information (CSI) is carried out, including two basic structures according to the positions of the diagonal weighted filter: decentralized at the receivers or centralized at the transmitter, which are named as dTHP or cTHP, respectively. The interference power in layers caused by imperfect CSI is first derived, then the comparison between these two THP structures and the linear zero-forcing (ZF) precoding is performed, in terms of interference power and system capacity. Moreover, the independence of the interference powers among layers is presented. Simulation results indicate that with imperfect CSI, THP leads to smaller interference power than linear-ZF, and dTHP leads to larger system capacity than cTHP and linear-ZF.Index Terms-Tomlinson-Harashima precoding (THP), multiple-input multiple-output (MIMO), multiuser, imperfect channel state information (CSI).
Abstract-This paper proposes a novel blind carrier frequency offset (CFO) estimator, namely the sparse recovery assisted CFO (SR-CFO) estimator, for the uplink orthogonal frequencydivision multiple access (OFDMA) systems. By exploiting the sparsity embedded in the OFDMA data, the CFO estimation is formulated as an optimization problem of sparse recovery with high-resolution. Meanwhile, in order to enhance the estimation accuracy of CFOs, background noise and sampling errors are mitigated by exploiting the structure of the noise covariances matrix in the transformed observation data, and the asymptotic distribution of the sampling errors. Furthermore, we propose an approach for deriving the regularization parameter used by the SR-CFO estimator, so as to control the trade-off between the data fitting error and the sparsity of solution. The performance of the proposed SR-CFO estimator along with other four existing estimators is investigated and compared. Numerical results show that the proposed SR-CFO estimator is superior to the state-ofthe-art estimators in terms of the estimation reliability.Index Terms-OFDMA, carrier frequency offset, estimator, sparse recovery.
In order to deploy minimum number of unmanned aerial vehicle (UAV)-mounted mobile base stations (MBSs) to service all given ground terminals, this paper proposes an MBS placement based on sparse recovery (MBS-PBSR) algorithm. By exploiting the sparsity inherent in the differences between any two dedicated MBSs, the problem of UAV-mounted MBS placement could be formulated as an 0-norm constrained optimization problem, which is then be solved by the reweighted 1-norm method. Subsequently, the resulted solutions to the MBS placement are adjusted by the iterative redundant circle deletion algorithm, eventually leading to the redundant MBSs removal as much as possible. Simulation results demonstrate that our proposed MBS-PBSR algorithm works well with affordable computational complexity, and is nearly optimum in the sense of the number of deployed UAV-mounted MBSs. INDEX TERMS Unmanned aerial vehicle, mobile base station, sparse recovery.
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