For massive multiple-input multiple-output (m-MIMO) uplink, the performances of the linear minimum mean-square error (MMSE) detector are considered near optimal, and they occupy benchmark place for most linear iterative detectors. However, the MMSE algorithm is known by its load computational complexity due to the implication of large-scale matrix inversions, and in other hand, iterative methods are often preferred in signal detection because of its low complexity. In this paper, we propose a New Damped Jacobi (NDJ) detector in order to improve the performance of the classical Jacobi linear algorithm. Starting from the classical Jacobi technique to our new proposal, we go through the development of two variants; one uses a damping factor and the other uses a stair-matrix. However, the NDJ incorporates a damping factor in its construction and basing also on stair matrix instead of diagonal matrix. The performances in terms of convergence and low complexity of each Jacobi variant studied in this paper are analyzed. Finally, some simulation examples are given to illustrate the advantages of the new proposed algorithm.

The radar analyst can develop and use mathematical and statistical techniques that lead to accurate prediction or adapting models for estimating the target detection performance. In radar detection theory, detection probability, false alarm probability, number of samples non-coherently integrated for a detection test, and signal-to-noise ratio (SNR) are closely interrelated. The present paper is intended to provide an overview of the calculations of radar probability of detection and its related parameters. The main methods and procedures for predicting the detection performance of either non-fluctuating or fluctuating targets are described. Performance’s analysis of the studied models is included, along with some graphical simulation examples.

This paper deals with the problem of the direction of arrival (DOA) estimation for diverse systems of wireless communication using an antenna array. This study provides an improved version of Capon’s direction of arrival algorithm. In fact, the proposed version uses an upper-triangular matrix extracted from the covariance matrix instead of the entire covariance matrix. The simulation results demonstrate that our proposed scheme can significantly improve the accuracy of direction of arrival estimation with low computation complexity.

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