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Abstract Hybrid precoding is a promising technique for millimeter wave (mmWave) large-scale multiple-input multiple-output (MIMO) systems. This paper considers a hybrid precoding architecture which is composed of a reductive digital precoder and a phase-only control radio frequency (RF) precoder, and proposes a Givens rotation based column-wise hybrid precoding algorithm. In the proposed algorithm, the digital precoding matrix and the RF precoding matrix are alternatively optimized column by column. For the optimization of the constant modulus RF precoding vector, it is interesting that the optimal solution can be expressed as a product of an arbitrary constant modulus vector and a series of Givens rotation matrices. Accordingly, the original problem is transformed into an unconstrained problem of the Givens rotations, which is easy to be solved. After obtaining the optimal RF precoding vector, the optimal digital precoding vector can be given through the least squares method. Simulation results show that the proposed algorithm can approach the performance of the fully digital precoder effectively and enjoy a higher spectral efficiency than previous works under various channel environments and different system settings. [ABSTRACT FROM AUTHOR]

In this paper, we propose a blind channel estimation and signal retrieving algorithm for two-hop multiple-input multiple-output (MIMO) relay systems. This new algorithm integrates two blind source separation (BSS) methods to estimate the individual channel state information (CSI) of the source-relay and relay-destination links. In particular, a first-order Z-domain precoding technique is developed for the blind estimation of the relay-destination channel matrix, where the signals received at the relay node are pre-processed by a set of precoders before being transmitted to the destination node. With the estimated signals at the relay node, we propose an algorithm based on the constant modulus and signal mutual information properties to estimate the source-relay channel matrix. Compared with training-based MIMO relay channel estimation approaches, the proposed algorithm has a better bandwidth efficiency as no bandwidth is wasted for sending the training sequences. Numerical examples are shown to demonstrate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Due to the unknown platform motion and/or signal propagation delays, phase errors are introduced to synthetic aperture radar (SAR) imagery. With the fact that the pixel values in the low-return region of the focused image are close to zero, we devise two autofocus SAR imaging methods with phase error correction in this paper. One is a fractional model to maximize the pixel values in the remaining region, termed high-return region, while minimizing those in the low-return region. And another is a model with the high-return region pixel value maximization and the controlled peak low-return pixel value. The resultant optimization problems are difficult to solve due to the coupled numerator and denominator of the quadratic fractional programming problem formulation with the constant modulus constraints and the controlled optimization problem with the nonconvex inequality constraints. To tackle them, we derive two iterative solutions via introducing auxiliary variables to decouple the numerator and denominator and the inequality constraints. Alternating direction method of multipliers is utilized in the algorithm development so that their convergence is guaranteed. Numerical examples are provided to show the effectiveness of the developed methods. [ABSTRACT FROM AUTHOR]

In this paper, we study waveform optimization methods for transmit beampattern matching with a multiple-input-multiple-output (MIMO) radar. The aim is to minimize the squared error between the desired beampattern and the actual one. We propose two algorithms, which are based on cyclic optimization and majorization-minimization (MM), to tackle the highly nonlinear and non-convex optimization problem. The proposed algorithms have simple closed-form solutions at every iteration, which reduce the computational complexity significantly. Moreover, the convergence of the proposed methods can be guaranteed, due to the descent property of MM algorithms and cyclic optimization. In addition, we extend the proposed algorithm to design waveforms for dual-function radar-communication systems. Finally, simulation results demonstrate the superiority of the proposed algorithms compared with state-of-the art methods. [ABSTRACT FROM AUTHOR]

This paper investigates the robust cognitive joint design of transmit waveform and receive filter to improve the system performance of airborne multiple-input multiple-output (MIMO) radar. Considering the target Doppler frequency and spatial cone angle uncertainties are present, we formulate the averaged signal-to-clutter-plus-noise ratio (SCNR) as the optimization goal. Specifically, the averaged SCNR is maximized under only constant modulus constraint and that under constant modulus and similarity constraints. Four iterative optimization algorithms are developed to deal with the joint design problem. The first kind of iterative optimization algorithm is based on semidefinite programming (SDP) relaxation and randomization or rank-one decomposition techniques. The second kind of computational efficient iterative optimization algorithm utilizes the fractional programming and power method-like iteration. The proposed algorithms can achieve a monotonic output SCNR enhancement and are robust against the inaccuracies of target parameters. Several simulations results are implemented to validate the superiority of the proposed algorithms considering the output SCNR, space-time beampattern and computational complexity. [ABSTRACT FROM AUTHOR]