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Intelligent reflecting surface (IRS) is of low-cost and energy-efficiency and will be a promising technology for the future wireless communications like sixth generation. To address the problem of conventional directional modulation (DM) that Alice only transmits single confidential bit stream (CBS) to Bob with multiple antennas in a line-of-sight channel, IRS is proposed to create friendly multipaths for DM such that two CBSs can be transmitted from Alice to Bob. This will significantly enhance the secrecy rate (SR) of DM. To maximize the SR (Max-SR), a general non-convex optimization problem is formulated with the unit-modulus constraint of IRS phase-shift matrix (PSM), and the general alternating iterative (GAI) algorithm is proposed to jointly obtain the transmit beamforming vectors (TBVs) and PSM by alternately optimizing one and fixing another. To reduce its high complexity, a low-complexity iterative algorithm for Max-SR is proposed by placing the constraint of null-space (NS) on the TBVs, called NS projection (NSP). Here, each CBS is transmitted separately in the NSs of other CBS and AN channels. Simulation results show that the SRs of the proposed GAI and NSP can approximately double that of IRS-based DM with single CBS for massive IRS in the high signal-to-noise ratio region. [ABSTRACT FROM AUTHOR]

It is a serious problem that the performance loss is suffered by traditional Direction-of-Arrival (DOA) estimation methods in non-ideal environment, such as mutual coupling of array elements, coherent sources, colored noise and plethora targets. A data-driven robust DOA estimation framework is proposed for MIMO radar via deep neural networks (DNN), so as to overcome the problems mentioned before. The framework consists of an autoencoder, a feedforward network, a network parameters database and a series of parallel directed acyclic graph networks (DAGN). Assisted with feedforward network for target-number determination, matching parameters of networks will be loaded from database. The autoencoder acts like a noise filter, it reconstructs the noise-free covariance from the noisy signal and thus the generalization burden of the subsequent DOA estimation DAGN will be decreased. Each sub-network of the parallel DAGN consists of a convolutional neural network (CNN) and two bidirectional long short-term memory (BiLSTM) networks, from which the estimation of DOA will be obtained by regression. The simulation results show that the proposed method is superior to the traditional methods in a non-ideal environment, and can also perform well when the number of targets reaches the upper limitation of the degrees of freedom of MIMO radar. [ABSTRACT FROM AUTHOR]

The polarization millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system has been deployed in next-generation wireless communication since it can provide a high-data stream and high space efficiency simultaneously. Polarization channel parameter estimation for polarized mmWave massive MIMO systems is extremely important for directional beamforming with data transmissions. In this paper, the base station (BS) equipped with a large polarization-sensitive array is considered in massive MIMO systems. The polarization channel consists of direction-of-arrival (DOA) and polarization parameters that are estimated from the coexistence of circular and non-circular signals. Based on the unconjugated covariance matrix, the initialized polarization channel estimation is achieved by multiple signal classification (MUSIC). Then, the high-accuracy polarization channel estimation for general non-circular rate signals is performed by reconstructing the corresponding noise matrix. The high-accuracy polarization channel estimation for circular signals is obtained based on covariance matrix differencing. Moreover, the dimension of parameter search is reduced based on the partial derivative of the spectrum function with respect to the non-circular phase. The high-accuracy polarization channel estimation for the maximum non-circular rate signal is finally achieved based on the initialized parameter estimation of the polarization channel. The proposed algorithm estimates different kinds of signals separately. The effect of different kinds of signals is reduced significantly, which means that the resolution probability of different kinds of signals can be dramatically improved. Numerical examples are provided to demonstrate the performance of the proposed algorithm, especially in small angular distances. [ABSTRACT FROM AUTHOR]

In this letter, the direction-of-arrival (DOA) estimation of uniform linear array under unknown mutual coupling is dealt with by proposing a reweighted regularized sparse recovery algorithm. The proposed method first formulates a block-sparse representation model without mutual coupling compensation and array aperture loss. Then, a reweighted $l_{1}$ -norm minimization scheme is formulated to recover the block-sparse matrix, in which a weighted matrix is designed with a novel MUSIC-Like spectrum function for enhancing the sparsity of solution. Finally, the spatial spectrum of the recovered matrix is utilized to estimate DOAs. Due to using the whole array aperture and enhanced sparsity of solution, the proposed method can achieve superior performance than the existing regularized sparse recovery methods. Some simulation results are carried out to demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

A reweighted l1‐norm minimisation algorithm for high‐resolution direction‐of‐arrival (DOA) estimation under unknown mutual coupling is proposed in this Letter. First, the proposed method forms a new block representation model by parameterising the steering vector. Then, a reweighted l1‐norm constraint based on the new data model is proposed, in which the principle of a novel multiple signals classification (MUSIC)‐like algorithm is used to construct a weighted matrix. Finally, the DOAs can be achieved by the recovered sparse matrix. Owing to the use of the whole received data and reweighted procedure, the performance of the proposed method is better than the state‐of‐the‐art methods. Extensive simulation experiments confirm that the above inference is correct. [ABSTRACT FROM AUTHOR]

A reweighted nuclear norm minimisation algorithm by considering the inherent rank sparsity of the submatrix block for direction‐of‐arrival (DOA) estimation with unknown mutual coupling in a uniform linear array (ULA) is proposed. A novel block overcomplete dictionary is first derived by parameterising the steering vector to avoid the unknown mutual coupling effect. Then, in order to take advantage of the inherent rank sparsity of the submatrix block, a reweighted nuclear norm minimisation algorithm is introduced, and the weighted matrix can be designed by the Capon spectrum for enhancing the sparsity of scheme. Finally, the DOA estimates can be obtained by the spatial spectrum of the reconstructed matrix. Simulation results verify that the proposed method has distinct advantages over state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]