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.

With the widespread use of color images, the copyright protection of those images using watermarks is one of the latest research topics. The use of color images as watermarks has advantages over binary and irreplaceable grayscale images. Color images are intuitive, rich, and lively; they have large amounts of copyright protection information and more easily recognized by human vision. To improve the security of watermark information and embedding positions and improve the algorithm’s robustness against various attacks, a Quaternion Fourier transform (QFT) based algorithm, based on Arnold transform and chaotic encryption, is proposed in this paper. Geometric algebra (GA) can deal with color images in vector form with each component of RGB handled individually. We used Quaternion, which is a sub-algebra of GA, and effectively handled color image processing by using Fourier transformation. After deriving the calculation process of the QFT with strong security by Arnold scrambling and chaotic encryption, this paper proposes a digital watermarking algorithm that resists geometric attacks by using color images as carriers. The robustness and quality of the proposed watermarking algorithm is tested with different with many statistical measures. Experimental outcomes show that the proposed approach is the best to solve conflict problems between quality and robustness. Also, the proposed approach exhibits worthy robustness against many attacks, such as, conventional attacks, and geometrical attacks.

The advent of sixth-generation (6G) networks promises ultrahigh bandwidth, reliable connectivity, and ultralow latency, enabling large-scale Internet of Things (IoT) deployment. Network slicing is central to these capabilities, but conventional deep learning approaches often suffer from privacy risks, high computational cost, and poor energy efficiency. To address these challenges, this work proposes a federated and explainable artificial intellige (AI) framework for energy-efficient IoT slicing in 6G. Federated learning (FL) enables collaborative training without sharing raw data, preserving privacy and reducing communication overhead. A transformer-based model captures complex traffic patterns, while a hybrid swarm-intelligence optimizer balances throughput, latency, and energy consumption. SHAP-based explainability enhances transparency in slice allocation. Experiments on real and simulated traffic confirm superior performance, with the proposed framework achieving 98.42% accuracy compared to convolutional and long short-term memory network (CNN+LSTM, 92.67%) and Harris Hawks optimization with CNN+LSTM (HHO-CNN+LSTM, 95.12%). These results demonstrate a scalable, privacy-preserving, and sustainable solution for future 6G IoT ecosystems.

An augmented coprime array systematically employs two sparse subarrays to produce a large-scale difference co-array with attractive merits, such as enhanced degrees of freedom (DOFs) and enlarged array aperture, whereas the interleaved subarrays are susceptible to mutual coupling. In this paper, we propose an unfolded augmented coprime array (UACA) obtained by careful crafting of small sparse subarrays to fill the holes in the difference co-array generated by unfolding operation. Specifically, UACA can significantly reduce the number of sensor pairs with small spacing and hence inherently weaken the mutual coupling effect. Meanwhile, an increase of the DOFs and improved direction of arrival (DOA) estimation accuracy can be achieved in the presence of mutual coupling. As an application of UACA, we propose a decoupled interference-plus-noise covariance matrix (INCM) reconstruction method for robust adaptive beamforming (RAB) with UACA. Therein, mutual coupling coefficients are estimated based on the remodeled contaminated steering vector and the noise subspace. The estimated mutual coupling matrix is utilized to reconstruct the decoupled covariance matrix which in turn is used to obtain refined DOA estimates, interferer power estimates, and the desired INCM. Extensive simulation results are provided to verify the effectiveness of UACA and the decoupled INCM reconstruction method for RAB.