Industrial applications can produce significant amounts of data that require low delay and high data rate communications. Multiple input multiple output filter bank multicarrier (MIMO-FBMC) communications employing offset quadrature amplitude modulation has been proposed for industrial big data (IBD) due to its reliability and high spectrum efficiency. One of the difficulties in implementing a MIMO-FBMC system is accurate channel estimation (CE). The main factor affecting CE performance is intrinsic imaginary interference, and conventional preamble-based CE is not effective in this case. Thus, in this paper, a low-complexity sparse adaptive CE scheme is proposed which is based on a dynamic threshold. This reduces the number of inner product calculations by considering only the columns of the measurement matrix greater than the threshold. Simulation results are presented which show that the proposed scheme is better than other well-known methods in terms of computational complexity and CE accuracy.

In frequency selective channel environment, channel estimation in hybrid precoding millimeter-wave (mmWave) massive multiple input multiple output (MIMO) system is a challenge issue. To solve this problem, we propose an effective channel estimation scheme for frequency selective channel, which is based on the training channel model in urban traffic environment. Considering that the practical mmWave MIMO channel is sparsity and the subcarrier multi-channels have the same sparse structure, we regard the channel estimation problem as the sparse channel recovery, and propose a multipath simultaneous matching tracking estimation method. It is assumed that the noise between the practical channels has a certain correlation, and the noise correlation has an impact on the selection of the optimal atomic support set in the process of channel recovery. Therefore, noise weighting is introduced in our proposed method. The simulation results prove the validity of this proposed method in frequency selective mmWave MIMO channel. Without increasing the complexity of the algorithm, the proposed method can achieve better local performance than the traditional classical methods.

The Internet of Things (IoT) has greatly facilitated our daily lives. Nevertheless, how to achieve higher spectral efficiency, large-scale device access, and lower latency for the next-generation IoT is still a challenge. Inspired by this, a nonorthogonal multiple access (NOMA)-assisted cognitive radio (CR) IoT network is proposed in this article, where the communication between the indoor secondary transmitter and secondary receivers is performed in the presence of an eavesdropper and under the constraint of secondary transmit power. In particular, we introduce simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) into the secondary network to assist the secondary transmitter to communicate with its receivers in different rooms. To characterize the reliability and security of the proposed system, we derive analytical approximate expressions for the outage probabilities (OPs) and intercept probabilities (IPs) by using the Gaussian–Chebyshev quadrature. With the aim of providing a deeper understanding, we also explore the impacts of transmission signal-to-noise ratios (SNRs), power allocation coefficient and the number of STAR-RIS elements on the system performance. Presented numerical results show that: 1) the OPs of near and far users gradually decrease with SNRs until floors appear at high SNR, and the floors of near user is always lower than that of far user; 2) IPs increasing with SNRs and near user is always less than far user, which proves that near user has better security; and 3) under appropriate parameters, the tradeoff between reliability and security of the considered system can be arisen.

The privacy protection and data security problems existing in the healthcare framework based on the Internet of Medical Things (IoMT) have always attracted much attention and need to be solved urgently. In the teledermatology healthcare framework, the smartphone can acquire dermatology medical images for remote diagnosis. The dermatology medical image is vulnerable to attacks during transmission, resulting in malicious tampering or privacy data disclosure. Therefore, there is an urgent need for a watermarking scheme that doesn't tamper with the dermatology medical image and doesn't disclose the dermatology healthcare data. Federated learning is a distributed machine learning framework with privacy protection and secure encryption technology. Therefore, this paper presents a robust zero-watermarking scheme based on federated learning to solve the privacy and security issues of the teledermatology healthcare framework. This scheme trains the sparse autoencoder network by federated learning. The trained sparse autoencoder network is applied to extract image features from dermatology medical image. Image features are undergone to two-dimensional Discrete Cosine Transform (2D-DCT) in order to select low-frequency transform coefficients for creating zero-watermarking. Experimental results show that the proposed scheme has more robustness to the conventional attack and geometric attack and achieves superior performance when comparing with other zero-watermarking schemes. The proposed scheme is suitable for the specific requirements of medical images, which neither changes the important information contained in medical images nor divulges privacy data.

Direction-of-arrival (DOA) estimation is the preliminary stage of communication, localization, and sensing. Hence, it is a canonical task for next-generation wireless communications, namely beyond 5G (B5G) or 6G communication networks. Both massive multiple-input multiple-output (MIMO) and millimeter wave (mmW) bands are emerging technologies that can be implemented to increase the spectral efficiency of an area, and a number of expectations have been placed on them for future-generation wireless communications. Meanwhile, they also create new challenges for DOA estimation, for instance, through extremely large-scale array data, the coexistence of far-field and near-field sources, mutual coupling effects, and complicated spatial-temporal signal sampling. This article discusses various open issues related to DOA estimation for B5G/6G communication networks. Moreover, some insights on current advances, including arrays, models, sampling, and algorithms, are provided. Finally, directions for future work on the development of DOA estimation are addressed.

In recent years, more and more attention has been paid to the unmanned aerial vehicle (UAV) cooperative task assignment. In order to complete the task with the lowest cost, some researchers use multi-objective optimization to solve the assignment problem. But few of them consider the complex dynamic scenarios. In this article, the time-varying resource supply and demands are provided by established digital twins (DTs) of UAVs and targets, thereby enabling accurate decision guidance for dynamic task assignment. It takes the scheduling cost, path cost, risk cost and total task time cost as the optimization objectives. To solve this model, an improved dynamic multi-objective adaptive weighted particle swarm Optimization algorithm (DMOAWPSO) is proposed. In the initialization stage, a heuristic method is used to increase the effectiveness of the solution. Besides, the adaptive mutation and subgroup methods are adopted to improve the diversity of the solution. Then, effective environment change detection and response strategies are designed to adapt to dynamic scenarios. Finally, the evaluation metrics are calculated in different instances. Compared with the popular and classic dynamic multi-objective algorithms, the simulation results verify that the proposed algorithm is effective and can cope with the environment changes better in solving the task assignment problem.