Hybrid-field channel estimation for extremely large-scale massive MIMO (XL-MIMO) system is discussed in this paper. By exploiting the structural characteristics of far-field path components in the angle domain and the sparsity of near-field path components in the polar domain, a channel estimation algorithm combining support detection and orthogonal matching pursuit (SD-OMP) is proposed. Specifically, the supports of the far-field path components are firstly detected according to components structure characteristics of the angle domain in the XL-MIMO system and then can be used to obtain the far-field path components. Next, effect on the XL-MIMO system induced by far-field path components can be removed firstly and the OMP algorithm is employed to obtain the near-field path components by exploiting its polar domain sparsity. Finally, the hybrid-field channel is recovered by superposing the given far-field path components and near-field path components. Experiment results show that the proposed algorithm can accurately recover channel with relatively low pilot overhead and computation complexity comparing with some classical channel estimation algorithms.

Aiming at high network energy consumption and data delay induced by mobile sink in wireless sensor networks (WSNs), this paper proposes a cluster-based energy optimization algorithm called Cluster-Based Energy Optimization with Mobile Sink (CEOMS). CEOMS algorithm constructs the energy density function of network nodes firstly and then assigns sensor nodes with higher remaining energy as cluster heads according to energy density function. Meanwhile, the directivity motion performance function of mobile sink is constructed to enhance the probability of remote sensor nodes being assigned as cluster heads. Secondly, based on Low Energy Adaptive Clustering Hierarchy Protocol (LEACH) architecture, the energy density function and the motion performance function are introduced into the cluster head selection process to avoid random assignment of cluster head. Finally, an adaptive adjustment function is designed to improve the adaptability of cluster head selection by percentage of network nodes death and the density of all surviving nodes of the entire network. The simulation results show that the proposed CEOMS algorithm improves the cluster head selection self-adaptability, extends the network life, reduces the data delay, and balances the network load.

Solving the nonconvex and non-differentiable objective function of traditional range-free node localization methods will result in low localization accuracy or high computational complexity. To this end, a novel iterative localization algorithm called CVX-DV-hop is proposed in this letter. It first performs matrix transformation to reformulate the original optimization problem into one with a convex function and nonconvex constraints. And then, the first-order Taylor expansions are employed to tighten the nonconvex constraints into linear inequality constraints. Finally, a successive convex approximation method is designed to iteratively solve the optimization problem. Simulation results show that the proposed algorithm has higher localization accuracy and lower computational burden than those of the particle swarm optimization algorithm.

This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper proposes an improved variational mode decomposition method with automatic optimization of the number of modes. This method overcomes the problems of the traditional VMD method, in that each parameter is set by experience and is greatly influenced by subjective experience. Secondly, the decomposed signal components are analyzed by correlation, and then high correlated components with the original signal are selected to reconstruct the original signal. The continuous wavelet transform (CWT) is employed to extract the two-dimensional time–frequency domain feature map of the fault signal. Finally, the deep learning method is used to construct a convolutional neural network. After feature extraction, the two-dimensional time-frequency image is applied to the neural network to identify fault features. Experiments verify that the proposed method can adapt to rotating machinery faults in complex environments and has a high recognition rate.

Physical layer security of wireless communication network with intelligent reflecting surface (IRS) is investigated in this letter. At the access point (AP) transmission power and user nonlinear energy harvest constraints, maximum secrecy rate of full-duplex (FD) user which employs simultaneous wireless information and power transfer (SWIPT) technology is achieved by jointly optimizing transmit beamformer of AP, IRS phase shift, and artificial noise power of FD user. Due to its undesirable properties, such as coupling of multiple optimization variables and non-convex objective function, original optimization problem is hard to be handled. We solved this problem by an alternating optimization algorithm consisting of semi-definite relaxation, Charnes-Cooper transform and continuous phase shifting discretization techniques to obtain a feasible suboptimal solution. Simulation results demonstrate that although may only guarantee to convergence a local optimal, proposed algorithm still achieves the best security rate comparing to the benchmark algorithms with the computational efficiency sacrificing.

In wireless sensor networks (WSNs), conventional clustering routing algorithms often lead to uneven distribution of cluster heads (CHs), resulting in imbalanced and high energy consumption. Addressing these challenges, this study introduces a novel algorithm, termed the energy distance function-based improved K-Means algorithm (EDFIKM). This algorithm synthesizes the principles of the low energy adaptive clustering hierarchy (LEACH) with the K-Means clustering methodology. Key to this approach is the formulation of an adaptive weighted energy distance function that accounts for nodes’ residual energy, their proximity to cluster centers, and dynamic weights for energy and distance factors. This function strategically mitigates the randomness in CH selection. Incorporating this function into the K-Means clustering process achieves a more uniform distribution of CHs and an equitable allocation of nodes across clusters. Importantly, this contributes to a balanced energy load and a reduction in overall network energy consumption. Simulation studies validate that the EDFIKM algorithm efficiently lowers network energy demands and extends the network lifespan, without adding to the time complexity.