Pilot contamination resulting from pilot reuse seriously restricts the communication performance of the cell-free massive multiple-input multiple-output (MIMO) networks. To deal with this problem, an efficient pilot assignment scheme using a weighted graphic framework is proposed in this correspondence paper. Specifically, a novel metric is first introduced for capturing the severity of potential mutual pilot contamination in cell-free topology. On this basis, the weighted pilot contamination graph is constructed to depict the dynamic interference relationship corresponding to the network. Then, the pilot assignment optimization is mapped to the Max k-Cut problem, and a heuristic algorithm is exploited to realize pilot decontamination. Numerical simulation results reveal the superior performance of the proposed scheme, which is capable of achieving a significant improvement in throughput with low complexity.

The time difference of arrival (TDOA) localization method has been widely applied in partial discharge (PD) localization in substations based on an ultra-high-frequency (UHF) antenna array. However, TDOA errors obtained from different UHF antennas can result in a poor localization accuracy. In this paper, a novel nonlinear transform localization algorithm based on regularization for PD in substations is proposed. First, to avoid the problem of difficult solutions and heavy calculations caused by solving nonlinear localization equations, the equations are transformed from nonlinear to linear by eliminating the second-order terms. Then, for the ill-conditioned problem of the localization equations caused by the antenna array positions and time difference errors, the equations are optimized by centralization and row balance to reduce their ill-conditioned degree. Finally, the Tikhonov regularization method is used to solve the localization equations with an ill-conditioned problem, and then, the PD position is determined. The experimental results show that the proposed localization algorithm can improve the accuracy of PD localization in substations and that the localization errors are within 2.23 m.

Denoising is a crucial step in the localization of partial discharge (PD) in a substation. In this paper, a novel multi-resolution generalized S-transform (GST) denoising algorithm for precise localization of PD in substations is proposed. The algorithm denoises the PD signal received by the ultra-high frequency (UHF) sensor in two steps. First, the GST with the high frequency resolution is used to analyze the PD signal, and a GST filter is designed to filter out periodic narrowband noises. Then, the S-transform (a particular case of GST) is applied to analyze the PD signal, and the white Gaussian noise is suppressed according to the statistical characteristic difference between the noise and effective signal. Finally, the denoised PD signal is obtained and applied to PD localization. The simulations results show that the proposed algorithm can effectively suppress noise and can extract accurate time delay data from the denoised PD signal. The experiment results show that, compared with the wavelet transform, the localization error of the proposed algorithm is the smallest, which is 1.59m. The proposed algorithm can realize the precise localization of PD.

Fault diagnosis of wind turbine planetary gearboxes is essential for maintaining their operational reliability; this paper introduces a novel method focused on fault diagnosis. Initially, raw vibration signals from the gearbox are transmitted to a data processing system where blind source separation and ensemble local mean decomposition are employed to extract sparse components. These sparse samples are used to optimize a deep fault diagnosis architecture based on multi-layer denoising autoencoders, which effectively extract fault features. By integrating the attention mechanism and chaotic quantum particle swarm optimization, the model enhances feature extraction, leading to improved fault classification accuracy. In two experiments based on different datasets, the diagnosis accuracy of fault types reaches 99.20% and 96.73%, respectively, while the diagnosis accuracy of corresponding fault severity is 99.12% and 93.60%. Experimental results validate the effectiveness of our method in the diagnosis of gearbox faults, demonstrating robust performance in complex operating environments.

The detection of partial discharge (PD) is a crucial method to evaluate the insulation status of transformers. The main difficulties of the current localization algorithms are the complexity of the solution and sensitivity to time delay errors. This article proposes a PD localization method in transformers based on linear conversion and density peak clustering (DPC). First, to reduce the complexity of solving the localization equations, the nonlinear localization equations are transformed into linear localization equations by eliminating the second-order terms. Then, to reduce the influence of time delay errors on localization accuracy, the initial localization values are located by multiple acoustic emission (AE) sensors. Finally, the optimal PD coordinates are determined by clustering the initial location values using density peaks clustering algorithm with automatic finding centers (AFC-DPC). The experimental results show that the proposed method can improve PD localization accuracy in transformers, and the average localization error is 5.30 cm.

In frequency-division duplexing mode, accurate channel state information (CSI) needs to be acquired at the base station by feedback from the terminals. However, the costly feedback overhead generated by large-scale arrays poses an arduous challenge to the traditional feedback scheme. In this letter, we propose a sparse learning-based CSI feedback method in a compressive sensing framework for massive MIMO systems. The key insights are to learn the sparse structure of CSI through the recursive least squares algorithm and process a continuous update of the sparse basis. Accordingly, a CSI sparse characteristic adaptive dictionary is constructed. The simulation results reveal that the proposed scheme achieves excellent performance in terms of both compression efficacy and recovery accuracy.