Hongwei Wang

Wireless-powered Internet of Things networks face critical challenges due to the limited energy of sensor nodes and the difficulty of maintaining data freshness. To address these issues, this paper proposes a multi–unmanned aerial vehicle (UAV)-assisted data collection framework to minimize the age of information (AoI). A hierarchical multiple decision strategy (HMDS) is developed to optimize UAV hovering time by dynamically selecting non-orthogonal multiple access or orthogonal frequency division multiple access for intra-cluster transmission according to energy constraints and channel conditions. In addition, a multi-collaborative trajectory optimization (MCTO) scheme is proposed to reduce UAV flight time through joint clustering, partitioning, and trajectory planning. Specifically, radius-adaptive K-means++ clustering, load-balanced spectral partitioning, and a hybrid K-nearest neighbors and elite ant strategy are employed. Simulation results show that the proposed HMDS and MCTO significantly reduce hovering and flight times, thereby effectively improving AoI performance.

We consider the channel acquisition problem for a wideband terahertz (THz) communication system, where an extremely large-scale array is deployed to mitigate severe path attenuation. In channel modeling, we account for both the spherical wavefront and beam-splitting phenomena of the wide-band near-field channel. We propose a frequency-independent orthogonal dictionary that generalizes the standard discrete Fourier transform (DFT) matrix by introducing an additional parameter to capture near-field effects. This dictionary enables an efficient two-dimensional (2D) block-sparse representation of the wideband near-field channel. By leveraging this structured sparsity, the wideband near-field channel estimation problem can be effectively solved within a customized compressive sensing framework. Numerical results demonstrate the significant advantages of our proposed 2D block-sparsity-aware method over conventional polar-domain-based approaches for near-field wideband channel estimation.

Trajectory reconstruction is essential for localizing and tracking maritime vehicles, but non-Gaussian process and measurement noises, as well as time-varying measurement loss, are present in the marine environment. To enhance TR accuracy in such challenging scenarios, this article proposes a state sequence estimation method based on the Rauch-Tung-Striebel smoother, which fully leverages the distributional characteristics of outliers. This approach models the likelihood density of non- Gaussian noise using two multivariate Laplace distributions. Unknown noise covariances are then modeled via an inverse Wishart distribution, while unknown measurement losses and their occurrence probabilities are addressed using Bernoulli and Beta distributions, respectively. On this basis, Variational Bayes inference is utilized to decouple fused anomalous processes, and infer states within a hierarchical model, yielding the proposed robust smoother. Experiments with an Unmanned Underwater Vehicle and numerical simulations demonstrate the effectiveness and robustness of this proposed method for TR tasks.

Millimeter wave/Terahertz (mmWave/THz) communication with extremely large-scale antenna arrays (ELAAs) offers a promising solution to meet the escalating demand for high data rates in next-generation communications. A large array aperture, along with the ever increasing carrier frequency over the mmWave/THz bands, leads to a large Rayleigh distance. As a result, the traditional planar-wave assumption may not hold valid for mmWave/THz systems featuring ELAAs. In this paper, we consider the problem of hybrid near/far-field channel estimation by taking spherical wave propagation into account. By analyzing the coherence properties of any two near-field steering vectors, we prove that the hybrid near/far-field channel admits a block-sparse representation on a specially designed unitary matrix. Specifically, the percentage of nonzero elements of such a block-sparse representation is in the order of 1/√N, which tends to zero as the number of antennas, N, grows. Such a block-sparse representation allows to convert channel estimation into a block-sparse signal recovery problem. Simulation results are provided to verify our theoretical results and illustrate the performance of the proposed channel estimation approach in comparison with existing state-of-the-art methods.

In this paper, we consider the problem of downlink beam training for extremely large-scale millimeter wave (mmWave)/Terahertz (THz) systems, where the far-field assumption which treats wavefronts as planar waves may not hold valid. For such hybrid far/near-field channels, beam training needs to identify the best beam alignment on a two-dimensional angle-range domain. An exhaustive search scheme sequentially scanning the entire angle-range space incurs a high training overhead. To address this issue, in this paper, we propose an efficient hybrid far/near-field beam training method. By utilizing the approximate orthogonality of near-field steering vectors of the same effective distance, we devise a multi-directional beam training sequence which can more efficiently scan the entire angle-range space. Based on the devised beam training sequence, we develop a simple estimation method at the receiver that can simultaneously identify the angle and the range associated with the dominant path. Simulation results show that the proposed method achieves better performance than the exhaustive search scheme, while with a much lower overhead cost. The proposed method also presents a clear advantage over other existing state-of-the-art hybrid far/near-field beam training methods in terms of performance and generality.

We consider the problem of channel estimation and joint active and passive beamforming for reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We show that, with a well-designed frame-based training protocol, the received pilot signal can be organized into a low-rank third-order tensor that admits a canonical polyadic decomposition (CPD). Based on this observation, we propose a CPD-based method for estimating the cascade channels associated with different subcarriers. The proposed method exploits the intrinsic low-rankness of the CPD formulation, which is a result of the sparse scattering characteristics of mmWave channels, and thus has the potential to achieve a significant training overhead reduction. Specifically, our analysis shows that the proposed method has a sample complexity that scales quadratically with the sparsity of the cascade channel. Also, by utilizing the singular value decomposition-like structure of the effective channel, this paper develops a joint active and passive beamforming method based on the estimated cascade channels. Simulation results show that the proposed CPD-based channel estimation method attains mean square errors that are close to the Cramer-Rao bound (CRB) and present a clear ad- ´ vantage over the compressed sensing-based methods. In addition, the proposed joint beamforming method can effectively utilize the estimated channel parameters to achieve superior beamforming performance.

We investigate the problem of joint active and passive beamforming for intelligent reflecting surface (IRS)-aided multi-user multiple-input multiple-output (MIMO) communication systems where the base station (BS) adopts low-resolution DACs to reduce the power consumption and hardware complexity. The additive quantization noise model (AQNM) is employed to model the nonlinear quantization operation. Given a pre-specified minimum achievable rate for each user, an optimization which aims to minimize the transmit power is formulated. We devise an iterative algorithm based on alternating optimization and semi-definite relaxation (SDR) techniques. Numerical results verify the effectiveness of the proposed method.

In real applications, non-Gaussian distributions are frequently caused by outliers and impulsive disturbances, and these will impair the performance of the Rauch-Tung-Striebel (RTS) smoother. In this study, a modified RTS smoothing algorithm combined with the minimum error entropy (MEE) criterion (MEE-RTS) is developed, and by employing the Taylor series linearization method, it is also expanded to the state estimation of nonlinear systems. The proposed methods improve the robustness of the conventional RTS smoother against complex non-Gaussian noises. In addition, we examine the MEE-RTS smoother's mean error behavior, mean square error behavior, and computational complexity, and the performance of the proposed algorithms is verified by comparing it with existing RTS -type smoothers.

Bugs are inevitable as software complexity increases and bug reports are crucial for program maintenance. Due to the variety of language expressions, there will be duplicate bug reports where different reports correspond to the same bug. Existing methods for detecting duplicate bug reports are supervised, but manually labeling data is time-consuming and difficult because it requires expertise to identify the specific bug in the report. To reduce the dependence of current duplicate bug report detection methods on data labels, we expect to explore an unsupervised approach for detecting duplicate bug reports based on GCN. Firstly, since the bug report dataset has no prior graph information, we utilize the estimated distribution of connectivity between two disjoint sets in a bipartite graph to build a graph. Then, anchor points are introduced to address the problem that traditional graph autoencoders are not suitable for large-scale datasets. Finally, we conduct extensive experiments to evaluate the performance of our unsupervised approach. Our work shows that unsupervised approaches are feasible for duplicate bug report detection.