Geometric distortions and repetitive or weak textures lead to uneven geometric control across large-format aerial images, posing challenges to precise registration and fusion. To overcome these issues, we propose an adaptive spatially constrained registration and fusion framework. An improved Adaptive Adaptive Threshold RANSAC (At-RANSAC) integrates spatial geometric similarity (SGS) and neighborhood gray continuity (NGC) for robust mismatch elimination. The spatial distribution of matched points is optimized using Non-Maximal Suppression with Square Covering (NSSC) and Local Self-Similarity Correlation (LSCC) to ensure uniform geometric control. A multi-threaded TIN-based differential rectification achieves subpixel-accurate registration and fusion of PAN and multispectral images. Experiments on six aerial datasets show that our method reduces registration error from 1.1 to 0.3 pixels, improves efficiency nearly fivefold, and enhances image sharpness by over 2%, outperforming existing methods in accuracy, robustness, and computational efficiency.

This survey articles focuses on emerging connections between the fields of machine learning and data compression. While fundamental limits of classical (lossy) data compression are established using rate-distortion theory, the connections to machine learning have resulted in new theoretical analysis and application areas. We survey recent works on task-based and goal-oriented compression, the rate-distortion-perception theory and compression for estimation and inference. Deep learning based approaches also provide natural data-driven algorithmic approaches to compression. We survey recent works on applying deep learning techniques to task-based or goal-oriented compression, as well as image and video compression. We also discuss the potential use of large language models for text compression. We finally provide some directions for future research in this promising field.

Aerodynamic and aeroacoustic measurements in the wind tunnel are essential for the structural design and optimization of the aircraft. Microphone arrays are widely used in wind tunnels for the identification of aircraft noise sources, where the arrays are exposed to strong background interference. Meanwhile, the background interference is varied in different non-anechoic chamber acoustic experiments, and background interference directly affects the identification of noise sources. A generalized array denoising algorithm is proposed to address the interference problem. A hierarchical Dirichlet process is developed to deal with background interference that has non-independent and non-identical distribution characteristics between different microphone channels. At the same time, the sound source signal is also modelled based on its low-rank characteristic. All involved parameters in the model are estimated by the variational Bayesian algorithm. Then, the source signal can be separated from complex background interference. The denoising algorithm is also applied in simulations and wind tunnel experiments to verify its effectiveness and robustness in suppressing complex background interference suppression.

Reconstruction of sound sources in the strong interference environment is difficult due to the low signal-to-noise ratio of muti-channel signals recorded by the microphone array. In this research, a loss function based on optimal transport is derived to suppress background interference for sound source reconstruction. The approximated distribution of uncontaminated data is obtained by training the snapshot matrix through weakly supervised learning, which means there is no need to collect paired data. The quantitative reconstruction of the actual amplitude of the sound source is realized by a normalization strategy with the inverse tangent function. In the numerical simulation and experiment, the proposed method is able to accurately reconstruct the sound pressure level of target sound sources in the strong interference environment.

We consider a mobile edge computing (MEC) framework empowered by unmanned aerial vehicle (UAV) and reflecting intelligent surface (RIS) serving multiple ground users in a practical environment, where mobile ground users generate movements and tasks randomly. Our objective is to optimize energy efficiency while ensuring long-term data queue stability, assuming knowledge of the channel state information. The problem is formulated as a stochastic optimization problem, and the Lyapunov method is applied to convert the initial problem into per-slot problems. Without the future knowledge of user movement, we consider the outage constraint into the per-slot problem to derive robust resource allocation and trajectory design in the MEC system. For each per-slot problem, an alternating optimization algorithm utilizing successive convex approximation technique is designed to solve it. This solution guarantees adherence to the UAV energy budget constraint while achieving a balance between system energy efficiency and the length of the queue backlog. Simulation results demonstrate that the proposed algorithm achieves better performance than other benchmark methods in terms of improving energy efficiency and maintaining queue stability.

Passive bistatic radar(PBR) frequently experiences interference from direct signal waves when detecting maritime targets, which can completely mask target echoes, particularly for distant targets or weak targets with low radar cross-section(RCS). To mitigate this, the paper proposes a direct signal interference(DSI) suppression method. The approach involves dual-channel reception of digital video broadcast satellites(DVB-S) signals from the China Sat-9, followed by signal preprocessing. The reference and surveillance channel signals are then segmented. After segmentation,the signals undergo fast Fourier transformation(FFT), and an adaptive filtering clutter suppression method is applied at each frequency point. Finally, an inverse fast Fourier transform(IFFT) is performed on the suppressed signals to obtain the DSI-suppressed output. Compared to traditional clutter suppression techniques, this method is not only faster but also achieves more effective suppression. Simulation experiments involving both single and multiple targets validate the superiority of the proposed algorithm.

Accurate identification of noise sources is the key technology for low noise design of products. Acoustic measurements are not always performed in the anechoic chamber due to practical constraints, and the signals measured by the microphone arrays may be contaminated by background interference. The results of acoustic imaging are blurred by background interference, making it difficult to identify the noise sources. A grid-based high-resolution acoustic deconvolution method via the score-based generative model (SGM) is proposed. The multi-step forward and reverse processes in the SGM are expected to alleviate the difficulty of predicting the real sound source distribution underlying the contaminated data through the neural network in a single step. In this research, the forward and reverse processes of the SGM following mean-reverting stochastic differential equations are utilized to relate the real source distribution to the output of conventional beamforming. The proposed deconvolution method is capable of accurately identifying the target sound source and suppressing the interference in numerical simulations of Gaussian and reverberant interference, as well as the closed test section wind tunnel experiment.

This paper explores a UAV-mounted active Reconfigurable Intelligent Surface(aRIS)network designed to enhance secure downlink communication for multiple users while mitigating the impact of multiple Eavesdroppers(EVs).The focus is on optimizing the UAV's trajectory,the Base Station's(BS)transmit beamforming,and the power-Amplified Programmable Reflecting Elements(APREs)of the aRIS to maximize the minimum secrecy rate in the presence of EVs.This is a complex non-convex problem due to multiple optimization variables,high-dimensional matrix operations,and log-determinant objective functions,which makes it challenging to solve.Hence,a Successive Convex Approximation(SCA)-based optimization strategy is developed to efficiently solve the subproblems related to the UAV's trajectory,aRIS's APREs,and BS's beamforming.By leveraging slack variables and approximation techniques,we solve the nonconvex subproblems by a sequence of convex subproblems.Simulation results demonstrate that the proposed UAV-aRIS network significantly outperforms its passive RIS counterpart in improving communication security,highlighting the effectiveness of the optimization strategy.

In this paper, we investigate the mobile edge computing framework with unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) collaboration, in which a dedicated non-orthogonal multiple access (NOMA) based protocol is introduced for task offloading. In the model, the UAV acts as a relay node and computation server to support multiple ground users for offloading computation tasks to remote access point with the assistance of the RIS. To explore the impact of the joint UAV and RIS design with NOMA on computation performance, the sum computation bits maximization problem is formulated by optimizing computation and offloading bits, RIS phase shift design, UAV trajectory and bandwidth allocation. In order to obtain the solution, the original problem is decomposed into three more tractable subproblems, which can be solved by applying semidefinite relaxation method and successive convex approximation technique. Finally, the numerical solution demonstrates that our proposed algorithm achieves improvements on computation capacity compared with the benchmark and outperforms considerably the scheme without collaboration of the UAV and RIS.

AbstractDetecting respiration and heartbeat information from human chest movement is crucial for non‐contact vital sign detection. Due to the small frequency difference between respiration and heartbeat signals and the relatively low amplitude of the heartbeat signal, it is challenging to separate and extract them completely. This letter proposes a successive variational mode extraction method based on spectrum trend for the extraction of respiration and heartbeat signals. The proposed method adaptively determines the initial centre frequency and extraction order of the desired signal modes by analyzing the spectrum trend of the signal to be decomposed. Additionally, a recursive framework is introduced to sequentially extract the desired signal modes. Both simulation and practical experiment results show that the method is effective in extracting respiration and heartbeat signals, significantly improving the accuracy of estimating respiration and heartbeat frequencies.

This paper considers a reconfigurable intelligent surface (RIS) aided multi-user multiple-input single-output (MU-MISO) mmWave downlink communication system. The hybrid beamforming (HBF) and the programmable reflecting elements (PREs) are respectively applied at the base station (BS) and the RIS, where the HBF consists of the digital beamforming (DBF) and the analog beamforming (ABF). To maximize the geometric mean of the users’ rates (GM-rate) for providing feasible links to all users in the aspect of the same time slot and same bandwidth, which results in the superiority of a rational rate distribution for the users without imposing a minimum quality of service (QoS) constraint. The joint PREs, ABF, and DBF optimization problems are formulated, the problem is generally non-convex due to the log-determinant as well as unit modulus constraints for both PREs and ABF. Efficient alternating descent iteration algorithms are developed to solve the intractable problem. Finally, simulation results are included to verify the efficiency of the proposed approaches, results also show that the proposed algorithms can improve the fairness among users and accommodate discrete phase shifts for both PREs and ABF.

This is the first treatise on multi-user (MU) beamforming designed for achieving long-term rate-fairness in fulldimensional MU massive multi-input multi-output (m-MIMO) systems. Explicitly, based on the channel covariances, which can be assumed to be known beforehand, we address this problem by optimizing the following objective functions: the users' signal-toleakage-noise ratios (SLNRs) using SLNR max-min optimization, geometric mean of SLNRs (GM-SLNR) based optimization, and SLNR soft max-min optimization. We develop a convex-solver based algorithm, which invokes a convex subproblem of cubic time-complexity at each iteration for solving the SLNR maxmin problem. We then develop closed-form expression based algorithms of scalable complexity for the solution of the GMSLNR and of the SLNR soft max-min problem. The simulations provided confirm the users' improved-fairness ergodic rate distributions.

This paper proposes a joint design strategy for enhancing individual user rates in a multi-user system by optimizing both the programmable reflecting elements (PREs) of an active reconfigurable intelligent surface (aRIS) and the transmit beamforming at a base station. Given that the aRIS's PREs are bound by discrete constraints due to low-resolution quantization, this design approach relies on large-scale mixed discrete-continuous problems, which are addressed through a new universal penalised optimization reformulations. Initially, we develop iterations based on convex quadratic solvers (CQ) to tackle the problem of maximizing the users' minimum rate (MR). Given that the computational complexity of these CQs is cubic, leading to high costs in large-scale computations, we introduce a pair of surrogate objectives. These objectives are designed in a way that their constrained optimization can be efficiently managed through iterations of closed-form expressions with scalable complexity, rendering them practical for large-scale computations. This pair of surrogate objectives comprises the maximization of the geometric mean of users' rates (GM-rate maximization) and the soft-maximization of users' MR (soft max-min rate optimization). Remarkably, they not only enhance MR but also contribute to the improvement of the sum-rate (SR). Building upon the GM-rate optimization, we further propose addressing the energy efficiency problem, which achieves a high ratio of SR to power consumption and MR to power dissipation through closed-form expressions. Comprehensive simulations are conducted to validate the efficacy of the proposed solutions.

Due to the rapid development of wireless network communications, this paper makes certain improvements to the Target Wake-up Time (TWT) mechanism adopted based on the IEEE 820.11ax standard protocol, and proposes a Wireless Local Area Network (WLAN) based on cross-cell TWT Service Quality Assurance Agreement. First, the wireless Access Point (AP) sleep mechanism is introduced, and then through the TWT mechanism, the AP arranges sleep time for the STA and informs the STA of its own sleep time. This mechanism further reduces energy consumption, and then introduces the cooperative APs. This concept ensures that high-priority services generated by the STA during the AP sleep process can be forwarded in time, thereby ensuring service quality. Through relevant simulation verification, the results show that the implementation of this solution effectively ensures the quality of communication services, especially network delay.

The high mobility of unmanned aerial vehicles (UAVs) enables them to improve system throughput by establishing line-of-sight (LoS) links. Nevertheless, in urban environments, these LoS links can be disrupted by complex urban structures, leading to potential interference issues. Reconfigurable intelligent surfaces (RIS) provide an innovative approach to enhance communication performance by intelligently reflecting incident signals. Recent studies suggest that utilizing multi-antenna transmission can increase system efficiency, while single-antenna transmission may be more prone to interference. To address these challenges, this article introduces a RIS-assisted multiple-input single-output (MISO) UAV communication system. Our objective is to optimize the minimum user rate, thereby guaranteeing equitable communication for all users. Nevertheless, the non-convexity inherent in this optimization problem complicates the pursuit of a direct solution. Hence, we decompose the problem into four subproblems: user scheduling optimization, RIS phase-shift optimization, UAV trajectory optimization, and UAV transmit beamforming optimization. To obtain suboptimal solutions, we have developed an alternating iterative optimization algorithm for addressing the four subproblems. Numerical results demonstrate that our algorithm effectively boosts the minimum user rate of the entire system.

In future 6G mobile communication systems, the use of reconfigurable intelligent surfaces (RIS) in conjunction with unmanned aerial vehicles (UAV) is leveraged to enhance air-to-ground communication system transmission performance. In this paper, the downlink of the RIS-assisted UAV communication system is considered, where the RIS is mounted beneath the UAV, allowing the downlink signals to be reflected from a multi-antenna base station through the RIS to multiple terminal devices. To enhance the system’s panoramic beamforming capability and maximize the total downlink transmission bits, an efficient alternating descent algorithm is proposed in this paper. This algorithm jointly optimizes the phase shift of the RIS and the transmit beamforming of the base station. The simulation results indicate that the algorithm proposed in this paper outperforms schemes that only optimize the phase shift of the RIS or the transmit beamforming of the base station in terms of system performance.

This paper investigates the optimization of an unmanned aerial vehicle (UAV) network serving multiple downlink users equipped with single antennas. The network is enhanced by the deployment of either a passive reconfigurable intelligent surface (RIS) or an active RIS. The objective is to jointly design the UAV’s trajectory and the low-bit, quantized, RIS-programmable coefficients to maximize the minimum user rate in a multi-user scenario. To address this optimization challenge, an alternating optimization framework is employed, leveraging the successive convex approximation (SCA) method. Specifically, for the UAV trajectory design, the original non-convex optimization problem is reformulated into an equivalent convex problem through the introduction of slack variables and appropriate approximations. On the other hand, for the RIS-programmable coefficient design, an efficient algorithm is developed using a penalty-based approximation approach. To solve the problems with the proposed optimization, high-performance optimization tools such as CVX are utilized, despite their associated high time complexity. To mitigate this complexity, a low-complexity algorithm is specifically tailored for the optimization of passive RIS-programmable reflecting elements. This algorithm relies solely on closed-form expressions to generate improved feasible points, thereby reducing the computational burden while maintaining reasonable performance. Extensive simulations are created to validate the performance of the proposed algorithms. The results demonstrate that the active RIS-based approach outperforms the passive RIS-based approach. Additionally, for the passive RIS-based algorithms, the low-complexity variant achieves a reduced time complexity with a moderate loss in performance.

In the extensive research on next-generation communication network architectures, task offloading and resource allocation problems become increasingly complex for mobile edge computing (MEC) systems with multiple base stations (BSs). This paper first formulates this multi-dimensional dynamic issue as an optimization problem. To minimize system overhead, we propose an attention-based multi-agent proximal policy optimization (A-MAPPO) algorithm. This algorithm employs a centralized training and decentralized execution (CTDE) framework, and leverages attention mechanisms to facilitate the convergence of the critic network, thereby enhancing the algorithm's performance. Experimental results show that the A-MAPPO algorithm can reduce system costs by up to 28.3% compared to other benchmark algorithms.

In this paper, the blockage problem of the optical link between AGV (autonomous ground vehicles) and AP (access point) in the logistics–warehousing VLC (visible light communication) network is analyzed. First, based on the random geometric model, a link-blockage model is proposed. Given the position of AGV, AP and obstacle, the blockage state of the VLC link between AGV and AP can be obtained through this model. Then, an AP-placement scheme based on the link-blockage model is proposed. Under this AP placement, AGVs in any position have a reliable link that is not affected by obstacles. During the movement of AGV, the VLC link of AGV will not be interrupted by a random blockage. Finally, the effectiveness of the link-blockage model is demonstrated by the shadow method. In this paper, the link outage probability and the data rate under different AP heights, AP spacings and the number of obstacles are simulated. Simulation results show that the VLC link can keep uninterrupted under the AP placement proposed in this paper.

Vehicle-to-vehicle (V2V) communication is the most typical application of the Internet of Vehicles, and it has many envisaged applications, such as driverless driving, collision warning and addressing traffic congestion. The realization of future intelligent transportation systems (ITSs) will require V2V communication technology, which in turn relies on accurate V2V channel estimation. As the possibilities that 5G technologies can be used for V2V communications are increasingly being explored, corresponding channel models used to describe the characteristics of V2V channels are also being updated, so the new channel estimation algorithms needed to be developed. Although the channel estimation method based on Convolutional Neural Network (CNN) has achieved remarkable success in communication problems in recent years, it cannot be well adapted to V2V channels under different scenarios and different modeling methods. In this article, a method is proposed for learning and estimating the channel structure in the 5G NR downlink that can adapt to the V2V channel under multi-scenes. The inherent block sparsity characteristics of the channel structure is adopted to combine the group lasso Alternating Direction Method of Multipliers (ADMM) algorithm with CNN, and residual dense network is employed to estimate and refine the V2V channel structure. Furthermore, the classifier and interpolation with pooling method are used to estimate the V2V channel structure under multi-scene and multi-resolution. Simulation results show that the performance of this method is better than other deep learning based estimation algorithms.