arXiv subjects

Aimin Wang

Publications and source records attributed to Aimin Wang.

14 recordsLinked to original sources

arXiv2026

UAV-Assisted Joint Data Collection and Wireless Power Transfer for Batteryless Sensor Networks

The development of wireless power transfer (WPT) and Internet of Things (IoT) offers significant potential but faces challenges such as limited energy supply, dynamic environmental changes, and unstable transmission links. This paper presents an unmanned aerial vehicle (UAV)-assisted data collection and WPT scheme to support batteryless sensor (BLS) networks in remote areas. In this system, BLSs harvest energy from the UAV and utilize the harvested energy to transmit the collected data back to the UAV. The goal is to maximize the collected data volume and fairness index while minimizing the UAV energy consumption. To achieve these objectives, an optimization problem is formulated to jointly optimize the transmit power and UAV trajectory. Due to the non-convexity and dynamic nature of the problem, a deep reinforcement learning (DRL)-based algorithm is proposed to solve the problem. Specifically, this algorithm integrates prioritized experience replay and the performer module to enhance system stability and accelerate convergence. Simulation results demonstrate that the proposed approach consistently outperforms benchmark schemes in terms of collected data volume, fairness, and UAV energy consumption.

cs.NI↗

arXiv2025

Secure Low-altitude Maritime Communications via Intelligent Jamming

Low-altitude wireless networks (LAWNs) have emerged as a viable solution for maritime communications. In these maritime LAWNs, unmanned aerial vehicles (UAVs) serve as practical low-altitude platforms for wireless communications due to their flexibility and ease of deployment. However, the open and clear UAV communication channels make maritime LAWNs vulnerable to eavesdropping attacks. Existing security approaches often assume eavesdroppers follow predefined trajectories, which fails to capture the dynamic movement patterns of eavesdroppers in realistic maritime environments. To address this challenge, we consider a low-altitude maritime communication system that employs intelligent jamming to counter dynamic eavesdroppers with uncertain positioning to enhance the physical layer security. Since such a system requires balancing the conflicting performance metrics of the secrecy rate and energy consumption of UAVs, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP). To solve this dynamic and long-term optimization problem, we first reformulate it as a partially observable Markov decision process (POMDP). We then propose a novel soft actor-critic with conditional variational autoencoder (SAC-CVAE) algorithm, which is a deep reinforcement learning algorithm improved by generative artificial intelligence. Specifically, the SAC-CVAE algorithm employs advantage-conditioned latent representations to disentangle and optimize policies, while enhancing computational efficiency by reducing the state space dimension. Simulation results demonstrate that our proposed intelligent jamming approach achieves secure and energy-efficient maritime communications.

cs.CR↗

arXiv2025

Cost Minimization for Space-Air-Ground Integrated Multi-Access Edge Computing Systems

Space-air-ground integrated multi-access edge computing (SAGIN-MEC) provides a promising solution for the rapidly developing low-altitude economy (LAE) to deliver flexible and wide-area computing services. However, fully realizing the potential of SAGIN-MEC in the LAE presents significant challenges, including coordinating decisions across heterogeneous nodes with different roles, modeling complex factors such as mobility and network variability, and handling real-time decision-making under partially observable environment with hybrid variables. To address these challenges, we first present a hierarchical SAGIN-MEC architecture that enables the coordination between user devices (UDs), uncrewed aerial vehicles (UAVs), and satellites. Then, we formulate a UD cost minimization optimization problem (UCMOP) to minimize the UD cost by jointly optimizing the task offloading ratio, UAV trajectory planning, computing resource allocation, and UD association. We show that the UCMOP is an NP-hard problem. To overcome this challenge, we propose a multi-agent deep deterministic policy gradient (MADDPG)-convex optimization and coalitional game (MADDPG-COCG) algorithm. Specifically, we employ the MADDPG algorithm to optimize the continuous temporal decisions for heterogeneous nodes in the partially observable SAGIN-MEC system. Moreover, we propose a convex optimization and coalitional game (COCG) method to enhance the conventional MADDPG by deterministically handling the hybrid and varying-dimensional decisions. Simulation results demonstrate that the proposed MADDPG-COCG algorithm significantly enhances the user-centric performances in terms of the aggregated UD cost, task completion delay, and UD energy consumption, with a slight increase in UAV energy consumption, compared to the benchmark algorithms. Moreover, the MADDPG-COCG algorithm shows superior convergence stability and scalability.

cs.LG↗

arXiv2025

Energy Transfer and Data Collection from Batteryless Sensors in Low-altitude Wireless Networks

The integration of wireless power transfer (WPT) with Internet of Things (IoT) offers promising solutions for sensing applications, but faces significant challenges when deployed in hard-to-access areas such as high-temperature environments. In such extreme conditions, traditional fixed WPT infrastructure cannot be safely installed, and batteries rapidly degrade due to hardware failures. In this paper, we propose an uncrewed aerial vehicle (UAV)-assisted data collection and WPT framework for batteryless sensor (BLS) networks deployed in these challenging environments. Specifically, we consider a practical scenario where a UAV first transfers energy to BLS nodes via WPT, enabling these nodes to subsequently transmit their collected data to the UAV through orthogonal frequency-division multiple access (OFDMA). Then, we formulate a multi-objective optimization problem that aims to maximize the fair data collection volume while minimizing the UAV energy consumption through joint optimization of transmit power allocation and flight trajectory planning. Due to the non-convex nature and dynamic characteristics of this problem, conventional optimization methods prove inadequate. To address these challenges, we propose an enhanced soft actor-critic algorithm with parameter-free attention, prioritized experience replay, and value-based reward centering (SAC-PPV), thereby improving the exploration efficiency and learning stability of the algorithm in complex WPT scenarios. Simulation results demonstrate that the proposed approach consistently outperforms benchmark algorithms under various network configurations.

cs.NI↗

arXiv2025

Practical 1-um GHz fiber comb on silica-based platform

We present a fully stabilized 1-GHz Yb-fiber laser frequency comb built on silica substrates, utilizing "optical cubes" to house all optical components, ensuring long-term stability and practical operation. Both the femtosecond laser and f-to-2f interferometer are constructed to silica bricks, with a compact footprint of 290 mm * 250 mm, and a total weight of 1.8 kg. This system provides a stable repetition rate, offset frequency, and a supercontinuum spanning 460-1560 nm without requiring amplification. The carrier-envelop offset frequency exhibits exceptional in-loop stability, with a fractional frequency instability of 3.07* 10^(-18) at a 1 second averaging time, improving to 2.12*10^(-20) at a 10,000 second, maintaining uninterrupted operation for over 60 hours. This work demonstrates a high-performance GHz fiber-based frequency comb, paving the way for applications beyond laboratory environments, including dual-comb spectroscopy, astronomical spectrograph calibration, and portable optical clocks.

physics.optics↗

arXiv2025

Low-altitude UAV Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning

Low Earth orbit (LEO) satellites can be used to assist maritime wireless communications for wide-area data transmission. However, the extensive coverage of LEO satellites, combined with the openness of channels, can cause the communication process to suffer from security risks. This paper presents a LEO satellite-maritime communication system assisted by low-altitude unmanned aerial vehicle (UAV) friendly-jamming to ensure data security at the physical layer. Since such a system requires balancing the conflicting performance metrics of secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor-critic (TransSAC) algorithm, which is a generative artificial intelligence-enabled deep reinforcement learning approach to solve the reformulated problem, thus capturing strong temporal correlations and diversely exploring weights. Simulation results demonstrate that the TransSAC algorithm outperforms comparative approaches and algorithms, maximizing the secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results identify more suitable constraints for the system.

cs.CR↗

arXiv2024

Dual UAV Cluster-Assisted Maritime Physical Layer Secure Communications via Collaborative Beamforming

Unmanned aerial vehicles (UAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of UAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the UAV relay for remote maritime communications. However, due to the open nature of UAV channels, security issue requires special consideration. This paper proposes a dual UAV cluster-assisted system via CB to achieve physical layer security in maritime wireless communications. Specifically, one UAV cluster forms a maritime UAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other UAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of UAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method.

cs.DC↗

arXiv2024

Multi-objective Optimization for Data Collection in UAV-assisted Agricultural IoT

The ground fixed base stations (BSs) are often deployed inflexibly, and have high overheads, as well as are susceptible to the damage from natural disasters, making it impractical for them to continuously collect data from sensor devices. To improve the network coverage and performance of wireless communication, unmanned aerial vehicles (UAVs) have been introduced in diverse wireless networks, therefore in this work we consider employing a UAV as an aerial BS to acquire data of agricultural Internet of Things (IoT) devices. To this end, we first formulate a UAV-assisted data collection multi-objective optimization problem (UDCMOP) to efficiently collect the data from agricultural sensing devices. Specifically, we aim to collaboratively optimize the hovering positions of UAV, visit sequence of UAV, speed of UAV, in addition to the transmit power of devices, to simultaneously achieve the maximization of minimum transmit rate of devices, the minimization of total energy consumption of devices, and the minimization of total energy consumption of UAV. Second, the proposed UDCMOP is a non-convex mixed integer nonlinear optimization problem, which indicates that it includes continuous and discrete solutions, making it intractable to be solved. Therefore, we solve it by proposing an improved multi-objective artificial hummingbird algorithm (IMOAHA) with several specific improvement factors, that are the hybrid initialization operator, Cauchy mutation foraging operator, in addition to the discrete mutation operator. Finally, simulations are carried out to testify that the proposed IMOAHA can effectively improve the system performance comparing to other benchmarks.

cs.IT↗

arXiv2024

Secure and Energy-efficient Unmanned Aerial Vehicle-enabled Visible Light Communication via A Multi-objective Optimization Approach

In this research, a unique approach to provide communication service for terrestrial receivers via using unmanned aerial vehicle-enabled visible light communication is investigated. Specifically, we take into account a unmanned aerial vehicle-enabled visible light communication scenario with multiplex transmitters, multiplex receivers, and a single eavesdropper, each of which is equipped with a single photodetector. Then, a unmanned aerial vehicle deployment multi-objective optimization problem is formulated to simultaneously make the optical power received by receiving surface more uniform, minimize the amount of information collected by a eavesdropper, and minimize the energy consumption of unmanned aerial vehicles, while the locations and transmission power of unmanned aerial vehicles are simultaneously optimized under certain constraints. Since the formulated unmanned aerial vehicle deployment multi-objective optimization problem is complex and nonlinear, it is challenging to be tackled by using conventional methods. For the purpose of solving the problem, a multi-objective evolutionary algorithm based on decomposition with chaos initiation and crossover mutation is proposed. Simulation outcomes show that the proposed approach is superior to other approaches, and is efficient at improving the security and energy efficiency of visible light communication system.

eess.SP↗

arXiv2024

UAV Deployment Optimization in UAV-assisted Wireless Communications

Due to the fact that the locations of base stations (BSs) cannot be changed after they are installed, it is very difficult to communicate directly with remote user equipment (UE), which will directly affect the lifespan of the system. Unmanned aerial vehicles (UAVs) offer a hopeful solution as mobile relays for fifth-generation wireless communications due to the flexible and cost-effective deployment. However, with the limited onboard energy of UAV and slow progress in energy storage technology, it is a key challenge to achieve the energy-efficient communication. Therefore, in this article, we study a wireless communication network using a UAV as a high-altitude relay, and formulate a UAV relay deployment optimization problem (URDOP) to minimize the energy consumption of system by optimizing the deployment of UAV, including the locations and number of UAV hover points. Since the formulated URDOP is a mixed-integer programming problem, it presents a significant challenge for conventional gradient-based approaches. To this end, we propose a self-adaptive differential evolution with a variable population size (SaDEVPS) algorithm to solve the formulated URDOP. The performance of proposed SaDEVPS is verified through simulations, and the results show that it can successfully decrease the energy consumption of system when compared to other benchmark algorithms across multiple instances.

eess.SP↗

arXiv2023

Joint Scheduling and Trajectory Optimization of Charging UAV in Wireless Rechargeable Sensor Networks

Wireless rechargeable sensor networks with a charging unmanned aerial vehicle (CUAV) have the broad application prospects in the power supply of the rechargeable sensor nodes (SNs). However, how to schedule a CUAV and design the trajectory to improve the charging efficiency of the entire system is still a vital problem. In this paper, we formulate a joint-CUAV scheduling and trajectory optimization problem (JSTOP) to simultaneously minimize the hovering points of CUAV, the number of the repeatedly covered SNs and the flying distance of CUAV for charging all SNs. Due to the complexity of JSTOP, it is decomposed into two optimization subproblems that are CUAV scheduling optimization problem (CSOP) and CUAV trajectory optimization problem (CTOP). CSOP is a hybrid optimization problem that consists of the continuous and discrete solution space, and the solution dimension in CSOP is not fixed since it should be changed with the number of hovering points of CUAV. Moreover, CTOP is a completely discrete optimization problem. Thus, we propose a particle swarm optimization (PSO) with a flexible dimension mechanism, a K-means operator and a punishment-compensation mechanism (PSOFKP) and a PSO with a discretization factor, a 2-opt operator and a path crossover reduction mechanism (PSOD2P) to solve the converted CSOP and CTOP, respectively. Simulation results evaluate the benefits of PSOFKP and PSOD2P under different scales and settings of the network, and the stability of the proposed algorithms is verified.

eess.SY↗

arXiv2023

DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images

Three-dimensional microscopy is often limited by anisotropic spatial resolution, resulting in lower axial resolution than lateral resolution. Current State-of-The-Art (SoTA) isotropic reconstruction methods utilizing deep neural networks can achieve impressive super-resolution performance in fixed imaging settings. However, their generality in practical use is limited by degraded performance caused by artifacts and blurring when facing unseen anisotropic factors. To address these issues, we propose DiffuseIR, an unsupervised method for isotropic reconstruction based on diffusion models. First, we pre-train a diffusion model to learn the structural distribution of biological tissue from lateral microscopic images, resulting in generating naturally high-resolution images. Then we use low-axial-resolution microscopy images to condition the generation process of the diffusion model and generate high-axial-resolution reconstruction results. Since the diffusion model learns the universal structural distribution of biological tissues, which is independent of the axial resolution, DiffuseIR can reconstruct authentic images with unseen low-axial resolutions into a high-axial resolution without requiring re-training. The proposed DiffuseIR achieves SoTA performance in experiments on EM data and can even compete with supervised methods.

eess.IV↗

arXiv2018

High contrast linking 6 lasers to a 1 GHz Ybfiber laser frequency comb

We demonstrate a 0.95 GHz repetition rate fully stabilized Yb:fiber frequency comb without optical amplification. Benefitted from the high mode power and high coherence, this comb achieved 35 dB to 42 dB signal to noise ratio on the direct heterodyne beat signals with at least 6 continuous wave lasers (at 580nm, 679nm, 698nm, 707nm, 813nm and 922 nm) while keeping >40 dB carrier envelop frequency signal. It can be used for the direct measurement of optical frequencies in visible and near-infrared wavelengths, and has a great potential on simultaneous comparison of multiple optical frequencies.

physics.optics↗

arXiv2018

Visible astro-comb filtered by a passively-stabilized Fabry-Perot cavity

We demonstrate a compact 29.3 GHz visible astro-comb covering the spectrum from 560nm to 700nm. A 837 MHz Yb:fiber laser frequency comb phase locked to a Rb clock served as the seed comb to ensure the frequency stability and high side mode suppression ratio. After the visible super-continuum generation, a cavity-length-fixed Fabry-Perot cavity made by ultra-low expansion glass was utilized to filter the comb teeth for eliminating the rapid active dithering. The mirrors were home-made complementary chirped mirrors pair with zero dispersion and high reflection to guarantee no mode skipping. These filtered comb teeth were clearly resolved in an astronomical spectrograph of 49,000 resolution, exhibiting sharp linetype, zero noise floor, and uniform exposure amplitude.

astro-ph.IM↗