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Random phase interference is the main factor leading to uneven optical power distribution, which will affect the positioning accuracy of the four-quadrant detector. This paper uses the power spectrum inversion method to simulate the light spot power distribution affected by random phase interference, and studies the subdivision capability characteristics of the four-quadrant detector under different random phase interference. The simulation experiment results show that when the radius of the light spot received by the detector is 1/2 the radius of the detector, both detection range and positioning accuracy can be taken into account. When the random phase interference is enhanced, the detector's subdivision capability decreases. When the random phase interference is small, the detector can achieve more than 100 subdivisions. When the random phase interference is large, the light spot can only achieve 3 subdivision at the edge of the detector target surface. An experimental system was built to test the subdivision capability of the four-quadrant detector, the experimental test results were basically consistent with the simulation results. The research results provide a technical reference for the application of four-quadrant detector. in free space environments.

Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) systems have emerged as promising solutions for enhancing the computational capabilities and reducing latency in next-generation wireless networks. However, the finite energy capacity of UAVs presents a significant challenge. In this study, we formulate a joint user association, computing resource allocation, and task offloading problem, called User Association and Computation Offloading (UACO), to minimize the energy consumption of both mobile devices and UAVs by considering the computational resource limitations of UAVs and the minimum user offloading data rate requirements. The UACO problem is a mixed-integer linear programming (MILP) problem that makes it NP-hard. We transform UACO into an Offload Exact Constrained Potential Game (OFECPG) based on game theory, which facilitates distributed execution. We propose the Best Response dynamics based on Local Decomposition (BR-LD) and Better Response dynamics based on the Local Switch operator (BR-LS) to enhance computational efficiency. We prove the existence of a pure strategy Nash Equilibrium (NE) and the convergence of the proposed algorithms. Extensive simulations demonstrated the effectiveness of the OFECPG, BR-LD, and BR-LS, showing significant improvements in system energy consumption compared to the baseline schemes. Our approach offers valuable insights into the design of efficient association and offloading schemes for multi-UAV MEC networks.

The rapid deployment of large-scale UAV-assisted cellular networks has emerged as an effective solution for delivering high-speed data services to ground users in areas where terrestrial base station (BS) infrastructure is inadequate. In this paper, we address a crucial problem in these networks by aiming to maximize the uplink total rate through joint optimization of Unmanned Aerial Vehicle (UAV) association and Orthogonal Frequency Division Multiple Access (OFDMA) subcarrier assignment. However, due to the mobility of pedestrians, fixed association and subcarrier assignments lack adaptation, and centralized optimization does not scale well, especially in large-scale UAV settings. To overcome these challenges, we formulate the joint optimization problem as an exact Constrained Potential Game, called the Subcarrier Assignment and UAV Association Game (SAUAG), based on game theory. In SAUAG, each user selfishness selects the most appropriate strategy with the objective of maximizing their own utility while still fulfilling the system’s Quality of Service (QoS) constraints. We analyze the conditions under which a Nash Equilibrium (NE) exists and provide rigorous proof of its existence.To mitigate the computational complexity typically associated with Best Response Dynamics (BRD), we introduce the Two-Step Better Response (TS-BR) algorithm, which significantly reduces the computational load while ensuring convergence to an NE. Furthermore, we proposed a distributed algorithm based on sequential play and demonstrated its effectiveness in achieving NE convergence. Through comprehensive simulations, the superiority of the proposed SAUAG and TS-BR algorithms was demonstrated, showing their potential in enhancing network performance and adaptability in UAV-assisted OFDMA networks with mobile ground users.

In this paper, we formulate Subcarrier Assignment and Discrete Power Allocation for multi-UAV millimeter-wave cooperative Orthogonal Frequency Division Multiple Access (OFDMA) networks as a joint optimization problem considering the heterogeneous user data rate quality-of-service (QoS) requirements. The formulated joint optimization problem, named Discrete Power and Subcarrier Allocation (DPSA), is a nonconvex and mixed-integer nonlinear programming (MINP) problem, making it NP-hard to solve. We then transform the DPSA into the Subcarrier and Power Resource Efficient Cooperative Potential Game (SRECPG) based on game theory which facilitates distributed execution. We analyze the conditions under which a Nash Equilibrium (NE) exists in the SRECPG and provide rigorous proof of its existence. Furthermore, to enhance computational efficiency, we propose a BR-SSO algorithm based on better response dynamics. BR-SSO dramatically reduces the computational burden compared to the best response dynamics based on local exhaustive search (BRLES) while still ensuring convergence to a NE. Through extensive simulations, we demonstrated the effectiveness of the proposed SRECPG and BR-SSO algorithms. The results show significant improvements in throughput, fairness, and QoS guarantees compared to the baseline schemes. Our approach offers valuable insights into the design of efficient resource allocation schemes for multi-UAV millimeter-wave networks with varying QoS demands.

This paper designs a new batching program to extract the original data, which helps to traverse the entire sample space quickly and provides a new approach for data extraction based on the motion stroke diagram. The designed program can read thousands of files out of many folders instantly and automatically. Meanwhile, thousands of time nodes are calculated based on the proportional coefficients. Finally, experimental data of many folders are separated into sample space easily and rapidly. The original data, the extracted stroke data, and the name and address of every folder and file are output in the result. The program designed in this paper at the maximum processing speed needs only 0.015 seconds to read, compute, and extract the correlative data information from one file (s/f), and the average time threshold is 0.0866 seconds. The Linear Theory (LT), optimizing Sparrow Search Algorithm (OSSA), and the automatic batch read file method can be employed to obtain the optimal result of data extraction. Through 744 rounds of nine experiments, the Average Processing Speed (APS) is less than 0.110 seconds per second data segment (s/ess). The APS is increased by 79.73%. The accuracy of fault classification and forecast of the eigenvalue extracted from the Automatic Batch Reading File (ABRF) and the Ensemble Empirical Mode Decomposition (EEMD) method is improved by 9% and 13% by Self-Organized Mapping (SOM). It is validated that our proposed data extraction method is faster and more progressive than the existing ones.

Space laser communication, with its strong anti-jamming capability, high transmission rate and good adaptability, offers hope for the establishment of 5G mobile networks in areas that are not conducive to the erection of cables, such as islands and remote land areas. Turbulent scintillation effect is one of the important factors affecting the performance of laser communication, which can lead to the increase of communication BER and even communication interruption. Based on the working principle of the photoelectric detector in atmospheric turbulence, an optical communication receiving system based on the detector gain factor regulation control is designed, which calculates the scintillation variance according to the received signal in real time, establishes the function conversion relationship between the scintillation variance and the gain factor, realizes the closed-loop regulation control of the detector gain factor, and improves the SNR of the receiving system. A ground-based 13km static laser communication experiment was set up to test the reception performance, and the test results show that the system reduces the communication BER by more than two orders of magnitude and can be kept at 1E-6 under medium to weak turbulence (discriminated by flash variance) at a communication rate of 2.5Gbps. The technology was applied to the test of laser 5G signal transmission between the airship and the ground, and for the first time, the mobile communication (access network) signal transmission with a downlink speed of 1.230Gbps and an uplink speed of 76.4Mbps is realized, which verified the performance of mobile signal transmission between the mobile base station and the fixed station on the ground.

At present, the energy consuming during the electrolytic copper foil preparation accounts for more than 75% of the total energy consumption. In real-life production, the process parameters are set by the operator empirically and the system may not work at the operating point with minimum energy consumption. Therefore, it is critical to establish an effective model for predicting electrolysis energy consumption to guide the parameters design. In this paper, a novel hybrid model (named PSVM-PMLP-MLR) based on stacked ensemble learning is proposed. The model is divided into two parts: the base-learning model and the meta-learning model. The support vector machine (SVM) model and multilayer perceptron (MLP) model with different input structures are established by the former first. Then the particle swarm algorithm is employed to determine the optimal value of SVM parameters and the optimal weight of MLP by minimizing the mean absolute percentage error (MAPE). The multiple linear regression (MLR) is finally employed as a meta-learning machine to compute the final predictions. Experimental results show that the regression coefficient of this model reached 0.987, and compared with the traditional SVM and MLP models, the accuracy of the model is improved by 10.29% and 8.28%, respectively.

A hybrid scheme that combines the pre-corrected fast Fourier transform (p-FFT) and iterative Kirchhoff approximation (IKA) is developed to solve the electromagnetic scattering from multiple complex targets above a three-dimensional (3D) rough surface. Based on the domain distribution method (DDM), multiple targets are divided into an FFT region, and the rough surface is divided into an IKA region. Scattering on the FFT region and IKA region is calculated by p-FFT and IKA, respectively. In addition, iterative strategies are considered in both solving interaction between facets in the IKA region and interactions between FFT region and IKA region. However, interactions between FFT region and IKA region are the most time-consuming in the entire computational process. To overcome this computational bottleneck, the combined multilevel fast multipole algorithm (MLFMA), ray-propagation fast multipole algorithm (RPFMA) and fast far-field approximation (FAFFA) are incorporated into interactions between FFT region and IKA region. They accelerate the matrix vector multiplication by “interpolation –translation –anterpolation”, while their translators are different, which result in their different computation efficiencies and application conditions. To effectively combine these three algorithms, the IKA region is divided into MLFMA area, RPFMA area and FAFFA area according to the distance, where interactions between these areas and the targets are accelerated by MLFMA, RPFMA and FAFFA, and the surface outside these areas is discarded. Compared with MLFMA, the accuracy and efficiency of this hybrid method are verified.

Space dynamic communication base stations are characterized by high mobility, flexible deployment and wide coverage. It has a wide range of needs in the fields of emergency communication, earthquake relief and island communication. To solve the problem of a limited number of users due to the limited bandwidth of space base stations, so we should improve the reliability and efficiency of data transmission between space mobile base stations. According to the characteristics of the airship platform, a blimp-borne laser communication terminal applied to space dynamic base stations is designed to meet the demand for bi-directional transmission of 5G signal laser carriers in space between airships. The experimental results show that the blimp-borne laser communication terminal achieves an overall technical index of capture probability is better than 98%, the average capture time is 5s, the tracking accuracy is better than 6–15 μrad, the communication rate is 2.5 Gbps and BER is 1E-7. In order to realize the real-time evaluation of the performance of laser communication system under the turbulent channel and guide the research of anti-turbulence fading method, a real-time test method of communication channel turbulence characteristics based on the principle of light intensity scintillation is proposed. And the real-time test of turbulence characteristics of airship inclined link and the horizontal link is completed. The research results play a positive role in promoting the development of 5G mobile technology and space optical communication technology.

The data transmission between 5G floating base stations will be an important application area of the free space optical (FSO) link, but the problem of atmospheric disturbance will directly affect the application effect. An atmospheric turbulence suppression algorithm based on avalanche photodiode adaptive gain control (APDAGC) for the laser transmission terminal was proposed to suppress atmospheric turbulence. The given design mechanism and experimental results demonstrate its function to improve the performance of FSO. When APDAGC is over 1 km FSO, the variance of intensity fluctuation is lowered from 0.046 to 0.009, with the bit error rate reduced from 4.82E-6 to 1E-12. Comparatively, as APDAGC is over 6.5 km FSO, the variance of intensity fluctuation is decreased from 1.767 to 0.376, with the bit error rate varying from 4.6E-2 to 2.4E-4. Moreover, it was used for the first FSO transmission of 5G floating base station signals on an airship platform.

Sparse recovery (SR) based space-time adaptive processing (STAP) has attracted much attention due to its small requirement of snapshots in the estimation of the clutter plus noise covariance matrix (CNCM). However, most of the existing SR STAP methods suffer from the grid mismatch effect of the dictionary matrix. In this paper, a novel grid-less total variation minimization (TVM) based STAP approach is proposed, which avoids the discretization of the spatial-temporal profile and possible mismatch of the spatial-temporal gird. The optimization problem is firstly introduced to estimate the clutter subspace steering vector by minimizing the defined atomic norm based on the TVM. Then the optimization problem is reformulated via utilizing the property of the radar space-time steering vector and approximation of the Bessel function. Finally, with a solution obtained by the optimization problem, a projection method is presented to obtain an accurate estimation of the CNCM. The proposed STAP method can be applied for both the side-looking and non-side-looking case. Numerical results validate its effectiveness compared with the other SR STAP methods.

Spatio-temporal filtering has been widely used for extracting discriminative features in the motor imagery-based brain-computer interface (MI-BCI). In order to obtain high performance, the algorithms need to enhance robustness or find class-discriminative bands for the spatial filter. However, the existing methods either cannot derive the spatial and spectral filters with a unique objective function for guaranteeing convergence or rarely consider the combined optimization of spatial-spectral filters and other patterns for enhancing the discrimination. In this study, we present a novel feature extraction method termed Spectrum-weighted Tensor Discriminant Analysis (SwTDA), which optimizes spectral filters along with spatial filters and other associated patterns by tensor-based discriminant analysis. The proposed method considers intrinsic spatial-spectral-temporal information contained by the physiological signal and hence can identify discriminative characteristics robustly. The effectiveness of the algorithm is demonstrated by comparing it with several state-of-the-art methods on two datasets involving 15 different subjects. Results indicate that the SwTDA method yields higher classification accuracies than the competing methods. Furthermore, interpretable spatial-spectral patterns that are determined by the algorithm can be used for further analysis of the MI-based EEG signal.

In this paper, we consider the problems of off-grid effects elimination and fast implementations for sparse recovery based space-time adaptive processing (SR-STAP) methods. To improve the computational efficiency of recently proposed atomic norm minimization based space-time adaptive processing (ANM-STAP) method, we derive a fast iterative scheme by exploiting the framework of the alternating direction method of multipliers (ADMM), where the unknown parameters are iteratively updated with closed-form expressions. Furthermore, to bypass the selection of regularization parameter in ANM-STAP, we also develop two novel gridless STAP methods by utilizing the covariance fitting criterion (CFC) and the properties of the clutter plus noise matrix (CNCM). Likewise, the corresponding ADMM-based fast implementations are also derived for both CFC-based methods to reduce their computational complexities. Simulation results with both simulated and Mountain-Top data demonstrate that high computational efficiency and good performance of proposed algorithms are achieved.

The construction of a navigation constellation with inter-satellite links (ISLs) has become one of the important development trends for new-generation global navigation satellite systems (GNSSs), and ISLs currently realize navigation and communication functions through separate low-rate omnidirectional telemetry, tracking, command and high-rate data service channels, respectively. If the above two functions are integrated into one channel, this will result in simplification of the onboard equipment, improvement of the electromagnetic compatibility, power consumption reduction and frequency resources savings, and we speculate that autonomous navigation will be achieved by ISLs with navigation and communication fusion. In this paper, a system capable of simultaneous high-data rate communication transmission and precision ranging is investigated, and a specific scheme is introduced by combining continuous phase modulation (CPM) and a pseudonoise (PN) ranging code denoted as CPM + PN. The chip pulse is one of the key factors to design the ranging code, which not only affects the ranging performance but also influences the properties of the CPM + PN scheme, such as its spectral characteristics, communication reliability, and acquisition time. To consider the above performance indexes, a new chip pulse based on a normally distributed wave is proposed. Theoretical analysis and simulation results show that compared to square-wave and half-sine wave cases, the normally distributed wave attains great advantages in the ranging accuracy and communication reliability, which become more notable with reasonable selection of the energy distribution index. Moreover, the proposed chip pulse achieves a similar acquisition time as the traditional wave. As a result, the normally distributed wave can be used as a better alternative to the ranging chip pulse for the CPM + PN waveform.

In recent years, wideband spectrum sensing combined with sub-Nyquist sampling and compressed sensing technology in the field of cognitive radio has received widespread attention. However, the existing broadband detection methods based on sub-Nyquist sampling do not fully consider the spectrum feature changes caused by the sampling structure, resulting in unnecessary computational complexity in the support reconstruction process. In this paper, a novel reconstruction algorithm, called nearest orthogonal matching pursuit (N-OMP), is proposed based on modulated wideband converter (MWC) sub-Nyquist sampling structure. This algorithm utilizes the special power spectrum slicing characteristics caused by pseudo-random sequence and low-pass filtering. After an occupied subband is detected, it calculates the correlation coefficient between the residual vector and the column vectors corresponding to two adjacent subbands, based on which we can directly judge the occupancy of two adjacent subbands by comparing the size of the two correlation coefficients, thereby reducing the number of iterations of the reconstruction algorithm. Theoretical derivation and simulation experiment results show that, compared with the orthogonal matching pursuit (OMP) algorithm, the proposed algorithm can reduce the computational complexity by up to 50%, while showing better support reconstruction accuracy.

Great advances have been made with the lattice Boltzmann (LB) method for complicated fluid phenomena and fundamental thermal processes over the past three decades. This paper presents a systematic overview of the LB method from 1990 to 2018, based on bibliometric analysis and the Science Citation Index Expanded (SCI-E) database. The results show that China took the leading position in this field, followed by the USA and UK. The Chinese Academy of Sciences had the most publications, while the Los Alamos National Laboratory was first as far as highest average citation per paper and h-index are concerned. Physical Review E was the most productive journal and “Mechanics” was the most frequently used subject category. Keyword analysis indicated that recent research has focused on the natural convection and heat transfer of nanofluid or multiphase flow in complex porous media. Hydrothermal treatment of nanofluid with shape factor on the conditions, such as variable magnetic fields, thermal radiation and slipping boundary, were the research hotspots. Further research perspectives mainly explore the multiscale models for coupling multiple transport phenomena, morphology optimization of porous parameters, new nanoparticles with shape factor, multicomponent LB method considering Knudsen diffusion effect, LB-based hybrid methods, radiation performance or boiling-heat transfer of nanofluid, and the active control of droplets, may continue to attract more attention. Moreover, some new applications, such as phase change of metal foam, erosion induced by nanaofluid, anode circulating, 3D modeling in thermal systems with vibration, and magnetohydrodynamics microfluid devices, could be of interest going forward.

Abstract Renewable energy based distributed generation (DG) has the potential to reach high penetration levels in the residential region. However, its integration at the demand side will cause rapid power fluctuations of the tie-line in the residential region. The traditional generators are generally difficult to manage rapid power fluctuations due to their insufficient efficiency requirements and low responding speed. With an effective control strategy, the demand side resources (DSRs) including DGs, electric vehicles and thermostatically-controlled loads at the demand side, are able to serve as the energy storage system to smooth the load fluctuations. However, it is a challenge to properly model different types of DSRs. To solve this problem, a unified state model is first developed to describe the characteristics of different DSRs. Then a load curve smoothing strategy is proposed to offset the load fluctuations of the tie-line of the residential region, where a control matrix deduced from the unified state model is introduced to manage the power outputs of different DSRs, considering the response order and the comfort levels. Finally, a residential region with households is used to validate the load curve smoothing strategy based on the unified state model, and the results show that the power fluctuation rate of the tie-line is significantly decreased. Meanwhile, comparative study results are shown to demonstrate the advantages of the unified state model based load curve smoothing strategy.