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Behavioral finance between the spot and futures markets based on multilayer network
Zhang Sicong, Dai Jianzhuo, Huang Wenjing, Mi Xinping
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 82-88.   DOI: 10.19682/j.cnki.1005-8885.2023.1013
Abstract886)      PDF(pc) (3117KB)(68)       Save
In order to study the financial behavior of investors in the spot market, the transmission process of futures prices to spot prices is analyzed. Firstly, a coarse-graining method is proposed to construct a dual-layer coupled complex network of spot price and futures price. Then, to characterize the financial behavior of investors in the spot market, a price coupling strength indicator is introduced to capture investors' overreaction and underreaction behavior. The simulation results show that, despite the focus of researchers on arbitrage opportunities between futures and spot markets, investors in the spot market will not overreact or delay when the acceptance level of price fluctuations remains unchanged. On the contrary, when the stable coefficient of the price difference between the futures and spot markets remains unchanged, investors undergo a nonlinear process of overreaction followed by underreaction as their acceptance level of price fluctuations increases.
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Dynamic event-triggered leader-follower consensus of nonlinear multi-agent systems under directed weighted topology
Wu Yue, Chen Xiangyong, Qiu Jianlong, Hu Shunwei, Zhao Feng
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 3-10.   DOI: 10.19682/j.cnki.1005-8885.2023.1019
Abstract675)      PDF(pc) (1536KB)(224)       Save
This paper studies the dynamic event-triggered leader-follower consensus of nonlinear multi-agent systems (MASs) under directed weighted graph containing a directed spanning tree, and also considers the effects of disturbances and leader of non-zero control inputs in the system. Firstly, a novel distributed control protocol is designed for uncertain disturbances and leader of non-zero control inputs in MASs. Secondly, a novel dynamic event-triggered control ( DETC) strategy is proposed, which eliminates the need for continuous communication between agents and reduces communication resources between agents. By introducing dynamic thresholds, the complexity of excluding Zeno behavior within the system is reduced. Finally, the effectiveness of the proposed theory is validated through numerical simulation.
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Stability and Hopf bifurcation analysis in DCTCP congestion control
Cheng Zunshui, Jiang Jingna, Sun Dongsheng
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 30-37.   DOI: 10.19682/j.cnki.1005-8885.2023.1015
Abstract675)      PDF(pc) (3657KB)(75)       Save
Traditional loss-based transports cannot meet the strict requirements of low latency and high throughput in data center networks (DCNs). Thus data center transmission control protocol (DCTCP) is proposed to better manage the congestion control in DCNs. To provide insight into improving the stability of the DCN, this paper focuses on the Hopf bifurcation analysis of a fluid model of DCTCP, and investigates the stability of the network. The round-trip time (RTT), being an effective congestion signal, is selected as the bifurcation parameter. And the network turns unstable and generates periodic solutions when the parameter is larger than the given critical value, which is given by explicit algorithms. The analytical results reveal the existence of Hopf bifurcation. Numerical simulations are performed to make a comparative analysis between the fluid model and the simplified model of DCTCP. The influence of other parameters on the DCN stability is also discussed.
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Linear-quadratic optimal control for time-varying descriptor systems via space decompositions
Lv Pengchao, Huang Junjie, Liu Bo
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 38-48.   DOI: 10.19682/j.cnki.1005-8885.2023.1011
Abstract645)      PDF(pc) (1663KB)(72)       Save
This paper aims at solving the linear-quadratic optimal control problems ( LQOCP) for time-varying descriptor systems in a real Hilbert space. By using the Moore-Penrose inverse theory and space decomposition technique, the descriptor system can be rewritten as a new differential-algebraic equation (DAE), and then some novel sufficient conditions for the solvability of LQOCP are obtained. Especially, the methods proposed in this work are simpler and easier to verify and compute, and can solve LQOCP without the range inclusion condition. In addition, some  numerical examples are shown to verify the results obtained.
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Node interdependent percolation of multiplex hypergraph with weak interdependence
Zhang Junjie, Liu Caixia, Liu Shuxin, Zang Weifei, Li Qian
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 49-59.   DOI: 10.19682/j.cnki.1005-8885.2023.1016
Abstract612)      PDF(pc) (3049KB)(84)       Save
In recent years, there has been considerable attention and research on the higher-order interactions that are prevalent in various real-world networks. Hypergraphs, especially in the study of complex systems, are proved effective in capturing these interactions. To better characterize the model in reality, this paper proposes a theoretical model of node interdependent percolation in multiplex hypergraphs, considering “ weak ” interdependence. The proposed model includes pairwise and higher-order interactions, where the removal of nodes triggers cascading failures. However, interdependent nodes connected to failed nodes experience partial loss of connections due to “ weak” interdependence, reflecting the self-sustaining capabilities of real-world systems. Percolation theory is applied to the analysis to investigate the properties of the percolation threshold and phase transition. Both analysis and simulation results show that as the strength of interdependence between nodes weakens, the network transitions from a discontinuous to a continuous phase, thereby increasing its robustness.
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Fairness optimization and power allocation in cognitive NOMA / OMA V2V network with imperfect SIC
Liang Xiaolin, Liu Qianlong, Cao Wangbin, Liu Shuaiqi, Zhao Shuhuan, Zhao Xiongwen
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 68-81.   DOI: 10.19682/j.cnki.1005-8885.2023.1020
Abstract601)      PDF(pc) (4592KB)(79)       Save
In order to improve the reliability and resource utilization efficiency of vehicle-to-vehicle (V2V) communication system, in this paper, the fairness optimization and power allocation for the cognitive V2V network that takes into account the realistic three-dimensional (3D) channel are investigated. Large-scale and small-scale fading are considered in the proposed channel model. An adaptive non-orthogonal multiple access ( NOMA) / orthogonal multiple access (OMA) scheme is proposed to reduce the complexity of successive-interference-cancellation (SIC) in decoding and improve spectrum utilization. Also, a fairness index that takes into account each user’s requirements is proposed to indicate the optimal point clearly. In the imperfect SIC, the optimization problem of maximizing user fairness is formulated. Then, a subgradient descent method is proposed to solve the optimization problem with customizable precision. And the computational complexity of the proposed method is analyzed. The achievable rate, outage probability and user fairness are analyzed. The results show that the proposed adaptive NOMA / OMA (A-NOMA / OMA) outperforms both NOMA and OMA. The simulation results are compared with validated analysis to confirm the theoretical analysis.
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Performance analysis of different coding schemes for wideband vehicle-to-vehicle MIMO systems
Liang Xiaolin, Rong Zhanyi, Cao Wangbin, Liu Shuaiqi, Zhao Xiongwen
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 89-98.   DOI: 10.19682/j.cnki.1005-8885.2023.1017
Abstract574)      PDF(pc) (2637KB)(98)       Save
The signal is subjected to lots of interferences in vehicle-to-vehicle (V2V) channel propagation, resulting in receiving error codes. Two-dimensional (2D) and three-dimensional (3D) geometrical channel models are used to depict the wideband V2V multiple-input multiple-output (MIMO) channels. Using the channel model, Turbo code and low-density parity-check (LDPC) code are investigated for wideband V2V MIMO system, and the encoding and the decoding schemes are investigated. The bit error rate (BER), channel capacity and outage probability of wideband V2V MIMO system using Turbo code and LDPC code are analyzed at different typical speeds. The results show that the performance of wideband V2V MIMO system using Turbo code outperform that using LDPC code. The performance is affected by transmitting and receiving speeds with the same coding scheme. And the channel capacity of the 3D channel is larger than that of 2D channel.
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GNN-based temporal knowledge reasoning for UAV mission planning systems
Chai Rong, Duan Xiaofang, Wang Lixuan
The Journal of China Universities of Posts and Telecommunications    2024, 31 (1): 12-25.   DOI: 10.19682/j.cnki.1005-8885.2024.2002
Abstract570)      PDF(pc) (2603KB)(50)       Save
Unmanned aerial vehicles (UAVs) are increasingly applied in various mission scenarios for their versatility, scalability and cost-effectiveness. In UAV mission planning systems (UMPSs), an efficient mission planning strategy is essential to meet the requirements of UAV missions. However, rapidly changing environments and unforeseen threats pose challenges to UMPSs, making efficient mission planning difficult. To address these challenges, knowledge graph technology can be utilized to manage the complex relations and constraints among UAVs, missions, and environments. This paper investigates knowledge graph application in UMPSs, exploring its modeling, representation, and storage concepts and methodologies. Subsequently, the construction of a specialized knowledge graph for UMPS is detailed. Furthermore, the paper delves into knowledge reasoning within UMPSs, emphasizing its significance in timely updates in the dynamic environment. A graph neural network (GNN)-based approach is proposed for knowledge reasoning, leveraging GNNs to capture structural information and accurately predict missing entities or relations in the knowledge graph. For relation reasoning, path information is also incorporated to improve the accuracy of inference. To account for the temporal dynamics of the environment in UMPS, the influence of timestamps is captured through the attention mechanism. The effectiveness and applicability of the proposed knowledge reasoning method are verified via simulations.
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Parameter optimization of complex network based on the change-point identification
Xu Xingtao, Tao Jiagui
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 22-29.   DOI: 10.19682/j.cnki.1005-8885.2023.1014
Abstract567)      PDF(pc) (6155KB)(81)       Save
This paper proposes a novel method for the parameter optimization of complex networks established through coarsening and phase space reconstruction. Firstly, we identify the change-points of the time series based on the cumulative sum ( CUSUM) control chart method. Then, we optimize the coarse-graining parameters and phase space embedding dimension based on the evolution analysis of the global topology index ( betweenness) at the mutation point. Finally, we conduct a simulation analysis based on real-time data of Chinese copper spot prices. The results show that the delay of the copper spot prices in Chinese spot market is 1 day, and the optimal embedding dimension of the phase space reconstruction is 3. The acceptance level of the investors towards the small fluctuations in copper spot prices is 0.2 times than the average level of price fluctuations, which means that an average price fluctuation of 0.2 times is the optimal coarse- graining parameter.
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Distributed consensus of Lurie multi-agent systems under directed topology: a contraction approach
Zhang Xiaojiao, Wu Xiang
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 11-21.   DOI: 10.19682/j.cnki.1005-8885.2023.1018
Abstract566)      PDF(pc) (3622KB)(128)       Save
This paper is devoted to investigate the consensus problems for the multi-agent systems with Lurie nonlinear dynamics in directed topology. Under some assumptions, some sufficient conditions for the systems reaching leaderless consensus and tracking consensus are established by using contraction analysis theory. Compared with the existing results, there is no need to formulate the multi-agent networks in compact form. These conditions are only related to the individual agent in lower-dimensional case and the communication topology of the network. Additionally, a generalized nonlinear function is introduced. Finally, three numerical examples are demonstrated to illustrate the effectiveness of the theoretical results.
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Characteristics and modeling of UAV-vehicle MIMO wideband channels
Liang Xiaolin, Ma Jiaxu, Cao Wangbin, Xu Jianpeng, Liu Shuaiqi, Zhao Xiongwen
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 60-67.   DOI: 10.19682/j.cnki.1005-8885.2023.1012
Abstract524)      PDF(pc) (2101KB)(65)       Save
A geometry-based stochastic model ( GBSM) for unmanned aerial vehicle to vehicle ( UAV-V) multiple-input multiple-output (MIMO) wideband channel is proposed to investigate the characteristics of UAV-V channel. Based on the proposed model, a three-dimensional (3D) wideband channel matrix regarding channel numbers, time and delay is constructed. And some important channel characteristics parameters, such as power delay profile (PDP), root mean square ( RMS) delay spread, RMS Doppler spread, channel gain and Doppler power spectral density (PSD) are investigated with different vehicle velocities. It is much simpler and clearer compared with the complex analytical derivations. The results are compared with validated analysis to confirm the theoretical analysis.
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SNR-adaptive deep joint source-channel coding scheme for imagesemantic transmission with convolutional block attention module
Yang Yujia, Liu Yiming, Zhang Wenjia, Zhang Zhi
The Journal of China Universities of Posts and Telecommunications    2024, 31 (1): 1-11.   DOI: 10.19682/j.cnki.1005-8885.2024.2001
Abstract500)      PDF(pc) (2443KB)(96)       Save
With the development of deep learning (DL), joint source-channel coding (JSCC) solutions for end-to-end transmission have gained a lot of attention. Adaptive deep JSCC schemes support dynamically adjusting the rate according to different channel conditions during transmission, enhancing robustness in dynamic wireless environment. However, most of the existing adaptive JSCC schemes only consider different channel conditions, ignoring the different feature importance in the image processing and transmission. The uniform compression of different features in the image may result in the compromise of critical image details, particularly in low signal-to-noise ratio (SNR) scenarios. To address the above issues, in this paper, a dual attention mechanism is introduced and an SNR-adaptive deep JSCC mechanism with a convolutional block attention module (CBAM) is proposed, in which matrix operations are applied to features in spatial and channel dimensions respectively. The proposedsolution concatenates the pooling feature with the SNR level and passes it sequentially through the channel attention network and spatial attention network to obtain the importance evaluation result. Experiments show that the proposed solution outperforms other baseline schemes in terms of peak SNR (PSNR) and structural similarity (SSIM), particularly in low SNR scenarios or when dealing with complex image content.
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Joint global constraint and Fisher discrimination based multi-layer dictionary learning for image classification
Hong PENG yaozong liu
The Journal of China Universities of Posts and Telecommunications    2023, 30 (5): 1-10.   DOI: 10. 19682 / j. cnki. 1005-8885. 2023. 0010
Abstract470)      PDF(pc) (890KB)(214)       Save

    A multi-layer dictionary learning algorithm that joints global constraints and Fisher discrimination (JGCFD-MDL) for image classification tasks was proposed. The algorithm reveals the manifold structure of the data by learning the global constraint dictionary and introduces the Fisher discriminative constraint dictionary to minimize the intra-class dispersion of samples and increase the inter-class dispersion. To further quantify the abstract features that characterize the data, a multi-layer dictionary learning framework is constructed to obtain high-level complex semantic structures and improve image classification performance. Finally, the algorithm is verified on the multi-label dataset of court costumes in the Ming Dynasty and Qing Dynasty, and better performance is obtained. Experiments show that compared with the local similarity algorithm, the average precision is improved by 3.34% . Compared with the single-layer dictionary learning algorithm, the one-error is improved by 1.00% , and the average precision is improved by 0.54% . Experiments also show that it has better performance on general datasets.

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Study on high-order frequency selective surface with interdigital capacitance loading
Zheng Guangming, Zhou Tianle, Lu Haiwei, Long Yifei, Bai Jing
The Journal of China Universities of Posts and Telecommunications    2024, 31 (1): 57-63.   DOI: 10.19682/j.cnki.1005-8885.2024.2006
Abstract462)      PDF(pc) (2161KB)(22)       Save
The application of frequency selection surfaces (FSSs) is limited by large area, narrow bandwidth, low stopband inhibition and large ripple in the passband. A method for designing high-order wide band miniaturized-element frequency selective surface (MEFSS) with capacitance loading is introduced. The proposed structure is composed of multiply sub-wavelength interdigital capacitance layer, sub-wavelength inductive wire grids separated by dielectric substrates. A simple equivalent circuit model, composed of short transmission lines coupled together with shunt inductors and capacitors, is presented for this structure. Using the equivalent circuit model and electromagnetic (EM) model, an analytical synthesis procedure is developed that can be used to synthesize the MEFSS from its desired system-level performance indicators such as the center frequency of operation, bandwidth and stopband inhibition. Using this synthesis procedure, a prototype of the proposed MEFSS with a third-order bandpass response, center frequency of 2.75 GHz, fractional bandwidth of 8% is designed, fabricated, and measured. The measurement results confirm the theoretical predictions and the design procedure of the structure and demonstrate that the proposed MEFSS has a stable frequency response with respect to the angle of incidence of the EM wave in the ±30° range incidence, and the in-band return loss is greater than 18 dB, and the rejection in the stopband is greater than 25 dB at the frequency of 3.2 GHz.
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Radar false alarm plots elimination based on multi-feature extraction and classification
Cheng Yi, Zhao Yan, Yin Peiwen
The Journal of China Universities of Posts and Telecommunications    2024, 31 (1): 83-92.   DOI: 10.19682/j.cnki.1005-8885.2024.2008
Abstract423)      PDF(pc) (1504KB)(26)       Save
Caused by the environment clutter, the radar false alarm plots are unavoidable. Suppressing false alarm points has always been a key issue in Radar plots procession. In this paper, a radar false alarm plots elimination method based on multi-feature extraction and classification is proposed to effectively eliminate false alarm plots. Firstly, the density based spatial clustering of applications with noise (DBSCAN) algorithm is used to cluster the radar echo data processed by constant false-alarm rate (CFAR). The multi-features including the scale features, time domain features and transform domain features are extracted. Secondly, a feature evaluation method combining pearson correlation coefficient (PCC) and entropy weight method (EWM) is proposed to evaluate interrelation among features, effective feature combination sets are selected as inputs of the classifier. Finally, False alarm plots classified as clutters are eliminated. The experimental results show that proposed method can eliminate about 90% false alarm plots with less target loss rate.
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Fractional order distance regularized level set method with bias correction
Cai Xiumei, He Ningning, Wu Chengmao, Liu Xiao, Liu Hang
The Journal of China Universities of Posts and Telecommunications    2024, 31 (1): 64-82.   DOI: 10.19682/j.cnki.1005-8885.2024.2007
Abstract395)      PDF(pc) (15845KB)(19)       Save
The existing level set segmentation methods have drawbacks such as poor convergence, poor noise resistance, and long iteration times. In this paper, a fractional order distance regularized level set segmentation method with bias correction is proposed. This method firstly introduces fractional order distance regularized term to punish the deviation between the level set function  (LSF)  and the signed distance function. Secondly a series of covering template is constructed to calculate fractional derivative and its conjugate of image pixel. Thirdly introducing the offset correction term and fully using the local clustering property of image intensity, the local clustering criterion of image intensity is defined and integrated with the neighborhood center to obtain the global criterion of image segmentation. Finally, the fractional distance regularization, offset correction, and external energy constraints are combined, and the energy optimization segmentation method for noisy image is established by level set. Experimental results show that the proposed method can accurately segment the image, and effectively improve the efficiency and robustness of exiting state of the art level set related algorithms.
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Energy-efficient computation offloading assisted by RIS-based UAV
Li Linpei, Zhao Chuan, Su Yu, Huo Jiahao, Huang Yao, Li Haojin
The Journal of China Universities of Posts and Telecommunications    2024, 31 (1): 37-48.   DOI: 10.19682/j.cnki.1005-8885.2024.2004
Abstract375)      PDF(pc) (1638KB)(42)       Save
The new applications surge with the rapid evolution of the mobile communications. The explosive growth of the data traffic aroused by the new applications has posed great computing pressure on the local side. It is essential to innovate the computation offloading methods to alleviate the local computing burden and improve the offloading efficiency. Mobile edge computing (MEC) assisted by reflecting intelligent surfaces (RIS)-based unmanned aerial vehicle (UAV) is a promising method to assist the users in executing the computation tasks in proximity at low cost. In this paper, we propose an energy-efficient MEC system assisted by RIS-based UAV, where the UAV with RIS mounted relays the computation tasks to the MEC server. The energy efficiency maximization problem is formulated by jointly optimizing the UAV's trajectory, the transmission power of all users, and the phase shifts of the reflecting elements placed on the UAV. Considering that the optimization problem is non-convex, we propose a deep deterministic policy gradient (DDPG)-based algorithm. By combining the DDPG algorithm with the energy efficiency maximization problem, the optimization problem can be resolved. Finally, the numerical results are illustrated to show the performance of the system and the superiority compared with the benchmark schemes.
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Wireless semantic communication based on semantic matching multiple access and intent bias multiplexing
Ren Chao, He Zongrui, Sun Chen, Li Haojin, Zhang Haijun
The Journal of China Universities of Posts and Telecommunications    2024, 31 (1): 26-36.   DOI: 10.19682/j.cnki.1005-8885.2024.2003
Abstract368)      PDF(pc) (1783KB)(49)       Save
This paper proposes a multi-access and multi-user semantic communication scheme based on semantic matching and intent deviation to address the increasing demand for wireless users and data. The scheme enables flexible management of long frames, allowing each unit of bandwidth to support a higher number of users. By leveraging semantic classification, different users can independently access the network through the transmission of long concatenated sequences without modifying the existing wireless communication architecture. To overcome the potential disadvantage of incomplete semantic database matching leading to semantic intent misunderstanding, the scheme proposes using intent deviation as an advantage. This allows different receivers to interpret the same semantic information differently, enabling multiplexing where one piece of information can serve multiple users with distinct purposes. Simulation results show that at a bit error rate (BER) of 0.1, it is possible to reduce the transmission by approximately 20 semantic basic units.
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Intelligent reflecting surfaces-assisted millimeter wave communication: Channel estimation based on deep learning
Kang Xiaofei, Wang Tian, Liang Xian
The Journal of China Universities of Posts and Telecommunications    2024, 31 (1): 49-56.   DOI: 10.19682/j.cnki.1005-8885.2024.2005
Abstract342)      PDF(pc) (1810KB)(32)       Save
In response to the challenge posed by the complexity of the system and the difficulty in obtaining accurate channel state information (CSI) for millimeter wave communication assisted by intelligent reflecting surfaces (IRS), we propose a deep learning-based channel estimation scheme. The proposed scheme employs a hybrid active/passive IRS architecture, wherein the least square (LS) algorithm is initially utilized to acquire the channel estimate from the active elements. Subsequently, this estimation is interpolated to obtain a preliminary channel estimation and ultimately refined into an accurate estimate of the channel using the channel super-resolution convolutional neural network (Chan-SRCNN) deep learning network. The simulation results demonstrate that the proposed scheme surpasses LS, orthogonal matching pursuit (OMP), synchronous OMP (SOMP), and deep neural network (DNN) channel estimation algorithms in terms of normalized mean squared error (NMSE) performance, thereby validating the feasibility of the proposed approach.
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Machine-learning based ocean atmospheric duct forecasting: a hybrid model-data-driven approach
Feng Yuting, Gong Haobing, Hao Xiaojing, Gao Hui, Guo Xiangming
The Journal of China Universities of Posts and Telecommunications    2023, 30 (4): 1-9.   DOI: 10.19682/j.cnki.1005-8885.2023.2011
Abstract156)            Save
The atmospheric duct is a vital radio wave environment. Conventional methods of forecasting the atmospheric duct mainly include statistical analysis based on sounding observation data and mesoscale numerical model-based prediction. The former can provide accurate duct information but is highly dependent on the acquisition of data sets. The latter is more practical but still lacks accuracy. This paper introduces machine learning to establish a novel meteorological parameter correction model for atmospheric duct prediction. In detail, using the weather research and forecasting (WRF) model data and spatiotemporal characteristics as input, sounding data as label and extreme gradient boosting (XGBoost) model for training, the meteorological parameter correction effect is the best, i. e. , the accuracy of forecast meteorological parameters is improved by about 65.4%. Combining the mapping relationship between meteorological parameters and corrected atmospheric refractive index ( CARI ), and the transition mechanism of CARI to duct parameters, a new duct forecasting mechanism is proposed. Due to the high efficiency of numerical model and the accuracy of sounding data, the new duct forecasting mechanism has excellent performance. By comparing the duct forecasting results, the forecasting accuracy of the new duct forecasting model is significantly higher than that of the mesoscale model.
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