Current Issue

• • 2024, Vol. 47 No. 1 Published date:26 February 2024 Last issue Next issue

  • Bayesian Blind Detection Algorithm Based on Multi-User Serial Interference Cancellation
  • WU Qi, SI Zhongwei, DAI Jincheng, WANG Sen, YUAN Yifei
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 1-6,37.
  • Abstract ( 299 )     　　
  • In the massive machine type communication, user devices are allowed to randomly access the network and transmit small packets occasionally by grant-free transmission. Correspondingly, receivers are required to perform the blind multi-user detection without scheduling and pilots. The Bayesian blind detection algorithm based on message passing can solve the above problem, but the parallel iterative calculation consumes massive computing resources with high computational complexity and unstable convergence. An algorithm combining serial interference cancellation with Bayesian message passing is proposed to improve the performance of the blind multi-user detection. By iteratively reconstructing and canceling the interference of correctly recovered users, the signal to interference plus noise ratio at the receiver is improved, which enhances the error performance and reduces the computational complexity. Meanwhile, the convergence stability is promoted by damping and re-initialization mechanisms. Simulation results show that the proposed algorithm has obvious advantages over the parallel Bayesian blind detection algorithm in the blind multiuser detection.
    
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  • A Signal Enhancement Algorithm for Simultaneous-Transmitting-and-Reflecting Reconfigurable Intelligent Surface Based on Non-Orthogonal Multiple Access Networks
  • HOU Tianwei, LI Jie, WANG Jun, SONG Zhengyu, SUN Xin
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 7-12.
  • Abstract ( 262 )     　　

  • In order to overcome the non-transition issue of the conventional reconfigurable intelligent surface, a signal enhancement algorithm based on non-orthogonal multiple access networks is proposed, aided by simultaneous-transmitting-and-reflecting reconfigurable intelligent surfaces. By utilizing the omni property of the simultaneous-transmitting-and-reflecting reconfigurable intelligent surface, it is possible to serve multiple users on both sides simultaneously. Since the direct signal and the reflected signals through simultaneous-transmitting-and-reflecting reconfigurable intelligent surface can be coherent, and by utilizing the Riemannian conjugate gradient algorithm, the signal power level of non-orthogonal multiple access users can be significantly boosted by the proposed signal enhancement based algorithm. As a result, the outage probability is decreased and the transmission rate performance is enhanced. Simulation results show that an improvement of 3.6 bit / (s· Hz) spectral efficiency is observed in the random phase shift scenario, compared to the scenario without simultaneous-transmitting-and-reflecting reconfigurable intelligent surface. Furthermore, an extra 2.3 bit / (s· Hz) gain on the spectral efficiency can be obtained when the proposed Riemannian conjugate gradient algorithm is employed, which validates the superiority of the proposed algorithm.

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  • Hybrid Precoding Method Based on Multi-Beam Trainings for Millimeter-Wave Channels
  • NIE Yifang, HE Xiping, TANG Lianggui, LI Yu, YANG Junchao
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 13-18.
  • Abstract ( 186 )     　　
  • Millimeter-wave communication systems have a large number of available frequency bands. In order to support multiple streams and reduce power consumption and cost, a hybrid precoder design method based on multi-beam training and iterative updating is proposed for millimeter-wave channels. In order to maximize the spectrum efficiency, the proposed method first utilize multi-beam trainings to estimate the angle of arrivals of the dominant paths of the channels and form the alternative beam collection. Then, the analog precoder is iteratively updated to find the optimal paths of the user channels. Furthermore, based on the determined equivalent channel, the digital precoder is determined by establishing the convex optimal mathematical model and solving the objective function on the condition that the spectrum efficiency should exceed the given threshold. The numerical and experimental results show that the spectrum efficiency of the proposed method is superior to those of existing hybrid precoding algorithms in highly correlated channels.
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  • Resource Optimization of UCA-Based OAM-MIMO System
  • LIN Chuting, TANG Jie, MA Ruoyan, YANG Jun
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 19-24.
  • Abstract ( 239 )     　　
  • For the orbital angular momentum (OAM) combined with multiple input multiple output (MIMO) system based on uniform circular array (UCA), the design of UCA affects the channel gain and the system performance. In order to further study the impact of the UCA design on the energy efficiency of OAM-MIMO system, the UCA radius, the number of antennas of UCA and transmission power are jointly optimized to maximize the energy efficiency, while improving the communication quality of each channel. Due to the coupling of several optimization variables, the considered optimization problem is non-convex and difficult to be solved directly. Therefore, an optimization algorithm combining genetic algorithm and particle swarm optimization with convex optimization method is proposed. Simulation results show that the proposed resource optimization algorithm is closed to the exhaustive algorithm combined with convex optimization, which can effectively improve the system energy efficiency.
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  • D2D-Assisted Task Offloading in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks
  • Ming-Lei TONG Li Song
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 25-30.
  • Abstract ( 239 )     　　
  • To solve the problem that the computing resources of ground equipment are not utilized effectively, the device-to-device ( D2D ) assisted task offloading in mobile edge computing-enabled satellite-terrestrial networks is studied. The utility maximization problem for all user equipment, service equipment, and the low earth orbit satellite is modeled and decomposed into two sub-problems, which are task offloading ratio optimization sub-problem and task offloading decision sub-problem. The analytical solution of the optimal task offloading ratio of D2D offloading problem and mobile edge computing offloading problem is obtained by the theoretical derivation, respectively. Besides, a matching game-based task offloading decision algorithm is designed and a joint optimization scheme of task offloading ratio and task offloading decision is proposed. The simulation results demonstrate that the proposed scheme has better performance than the reference schemes.
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  • User Clustering and Power Allocation Algorithm for Unmanned Aerial Vehicle Assisted NOMA Downlink
  • YANG Qingqing, HAN Zhuoting, PENG Yi
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 31-37.
  • Abstract ( 292 )     　　
  •  A clustering and power allocation algorithm is proposed for unmanned aerial vehicle assisted non-orthogonal multiple access (NOMA) downlink communication systems with non-uniformly distributed users. The optimization problems for maximizing the sum-rate and ensuring quality of service are established, and a three-step strategy is proposed to reduce the computation complexity. First, the optimal deployment position for the unmanned aerial vehicle is determined. Then, based on the unmanned aerial vehicle position, an angle-density based spatial clustering of applications with noise algorithm is proposed. Finally, a suboptimal solution to the power allocation problem is obtained using the successive convex approximation method. Simulation results show that the proposed algorithm outperforms traditional algorithms in terms of clustering effectiveness and communication performance in the same scenario.
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  • Channel Estimation Based on Deep Compression Sensing in RIS Assisted Communication System
  • LIU Feng, YANG Liu, ZHAO Lei
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 38-44.
  • Abstract ( 376 )     　　
  •  A channel estimation algorithm based on deep compressed sensing is proposed to solve the problem of high cost and limited accuracy of channel estimation pilots in reconfigurable intelligent surface (RIS) assisted multi-user communication system. To reduce the pilot cost of the traditional orthogonal matching pursuit (OMP) algorithm, an improved OMP algorithm is proposed by using the unique double-structured sparse property of cascaded channels to obtain rough estimation of the cascaded channel. In order to further improve the accuracy of channel estimation, a deep learning model is designed, which regards the coarsely estimated channel matrix as a low-resolution image, and uses the multi-convolutional network to learn the implicit noise features to the maximum extent. Finally, the multi-convolutional network structure based on residual connection (RMCN) is proposed. By using the spatial characteristics and additivity of noise, the influence of noise on the channel matrix is eliminated, and a cascade channel matrix with high resolution is output to complete the channel estimation. The simulation results show that compared with the traditional OMP scheme, the normalized mean square error of the proposed RMCN-OMP algorithm is reduced by about 2.5 dB while reducing pilot overhead and achieving higher estimation accuracy.
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  • Superimposed Pilot Optimization Design in Intelligent Reflecting Surface-Aided Massive MIMO System
  • SONG Yu, YANG Jianxin, JIN Sinian, JU Moran
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 45-50,99.
  • Abstract ( 303 )     　　
  • Compared with time-multiplexed pilot, superimposed pilot has been widely concerned for high bandwidth utilization and fast transmission rate. Therefore, the intelligent reflecting surface-aided massive multiple-input multiple-output ( MIMO) system in superimposed pilot mode is studied. Besides, the closed-form expression of sum achievable rate is derived when base stations use least square channel estimation and maximum ratio combining detection. In addition, an iterative optimization algorithm based on geometric programming and genetic algorithm is proposed to improve the mutual interference between the pilot signal and the data signal in superimposed pilot mode. The proposed algorithm can maximize the sum achievable rate of the system by cooperatively controlling the power control coefficient of the pilot signal, the power control coefficient of the data signal and the phase of the intelligent reflecting surface. The simulation results verify the accuracy of the closed-form expression of the achievable rate, and show that the proposed algorithm improves effectively the system rate performance.
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  • Trajectory Optimization and Resource Allocation for Full-Duplex Unmanned Aerial Vehicle-Relaying
  • TANG Jingmin, WANG Bingwen, HUANG Jiaqi, SONG Yaolian
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 51-57.
  • Abstract ( 242 )     　　
  • In order to mitigate the adverse effect of blockages of wireless communication in urban scenes, a trajectory optimization and resource allocation algorithm based on full-duplex unmanned aerial vehicle assisted communication system is proposed. A minimum transmission rate of the user is maximized by jointly optimizing user scheduling, flight trajectory, beamforming, base station and unmanned aerial vehicle’s transmit power. To solve this complicated complex non-convex optimization problem, the original problem is decomposed into four sub-problems via the block coordinate descent, which are tackled by introducing the slack variables, successive convex approximation and alternating interference suppression. Simulation results show that the proposed algorithm has a desirable convergence and can effectively improve the minimum transmission rate of the user.
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  • Dynamic Diffusion Community Detection Algorithm Based on Central Node
  • ZHUO Xinjian, TAN Wenze
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 58-64.
  • Abstract ( 192 )     　　
  • Community detection is one of the key research directions in the study of complex networks. Most of the existing work focuses on network topology but ignores the dynamic process on the network. Thus, a dynamic diffusion community detection algorithm based on central node is proposed. First, a node centrality measure metric is proposed based on the number of non-backtracking path. Then, in order to model the multi-scale social interaction mode occurring on the network, a new edge membership vector is designed to represent the community belonging of nodes which links the central node with community detection. Besides, a dynamic system is designed to represent the dynamic distribution process of community members to complete overlapping community detection. Finally, the proposed algorithm is applied to real networks and artificial networks to verify its effectiveness. The experimental results show the proposed algorithm has great advantages in detection accuracy.
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  • Optimization Method for Large-Scale Multi-Site Unmanned Aerial Vehicle mergency Rescue Based on Dynamic Divide-and-Conquer Strategy
  • SU Lichen, ZHAO Haoran, GUO Tong, DU Wenbo, LI Yumeng
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 65-71.
  • Abstract ( 506 )     　　
  •  Since the emergency rescue tasks need tight time, large demand, and large scale of rescue points, a large-scale multi-site unmanned aerial vehicle emergency rescue optimization method based on dynamic divide and conquer is proposed. In particular, a multi-station unmanned aerial vehicle emergency rescue model is established with the goal of minimizing the cumulative resume time. Besides, the model consideres the unmanned aerial vehicle platform constraints and emergency rescue mission constraints. Based on the model, a dynamic divide-and-conquer optimization framework based on path similarity is established and spatial clustering is performed based on the coupling relationship of rescue points. Then, large-scale problems are decomposed into several smaller-scale sub-problems with low coupling degrees. Finally, a variable neighborhood search algorithm for adaptive perturbation neighborhoods is proposed to achieve efficient optimization of large-scale emergency delivery plans through collaborative search and dynamic interaction of multi-dimensional neighborhoods. The simulation takes typical samples as an example and compares the proposed method with the advanced metaheuristic method on samples of different sizes. It is verified that the proposed method can effectively shorten the emergency delivery time and provide technical support for efficient post-disaster emergency rescue missions.
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  • Virtual Network Recovery Strategy for Single Node Failure in Multi-Domain Networks
  • LING Shen, WU Muqing, ZHAO Min
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 72-77.
  • Abstract ( 175 )     　　
  • Most existing research on virtual network recovery for node failure is focused on single-domain underlying network, while the real network is multi-domain distributed. Therefore, a virtual network recovery strategy is proposed for single node failure in multi-domain networks ( SNFMDN). In order to maximize the recovery rate and minimize the recovery cost, an integer programming model of the SNFMDN problem is established. This optimization problem cannot be solved quickly. Besides, two virtual network recovery algorithms based on bandwidth consumption are proposed to solve the SNFMDN problem. The experimental results show that the proposed recovery strategy can effectively recover the affected virtual network, and the recovery cost is relatively low.
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  • Asymmetrically Clipped Optical OFDM System with Enhanced Index Modulation
  • WANG Huiqin, HE Jie, CAO Minghua, PENG Qingbin, WANG Bin
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 78-84.
  • Abstract ( 195 )     　　
  • To solve the problem that the transmission rate of conventional asymmetrically clipped optical OFDM system with index modulation (OFDM) is not ideal, an asymmetrically clipped optical OFDM with enhanced index modulation system is proposed by selecting the active carrier twice to increase the index information carried by the active subcarrier and transmitting different modulation symbols on the active subcarrier. Meanwhile, the subcarrier selection algorithm is proposed to improve the bit error rate of the system. In particular, the modulation mapping principle of the proposed system is introduced in detail. Then, its theoretical bit error rate is derived by using the joint bound technique. Finally, the proposed system is compared with the conventional systems. The simulation results show that the proposed system not only increases the transmission rate but also improves the bit error rate. Specifically, when the turbulence is strong and the bit error rate is 1×10^-3, the transmission rate of (8,3,3) the proposed system is 1.25 times higher than that of(8,6) conventional system, meanwhile the signal to noise ratio is improved by about 1.4 dB.
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  • Improved Adaptive Lion Swarm Optimization Algorithm Based on Multi-Strategy
  • LIU Miaomiao, ZHANG Yuying, GUO Jingfeng, CHEN Jing
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 85-93.
  • Abstract ( 235 )     　　
  • To solve the problems of low diversity, slow convergence speed and easy to fall into local extremum of the lion swarm optimization algorithm, an improved adaptive algorithm based on multi-strategy is proposed. Specifically, the adaptive parameters are introduced to improve Tent chaotic map for population initialization, which ensures random distribution and improves diversity and uniform ergodicity. Then, based on differential evolution mechanism, the disturbance factor of lioness position update is introduced to enhance the ability of the algorithm to jump out of the local optimum. Finally, the second order norm and information entropy are combined to form a step size disturbance factor, which adaptively adjusts the selection probability of different behavior modes of the cub, therefore inhibits the premature convergence of the algorithm. Based on adaptive Tent chaotic search, individuals with poor fitness are improved through multiple neighborhood points of local optimal solution to further enhance the optimization speed and accuracy. Comparing with various intelligence algorithms, the better performance of the proposed algorithm is verified through 16 multi-type test functions. To further evaluate the effectiveness of the proposed algorithm, it is used to optimize the initial weights and thresholds of back propagation neural networks. Experimental results on the two datasets show the proposed algorithm has higher classification accuracy compared with the other three algorithm.
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  • Variable Scale Factor Diffusion Fair Algorithm Based on P-norm
  • HUO Yuanlian, XU Tianci, QI Yongfeng, XU Yurong, ZHANG Yin
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 94-99.
  • Abstract ( 152 )     　　
  • A new P-norm based variable scale DFAIR diffusion fair cost function algorithm is proposed to further enhance the performance of diffusion based adaptive filtering algorithms in different noise environments. The absolute value of the error term is augmented with a P-norm and a scale factor that varies with the error is established using a tongue-like function to control the steepness of the algorithm. As a result, the convergence speed of the algorithm is accelerated and the steady-state error is reduced. The simulation results in Gaussian noise environments, as well as non-Gaussian noise environments with alpha stable distribution and Bernoulli Gaussian distribution, demonstrate the algorithm has stronger robustness and lower steady-state error. The performance of the proposed algorithm surpasses that of the comparison algorithm in various noise environments.
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  • Optical Flow-Camera Calibration Fusion Positioning Algorithm Based on Optical Camera
  • YUAN Zhenbo, BAI Bo, LUO Liujun, ZHANG Xiaowei, SHANG Tao
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 100-105,119.
  • Abstract ( 210 )     　　
  • In order to solve the problem that the positioning error of the optical flow algorithm is too large in the case of large camera rotation angle and camera calibration positioning algorithm is unable to locate when the number of LEDs is insufficient, an optical flow-camera calibration fusion positioning algorithm that combines the camera calibration positioning algorithm and optical flow algorithm with Kalman filtering is proposed. In particular, the camera calibration and positioning algorithm is used for indoor positioning. Then the optical flow algorithm is introduced to compensate the calculation results of the camera calibration and positioning algorithm. Finally, the Kalman filter is used to fuse the positioning results of the two algorithms. An experimental platform is built to verify the positioning performance of the optical flow-camera calibration fusion positioning algorithm. The experimental results show that the proposed algorithm can solve the problem that the camera calibration positioning algorithm can not locate when the number of LEDs is insufficient, and can reduce positioning error of the optical flow algorithm in the case of large rotation angles. Specifically, the average positioning error of the camera at large rotation angles has been reduced from 6.86 cm to 1.01 cm.
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  • ORB-SLAM Algorithm for Low Light Environment
  • LI Ping, CAO Chaochao
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 106-111.
  • Abstract ( 679 )     　　
  • Aiming at the localization failure and tracking loss of visual simultaneous localization and map building (SLAM) in complex environments such as weak lighted or even totally dark, a vision SLAM algorithm suitable for weak light environment is proposed based on ORB-SLAM2, to which, a new adaptive image enhancement algorithm is applied. Image brightness is adapted by means of correction factor , which can be dynamically adjusted according to illuminance component of input image extracted by multi-scale Gaussian function. Performance of the algorithm is tested on public dataset. Simulation results show that the algorithm can efficiently help feature matching in complex environments such as weak lighted or even totally dark, consequently, the robustness of ORB-SLAM is improved effectually. 
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  • Electro-Optical Effects of Chalcogenide Mid-Infrared Few-Mode Fibers with Lithium Niobate Cladding
  • DUAN Bo, HOU Shanglin, LEI Jingli, WU Gang, YAN Zuyong
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 112-119.
  • Abstract ( 134 )     　　
  • A mid-infrared chalcogenide few-mode fiber with cladding made by LiNbO₃ crystals in which the optical axis is running along the axis of optical fiber is proposed. Besides, the effect of the adding electric field on the rate of the extraordinary to the ordinary ray refractive index of LiNbO₃ crystals and the transmission characteristics of optical modes are studied by using the full-vector finite element method. The results show that the effective refractive index and power confinement factor both decrease with increasing wavelength, but the differential mode delay shows an increasing trend at the wavelength of 2.25 ~ 3.85 μm. Besides, the external axial electric field not only reduces the effective refractive index and differential mode delay of each optical mode, but also increases energy confinement for each mode. And the higher the mode orders is, the stronger energy confinement becomes. The dispersion of the HE₁₁ mode rises with wavelength drop without an external electric field, whilst the HE₂₁, TE₀₁, and TM₀₁ modes exhibit parabolic distribution with the wavelength increasing, and there exist two zero dispersion wavelengths. The dispersions of optical modes increase as the electrical field increases, which contributes to a blue shift of the dispersion zero point. In comparison with and without an electric field of 4 ×10⁹ V/ m, the zero dispersion wavelengths at the short wavelengths of HE₁₁, HE₂₁, TE₀₁ and TM₀₁ modes are blue-shifted by 0.569 5 μm, 0.391 5 μm, 0.386 2 μm and 0.559 4 μm, respectively.
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  • Redactable blockchain based on committee mechanism
  • LIU Ya, LI Changhui, ZHAO Fengyu, REN Yanli
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 120-126.
  • Abstract ( 248 )     　　
  • The blockchain has the characteristics of decentralization, traceability, and immutability. However, its immutability makes some illegal information on the chain permanently retained. To solve this problem, a redactable blockchain scheme based on committee mechanism is proposed. First, the committee members are selected by applying the random sortition algorithm. Then, a decentralized editing strategy of voting consensus with the consensus mechanism is adopted based on proof of stake for users’ editing requests. In addition, the chameleon hash function is used to modify the data on the chain and an accountability mechanism is introduced to prevent users from malicious actions. Finally, the security and convergence of the scheme are demonstrated theoretically, and simulation results verify that the proposed scheme can efficiently modify the block content in a decentralized environment without affecting the consistency of the blockchain.
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  • Aspect-Level Sentiment Analysis Based on Self-Attention and Graph Convolutional Network
  • CHEN Kejia, HUANG Chunxiang, LIN Hongxi
  • Journal of Beijing University of Posts and Telecommunications. 2024, 47(1): 127-132.
  • Abstract ( 350 )     　　
  • In view of the lack of interactive information between aspect words and the context in most aspect-level sentiment studies, and the inability to make full use of semantic information. To address the problems above, a model based on self-attention and graph convolution network is proposed. In order to improve the semantic representation ability of the model, the multi-head self-attention mechanism is used to obtain the long-distance dependency relationship of the text, combined with the dependency type matrix. Then, the weight matrix that combines the location information and the relationship type information is calculated and is inputted to the graph convolution network to obtain the text feature representation. Besides, the text aspect attention layer is employed to extract the context-sensitive aspect features, and it is inputted to graph convolution network to obtain aspect feature representation. Finally, the two vectors above are connected to complete the task of sentiment analysis. Simulation results show that the overall performance of the proposed model is better than that these of other comparison models in two open datasets.
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