Current Issue

• • 2021, Vol. 44 No. 3 Published date:28 June 2021 Last issue Next issue

  • Status and Analysis of Wireless Avionics Intra-Communications Network Protocol
  • LI Shi-ning, FAN Xiang-hui, LIU Zhou-zhou, CHEN Chang-sheng, CHENG Tao
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 1-8. DOI:10.13190/j.jbupt.2020-151
  • Abstract ( 1358 )     HTML( 230 )     　　
  • In order to develop the wireless avionics intra-communications(WAIC) network protocol, referring to five layer protocol model of network communication, the reliability and security requirements of airborne wired communication network, the industrial wireless network protocol and the latest research results of WAIC network, the WAIC network system model is given. The application model, the topology model, the communication protocol model, and the technical difficulties of each layer of WAIC network communication protocol are analyzed as the key points and solutions. The physical layer uses 4.2~4.4 GHz frequency band, improves the anti-interference ability through orthogonal frequency division multiplex-ing technology, and selects quadrature amplitude modula and quadrature phase shift keying coding mode for high and low speed respectively; The data link layer uses passive neighbor discovery strategy, time division multiple access scheduling mode and active/standby channel transmission mechanism; The network layer uses static graph routing protocol to provide primary link fault redundancy; The transmission layer uses connectionless transmission service based on bidirectional identification; The application layer uses anonymous subscriber messaging protocol to decouple the application from the network to improve the diversity and compatibility of WAIC network application scenarios.
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  • Dual-Mode Navigation Satellite Selection Algorithm Based on Differential Evolution and Geometry
  • ZHU Jun, XU Shi-jie, LI Kai
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 9-14. DOI:10.13190/j.jbupt.2020-168
  • Abstract ( 611 )     HTML( 171 )     　　
  • Based on the dual-system integration scenario, the geometric dilution of precision is modeled. Due to the low real-time performance of the traditional algorithm, a satellite selection algorithm based on the geometric distribution of satellites and differential evolution is proposed. According to the distribution of the elevation angle and the system type, the distribution of the satellite combination is determined. By setting different fitness thresholds and adaptively changing and the population size according to the number of remaining satellites, the rapid satellite selection is realized. Simulations show that, compared with the traditional algorithms, the proposed algorithm has a difference range from 0 to 0.25. And the average time to select a hundred times at a single time is 8.09% of the traditional algorithm; Moreover, this algorithm can be applied to dual-mode navigation scenarios where the number of visible satellites increases.
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  • Selection Algorithm of eMBB/URLLC Multiplexing Schemes Based on Fuzzy Logic
  • GAO Yue-hong, YANG Hao-tian, CHEN Lu, YANG Hong-wen, YIN Ning
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 15-20,34. DOI:10.13190/j.jbupt.2020-231
  • Abstract ( 907 )     HTML( 202 )     　　
  • The coexistence mechanism between enhanced mobile broadband(eMBB) and ultra reliability low latency communication(URLLC) is an important problem in the evolution of the fifth generation of mobile communications system. Existing downlink multiplexing mechanisms include resource reservation and puncture preemption with different characteristics and performance. A fuzzy logic based multiplexing mechanism selection algorithm is proposed to make optimal selections between resource reservation and puncture preemption according to the characteristics of URLLC services. The algorithm improves the system performance by combining the advantages of the two mechanisms. Simulations show that the queueing delay of URLLC services is reduced by nearly 50% after the optimal selection. At the same time, the reduction in throughput of eMBB services caused by URLLC services is less than 4%.
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  • Convolutional Memory Graph Collaborative Filtering
  • LIU Guo-zhen, CHEN Hong-long
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 21-26. DOI:10.13190/j.jbupt.2020-226
  • Abstract ( 483 )     HTML( 136 )     　　
  • An end-to-end graph neural networks with memory unit is proposed for user vector representations and items in recommender systems. Gated recurrent unit is introduced to reduce the information loss between high-order connected nodes. This enables users and items nodes to obtain more complete feature information from high-order neighbor nodes. The convolutional neural networks are used to fuse feature vectors between different output layers to obtain users' preferences at different stages. Experiments on 4 datasets show that compared with the optimal comparison algorithms, the performance of proposed algorithm achieves gain of 1.98%, 4.17%, 9.27% and 2.7%, respectively.
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  • Stereo Matching Method Based on Multiscale Attention Network
  • BIAN Ji-long, WANG Hou-bo, LI Jin-feng
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 27-34. DOI:10.13190/j.jbupt.2020-204
  • Abstract ( 570 )     HTML( 209 )     　　
  • Aiming at the problem that the depth of network is related to the size of training image patches and improving the matching accuracy for the weak texture and edge regions, a multiscale attention network for stereo matching is presented. The method is divided into two stages:in the first stage, a deep network for computing matching cost is proposed, which is composed of basic layer and scale layer, in the second stage, a disparity refinement network based on multiscale attention is proposed, in which multiple disparity clues are combined and multiscale attention is added to boost stereo matching accuracy. The percentage of 3-pixel bad points on KITTI 2012, KITTI 2015, and SceneFlow is 1.13%, 1.87%, and 2.29%, respectively. Experiments show that compared with the current domestic and foreign advanced methods, a stereo matching method based on multiscale attention network made a great improvement in matching accuracy, especially better improvement for the weak texture and edge regions.
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  • Design and Analysis of the Event-Triggered Power Control Algorithm
  • HAN Kang-rong, LI Na, QIAN Rong-rong
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 35-40. DOI:10.13190/j.jbupt.2020-199
  • Abstract ( 619 )     HTML( 197 )     　　
  • The existing power controls in wireless networks usually uses time-based feedback mechanism that has the problems of low efficiency and waste of feedback channel resources. The power control is modeled by control-theoretic methods, and the event-triggered mechanism is introduced so as to reduce the execution times of power control feedback and thus reduce the use of feedback-channel resources. The study integrates the event-trigger mechanism developed in control field into the power control algorithm, designs an event-triggered power control algorithm, analyzes, and verifies the stability of the algorithm. The focus is also on the event-triggered Zeno effect analysis. Simulations verify that the designed power control algorithm is the asymptotic stable and the Zeno effect does not exist.
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  • A Joint Beamforming Design for Optimizing Service State of Base Station in C-RAN
  • HAO Wan-ming, YAO Zhuang, SUN Gang-can, ZHU Zheng-yu, ZHOU Yi-qing
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 41-46. DOI:10.13190/j.jbupt.2020-190
  • Abstract ( 474 )     HTML( 216 )     　　
  • In order to achieve low-latency data transmission in the cloud wireless access network (C-RAN), a base station caching and fronthaul link multicast transmission mode is proposed to minimize the system transmission delay by jointly optimizing fronthaul link beamforming and access link beamforming. The non-convex time delay minimization problem is transformed into a convex one by using L0 norm approximation, successive convex approximation and semi-definite relaxation techniques. An effective iterative algorithm is proposed to obtain the solution of the problem. The proposed scheme can determine the service status of base stations, which are suspended service, continuing service status according to the content cached by base stations and the channel quality of fronthaul link. They are different from the traditional multicast transmission for cache-enabled C-RAN. Simulations show that compared with the traditional scheme, the proposed scheme can effectively reduce the system transmission delay.
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  • An Short-Term Residential Load Forecasting Scheme Using Multi-Task Learning
  • WANG Yu-feng, XIAO Can-bin, CHEN Yan, JIN Qun
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 47-52. DOI:10.13190/j.jbupt.2020-187
  • Abstract ( 479 )     HTML( 204 )     　　
  • In smart grid regarded as specific embodying of cyber-physical-social system, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly role in planning and operation of smart power system. Considering the similarity of electricity consumption between users, inspired by multi-task learning, the article puts forward an effective residential load forecasting based on multi-task learning model. In detail, the K-means clustering technology and Pearson correlation coefficient are used to select two similar users. Then these two user's load data are merged as input, the bidirectional long short-term memory network is used as a sharing layer to fully capture the relationship between the data of the two users, and then two fully-connection task-specific output layers are respectively built. Based on real datasets, the proposed scheme is thoroughly compared with several typical deep learning based load forecasting schemes. Experiments show that proposed multi-task learning scheme improves the prediction accuracy compared with the existing deep learning prediction scheme.
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  • A Pedestrian Dead Reckoning Algorithm Based on Online Learning Magnetometer Calibration
  • BAI Yan-ru, LUO Hai-yong, CAO Cheng-lin, WANG Qu, XIONG Hao
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 53-60. DOI:10.13190/j.jbupt.2020-165
  • Abstract ( 725 )     HTML( 193 )     　　
  • Currently commonly-used micro electro mechanical system magnetic sensors have time-varying soft magnetic and hard magnetic errors, which seriously affect the performance of geomagnetic-based heading estimation and geomagnetic matching positioning algorithms. Using the opportunistic natural rotation of pedestrians during normal walking, the gyroscope is used to sense the small-scale attitude changes of magnetic sensors, and a nonlinear objective cost function based on residual dynamic weighting is constructed, which contains multiple optimal magnetic observation pairs. The cuckoo nonlinear optimization algorithm with global optimal solution is used to dynamically estimate the soft and hard magnetic errors online. The average heading error of 3.09 degrees can be reduced by using the proposed magnetic calibration algorithm, and the relative error of the navigation estimation is 2.09% when tested on the pedestrian dead reckoning algorithm.
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  • The Beacons Self-Calibration Technology Combined with Building Information Modeling
  • WANG Yu-wei, WANG Xiao-xuan, SHEN Jie, WANG Zhi
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 61-66. DOI:10.13190/j.jbupt.2020-152
  • Abstract ( 551 )     HTML( 233 )     　　
  • The global navigation satellite system does not work ideally, because the satellite signal decays rapidly indoors due to the occlusion and absorption of building materials. As a result, the localization technologies based on beacons become state-of-art indoor localization technologies. The accuracy of these localization technologies is tightly related to the beacons' calibration accuracy. Based on the fact that beacons are usually set on the walls, a beacons self-calibration technology based on building information modeling is proposed to improve the performance of self-calibration. In the meantime, Cramer-Rao lower bound (CRLB) is used to analyse its performance in theory. Based on the semidefinite programming self-calibration algorithm, the proposed algorithm considers to use the building information modeling. CRLB analysis and simulation shows the proposed algorithm efficiently improve the accuracy of self-calibration.
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  • Person Re-Identification Method Based on Image Style Transfer
  • WANG Chen-kui, CHEN Yue-lin, CAI Xiao-dong
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 67-72. DOI:10.13190/j.jbupt.2020-147
  • Abstract ( 621 )     HTML( 194 )     　　
  • The training set of the existing person re-identification model comes from limited fixed collection equipment, and the sample style lacks diversity. Through the cyclic generative adversarial network, the image data captured by different cameras can be styled and style transferred, which can improve the diversity of sample styles at a lower cost. In order to improve the generalization ability of the model, a new training mechanism of positive and negative samples fusion is designed. Firstly, the samples after the style transfer are regarded as negative samples, and the samples before the style transfer are regarded as positive samples. The positive and negative samples are sent to the model training at the same time. Furthermore, in order to prevent over fitting and consider the loss of false labels positions, label smoothing regularization is adopted. At the same time, in order to pay more attention to difficult and error-prone samples, and to optimize the loss of negative samples, a focal loss function is adopted. Experiments show that there is a significant increase of 1.51% and 2.07% on the Market-1501 and DukeMTMC-reID datasets, respectively.
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  • The Epidemic Spreading Mechanism and Dynamic Characteristics on Signatured Networks
  • ZHUO Xin-jian, WANG Wen-xuan, LI Hui-jia
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 73-78. DOI:10.13190/j.jbupt.2020-145
  • Abstract ( 609 )     HTML( 251 )     　　
  • Spreading behavior and its dynamic evolution in the context of big data are diversified, and thousands of information dissemination models have been developed. In this paper, aiming at the signatured network model with multi-types individuals, the spreading behavior and dynamical properties were explored. Firstly, the improved signatured-susceptible-infected-susceptible (S-SIS) model is applied to the signatured network, and then in order to simulate the spreading dynamical process. Specially, and simulate the dynamic evolution of the infection, the assumption in the existing model that the infection probability is constant is improved, an infection probability equation that changes in real time is constructed, and the spreading behavior in the signatured network is accurately simulated. Finally, mean filed analysis and Monte Carlo simulation is made to get the simulation result of the model. By adjusting the parameters and comparing the results, useful conclusions can be drawn. In order to observe the performance of networks with different topologies, Erdos-Rainey random network and scale-free network are simulated to observe the characteristic with different topology of networks, and the difference is observed in the simulation results.
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  • An Adaptive Hybrid Filter Algorithm for Attitude Estimation
  • LI Xing-hua, LIU Xiao-ping, WANG Gang, ZHAO Yun-long, LI Yong-wei
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 79-86. DOI:10.13190/j.jbupt.2020-207
  • Abstract ( 668 )     HTML( 183 )     　　
  • In order to solve the problems such as low precision, poor anti-jamming ability in attitude estimation algorithms based on filtering for low-cost inertial sensors, a new adaptive hybrid filter algorithm based on conjugate gradient method and complementary filter is proposed. The conjugate gradient algorithm adopted to process the data measured by accelerometer and magnetometer at first, and estimated their attitude quaternion iteratively. Then, the complementary filter is used to fuse the information of the gyro updated attitude and the iterative optimized attitude of the conjugate gradient method. Finally, according to the motion state of the carrier, the complementary filtering parameter is adjusted adaptively to obtain the optimal attitude estimation. To verify that the algorithm is feasible and anti-interference, it is compared with other filter fusion algorithms in the experiments of anti-magnetic interference and anti-interference of motion acceleration. It is shown that the proposed algorithm effectively reduces the interferences caused by magnetic field and motion acceleration, and has better precision of attitude angle estimation than that of traditional gradient descent algorithm, Gauss Newton algorithm and conjugate gradient algorithm.
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  • Design and Analysis of Vehicle Seat Suboptimal Control Active Suspension
  • YU Yue-wei, ZHAO Lei-lei, ZHOU Chang-cheng, WANG Song, YUAN Jian
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 87-93. DOI:10.13190/j.jbupt.2020-219
  • Abstract ( 634 )     HTML( 215 )     　　
  • In order to improve the ride comfort of vehicles, and effectively solve the problems of linear quadratic Gaussian optimal controller in practical application, such as many state variables to be mea-sured, difficulty in obtaining road parameters, and high application cost, a vehicle seat active suspension based on suboptimal control theory is designed according to 1/4 vehicle seat active suspension system model, and the performance of the vehicle seat suboptimal active suspension under different state variables is discussed. An optimal control strategy of vehicle seat active suspension based on suboptimal control theory is obtained. The reliability of the designed vehicle seat suboptimal control active suspension is verified by comparing with the traditional vehicle seat passive suspension and optimal control active suspension.
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  • An Electroencephalogram Signal Processing Method Fusing Wavelet Packet and Neural Network
  • LI Duan-ling, CHENG Li-wei, YU Gong-jing, ZHANG Zhong-hai, YU Shu-yue
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 94-99. DOI:10.13190/j.jbupt.2020-208
  • Abstract ( 658 )     HTML( 209 )     　　
  • Aiming at the classification accuracy of the motor imagery electroencephalogram in processing is low, a processing method based on the combination of energy (second-order moment) wavelet packet transform and Levenberg-Marquardt neural network is proposed. Firstly, the energy method is used to analyze signal in the time domain, and the effective time sequence is selected. Then, wavelet packet transform is used to decompose the time-frequency of each pilot signal in the selected effective time-domain segment, and the frequency information related to the imagination task is selected to reconstruct the signal characteristics. Finally, the features reconstructed by each guide signal are concatenated and imported into the neural network based on the Levenberg-Marquardt training algorithm to realize the task classification. The method was verified by two kinds of electroencephalogram signal standard competition database, and the classification accuracy is 95.62% and 90.13%, respectively. Compared with some recent research results, this algorithm has a better processing effect.
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  • Communication Emitter Identification Method Based on Steady-State Cyclic Spectrum Characteristics
  • ZHOU Kai, HUANG Sai, ZENG Yu-qi, GAO Hui, LU Hua
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 100-105. DOI:10.13190/j.jbupt.2020-197
  • Abstract ( 632 )     HTML( 224 )     　　
  • In order to realize high-precision identification of multiple communication emitters in low signal-to-noise ratio (SNR) environment, a method of communication emitter identification based on steady-state cyclic spectrum characteristics is proposed. By using the strong robustness of cyclic spectrum's cross-sectional spectrum in frequency domain to Gaussian noise, the intrinsic differences between shaping filters of different emitters are extracted for identification. Specifically, the cyclic spectrum's cross-sectional spectra in frequency domain are extracted from the received steady-state signals, and the dimensions are reduced by principal component analysis. Then the emitters' categories are determined by Pearson correlation coefficient method, probabilistic neural network and Fréchet distance method, etc. Simulation shows that the proposed feature is superior to the traditional slice feature in cyclic frequency domain by using probabilistic neural network and Pearson correlation coefficient, which proves that it has certain application value.
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  • Scalable Recommendation Models Fusing Multi-Source Heterogeneous Data
  • JI Zhen-yan, WU Meng-dan, YANG Chun, LI Jun-dong
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 106-111. DOI:10.13190/j.jbupt.2020-229
  • Abstract ( 751 )     HTML( 206 )     　　
  • Social relationship plays an important role in life, and users are often affected by their friends' preferences. It is easier for users to choose items that their friends have purchased. In order to solve the cold start problem of the recommended system, a recommendation system that integrates social relationships is studied, and Bayesian personalized ranking review score social model and scalable Bayesian personalized ranking review score social model are proposed. The proposed fusion recommendation models integrate multi-source heterogeneous data such as scores, reviews, and social relationships from the data source level, introduce social relationships into the recommendation system through the user friend trust model, use the paragraph vector-distributed memory model to process review, use the fully connected neural network to process rating, and use an improved Bayesian personalized ranking model to optimize the ranking results. Experiments are conducted on the Yelp public dataset. It is shown that the recommendation accuracy of the two proposed models are better than other recommendation models.
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  • Language Identification Based on Vocal Tract Spectrum Parameters
  • SHAO Yu-bin, LIU Jing, LONG Hua, DU Qing-zhi, LI Yi-min
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 112-119. DOI:10.13190/j.jbupt.2020-228
  • Abstract ( 661 )     HTML( 184 )     　　
  • Aiming at the problem of low accuracy of language identification under low signal to noise ratio, a fusion identification method is proposed, using spectral parameters of channel impulse response and Teager energy operators cepstral coefficients. Considering the distribution of different feature information in speech, a low-pass filter is introduced to filter out the high-frequency part of the signal in the front-end of feature extraction. The resampling method is used to reduce the rate. And then, the spectral parameters of channel impulse response of vocal tract are extracted, and fused with the Teager energy operators cepstral coefficients. Finally, a Gaussian mixture model-universal background model is used to perform the language identification. Experiments under different signal to noise ratio conditions show that the proposed methold significantly improves the language identification accuracy with 15 dB gain at low signal to noise ratio compared with the single Mel frequency cepstrum coefficient feature, single Gammatone frequency cepstrum coefficient feature and log Mel-scale filter bank energies feature.
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  • Design and Implementation of a Novel Two-Level Ultra-Wideband Chaotic Circuit
  • ZHU Wei-guang, ZHENG Hai-yu, ZHANG Xin-lei, ZHANG Zhi-tao, HU Shan-wen
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 120-124. DOI:10.13190/j.jbupt.2020-096
  • Abstract ( 1208 )     HTML( 278 )     　　
  • In order to meet the requirements of chaotic signal sources for ultra-wideband chaotic communication systems, a new two-stage ultra-wideband chaotic circuit design is proposed to improve the bandwidth of chaotic signals effectively. Compared with the classic Colpitts chaotic circuit structure, the new structure uses an emitter follower with a collector inductor as the signal output end and introduced high-frequency resonance network. Thereafter, the signal power spectral density is compensated at high frequencies and the bandwidth was extended. The numerical solution of the new two-stage ultra-wideband chaotic circuit is obtained by MATLAB, and the bifurcation diagram and phase diagram of the new ultra-wideband chaotic circuit are obtained. The time domain diagram, spectrum diagram of the circuit output are also obtained through simulation analysis. It is shown that the new two-stage ultra-wideband chaotic circuit can generate 7.5 GHz wideband chaotic signal with a power within 20 dB, which meets the requirements of ultra-wideband signal indicators(3.1~10.6 GHz) for chaotic signal sources.
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  • Link Quality Estimation Based on Extremely Fast Decision Tree
  • LIU Lin-lan, XIAO Ting-zhong, XIA Yang, SHU Jian
  • Journal of Beijing University of Posts and Telecommunications. 2021, 44(3): 125-130. DOI:10.13190/j.jbupt.2020-163
  • Abstract ( 624 )     HTML( 194 )     　　
  • To estimate link quality for wireless sensor networks accurately and rapidly, an approach of link quality estimation is proposed based on improved extremely fast decision tree. After analyzing the relationship between the physical parameters and the packet reception rate in different time periods, the received signal strength indicator mean, the link quality indicator mean and the signal to noise ratio mean are selected as the link quality parameters;The evaluation index is determined by the link quality levels divided by packet reception rate. A link quality estimation model is constructed based on extremely fast decision tree, and Gini index is employed as heuristic measure of decision node;the computing method of sample number, with which decision nodes look for the best attributes, is improved in terms of the height of decision node. In scenarios of indoor, corridor and parking lot, the experiment shows that compared with fuzzy logic, very fast decision tree, the earlier extremely fast decision tree etc, the proposed method has better estimation accuracy and lower time complexity.
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