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Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

Current Issue

    • A comprehensive crosstalk-aware routing and spectrum allocation scheme— in Space-Division-Multiplexing Elastic Optical Networks
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 1-7.
    • Abstract ( 293 )     PDF (987KB) ( 214 )   
    • Aiming at the survivability requirements of space division multiplexing elastic optical networks (SDM-EONs), this paper proposes a comprehensive crosstalk-aware routing and spectrum allocation (IA-IRSA) scheme. Firstly, it establishes a crosstalk evaluation model, including adjacent crosstalk and band-limited crosstalk, and combine them with spectrum resource occupancy to form a comprehensive crosstalk model. Secondly, it updates the cost matrix of the fiber link by sensing the comprehensive crosstalk on the link in real time, so as to minimize the crosstalk between the allocated fiber links. At the same time, it detects the location that causes less blocking to perform spectrum allocation, so as to realize the decrease of network blocking probability and the increase of network resource utilization. Simulation results show that compared with the traditional scheme, this work significantly lower crosstalk, improves network survivability, and improves performance such as network blocking probability and network resource utilization.
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    • Construction of Optimal Locally Repairable Codes Based on Latin Square
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 8-14.
    • Abstract ( 196 )       
    • Locally repairable codes (LRCs) with (r,t)-locality could provide the requirements of local repair and parallel reading in distributed storage systems, which has attracted extensive attention. At present, LRCs with (r,t)-locality are seldom able to achieve the optimal minimum distance and optimal code rate at the same time, therefore, this paper proposes a construction algorithm of LRCs based on Latin square. More specifically, the digits in Latin square are replaced by binary numbers according to certain rules, and then the required check matrix can be constructed by the Kronecker product. Thus, binary locally repairable codes (BLRCs) with (r,2)-locality of information symbols are constructed. Furthermore, a construction algorithm of LRCs based on Mutually Orthogonal Latin Squares (MOLS) is proposed, which could construct BLRCs with arbitrary availability. Theoretical analyses show that, both of the two constructed codes satisfies the minimum distance bound, and their code rates are higher than that of the LRCs constructed based on direct product codes and array LDPC codes, and the LRCs based on Latin square achieve the optimal code rate.
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    • Design of KNN cascade equalizer improved by rough set theory and LED nonlinearity suppression study
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 15-21.
    • Abstract ( 177 )       
    • To address the problem that the nonlinear response of light-emitting diodes (LEDs) leads to serious degradation of visible light communication (VLC) performance, a K-nearest neighbor (KNN) algorithm improved based on rough set theory is proposed, and further, a cascaded equalizer is designed by combining it with least mean square (LMS). First, the training set data space is divided into different regions according to the distribution characteristics of constellation points at the receiver side, and different classification strategies are used for different regions to reduce the computational complexity of the traditional KNN algorithm. Then, the LMS and improved KNN cascade equalizer are proposed, and the first stage LMS algorithm can reduce the dispersion of sample points, which provides conditions to improve the classification accuracy and reduce the computational complexity of the second stage improved KNN. Finally, Monte Carlo BER simulation is used, and the results show that the complexity of the improved KNN algorithm is about 1/9 of the traditional KNN algorithm without sacrificing the classification accuracy; meanwhile, the proposed LMS with improved KNN cascade equalizer can significantly improve the BER performance.
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    • Joint Multi-Antenna Selection Mapping for Generalized Spatial Modulation
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 22-27.
    • Abstract ( 145 )       
    • A novel modulation scheme called joint multi-antenna selection mapping generalized space modulation (JAS-GSM) is proposed to solve the problems of inter-channel interference and high correlation between antennas in Generalized Spatial Modulation (GSM) systems. A low-complexity Transmit Antenna Combination (TAC) optimization algorithm is designed to reduce inter-channel interference and computational complexity based on the maximizing minimum Hamming distance criterion. At the same time, all antenna combinations participate in the joint mapping of the codebook by using the redundant characteristics of the optimized TAC, and an efficient screening algorithm for the joint mapping codebook is designed based on the maximizing minimum Euclidean distance criterion to improve bit error rate performance further. By deriving the theoretical boundary formula of the bit error rate of the JAS-GSM scheme, the correctness of the simulation results is verified, and the joint mapping codebook screening algorithm design is assisted. Simulation results show that the proposed JAS-GSM scheme can achieve better bit error rate (BER) performance than existing antenna selection techniques and GSM variant techniques.
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    • Fractional Normalized Subband Adaptive Filtering Algorithm Based On mixture Correntropy
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 28-34.
    • Abstract ( 83 )       
    • In order to improve the filtering performance of the normalized subband adaptive filter (NSAF) in a non-Gaussian noise environment, the maximum mixture correntropy criterion and fractional-order differentiation are applied to the NSAF algorithm in this paper. On the one hand, the robustness of the maximum mixture correntropy criterion is used to effectively suppress the effect of anomalous noise values on the performance of the algorithm. On the other hand, to describe the actual system more accurately, a fractional-order differentiation component is added to the weight update. The proposed algorithm is applied to system identification and nonlinear channel equalization in a non-Gaussian impact noise and colored noise environment. Simulation results show that the proposed algorithm has stronger robustness and better system tracking and estimation capability compared with existing robust algorithms.
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    • Influence of the ionosphere E layer on the characteristics of satellite-to-earth quantum key distribution
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 35-40.
    • Abstract ( 106 )       
    • The ionosphere E layer is an important part of the atmosphere, which contains a large number of charged ions that will have an impact on the quantum signals passing through the ionosphere E layer. Therefore, this paper studies the influence of the ionospheric E layer on the characteristics of the satellite-to-earth quantum key distribution system using three different wavelengths. A transmission model of the ionospheric E layer is established first and then it is used to analyze the influence of the solar zenith angle and transmission distance on the link attenuation of the signal with three different wavelengths. In addition, simulations of the channel capacity, entanglement fidelity, quantum bit error rate, and secret-key rate as a function of solar zenith angle and transmission distance are performed. The results show that, in order to obtain better quantum communication characteristics, it is recommended using short-wavelength signals in practice, and to run communication at the time that avoid noon.
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    • A High-precision Carrier Phase Positioning Algorithm in Dynamic Topology Environment for 5G NR
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 41-46.
    • Abstract ( 227 )       
    • The 5th generation mobile communication system (5G), evolving in positioning technology, provides more solutions for high-precision positioning. To improve the positioning accuracy in dynamic topology environment, a carrier phase positioning method for 5G NR is proposed. Due to the mobility of nodes and terminal in the dynamic topological environment, it is difficult to ensure a reasonable geometric layout, for which a node selection method based on geometric dilution of precision (GDOP) is proposed. On the basis of node selection, by introducing a reference terminal, a multidimensional differential processing of the measurement is performed to eliminate clock offsets. Considering the mobility of access points in dynamic topology environment, the carrier phase measurement equations are established, from which the time-domain difference equations are obtained to estimate the relative position with high accuracy. Then the relative position estimations are fused with TDoA to obtain high accuracy position estimations. With these estimations, the carrier phase measurement equations of multiple moments are linearized to solve integer ambiguities. Simulation results show that the proposed method achieves high accuracy resolution of integer ambiguities and positioning.
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    • Mechanism design for the end-to-end deterministic transmissions with decoupled time domains
    • Shuo WANG Wei-Qian TAN
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 47-52.
    • Abstract ( 155 )       
    • To achieve delay-bounded transmissions for time-sensitive applications, we propose an innovative cross-domain deterministic network mechanism. The proposed mechanism deploys discrete shapers at the network edge to decouple neighbor domains from the time synchronization, which helps to reduce the complexity. In other words, we require no time/frequency synchronization between neighbor domains; and the network could still provide delay-bunded transmission services. Meanwhile, the proposed mechanism enhances the availability of deterministic networking. Other than the periodic deterministic traffic, more types of traffic could be served, including aperiodic deterministic traffic and stochastic flows. Finally, we develop an auction-based online scheduling algorithm to improve network efficiency and reduce cost. The simulation results show that the proposed mechanism can effectively realize the end-to-end delay-bounded transmission across multiple domains. At the same time, the cross-domain latency could also be reduced compared to the existing methods.
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    • Anti-Multi-Stream Interference Cognitive Network Coding Routing and Payload Balancing
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 53-59.
    • Abstract ( 55 )       
    • To solve problems of node decoding failure caused by multi-stream interference in wireless communication networks and low resource utilization ratio, a physical layer network code aware routing scheme with payload balancing is proposed in multi-stream wireless networks. According to the time-varying characteristics of wireless networks, it uses two processes of routing request and response, and analyzes the relative positions of nodes in space to perform a reasonable selection of network topology. The scheme uses the relative spatial position between nodes to reasonably select the network topology, calculate the dynamic data distribution ratio, and reduce the congestion of intermediate nodes. A new routing metric based on bit error rate (BER) calculation is proposed to expand the scope of code-aware routing based on solving multi-stream interference. The simulation shows that when the BER is 10-4, network state information-aware physical layer network coding routing scheme against multi-stream interference compared with the coding opportunity entity (COPE) schemes, the proposed scheme reduces the required signal-to-noise ratio (SNR) by about 6%, compared with the COPE scheme, the proposed scheme reduces the expected transmissions count metric (ETX) of the required physical layer by 0.25 times under the same BER. Therefore, the scheme can be used in the space-ground integration network (SGIN) under multi-stream interference.
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    • An Optimization Scheme of Edge Caching for Panorama Video based on DQN
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 60-65.
    • Abstract ( 194 )       
    • To solve the edge caching problem of cloud server and edge server in panorama video service, optimizing the edge caching mechanism to reduce the time delay for obtaining video resources, a method of generating cache strategy by using DQN as deep reinforcement learning algorithm is proposed. First, the problem is modeled as Markov decision process with the goal of total time saving. Then, DQN algorithm is used for training to obtain the best cache strategy in the iteration. Simulation shows that DQN algorithm has high convergence speed and the best performance. And when the constraints change, it can actively change the edge caching strategy to stably improve the performance of the algorithm.
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    • Research On Pedestrian Detection Algorithm Based on Multi-camera Feature Fusion
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 66-71.
    • Abstract ( 210 )       
    • Monocular pedestrian detection usually suffers from occlusion problems in complex and crowded scenes, which can lead to serious false positives. Multi-view pedestrian detection can effectively solve the occlusion problem by combining data from multiple views. In the previous multi-view detection algorithms, only single-level feature maps are used, which cannot detect multi-scale targets well. In this paper we propose a new multi-view detection algorithm which uses a newly introduced Dilated Encoder method to aggregate the information of multiple views. Dilated Encoder is a method that uses different dilated convolutions of the expansion rate so that a single layer of features gets different scale perceptual fields, covering all scale ranges of the target and improving the capability of multi-scale targets. Our proposed method achieves 90.7% MODA on the Wildtrack dataset, which is a very strong competitive result compared to the current state-of-the-art algorithms.
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    • Multi-user-oriented SWIPT-MEC Task Hierarchical Processing Offloading Mechanism
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 72-79.
    • Abstract ( 308 )       
    • In order to ensure the multi-user mobile edge computing (MEC) network based on simultaneous wireless information and power transfer (SWIPT), the delay and energy consumption during the offloading process can be minimized under the condition of limited channel resources.This paper proposes a multi-task hierarchical processing mechanism (MHPM) by planning the offloading ratio of computing tasks and the channel allocation during the link transmission ,which realizes the rational scheduling of channel resources in the process of computing offloading.According to the average time consumption and energy consumption of mobile equipment in the process of MEC offloading, a mathematical model of constrained multi-objective optimization problem was established, and the model was solved by combining MHPM and sorting genetic algorithm ⅱ optimization algorithm, which maximized the relationship between device delay and energy consumption.Simulation results show that MHPM can reduce the average time consumption and energy consumption of equipment in offloading process, and the optimal solution of objective function can be obtained by using constrained multi-objective optimization algorithm.
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    • Weighted Eigenvector-based Unsupervised DOA Estimation Algorithm
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 80-86.
    • Abstract ( 169 )       
    • Subspace direction of arrival estimation algorithm and sparse reconstruction DOA estimation algorithm are driven by algorithm model, and have poor robustness to algorithm modelled errors such as antenna array position deviation and incident source statistical characteristic deviation. The DOA estimation algorithm based on deep learning is data-driven and can effectively improve the robustness of the algorithm to model errors. However, in the DOA estimation algorithm based on supervised deep learning strategy, a large number of labeled training data sets generated by known standard signal sources are required, which affects the estimation performance of the algorithm for unknown sources and is not conducive to practical application. To solve the above problems, this paper proposes a weighted eigenvector-based unsupervised DOA estimation algorithm (WEUDA) using unsupervised deep learning strategy. The WEUDA algorithm establishes the unlabeled training data set according to the weighted eigenvector of the multi snapshot data covariance matrix of the random incident source received by the antenna array of the unknown direction, so that the algorithm no longer depends on the known standard signal source, which is more convenient for engineering implementation. At the same time, it also improves the estimation performance of the network on the unknown test data set. Simulation results show that compared with subspace class, sparse reconstruction class and supervised DOA estimation algorithm, the proposed WEUDA algorithm has better estimation performance under the same simulation conditions.
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    • Emotion recognition based on meta bi-modal learning model
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 87-92.
    • Abstract ( 173 )       
    • In the existing emotion recognition models, there are some problems, such as ambiguity in single-mode rep-resentation, different ways of expressing emotion for each person, and ignoring discrete emotion and con-tinuous emotion. To solve these problems, the author proposes meta Bi-modal learning (MBL) model, which realizes single-mode continuous emotion, namely valence activation control (V-A-D), to assist in the recog-nition of dual-mode discrete emotion, Bi-modal feature fusion used cross modal self attention, which effec-tively solved the problem of modal sequence data alignment. At the same time, in the process of auxiliary task training, the V-A-D three-dimensional information interaction was realized through the sharing of hard pa-rameters in multi-task learning. And the learning model taked each speaker's sentence as a small sample, which improved the ability of the model to adapt to different speakers and make the model more generalized. The experiments show that the MBL model has achieved 71.24% and 69.12% emotion recognition rates on the script and dialogue data sets of the corpus IEMOCAP, respectively, showing good performance.
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    • Research on Behavior Recognition Based on Key-frame Sampling for Heterogeneous Time Series Data
    • 张 涛 Ma Chunmei
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 93-98.
    • Abstract ( 126 )       
    • Abstract:It is very important for the development of human-computer interaction that how to accurately recognize various behaviors from the contact sensing data. Based on the fact that there is redundancy between different behavior data and the action of identifying behavior is often only at some key points, a Key-frame DynAmic sampling network KDAS for behavior recognition is proposed, which aims to mine key-frames representing behaviors, eliminate redundant frames of behaviors, and improve the discrimination of different behaviors from the data source. Then, the discrimination of their deep features is enhanced and the recognition accuracy is improved. First, a pre-processing module for behavior recognition based on BLSTM network is established, which is used to initialize the original perceived data state and obtain the initial behavior prediction result. Second, based on the initialization information of each data frame, a key frame selection network is established using BLSTM, through which the probability of each frame being selected is predicted and then the key frame is determined. Finally, the selected key frames are used for behavior prediction again. The two prediction results are used to form a utility function for the key frame selection network training. The experiment is conduct on three public data sets UCI HAR, Opportunity and UCI MHEALTH. The experimental results show that compared with several existing advanced behavior recognition models, KDAS can obtain higher behavior recognition accuracy.
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    • The Decoding Algorithm Based on Dynamic Threshold Truncation Strategy for Nonbinary LDPC Codes
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 99-105.
    • Abstract ( 147 )       
    • A new dynamic threshold truncation strategy based on the distributions of the message-vector reliability is designed for solving the high complexity problem of non-binary LDPC decoding algorithms. The presented strategy dynamically selects the truncation threshold based on the difference between the maximum and the sub-maximum reliability values, which can reduce the finite filed elements involved in message computing. In the iterative process, the decoding messages are determined by the truncation threshold, which can effectively reduce the number of the states and branches in the trellis, resulting in lower decoding complexity in average. A dynamic threshold extended min-sum (DT-EMS) algorithm is further presented based on the new truncation strategy. Simulation results show that the proposed DT-EMS algorithm performs almost as well as the well-known Q-ary sum-product algorithm (QSPA) and the T-EMS algorithm. Moreover, the proposed algorithm achieves lower decoding complexity than the T-EMS algorithm and has much lower complexity than the QSPA .
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    • PERC Roberta:Emotion Recognition in Conversation using ERC Roberta with Learning
    • Qi-Wei GONG
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 106-111.
    • Abstract ( 278 )       
    • With a broad area of its applications, the task of emotion recognition in conversation has increasingly attracted attention. The text in the dialogue contains information about the speakers and links closely with the preceding ones, thus a particular word order and structural features are represented by it. Excellent results have been obtained in studies on emotion recognition in conversation using transformer-based pre-training models. However, its traditional classification approaches cannot take into account conversational word order and structural feature. And a mismatch will occur between the downstream task and the pre-trained task. learning can narrow the gap between them by reconstructing downstream tasks. Therefore, the PERC Roberta model is proposed. This model first learns word order and structural features of the dialogue by predicting masked texts and then reconstructs the downstream task through ing learning, thus a richer dialogue knowledge distributed in the pre-training model can be further stimulated. The experiments conducted on two public data, MELD and EmoryNLP, demonstrate the superior performance of the proposed PERC Roberta model. Further, the ablation experimental results also prove the effectiveness of each step in the PERC Roberta model. The code is publicly available on GitHub repository1.
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    • Cross-modal Retrieval Algorithm for Image and Text Based on Pre-trained Models and Encoders
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 112-117.
    • Abstract ( 314 )       
    • With the advent of the Internet era, the amount of image and text data on the web has grown exponentially. How to efficiently and accurately retrieve the information people need from massive amounts of data is a pressing issue. At present, the mainstream image-text cross-modal retrieval model architectures are mainly based on dual encoders or fusion encoders. The former encodes the image and text respectively, and then calculates the similarity distance between the image and text vectors, although the retrieval efficiency is high, the accuracy is insufficient. The latter obtains the similarity score between images and texts by jointly encoding the data of images and texts, which has high retrieval accuracy but low efficiency. In order to solve the problems of the above model architecture, this paper proposes a cross-modal image retrieval algorithm based on pre-trained model and encoder. Firstly, a recall sequencing strategy is proposed, which uses dual encoder to achieve rough recall and fusion encoder to achieve precise sequencing. Secondly, a method to build dual encoders and fusion encoders based on multi-channel Transformer pre-trained model is proposed to achieve high-quality semantic alignment between texts and images and improve retrieval performance. Experiments on two public datasets MSCOCO and Flickr30k demonstrate the effectiveness of the proposed algorithm.
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    • Scheme of Maximizing Tradeoff for the IRS-aided Downlink NOMA Systems
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 118-124.
    • Abstract ( 146 )       
    • A resource allocation scheme for maximizing system rate and energy efficiency tradeoff is proposed for single cluster downlink non-orthogonal multiple access systems assisted by intelligent reflecting surface. Firstly, the optimization problem of maximizing the rate and energy efficiency tradeoff is established, with power of each user and IRS phase shifts as the optimization parameters. Secondly, the optimization problem is sim-plified, after which the parameters include total power and IRS phase shifts. Then, the IRS phase shifts are solved by maximizing the sum of the equivalent channel gains, and with given IRS phase shifts, the total power of maximizing the rate and energy efficiency tradeoff is find by using the function extremum method. Finally, the power of each user is calculated based on the obtained IRS phase shifts and the total power. The simulation results show that the tradeoff of the proposed scheme is higher than that of existing schemes in the same scenario under the condition of the same rate requirement.
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    • Fine-grained emotion analysis of online comments based on the fusion of ontology and deep learning
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 125-131.
    • Abstract ( 259 )       
    • Fine grained emotion analysis analyzes the author's emotional tendency from the perspective of the evaluation object and its attributes through the text. Its main tasks include the recognition of the evaluation object and its attributes (topic recognition) and emotion recognition. To solve the problems of poor fine grained emotion recognition and poor interpretability of deep learning methods in previous studies, a fine-grained emotion analysis model integrating ontology and deep learning is proposed. The model uses domain ontology and CNN fusion methods to identify explicit and implicit topics, and combines emotion dictionary and Bi LSTM+Attention model to identify fine-grained emotions of online comment texts. The experimental results show that the proposed fine-grained sentiment analysis method has advantages over other methods in accuracy, recall and F value.
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    • Key Recognition Technology Based on Vibration Perception
    • Journal of Beijing University of Posts and Telecommunications. 2023, 46(5): 132-138.
    • Abstract ( 139 )       
    • With the development of mobile communication technology, the traditional Internet is migrating to the mobile Internet, and the development of intelligent wearable devices is very rapid. In particular, the smartwatch has become one of the most popular wearable devices because of its small size and ease of carrying. However, the smartwatch has an unsatisfied experience with text input. For example, keyboard input often leads to input errors because the screen is small and the number of keyboards is large. Speech recognition is vulnerable to environmental noise and privacy disclosure risks. In order to solve the above problems, a new text input method is studied, which is based on vibration perception to recognize user keystroke behavior. Design and implement a method to recognize which finger joint is tapped. Different finger joints are mapped to different keys. And then, classifying their tapping vibration signals can make up for the inconvenience of keyboard input on the small screen. Firstly, the accelerometer of LPMS-B2 module with a low sampling rate is used to collect the vibration signal generated by tapping finger joints. Then, design a algorithm to process the signal and extract features from the time-frequency map. Finally, use the Naive Bayes classification to recognize the tapped finger joint. The experimental results show that the accuracy of three classifications of finger joints is more than 90%, and the accuracy of five classifications is about 75%.
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