Current Issue

• • 2025, Vol. 48 No. 1 Published date:26 February 2025 Last issue

  • Relay-Enabled Backscatter Communication Networks： Hybrid Transmission Scheme's Design and Optimization
  • Ynghui Ye
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 461 )     　　
  • For the relay-enabled backscatter communication network, the entire transmission block is divided into the backscatter phase and the symmetric transmission scheme based relay-enabled backscatter phase, and a hybrid transmission scheme is proposed, based on which a throughput maximization resource allocation scheme is designed. Specifically, a multidimensional resource allocation problem is formulated to maximize the throughput by jointly optimizing the time allocation factor, the power reflection coefficients in each phase of the Internet of Things node and the transmitting power in each phase of the hybrid access point. As the formulated optimization problem includes couplings among optimization variables and the max-min function, it is non-convex and can not be solved by the existing convex tools. Therefore, a series of slack variables and auxiliary variables are introduced and the situational discussion is applied to convert the formulated problem into a convex one. Finally, computer simulations validate the superiority of the proposed scheme.
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  • Dynamic weighted non-binary LDPC decoding algorithm based on channel information
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 65 )     　　
  • For multi-component LDPC codes, the channel information based dynamic weighted multi-component LDPC decoding algorithm (CIW-AlgB) replaces the binary Hamming distance between hard decision information and external information in reliability calculation by considering the correlation reliability between the initial message from the channel and external information, and proposes a weighted correction coefficient based on channel information. The decoding algorithm can be dynamically weighted according to the channel information. Compared with the non-binary weighted symbol flipping algorithm, the proposed algorithm makes an effective compromise between decoding performance and decoding complexity. The simulation results show that compared with the weighted symbol flipping decoding algorithm, the proposed algorithm achieves significant performance improvement and has faster decoding convergence speed.
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  • On the Age of Information in NOMA-MEC based Status Update Systems
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 260 )     　　
  • The freshness of information is crucial to the accurate decision-making and reliable operation of the status update system. In order to characterize the information freshness of a non-orthogonal multiple access (NOMA) based status update system with mobile edge computing (MEC), the age of information (AoI) has been investigated while taking into account both the local and the edge computing schemes. First, queuing theory is utilized to mimic the transmission and computation of status update information. Then, the service rate of transmission queue, which hinges on the quality of NOMA transmission, is analyzed, and closed-form expressions for the system average AoI are derived. Monte Carlo simulations verify the validity of the obtained analytical results. It follows from the simulation results that compared to orthogonal multiple access (OMA), the system AoI performance can be enhanced more dramatically by applying NOMA-MEC with higher server computation rate. In addition, simulation results demonstrate the effects of the system parameters, such as server computation rate, status update information size, and transmission power, on the AoI performance.
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  • Active reconfigurable intelligent surface topology optimization and self-interference elimination scheme
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 115 )     　　
  • In order to suppress the self-interference between active reconfigurable intelligent surface (RIS) components and further improve the system capacity, a joint optimization of active RIS topology and precoding design scheme is proposed. Considering the maximum power constraints of base station and active RIS, the optimization problem is established based on the criterion of maximum weighted summation rate (WSR). In order to solve the non-convex problem, the problem is decomposed into two subproblems, active RIS topology and precoding design, and the subproblem is transformed and iteratively optimized by using adaptive tabu search, Lagrange multiplier method and two-dimensional grid search, and the approximate suboptimal solution is obtained. The simulation results show that the WSR of the optimized active RIS is 3.75bit/s/Hz higher than that of the conventional active RIS under ideal conditions. In the presence of self-interference, the proposed scheme only reduces WSR by 0.46% compared with the ideal state, while the conventional active RIS decreases by 1.6%, which proves that the pro-posed scheme can effectively suppress the self-interference between active components.
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  • Edge Intelligence Based Field Road Defect Detection Method
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 77 )     　　
  • Academic semantic translation using computers In the autonomous operation task of smart agricultural machinery, in addition to autonomous navigation tasks, it is also necessary to pay attention to the problem of field road defect detection during autonomous operation of agricultural machinery. In the task of detecting potholes in the field, we use the publicly available conventional road pothole data on Kaggle for learning and training, and then transfer learning from the self-built farmland pothole dataset in the laboratory environment to simulate the detection of potholes in the field road. In terms of model design, we adopt the lightweight version of YOLOv5s for network design, and introduce the CBAM attention mechanism to improve network accuracy. Finally, using Jetson Nano as a carrier, we apply TensorRT to simulate the detection of potholes in the field road. The model obtained from the pothole detection task is deployed on the edge, significantly improving the running speed while appropriately reducing the accuracy, meeting the requirements of real-time detection on the edge, and realizing the edge deployment of field road defect detection technology in autonomous agricultural machinery operation tasks.
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  • Minimum Rate Maximization Optimization Algorithm For IRS-assisted Wireless Powered Communication Networks
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 81 )     　　
  • This paper studies the optimization of wireless powered communication network assisted by intelligent reflecting surface (IRS). In order to improve the overall transmission rate of the system while guaranteeing the fairness of user service, beamforming, time allocation, user transmission power and IRS phase shift are jointly optimized to maximize the minimum user transmission rate. The original optimization problem is a multi-variable non-convex problem, which is transformed by introducing slack variables and using semi-definite relaxation. Then, the transformed problem is solved by using the method including golden section search, successive convex approximation and alternate iterative optimization. The simulation results show that compared with the optimization scheme aiming at the maximum sum rate, the proposed optimization algorithm can obtain a significantly higher minimum user rate, and the service fairness among users is significantly improved. Compared with the scheme without IRS, which maximize the minimum user rate, both the minimum user rate and the sum rate are higher, and the performance advantage increases with the increase of the number of IRS units in our proposed scheme, which shows that IRS can effectively improve the energy and information transmission efficiency.
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  • Blockchain Ciphertext Data Sharing and Access Control Scheme based on Zero-knowledge Proof
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 201 )     　　
  • The integration of blockchain technology with ciphertext policy attribute-based encryption (CP-ABE) has to some extent alleviated issues concerning key security auditing and privacy data leakage in data sharing and access control. However, most current solutions still retain authorization authorities on the blockchain, exacerbating the "impossible triangle" problem. Therefore, this paper introduces a zero-knowledge proof mechanism and proposes a blockchain-based ciphertext access control scheme using zero-knowledge proofs. Firstly, this scheme employs a threshold proxy re-encryption protocol, enabling multi-party secure management and distribution of the attribute-based encryption algorithm's main key without the involvement of authorization authorities. Secondly, we design a NIZKP (Non-Interactive Zero-Knowledge Proof) based on the Fiat-Shamir transformation to verify the correctness of off-chain computations for proxy re-encryption. Lastly, we develop a blockchain ciphertext access control transaction aggregation circuit based on zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) to enhance system scalability and reduce on-chain costs. Simulation experiment results demonstrate that the proposed solution achieves secure and efficient data sharing and access control and effectively reduces on-chain overhead, with a gas reduction exceeding 61%.
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  • Opportunistic Network Link Prediction Based on Temporal Generative Adversarial Networks
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 90 )     　　
  • The frequent node mobility of opportunistic networks leads to the challenges of link prediction. To better reflect the temporal changes in topology, an opportunistic link prediction method based on temporal generative adversarial networks (ONLP-TGAN) is proposed. The network volatility is defined for calculating the network segmentation time slot so that an opportunistic network is sliced into fine-grained snapshots. Information matrices are extracted from these snapshots， and a fusion matrix is obtained by integrating the information from both the spatial and temporal dimensions. Network feature vector matrices are constructed by graph embedding methods. Combining gated recurrent units and generative adversarial network, a temporal generative adversarial network model is constructed. It learns the evolutionary features of network topology, so as to achieve link prediction in networks at future time. Experimental results on three real datasets， ITC、Infocom06, and MIT， demonstrate that the proposed mothed ONLP-TGAN outperforms the baseline model in terms of Precision， Accuracy， AUC, and GMAUC metrics.
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  • Evaluation of Node Importance in Opportunistic Networks Based on Node Embedding
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 62 )     　　
  • To improve the accuracy of node importance evaluation in opportunistic networks, a node embedding-based node importance evaluation method is proposed. Considering the time-varying nature of opportunistic networks, time window aggregation graph is employed to represent the networks, so that network topology and temporal connections can be obtained in the window. Graph attention mechanism is utilized to extract the topological features of nodes, obtaining the topological embedding representation. Additionally, temporal embedding representation is obtained by using temporal encoding and self-attention mechanism to extract node temporal features. Node embedding vectors are achieved by integrating two representations. To reflect the information interaction between nodes, the cluster importance for nodes is introduced. The transition probability matrix is constructed, and the node importance is obtained by combining the PageRank algorithm. On three real opportunistic network datasets, experimental results demonstrate that the proposed method exhibits better evaluation accuracy compared to similar approaches such as f-PageRank and dynamic graph convolutional network (DGCN).
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  • Adaptive density peak clustering based on comparative quantities
  • FU Wei-Hong
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 76 )     　　
  • ABSTRACT:Peak Clustering(DPC) was proposed in journal Science in 2014,which has aroused widespread discussion and application due to its efficiency and simplicity.However,some obvious shortcomings had been found in studies. To overcome these deficiencies,a novel clustering algorithm named Adaptive density peak clustering based on comparative quantities is proposed. In the improved algorithm, a new quantity named relative local density was used to assess the similarity between clusters,which greatly improved its applicability to datasets,another quantity called relative connectivity distance was applied for measuring the similarity between clusters,which effectively eliminates the influence of different sizes of clusters in the dataset. The applicability of the algorithm on different datasets was enhanced. By constructing a comentropy function, parameters could be adaptively determined according to the characteristics of the datasets,which improved the intelligence of the algorithm.A new allocation strategy is proposed to avoid the effects of the ‘chain reaction’.Simulations show that compared with the DPC and its improved algorithm,the performance of ACDPC algorithm is greatly improved.
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  • Phoneme Recognition Based on B-Wave-U-Net Feature Enhancement at Low Signal-to-Noise Ratio
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 66 )     　　
  • To address the problem of low phoneme recognition accuracy under low signal-to-noise ratio (SNR), a phoneme recognition method based on B-Wave-U-Net feature enhancement is proposed. Firstly, Bidirectional Long-Short-Term Memory(BLSTM) module is integrated into the starting end of Wave-U-Net encoder, and information flow is derived from it, which is then jump-connected to the end of the decoder, followed by the addition of a fully connected layer, thus forming the B-Wave-U-Net network. Subsequently, the log spectrogram extracted from the speech signal is used to enhance and denoise the image by the B-Wave-U-Net, resulting in a new feature spectrogram，which is finally input into a Mel filter bank to obtain Fbank feature output. Under the condition of a SNR of 0dB and white noise as the noise source, phoneme recognition testing was conducted using the THCHS30 dataset and a ResNet-BLSTM-CTC model. The results reveal that the proposed B-Wave-U-Net based feature enhancement network can reduce the phoneme error rate by 2.5%, 2.1%, 1.6%, and 0.9% compared to CRN, GCRN, DCCRN, and GDCRN networks, respectively. Additionally, there is also a certain reduction in phoneme error rate under other SNR conditions.
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  • DDPG-based AP Selection in Cell-free Massive MIMO System
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 132 )     　　
  • Cell-free Massive MIMO achieves uniform network coverage and eliminates cell-edge effect by dispersive deployment of large number of access points (APs). In cell-free Massive MIMO system, APs cooperate to provide services for users by interacting with the central processing unit through the fronthaul link. To avoid the overloading of the fronthaul link caused by all APs simultaneously serving all UEs, the AP selection strategy in CF M-MIMO is studied. For this purpose, the average spectral efficiency per link is defined as the spectral efficiency obtained per AP-UE link, which is used to measure the effectiveness of the access point selection algorithm. Considering user quality of service (QoS) requirements, an access point selection scheme based on the Deep Deterministic Policy Gradient (DDPG) algorithm is proposed. The simulation results show that compared with the existing access point selection scheme, the proposed DDPG-based access point selection scheme can effectively improve the average link spectral efficiency of the CF M-MIMO system and meet the user's QoS requirements.
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  • Fast Clustering-based Adaptive Rate Block Compression Sensing for Surveillance Video
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 72 )     　　
  • Existing video surveillance implementations often use traditional video coding methods, which have high requirements on equipment cost, deployment environment and data storage due to high coding complexity. Compression sensing has great potential in solving problems related to video surveillance due to its natural distributed nature. However, there are still some difficulties in realizing adaptive rate compression sensing when the sparsity of the original video signal is unknown. In order to realize adaptive rate compression sensing in practical surveillance video applications, a time-domain adaptive rate sampling method and a time-frequency domain adaptive rate sampling method based on sub-block statistical characteristic estimation are proposed. The video is divided into appropriate sub-blocks, and the similar sub-blocks are quickly clustered by statistical characteristic estimation and clustering algorithm, and further the sampling rate is reasonably assigned to each type of sub-blocks to realize the adaptive rate compression sensing, which reduces the total sampling rate without degrading the reconstruction quality and significantly increasing the computational complexity of sampling. Simulation results show that, compared with the existing video adaptive rate compression sensing scheme, the proposed method can more reasonably allocate the sampling rate for sub-blocks with different sparsity and reduce the total sampling rate, and at the same time the sampling computational complexity is not high, which can be accepted by the actual sampling equipment.
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  • A method to maximize Energy Efficiency for STAR-RIS assisted Uplink NOMA Systems
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 81 )     　　
  • A method to maximize energy efficiency is proposed for uplink non-orthogonal multiple access (NOMA) systems assisted by simultaneously transmitting and reflecting intelligent surface(STAR-RIS). Firstly, the optimization problem of maximizing energy efficiency is constructed, with base station beamforming, STAR-RIS phase shift, users’ power and time allocation as variables. Then, the optimization problem is sim-plified by deducing the relationship between beamforming and STAR-RIS phase shift and the minimum power required by the users. Finally, an iterative method is proposed to optimize STAR-RIS phase shift, power al-location and time allocation alternately. In each iteration, semi-definite programming method (SDP) is used to optimize STAR-RIS phase shift, Dinkelbach algorithm to optimize power allocation, and function extremal method to optimize time allocation. The simulation results show that for the time switching protocol, time allocation has little effect on energy efficiency, while random phase shift has great effect on energy efficiency.
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  • Multi-party Human-computer Active Dialogue Strategy Based On Knowledge Enhancement
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 54 )     　　
  • In view of the existing multi-party human-computer dialogue system, it is easy to ignore the speakers who may be forgotten in the dialogue process, resulting in weak user interaction initiative and poor user dialogue experience. This paper proposes a multi-party human-computer active dialogue strategy based on knowledge enhancement. By using the knowledge graph as external knowledge, the strategy learns the preferences of specific individuals who may be forgotten in the dialogue, and designs a personalized response strategy combining with the current group dialogue needs to actively promote the participation of individuals and groups in the dialogue. Firstly, the graph attention mechanism is used to learn the representation of specific individuals’ interest entities, and the deep preference representation of individuals is obtained by introducing time weight aggregation. Then, multiple neighbor sets are triggered along the path of the current dialogue topic knowledge subgraph to actively capture the high-level personalized interest topics of individuals. Finally, by analyzing the current context and needs of group dialogue at the semantic level, the group's satisfaction with the candidate topics is assessed, and the optimal dialogue topics that can meet individual preferences and take care of group feelings are obtained. The experimental results show that the multi-party human-computer dialogue system that integrates external knowledge and focuses on specific speakers can effectively improve the content richness of response and participant interaction satisfaction, and promote the continuous multi-party dialogue.
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  • Financial Time Series Forecasting with the Neural Network Ensemble Models
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 162 )     　　
  • Accurately forecasting the financial time series data plays an essential role in the operation and management of financial markets. A new ARMA-CNN-LSTM model for forecasting financial time series data is proposed based on neural networks and ensemble learning. The above model combines the Convolutional Neural Network (CNN), Long Short-term Memory (LSTM) network, and Autoregressive Moving Average (ARMA) model in an integrated framework. To achieve the hybrid modeling of linear and nonlinear features in financial time series data, the CNN-LSTM model is used to model the spatiotemporal features of data and the ARMA model is used to model the autocorrelation features of data. The experimental results show that compared with the benchmark individual model, the proposed model has excellent accuracy and robustness in forecasting financial time series data.
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  • Deep Analysis and Detection of Anomalous Data Based on Dual-Layer Attention
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 101 )     　　
  • With the advancement of intelligent driving technology, the connection between vehicles and the external environment through in-vehicle networks is becoming increasingly frequent. The Controller Area Network (CAN) is the primary in-vehicle network currently, and attackers can exploit security vulnerabilities in the CAN network to gain control of vehicles, posing significant safety threats to occupants. In response to this issue, this paper proposes an anomaly detection model based on Long Short-Term Memory (LSTM) and attention mechanism. The model employs a dual-layer attention encoder to deeply mine information between local and global features of CAN data streams. By efficiently searching and learning sequential patterns between features, the proposed model significantly reduces feature information redundancy, leading to improved detection performance, enhanced efficiency, and accuracy in anomaly detection. The model also demonstrates effectiveness in handling imbalanced classification tasks. Finally, multiple independent repeated tests are conducted on the CarHacking dataset. The results show that the proposed method achieves detection accuracy above 99.2% for all attack categories in the CarHacking dataset, significantly outperforming other anomaly detection methods. Additionally, this paper accomplishes a multi-classification task that other methods have not achieved, with an average detection accuracy of 99.26% for the proposed method.
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  • The low latency routing solution for burst services in OTN
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 56 )     　　
  • When short-term and unconventional frequency services(i.e. burst services) are issued in OTN, it will be difficult to ensure that the blockage rate and transmission latency of services will be guaranteed due to insufficient bandwidth resources. In response to the above issue, an burst services problem model in OTN was established. This model can increase bandwidth resources by reducing the maximum bandwidth occupation rate to reduce the blocking rate of burst services.According to this model, a joint optimization algorithm based on latency and maximum bandwidth occupancy was proposed, which reduces the average latency of services while reducing the blocking rate of burst services.The experimental results also indicate that compared with other reserved bandwidth based algorithms, the joint optimization algorithm has the lowest service blocking rate (the highest blocking rate is 62.10% lower than the reserved bandwidth based multiplexing algorithm), and the average service latency has always remained low (the highest service average latency is 0.36ms lower than the reserved bandwidth based multiplexing algorithm, and the maximum value does not exceed 5.66ms).
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  • A PAPR Reduction SLM Method for OTFS Signals without Transmitting Side Information
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 121 )     　　
  • Orthogonal time frequency space (OTFS) modulation has an excellent performance in high-speed mobile scenarios. However, OTFS is susceptible to the problem of high peak-to-average power ratio (PAPR). Regarding this issue, a selective mapping method without transmitting side information is proposed to reduce the PAPR of OTFS signal. The random phase sequences are employed to scramble the transmitted symbols in the delay-Doppler (DD) domain, and the unique degree of freedom of OTFS pulse pilot position are used to carry the side information, which can be extracted by detecting the difference in energy distribution of the received symbols in the DD domain. Finally, the input-output relationship in the DD domain was derived, and an improved channel estimation and signal detection algorithm was provided. Simulation results show that the proposed method can effectively reduce the PAPR by about 2.4dB, while the performance loss of bit error rate is only 0.4dB with the estimated channel state information.
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  • A Pipeline Approach Based on Multi-semantic Representation for Interactive Argument Pair Extraction
  • Journal of Beijing University of Posts and Telecommunications. 2025, 48(1): 0-0.
  • Abstract ( 1 )     　　
  • Interactive argument pair extraction aims to identify multiple argument pairs within two related argumentative texts. Current methods decompose this task into two subtasks: argument mining and argument pair extraction, which are then jointly modeled for multi-task learning. However, these methods often share the same sentence semantic representations, leading to potential information bias within each subtask. The sentence semantic representation for the argument mining task requires more integration of the content and structure of a single text, while the argument pair extraction task needs to focus more on the specific content expressed by the two arguments. Therefore, a two-stage interactive argument pair extraction method based on multi-semantic representation is proposed, which includes an argument mining model and an argument pair extraction model. The argument mining model uses a pre-trained language model and a bidirectional encoder to capture the semantic information of a single text for the identification of arguments. The argument pair extraction model directly models the two arguments as a text pair for semantic representation, and uses a large language model to extract key information from lengthy argument content, generates new samples, introduces key semantic information, and ultimately completes the identification of interactive argument pairs. Experiments are conducted on two benchmark datasets, and the results achieve the best performance, proving the effectiveness of the method.
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