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    20 November 2024, Volume 40 Issue 11
    Research and Development
    Multi-stream adaptive offloading scheme based on mobile edge computing
    Dieli HU, Zheming YANG, Wen JI
    2024, 40(11):  1-15.  doi:10.11959/j.issn.1000-0801.2024233
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    The transmission and analysis of massive video streams require significant edge bandwidth and computational resources, posing severe challenges to the current multimedia frameworks based on mobile edge computing (MEC). To address this issue, an adaptive offloading scheme based on a multi-stream collaborative optimization framework was proposed. Firstly, under the constraint of the long-term MEC energy budget, the processing cost of video tasks was minimized by optimizing the data stream selection decisions, server offloading decisions, bandwidth resource allocation, and computing resource allocation. Then, based on the Lyapunov optimization method, the long-term optimization problem was transformed into independent deterministic subproblems for each time slot, and the mixed-integer nonlinear programming problems for each time slot were solved by Markov approximation and KKT conditions. Simulation results indicate that the proposed scheme not only meets the long-term MEC energy constraint, but also significantly outperforms existing benchmark schemes in terms of cost performance.

    STAR-RIS assisted multi-user wireless system beamforming design in TS mode
    Gang LIU, Mingtai LI, Cheng PAN, Yi GUO, Shaozhong FU
    2024, 40(11):  16-26.  doi:10.11959/j.issn.1000-0801.2024244
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    Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has the capability of simultaneous transmission and reflection, extending the communication capacity of conventional RIS (CRIS) from half-space to full-space. For the multi-user multiple-input single-output (MISO) communication system assisted by STAR-RIS, a beamforming algorithm based on time switch (TS) mode was proposed to minimize transmission power. Firstly, maximum ratio transmission (MRT) was used to get the initial BS transmitting beamforming. Secondly, TS slot orthogonality and channel gain maximum (CGM) were used to decouple the non-convex problem into the constant mode non-convex problem and the semi-definite programming (SDP) problem. Finally, manifold optimization and interior point method were used to jointly solve these problems. The simulation results show that, STAR-RIS can effectively reduce the transmission power compared with conventional RIS. Under the same SINR(signal-to-noise ratio). The proposed algorithm has lower transmitting power than the mainstream algorithm. TS mode has lower transmitting power than energy splitting (ES) mode at low SINR.

    Ultra-wideband digital channel modeling based on generative adversarial network
    Bin ZHUGE, Zhengxian WANG, Ying WANG, Xiaodan CAI, Ligang DONG, Zitian ZHANG, Xian JIANG, Hua LI, Yueqian XU
    2024, 40(11):  27-39.  doi:10.11959/j.issn.1000-0801.2024242
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    In ultra-wideband communication technology, high-quality channel impulse response data is crucial for system design and performance optimization. A least squares generative adversarial network (LSGAN) and an improved loss function were introduced, which significantly enhanced the ability to capture and reproduce channel data. By combining feature matching techniques with conditional generative adversarial networks (CGAN), it was able to improve the detail accuracy and diversity of the generated data. The model was allowed to generate data according to different communication environments and signal scenarios. During the model training phase, reconstructed channel data representing global features were used, while actual channel data experiencing wireless fading were employed during the testing phase. Experimental results demonstrate that the model outperforms the WGAN-GP in small sample datasets and complex fading channel environments, with a 4.8% increase in recognition accuracy and a 5% reduction in mode collapse issues.

    A method of synthetic spoofing speech detection using self-supervised contrastive learning
    Man YANG, Zhihua JIAN, Chenghan LIANG
    2024, 40(11):  40-49.  doi:10.11959/j.issn.1000-0801.2024236
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    In order to eliminate the impact of the imbalance of the sample size of bonafide speech and fake speech in the training dataset on the performance of synthetic speech detection system and further improve the accuracy of synthetic speech detection, a method of synthetic speech detection was proposed based on self-supervised contrastive learning. In this method, the samples after pitch transformation were regarded as negative samples, and the neural network was trained to make the anchor sample features different from the negative sample features, so that the network could extract the features sensitive to pitch transformation. And the deep residual network was used as the back-end classifier to judge the authenticity of the speech. Experimental results show that, compared with the traditional hand-crafted acoustic features, the deep learning-based and the end-to-end spoofing speech detection systems, the proposed method significantly reduces the equal error rate of the system. The synthetic forged speech detection method based on self-supervised contrastive learning can train the network to extract features sensitive to pitch transformation and will not affect the accuracy of synthetic speech detection because of the imbalance of bonafide and fake speech in the dataset, so the accuracy of synthetic forged speech detection is significantly improved.

    Non-contact ECG reconstruction algorithm based on millimeter wave radar
    Jingxue LUO, Yuanhui ZHANG, Xiao DAI, Duo FU, Kang LIU
    2024, 40(11):  50-65.  doi:10.11959/j.issn.1000-0801.2024238
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    With the wide application of millimeter-wave radar signals in medical monitoring, accurately mapping these signals to ECG signals has become a key challenge in meeting the needs for daily continuous non-contact ECG monitoring. The signal processing flow of millimeter-wave radar was introduced in detail, the fine-grained mapping relationship between radar signals and ECG signals was explored, and the nonlinear transformation from radar signals to electrocardiograms was achieved through the introduction of the CAE-BiLSTM deep learning network, which was a hybrid of a convolutional autoencoder (CAE) and bi-directional long short-term memory (BiLSTM), incorporating the convolutional block attention module (CBAM).The results show that the median morphological accuracy of the proposed method is 0.92, and the feature peak prediction error is less than 50 ms. The proposed approach significantly enhances the mapping relationship between radar and ECG signals and offers a new idea for generating non-contact ECG signals.

    Multi-pattern time-aware sequential recommendation with data augmentation
    Jiale LI, Ruiqin WANG, Yang YU
    2024, 40(11):  66-78.  doi:10.11959/j.issn.1000-0801.2024234
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    In sequential recommendation systems, explicit user interaction sequences are used as context to infer the user's next possible action. Time-aware sequential recommendation models explore the temporal information within the sequence and consider the impact of time on user decisions. However, existing time-aware sequential recommendation models only utilize raw temporal information, while many additional pieces of information in the original sequence are not fully exploited, such as user ratings, item attributes, item popularity, and textual information like item titles and reviews. Therefore, the DMTiSASRec model was proposed, which not only efficiently extracted relevant orders beyond temporal information but also leveraged techniques like contrastive learning and multi-modal methods to mine different types of additional information. Experiments on five publicly available datasets across different domains and scales show that DMTiSASRec outperforms existing models in terms of effectiveness.

    Intelligent automotive network security evaluation model based on dynamic weight allocation
    Rongxin MI, Zhiqiang LIN, Jiahao QI, Wenwen YAO, Jinxin ZUO, Yueming LU
    2024, 40(11):  79-90.  doi:10.11959/j.issn.1000-0801.2024239
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    The determination of the weight of evaluation indicators is one of the important factors affecting the security evaluation of intelligent vehicle networks. A network security evaluation model based on dynamic weight allocation was proposesd to address the problem of traditional property rights confirmation methods ignoring the impact of changes in indicator attribute states on the weight of evaluation indicators. The model first decomposed and analyzed the security objectives of the vehicle Ad Hoc network (VANET), and constructed its security evaluation index system. Based on the constructed security evaluation index system, the correlation analysis of security indicators was carried out using a sorting and confirmation algorithm. Then, the proposed dynamic weight allocation algorithm was used to calculate the dynamic weights of each indicator in the index system, thereby achieving the security evaluation of the intelligent vehicle VANET and obtaining the security level evaluation results. The experimental results indicate that the model can improve the rationality of intelligent vehicle VANET evaluation.

    Multi-user hybrid precoding design based on QR decomposition and beam gain alignment
    Tingting ZHANG, Haiyan CAO, Bin LI, Yangrui JI, Haikun LING, Fangmin XU
    2024, 40(11):  91-102.  doi:10.11959/j.issn.1000-0801.2024232
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    Millimeterwave massive MIMO systems based on lens antenna arrays can effectively reduce the number of required RF chains without loss of performance. In order to solve the problems of leakage power and multiuser interference in existing beamspace channels, a hybrid precoding scheme was proposed that combining QR decomposition and beam gain alignment (BGA). Firstly, a phase shift network (PSN) structure was designed, so that each RF chain in beamspace MIMO can select multiple beams to collect the leakage power. Secondly, QR decomposition was performed on the beam channel matrix, with the achievable sum rate as the goal, and appropriate beams were selected one by one in increasing order until the optimal beam selection matrix was selected.Finally, the beam gain alignment (BGA) strategy was used to design the analog precoding. In order to eliminate multiuser interference in the system, a digital precoder was designed to diagonalize the channel. Simulation results show that the proposed algorithm has lower complexity and higher achievable sum rate and energy efficiency.

    Network attack detection method for CPS of active distribution network with renewable energy based on FP-Growth algorithm
    Rui LI, Shan LIU, Lei YAN
    2024, 40(11):  103-113.  doi:10.11959/j.issn.1000-0801.2024229
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    In order to effectively analyze and identify the cyber physical system (CPS) status of active distribution network, a network attack detection method based on FP-Growth algorithm for active distribution network cyber physical system was proposed. Firstly, the active distribution network control model considering network attack and the impact mechanism of CPS network attack were analyzed, and the raw data was obtained by monitoring the CPS information side and physical side of the active distribution network through the real-time simulation platform. Then, the data discretization rules were formulated through the rated voltage and current values, and the original data were discretized and quantized according to the rules to generate event sequences. On this basis, the FP-Growth algorithm was used to mine the frequent items and strong correlations of abnormal signals in historical data, and new attack categories and fault points were identified through the existing frequent sequence features. The detection of CPS network attack on active distribution network was realized. Finally, the feasibility and effectiveness of the proposed method were verified by a simulation experiment.

    Technical evolution and development trend of quantum computing cloud platform
    Yuanchen HAO, Yuheng XIE, Jianjun TANG
    2024, 40(11):  114-124.  doi:10.11959/j.issn.1000-0801.2024240
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    Currently, quantum computing cloud platforms provide a convenient, on-demand mechanism for accessing quantum computing services, effectively utilizing classical networks to deliver these capabilities. A thorough analysis of the architecture, service models, and global development trends of quantum computing cloud platforms were conducted, identifying several critical challenges: insufficient real-time user experience, the adverse effects of noise and errors on algorithm performance, a lack of standardization in quantum programming, and limitations in resource sharing and collaboration among platforms. To address these challenges, a clear trajectory for the ongoing optimization of quantum computing cloud platforms was delineated. The strategic recommendations aimed at enhancing user experience, accelerating the research and development of NISQ algorithms, improving compatibility across programming frameworks, and fostering collaborative efforts between platforms were proposed. These recommendations are designed to provide substantial support for the further evolution of quantum computing cloud platforms, unlocking their inherent advantages and facilitating their widespread application and continuous innovation across diverse sectors.

    Analysis and research on the impact of medium voltage power line carrier communication
    Tonghan ZHAO, Yuzhong SU
    2024, 40(11):  125-134.  doi:10.11959/j.issn.1000-0801.2024241
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    Addressing the challenges of low data transmission efficiency, weak interference resistance, and poor signal transmission reliability in medium-voltage power line carrier communication due to the complex characteristics of power line channels and dynamic noise environments,a digital signal processing (DSP) as the core control unit of the medium-voltage power line carrier communication system was proposed. By integrating orthogonal frequency division multiplexing (OFDM) modules and an long short-term memory (LSTM) channel noise model, an advanced, highly integrated, and intelligent communication technology system has been developed. This system adaptively allocates subcarrier resources, employs efficient modulation and demodulation techniques, and performs real-time noise prediction and suppression, significantly enhancing the spectral efficiency, interference resistance, and reliability of signal transmission in the communication system. Experimental results demonstrate that the proposed design achieves a mean absolute percentage error (MAPE) of only 10.62% and a root mean square error (RMSE) of merely 7.2%. This innovative technological system offers an efficient and precise solution for medium-voltage power line carrier communication, facilitating further advancements in smart grid and remote monitoring fields.

    Engineering and Application
    Research on the application of quantum-secure algorithms in 5G network
    Congli WANG, Jinhui LI, Jingran WANG, Weijia XUE, Jinhua WANG, Qianran WANG
    2024, 40(11):  135-147.  doi:10.11959/j.issn.1000-0801.2024231
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    With the widespread application of 5G networks, data transmission speed and capacity have been significantly improved, promoting innovative development in fields such as the Internet of things, autonomous driving, and telemedicine. At the same time, higher requirements are put forward for network security. The progress of quantum computing technology poses a major threat to the traditional public key cryptosystem. Shor's algorithm and Grover's algorithm can crack public key cryptography based on the holeyer factorization and discrete logarithm problems respectively, and improve the cracking efficiency of symmetric cryptography and hash algorithms. To address this challenge, multiple global standardization organizations are actively promoting the research and standardization of quantum-secure cryptography. Firstly, the 5G network architecture and its security requirements were outlined, and the impact of quantum computing on traditional cryptography was analyzed. Subsequently, the latest progress of quantum-secure cryptography algorithms was introduced, and the integration methods of these algorithms in 5G network terminal access and data transmission security were discussed. Finally, a quantum-secure cryptography application strategy was proposed and its technical feasibility was verified, in order to provide theoretical and technical references for building a more secure and reliable 5G network.

    Research on architecture of trusted data service center for enterprise groups based on blockchain technology
    Xiaofu LANG, Min TAN, Yanqiang ZHANG, Xin LI, Guodong LIU
    2024, 40(11):  148-159.  doi:10.11959/j.issn.1000-0801.2024220
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    As a complex organizational entity characterized by platformization and diversification, large enterprise groups put forward requirements for data abilities, such as data services rapid innovation, information collaboration among multiple systems, trusted data governance, and data assets management. The typical architecture and problems of enterprise data center in non-trusted system environments were analyzed. The technical path, system architecture and credibility evaluation index model for trusted data service center based on blockchain technology were proposed. And four key mechanisms were introduced, including multi-portal for data services, multiple systems collaboration, data functions modularized, and "cloud-network-blockchain" infrastructure. Solutions for enterprises were provided to apply trusted data technology and mechanism innovation, and stimulate business development. Furthermore, combined with the case practice of blockchain service network, the application effectiveness of the proposed architecture and mechanisms was presented.

    Research on the application of Prophet model in intelligent prediction of call volume
    Yong CHEN, Yifeng LIU, Yin WANG, Mengyu ZHANG
    2024, 40(11):  160-169.  doi:10.11959/j.issn.1000-0801.2024235
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    For telecommunications operators, call volume prediction is crucial in business operations, IT system construction planning, and system operation and maintenance. The traditional prediction method is mainly achieved through manual prediction, supplemented by program script statistics. The accuracy of predictions is greatly influenced by human factors, and fixed rules cannot represent the variation pattern of call volume affected by multiple factors. Therefore, it is necessary to introduce artificial intelligence models for dynamic prediction of call volume. Commonly used artificial intelligence prediction models were studied and tested. The Prophet model, improved based on business characteristics, was adopted for forecasting call volume of the inter-network settlement systems. The prediction effect has been significantly improved compared to traditional prediction.

    Mechanism analysis and suppression of bus voltage low frequency oscillation of multi-VSC connected substation
    Liang CHENG, Pengyang DUAN, Hongbang SU, Guisheng MA, Shengjuan TIAN, Zheng DAI
    2024, 40(11):  170-182.  doi:10.11959/j.issn.1000-0801.2024237
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    Large new energy stations are located in remote areas and integrate into boost substation, which is transmitted to load center through long-distance AC lines. This results in weak electrical connections between boost substation busbar and load center, and causes low-frequency oscillation instability. To reveal the mechanism of low-frequency oscillation, a universal model for low-frequency dynamic analysis of multi-VSC system was established. Focusing on low-frequency dynamics, each VSC and AC power grid were independently established, which is applicable to different quantities of grid-tied VSC with various control strategies. Based on the proposed model, the impact mechanism of various factors such as power grid strength, VSC operating point, and coupling interaction of various VSC’s dynamic loops on low-frequency stability of multi-VSC system were revealed by state space analysis. Based on the analysis results, a virtual point of common coupling control strategy was proposed, which takes the voltage of substation busbar as the unified control objective of each VSC. This strategy can effectively suppress the low-frequency oscillations caused by dynamic interaction of different VSC, and a parameter self-tuning function was designed to eliminate errors caused by variation of line parameters. Finally, the correctness of the impact of various elements on low-frequency dynamics is verified through PSCAD/EMTDC simulation based on proposed model, as well as the effectiveness of proposed control strategy for low-frequency oscillation suppression.

Copyright Information
Authorized by: China Association for Science and Technology
Sponsored by: China Institute of Communications
Posts and Telecom Press Co., Ltd.
Publisher: Beijing Xintong Media Co., Ltd.
Editor-in-Chief: Chen Shanzhi
Editorial Director: Li Caishan
Address: F2, Beiyang Chenguang Building, Shunbatiao No.1 Courtyard, Fengtai District, Beijing, China
Postal Code: 100079
Tel: 010-53879277
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E-mail: dxkx@ptpress.com.cn
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ISSN 1000-0801
CN 11-2103/TN
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