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    Differential Quasi-Yagi Antenna and Array
    ZHU Zhihao, ZHANG Yueping
    ZTE Communications    2023, 21 (3): 37-44.   DOI: 10.12142/ZTECOM.202303006
    Abstract194)   HTML10)    PDF (2723KB)(137)       Save

    A novel differential quasi-Yagi antenna is first presented and compared with a normal single-ended counterpart. The simulated and measured results show that the differential quasi-Yagi antenna outperforms the conventional single-ended one. The differential quasi-Yagi antenna is then used as an element for linear arrays. A study of the coupling mechanism between the two differential and the two single-ended quasi-Yagi antennas is conducted, which reveals that the TE0 mode is the dominant mode, and the driver is the decisive part to account for the mutual coupling. Next, the effects of four decoupling structures are respectively evaluated between the two differential quasi-Yagi antennas. Finally, the arrays with simple but effective decoupling structures are fabricated and measured. The measured results demonstrate that the simple slit or air-hole decoupling structure can reduce the coupling level from -18 dB to -25 dB and meanwhile maintain the impedance matching and radiation patterns of the array over the broad bandwidth. The differential quasi-Yagi antenna should be a promising antenna candidate for many applications.

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    Boundary Data Augmentation for Offline Reinforcement Learning
    SHEN Jiahao, JIANG Ke, TAN Xiaoyang
    ZTE Communications    2023, 21 (3): 29-36.   DOI: 10.12142/ZTECOM.202303005
    Abstract175)   HTML19)    PDF (1865KB)(260)       Save

    Offline reinforcement learning (ORL) aims to learn a rational agent purely from behavior data without any online interaction. One of the major challenges encountered in ORL is the problem of distribution shift, i.e., the mismatch between the knowledge of the learned policy and the reality of the underlying environment. Recent works usually handle this in a too pessimistic manner to avoid out-of-distribution (OOD) queries as much as possible, but this can influence the robustness of the agents at unseen states. In this paper, we propose a simple but effective method to address this issue. The key idea of our method is to enhance the robustness of the new policy learned offline by weakening its confidence in highly uncertain regions, and we propose to find those regions by simulating them with modified Generative Adversarial Nets (GAN) such that the generated data not only follow the same distribution with the old experience but are very difficult to deal with by themselves, with regard to the behavior policy or some other reference policy. We then use this information to regularize the ORL algorithm to penalize the overconfidence behavior in these regions. Extensive experiments on several publicly available offline RL benchmarks demonstrate the feasibility and effectiveness of the proposed method.

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    Federated Learning for 6G: A Survey From Perspective of Integrated Sensing, Communication and Computation
    ZHAO Moke, HUANG Yansong, LI Xuan
    ZTE Communications    2023, 21 (2): 25-33.   DOI: 10.12142/ZTECOM.202302005
    Abstract169)   HTML2)    PDF (1107KB)(14)       Save

    With the rapid advancements in edge computing and artificial intelligence, federated learning (FL) has gained momentum as a promising approach to collaborative data utilization across organizations and devices, while ensuring data privacy and information security. In order to further harness the energy efficiency of wireless networks, an integrated sensing, communication and computation (ISCC) framework has been proposed, which is anticipated to be a key enabler in the era of 6G networks. Although the advantages of pushing intelligence to edge devices are multi-fold, some challenges arise when incorporating FL into wireless networks under the umbrella of ISCC. This paper provides a comprehensive survey of FL, with special emphasis on the design and optimization of ISCC. We commence by introducing the background and fundamentals of FL and the ISCC framework. Subsequently, the aforementioned challenges are highlighted and the state of the art in potential solutions is reviewed. Finally, design guidelines are provided for the incorporation of FL and ISCC. Overall, this paper aims to contribute to the understanding of FL in the context of wireless networks, with a focus on the ISCC framework, and provide insights into addressing the challenges and optimizing the design for the integration of FL into future 6G networks.

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    Deep Learning-Based Semantic Feature Extraction: A Literature Review and Future Directions
    DENG Letian, ZHAO Yanru
    ZTE Communications    2023, 21 (2): 11-17.   DOI: 10.12142/ZTECOM.202302003
    Abstract165)   HTML1)    PDF (390KB)(58)       Save

    Semantic communication, as a critical component of artificial intelligence (AI), has gained increasing attention in recent years due to its significant impact on various fields. In this paper, we focus on the applications of semantic feature extraction, a key step in the semantic communication, in several areas of artificial intelligence, including natural language processing, medical imaging, remote sensing, autonomous driving, and other image-related applications. Specifically, we discuss how semantic feature extraction can enhance the accuracy and efficiency of natural language processing tasks, such as text classification, sentiment analysis, and topic modeling. In the medical imaging field, we explore how semantic feature extraction can be used for disease diagnosis, drug development, and treatment planning. In addition, we investigate the applications of semantic feature extraction in remote sensing and autonomous driving, where it can facilitate object detection, scene understanding, and other tasks. By providing an overview of the applications of semantic feature extraction in various fields, this paper aims to provide insights into the potential of this technology to advance the development of artificial intelligence.

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    Future Vision on Artificial Intelligence Assisted Green Energy Efficiency Network
    CHEN Jiajun, GAO Yin, LIU Zhuang, LI Dapeng
    ZTE Communications    2023, 21 (2): 34-39.   DOI: 10.12142/ZTECOM.202302006
    Abstract130)   HTML4)    PDF (691KB)(71)       Save

    To meet the key performance requirement of the 5G network and the demand of the growing number of mobile subscribers, millions of base stations are being constructed. 5G New Radio is designed to enable denser network deployments, which raises significant concerns about network energy consumption. Machine learning (ML), as a kind of artificial intelligence (AI) technologies, can enhance network optimization performance and energy efficiency. In this paper, we propose AI/ML-assisted energy-saving strategies to achieve optimal performance in terms of cell shutdown duration and energy efficiency. To realize network intelligence, we put forward the concept of intrinsic AI, which integrates AI into every aspect of wireless communication networks.

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    Intelligent 6G Wireless Network with Multi-Dimensional Information Perception
    YANG Bei, LIANG Xin, LIU Shengnan, JIANG Zheng, ZHU Jianchi, SHE Xiaoming
    ZTE Communications    2023, 21 (2): 3-10.   DOI: 10.12142/ZTECOM.202302002
    Abstract121)   HTML3)    PDF (737KB)(103)       Save

    Intelligence and perception are two operative technologies in 6G scenarios. The intelligent wireless network and information perception require a deep fusion of artificial intelligence (AI) and wireless communications in 6G systems. Therefore, fusion is becoming a typical feature and key challenge of 6G wireless communication systems. In this paper, we focus on the critical issues and propose three application scenarios in 6G wireless systems. Specifically, we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems. Then, we introduce the wireless AI technology architecture with 6G multi-dimensional information perception, which includes the physical layer technology of multi-dimensional feature information perception, full spectrum fusion technology, and intelligent wireless resource management. The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.

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    Double Deep Q-Network Decoder Based on EEG Brain-Computer Interface
    REN Min, XU Renyu, ZHU Ting
    ZTE Communications    2023, 21 (3): 3-10.   DOI: 10.12142/ZTECOM.202303002
    Abstract119)   HTML12)    PDF (1551KB)(155)       Save

    Brain-computer interfaces (BCI) use neural activity as a control signal to enable direct communication between the human brain and external devices. The electrical signals generated by the brain are captured through electroencephalogram (EEG) and translated into neural intentions reflecting the user's behavior. Correct decoding of the neural intentions then facilitates the control of external devices. Reinforcement learning-based BCIs enhance decoders to complete tasks based only on feedback signals (rewards) from the environment, building a general framework for dynamic mapping from neural intentions to actions that adapt to changing environments. However, using traditional reinforcement learning methods can have challenges such as the curse of dimensionality and poor generalization. Therefore, in this paper, we use deep reinforcement learning to construct decoders for the correct decoding of EEG signals, demonstrate its feasibility through experiments, and demonstrate its stronger generalization on motion imaging (MI) EEG data signals with high dynamic characteristics.

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    Machine Learning Driven Latency Optimization for Internet of Things Applications in Edge Computing
    AWADA Uchechukwu, ZHANG Jiankang, CHEN Sheng, LI Shuangzhi, YANG Shouyi
    ZTE Communications    2023, 21 (2): 40-52.   DOI: 10.12142/ZTECOM.202302007
    Abstract115)   HTML2)    PDF (2054KB)(43)       Save

    Emerging Internet of Things (IoT) applications require faster execution time and response time to achieve optimal performance. However, most IoT devices have limited or no computing capability to achieve such stringent application requirements. To this end, computation offloading in edge computing has been used for IoT systems to achieve the desired performance. Nevertheless, randomly offloading applications to any available edge without considering their resource demands, inter-application dependencies and edge resource availability may eventually result in execution delay and performance degradation. We introduce Edge-IoT, a machine learning-enabled orchestration framework in this paper, which utilizes the states of edge resources and application resource requirements to facilitate a resource-aware offloading scheme for minimizing the average latency. We further propose a variant bin-packing optimization model that co-locates applications firmly on edge resources to fully utilize available resources. Extensive experiments show the effectiveness and resource efficiency of the proposed approach.

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    Statistical Model of Path Loss for Railway 5G Marshalling Yard Scenario
    DING Jianwen, LIU Yao, LIAO Hongjian, SUN Bin, WANG Wei
    ZTE Communications    2023, 21 (3): 117-122.   DOI: 10.12142/ZTECOM.202303015
    Abstract88)   HTML6)    PDF (1448KB)(41)       Save

    The railway mobile communication system is undergoing a smooth transition from the Global System for Mobile Communications-Railway (GSM-R) to the Railway 5G. In this paper, an empirical path loss model based on a large amount of measured data is established to predict the path loss in the Railway 5G marshalling yard scenario. According to the different characteristics of base station directional antennas, the antenna gain is verified. Then we propose the position of the breakpoint in the antenna propagation area, and based on the breakpoint segmentation, a large-scale statistical model for marshalling yards is established.

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    Content Popularity Prediction via Federated Learning in Cache-Enabled Wireless Networks
    YAN Yuna, LIU Ying, NI Tao, LIN Wensheng, LI Lixin
    ZTE Communications    2023, 21 (2): 18-24.   DOI: 10.12142/ZTECOM.202302004
    Abstract85)   HTML2)    PDF (1176KB)(47)       Save

    With the rapid development of networks, users are increasingly seeking richer and high-quality content experience, and there is an urgent need to develop efficient content caching strategies to improve the content distribution efficiency of caching. Therefore, it will be an effective solution to combine content popularity prediction based on machine learning (ML) and content caching to enable the network to predict and analyze popular content. However, the data sets which contain users’ private data cause the risk of privacy leakage. In this paper, to address this challenge, we propose a privacy-preserving algorithm based on federated learning (FL) and long short-term memory (LSTM), which is referred to as FL-LSTM, to predict content popularity. Simulation results demonstrate that the performance of the proposed algorithm is close to the centralized LSTM and better than other benchmark algorithms in terms of privacy protection. Meanwhile, the caching policy in this paper raises about 14.3% of the content hit rate.

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    Robust Beamforming Under Channel Prediction Errors for Time-Varying MIMO System
    ZHU Yuting, LI Zeng, ZHANG Hongtao
    ZTE Communications    2023, 21 (3): 77-85.   DOI: 10.12142/ZTECOM.202303011
    Abstract83)   HTML8)    PDF (1578KB)(66)       Save

    The accuracy of acquired channel state information (CSI) for beamforming design is essential for achievable performance in multiple-input multiple-output (MIMO) systems. However, in a high-speed moving scene with time-division duplex (TDD) mode, the acquired CSI depending on the channel reciprocity is inevitably outdated, leading to outdated beamforming design and then performance degradation. In this paper, a robust beamforming design under channel prediction errors is proposed for a time-varying MIMO system to combat the degradation further, based on the channel prediction technique. Specifically, the statistical characteristics of historical channel prediction errors are exploited and modeled. Moreover, to deal with random error terms, deterministic equivalents are adopted to further explore potential beamforming gain through the statistical information and ultimately derive the robust design aiming at maximizing weighted sum-rate performance. Simulation results show that the proposed beamforming design can maintain outperformance during the downlink transmission time even when channels vary fast, compared with the traditional beamforming design.

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    Special Topic on Federated Learning over Wireless Networks
    CUI Shuguang, YIN Changchuan, ZHU Guangxu
    ZTE Communications    2023, 21 (1): 1-2.   DOI: 10.12142/ZTECOM.202301001
    Abstract75)   HTML12)    PDF (506KB)(133)       Save
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    UAV Autonomous Navigation for Wireless Powered Data Collection with Onboard Deep Q-Network
    LI Yuting, DING Yi, GAO Jiangchuan, LIU Yusha, HU Jie, YANG Kun
    ZTE Communications    2023, 21 (2): 80-87.   DOI: 10.12142/ZTECOM.202302011
    Abstract72)   HTML2)    PDF (1114KB)(15)       Save

    In a rechargeable wireless sensor network, utilizing the unmanned aerial vehicle (UAV) as a mobile base station (BS) to charge sensors and collect data effectively prolongs the network’s lifetime. In this paper, we jointly optimize the UAV’s flight trajectory and the sensor selection and operation modes to maximize the average data traffic of all sensors within a wireless sensor network (WSN) during finite UAV’s flight time, while ensuring the energy required for each sensor by wireless power transfer (WPT). We consider a practical scenario, where the UAV has no prior knowledge of sensor locations. The UAV performs autonomous navigation based on the status information obtained within the coverage area, which is modeled as a Markov decision process (MDP). The deep Q-network (DQN) is employed to execute the navigation based on the UAV position, the battery level state, channel conditions and current data traffic of sensors within the UAV’s coverage area. Our simulation results demonstrate that the DQN algorithm significantly improves the network performance in terms of the average data traffic and trajectory design.

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    Research Towards Terahertz Power Amplifiers in Silicon-Based Process
    CHEN Jixin, ZHOU Peigen, YU Jiayang, LI Zekun, LI Huanbo, PENG Lin
    ZTE Communications    2023, 21 (2): 88-94.   DOI: 10.12142/ZTECOM.202302012
    Abstract70)   HTML4)    PDF (2811KB)(18)       Save

    In view of the existing design challenges for Terahertz (THz) power amplifiers (PAs), the common design methods and the efforts of the State Key Laboratory of Millimeter Wave, Southeast University, China in the development of silicon-based THz PAs, mainly including silicon-based PAs with operating frequencies covering 100–300 GHz, are summarized in this paper. Particularly, we design an LC-balun-based two-stage differential cascode PA with a center frequency of 150 GHz and an output power of 14 dBm. Based on a Marchand balun, we report a 220 GHz three-stage differential cascode PA with a saturated output power of 9.5 dBm. To further increase the output power of THz PA, based on a four-way differential power combining technique, we report a 211–263 GHz dual-LC-tank-based broadband PA with a recorded 14.7 dBm Psat and 16.4 dB peak gain. All the above circuits are designed in a standard 130 nm silicon germanium (SiGe) BiCMOS process.

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    Reliable and Privacy-Preserving Federated Learning with Anomalous Users
    ZHANG Weiting, LIANG Haotian, XU Yuhua, ZHANG Chuan
    ZTE Communications    2023, 21 (1): 15-24.   DOI: 10.12142/ZTECOM.202301003
    Abstract69)   HTML11)    PDF (1351KB)(393)       Save

    Recently, various privacy-preserving schemes have been proposed to resolve privacy issues in federated learning (FL). However, most of them ignore the fact that anomalous users holding low-quality data may reduce the accuracy of trained models. Although some existing works manage to solve this problem, they either lack privacy protection for users’ sensitive information or introduce a two-cloud model that is difficult to find in reality. A reliable and privacy-preserving FL scheme named reliable and privacy-preserving federated learning (RPPFL) based on a single-cloud model is proposed. Specifically, inspired by the truth discovery technique, we design an approach to identify the user’s reliability and thereby decrease the impact of anomalous users. In addition, an additively homomorphic cryptosystem is utilized to provide comprehensive privacy preservation (user’s local gradient privacy and reliability privacy). We give rigorous theoretical analysis to show the security of RPPFL. Based on open datasets, we conduct extensive experiments to demonstrate that RPPEL compares favorably with existing works in terms of efficiency and accuracy.

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    Learning-Based Admission Control for Low-Earth-Orbit Satellite Communication Networks
    CHENG Lei, QIN Shuang, FENG Gang
    ZTE Communications    2023, 21 (3): 54-62.   DOI: 10.12142/ZTECOM.202303008
    Abstract65)   HTML14)    PDF (1188KB)(177)       Save

    Satellite communications has been regarded as an indispensable technology for future mobile networks to provide extremely high data rates, ultra-reliability, and ubiquitous coverage. However, the high dynamics caused by the fast movement of low-earth-orbit (LEO) satellites bring huge challenges in designing and optimizing satellite communication systems. Especially, admission control, deciding which users with diversified service requirements are allowed to access the network with limited resources, is of paramount importance to improve network resource utilization and meet the service quality requirements of users. In this paper, we propose a dynamic channel reservation strategy based on the Actor-Critic algorithm (AC-DCRS) to perform intelligent admission control in satellite networks. By carefully designing the long-term reward function and dynamically adjusting the reserved channel threshold, AC-DCRS reaches a long-run optimal access policy for both new calls and handover calls with different service priorities. Numerical results show that our proposed AC-DCRS outperforms traditional channel reservation strategies in terms of overall access failure probability, the average call success rate, and channel utilization under various dynamic traffic conditions.

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    Multi-Agent Hierarchical Graph Attention Reinforcement Learning for Grid-Aware Energy Management
    FENG Bingyi, FENG Mingxiao, WANG Minrui, ZHOU Wengang, LI Houqiang
    ZTE Communications    2023, 21 (3): 11-21.   DOI: 10.12142/ZTECOM.202303003
    Abstract58)   HTML11)    PDF (1238KB)(148)       Save

    The increasing adoption of renewable energy has posed challenges for voltage regulation in power distribution networks. Grid-aware energy management, which includes the control of smart inverters and energy management systems, is a trending way to mitigate this problem. However, existing multi-agent reinforcement learning methods for grid-aware energy management have not sufficiently considered the importance of agent cooperation and the unique characteristics of the grid, which leads to limited performance. In this study, we propose a new approach named multi-agent hierarchical graph attention reinforcement learning framework (MAHGA) to stabilize the voltage. Specifically, under the paradigm of centralized training and decentralized execution, we model the power distribution network as a novel hierarchical graph containing the agent-level topology and the bus-level topology. Then a hierarchical graph attention model is devised to capture the complex correlation between agents. Moreover, we incorporate graph contrastive learning as an auxiliary task in the reinforcement learning process to improve representation learning from graphs. Experiments on several real-world scenarios reveal that our approach achieves the best performance and can reduce the number of voltage violations remarkably.

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    RIS-Assisted Federated Learning in Multi-Cell Wireless Networks
    WANG Yiji, WEN Dingzhu, MAO Yijie, SHI Yuanming
    ZTE Communications    2023, 21 (1): 25-37.   DOI: 10.12142/ZTECOM.202301004
    Abstract53)   HTML10)    PDF (1325KB)(81)       Save

    Over-the-air computation (AirComp) based federated learning (FL) has been a promising technique for distilling artificial intelligence (AI) at the network edge. However, the performance of AirComp-based FL is decided by the device with the lowest channel gain due to the signal alignment property. More importantly, most existing work focuses on a single-cell scenario, where inter-cell interference is ignored. To overcome these shortages, a reconfigurable intelligent surface (RIS)-assisted AirComp-based FL system is proposed for multi-cell networks, where a RIS is used for enhancing the poor user signal caused by channel fading, especially for the device at the cell edge, and reducing inter-cell interference. The convergence of FL in the proposed system is first analyzed and the optimality gap for FL is derived. To minimize the optimality gap, we formulate a joint uplink and downlink optimization problem. The formulated problem is then divided into two separable nonconvex subproblems. Following the successive convex approximation (SCA) method, we first approximate the nonconvex term to a linear form, and then alternately optimize the beamforming vector and phase-shift matrix for each cell. Simulation results demonstrate the advantages of deploying a RIS in multi-cell networks and our proposed system significantly improves the performance of FL.

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    A 220 GHz Frequency-Division Multiplexing Wireless Link with High Data Rate
    ZHANG Bo, WANG Yihui, FENG Yinian, YANG Yonghui, PENG Lin
    ZTE Communications    2023, 21 (3): 63-69.   DOI: 10.12142/ZTECOM.202303009
    Abstract50)   HTML3)    PDF (2704KB)(86)       Save

    With the development of wireless communication, the 6G mobile communication technology has received wide attention. As one of the key technologies of 6G, terahertz (THz) communication technology has the characteristics of ultra-high bandwidth, high security and low environmental noise. In this paper, a THz duplexer with a half-wavelength coupling structure and a sub-harmonic mixer operating at 216 GHz and 204 GHz are designed and measured. Based on these key devices, a 220 GHz frequency-division multiplexing communication system is proposed, with a real-time data rate of 10.4 Gbit/s for one channel and a transmission distance of 15 m. The measured constellation diagram of two receivers is clearly visible, the signal-to-noise ratio (SNR) is higher than 22 dB, and the bit error ratio (BER) is less than 10-8. Furthermore, the high definition (HD) 4K video can also be transmitted in real time without stutter.

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    Massive Unsourced Random Access Under Carrier Frequency Offset
    XIE Xinyu, WU Yongpeng, YUAN Zhifeng, MA Yihua
    ZTE Communications    2023, 21 (3): 45-53.   DOI: 10.12142/ZTECOM.202303007
    Abstract50)   HTML8)    PDF (1438KB)(106)       Save

    Unsourced random access (URA) is a new perspective of massive access which aims at supporting numerous machine-type users. With the appearance of carrier frequency offset (CFO), joint activity detection and channel estimation, which is vital for multiple-input and multiple-output URA, is a challenging task. To handle the phase corruption of channel measurements under CFO, a novel compressed sensing algorithm is proposed, leveraging the parametric bilinear generalized approximate message passing framework with a Markov chain support model that captures the block sparsity structure of the considered angular domain channel. An uncoupled transmission scheme is proposed to reduce system complexity, where slot-emitted messages are reorganized relying on clustering unique user channels. Simulation results reveal that the proposed transmission design for URA under CFO outperforms other potential methods.

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    Adaptive Retransmission Design for Wireless Federated Edge Learning
    XU Xinyi, LIU Shengli, YU Guanding
    ZTE Communications    2023, 21 (1): 3-14.   DOI: 10.12142/ZTECOM.202301002
    Abstract49)   HTML5)    PDF (1432KB)(103)       Save

    As a popular distributed machine learning framework, wireless federated edge learning (FEEL) can keep original data local, while uploading model training updates to protect privacy and prevent data silos. However, since wireless channels are usually unreliable, there is no guarantee that the model updates uploaded by local devices are correct, thus greatly degrading the performance of the wireless FEEL. Conventional retransmission schemes designed for wireless systems generally aim to maximize the system throughput or minimize the packet error rate, which is not suitable for the FEEL system. A novel retransmission scheme is proposed for the FEEL system to make a tradeoff between model training accuracy and retransmission latency. In the proposed scheme, a retransmission device selection criterion is first designed based on the channel condition, the number of local data, and the importance of model updates. In addition, we design the air interface signaling under this retransmission scheme to facilitate the implementation of the proposed scheme in practical scenarios. Finally, the effectiveness of the proposed retransmission scheme is validated through simulation experiments.

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    Log Anomaly Detection Through GPT-2 for Large Scale Systems
    JI Yuhe, HAN Jing, ZHAO Yongxin, ZHANG Shenglin, GONG Zican
    ZTE Communications    2023, 21 (3): 70-76.   DOI: 10.12142/ZTECOM.202303010
    Abstract46)   HTML6)    PDF (537KB)(98)       Save

    As the scale of software systems expands, maintaining their stable operation has become an extraordinary challenge. System logs are semi-structured text generated by the recording function in the source code and have important research significance in software service anomaly detection. Existing log anomaly detection methods mainly focus on the statistical characteristics of logs, making it difficult to distinguish the semantic differences between normal and abnormal logs, and performing poorly on real-world industrial log data. In this paper, we propose an unsupervised framework for log anomaly detection based on generative pre-training-2 (GPT-2). We apply our approach to two industrial systems. The experimental results on two datasets show that our approach outperforms state-of-the-art approaches for log anomaly detection.

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    A Hybrid Five-Level Single-Phase Rectifier with Low Common-Mode Voltage
    TIAN Ruihan, WU Xuezhi, XU Wenzheng, ZUO Zhiling, CHEN Changqing
    ZTE Communications    2023, 21 (4): 78-84.   DOI: 10.12142/ZTECOM.202304010
    Abstract40)   HTML3)    PDF (2986KB)(18)       Save

    Rectifiers with high efficiency and high power density are crucial to the stable and efficient power supply of 5G communication base stations, which deserves in-depth investigation. In general, there are two key problems to be addressed: supporting both alternating current (AC) and direct current (DC) input, and minimizing the common-mode voltage as well as leakage current for safety reasons. In this paper, a hybrid five-level single-phase rectifier is proposed. A five-level topology is adopted in the upper arm, and a half-bridge diode topology is adopted in the lower arm. A dual closed-loop control strategy and a flying capacitor voltage regulation method are designed accordingly so that the compatibility of both AC and DC input is realized with low common voltage and small passive devices. Simulation and experimental results demonstrate the effectiveness and performance of the proposed rectifier.

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    Special Topic on Reinforcement Learning and Intelligent Decision
    GAO Yang
    ZTE Communications    2023, 21 (3): 1-2.   DOI: 10.12142/ZTECOM.202303001
    Abstract34)   HTML8)    PDF (334KB)(91)       Save
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    Perceptual Quality Assessment for Point Clouds : A Survey
    ZHOU Yingjie, ZHANG Zicheng, SUN Wei, MIN Xiongkuo, ZHAI Guangtao
    ZTE Communications    2023, 21 (4): 3-16.   DOI: 10.12142/ZTECOM.202304002
    Abstract32)   HTML1)    PDF (1376KB)(52)       Save

    A point cloud is considered a promising 3D representation that has achieved wide applications in several fields. However, quality degradation inevitably occurs during its acquisition and generation, communication and transmission, and rendering and display. Therefore, how to accurately perceive the visual quality of point clouds is a meaningful topic. In this survey, we first introduce the point cloud to emphasize the importance of point cloud quality assessment (PCQA). A review of subjective PCQA is followed, including common point cloud distortions, subjective experimental setups and subjective databases. Then we review and compare objective PCQA methods in terms of model-based and projection-based. Finally, we provide evaluation criteria for objective PCQA methods and compare the performances of various methods across multiple databases. This survey provides an overview of classical methods and recent advances in PCQA.

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    Special Topic on Evolution of AI Enabled Wireless Networks
    WANG Ling, GAO Yin
    ZTE Communications    2023, 21 (2): 1-2.   DOI: 10.12142/ZTECOM.202302001
    Abstract31)   HTML5)    PDF (416KB)(84)       Save
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    A Practical Reinforcement Learning Framework for Automatic Radar Detection
    YU Junpeng, CHEN Yiyu
    ZTE Communications    2023, 21 (3): 22-28.   DOI: 10.12142/ZTECOM.202303004
    Abstract29)   HTML4)    PDF (456KB)(90)       Save

    At present, the parameters of radar detection rely heavily on manual adjustment and empirical knowledge, resulting in low automation. Traditional manual adjustment methods cannot meet the requirements of modern radars for high efficiency, high precision, and high automation. Therefore, it is necessary to explore a new intelligent radar control learning framework and technology to improve the capability and automation of radar detection. Reinforcement learning is popular in decision task learning, but the shortage of samples in radar control tasks makes it difficult to meet the requirements of reinforcement learning. To address the above issues, we propose a practical radar operation reinforcement learning framework, and integrate offline reinforcement learning and meta-reinforcement learning methods to alleviate the sample requirements of reinforcement learning. Experimental results show that our method can automatically perform as humans in radar detection with real-world settings, thereby promoting the practical application of reinforcement learning in radar operation.

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    Special Topic on 3D Point Cloud Processing and Applications
    SUN Huifang, LI Ge, CHEN Siheng, LI Li, GAO Wei
    ZTE Communications    2023, 21 (4): 1-2.   DOI: 10.12142/ZTECOM.202304001
    Abstract28)   HTML3)    PDF (624KB)(48)       Save
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    Multi-User MmWave Beam Tracking via Multi-Agent Deep Q-Learning
    MENG Fan, HUANG Yongming, LU Zhaohua, XIAO Huahua
    ZTE Communications    2023, 21 (2): 53-60.   DOI: 10.12142/ZTECOM.202302008
    Abstract25)   HTML2)    PDF (792KB)(36)       Save

    Beamforming is significant for millimeter wave multi-user massive multi-input multi-output systems. In the meanwhile, the overhead cost of channel state information and beam training is considerable, especially in dynamic environments. To reduce the overhead cost, we propose a multi-user beam tracking algorithm using a distributed deep Q-learning method. With online learning of users’ moving trajectories, the proposed algorithm learns to scan a beam subspace to maximize the average effective sum rate. Considering practical implementation, we model the continuous beam tracking problem as a non-Markov decision process and thus develop a simplified training scheme of deep Q-learning to reduce the training complexity. Furthermore, we propose a scalable state-action-reward design for scenarios with different users and antenna numbers. Simulation results verify the effectiveness of the designed method.

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    Lossy Point Cloud Attribute Compression with Subnode-Based Prediction
    YIN Qian, ZHANG Xinfeng, HUANG Hongyue, WANG Shanshe, MA Siwei
    ZTE Communications    2023, 21 (4): 29-37.   DOI: 10.12142/ZTECOM.202304004
    Abstract24)   HTML4)    PDF (905KB)(42)       Save

    Recent years have witnessed that 3D point cloud compression (PCC) has become a research hotspot both in academia and industry. Especially in industry, the Moving Picture Expert Group (MPEG) has actively initiated the development of PCC standards. One of the adopted frameworks called geometry-based PCC (G-PCC) follows the architecture of coding geometry first and then coding attributes, where the region adaptive hierarchical transform (RAHT) method is introduced for the lossy attribute compression. The upsampled transform domain prediction in RAHT does not sufficiently explore the attribute correlations between neighbor nodes and thus fails to further reduce the attribute redundancy between neighbor nodes. In this paper, we propose a subnode-based prediction method, where the spatial position relationship between neighbor nodes is fully considered and prediction precision is further promoted. We utilize some already-encoded neighbor nodes to facilitate the upsampled transform domain prediction in RAHT by means of a weighted average strategy. Experimental results have illustrated that our proposed attribute compression method shows better rate-distortion (R-D) performance than the latest MPEG G-PCC (both on reference software TMC13-v22.0 and GeS-TM-v2.0).

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    Beyond Video Quality: Evaluation of Spatial Presence in 360-Degree Videos
    ZOU Wenjie, GU Chengming, FAN Jiawei, HUANG Cheng, BAI Yaxian
    ZTE Communications    2023, 21 (4): 91-103.   DOI: 10.12142/ZTECOM.202304012
    Abstract21)   HTML3)    PDF (1676KB)(18)       Save

    With the rapid development of immersive multimedia technologies, 360-degree video services have quickly gained popularity and how to ensure sufficient spatial presence of end users when viewing 360-degree videos becomes a new challenge. In this regard, accurately acquiring users’ sense of spatial presence is of fundamental importance for video service providers to improve their service quality. Unfortunately, there is no efficient evaluation model so far for measuring the sense of spatial presence for 360-degree videos. In this paper, we first design an assessment framework to clarify the influencing factors of spatial presence. Related parameters of 360-degree videos and head-mounted display devices are both considered in this framework. Well-designed subjective experiments are then conducted to investigate the impact of various influencing factors on the sense of presence. Based on the subjective ratings, we propose a spatial presence assessment model that can be easily deployed in 360-degree video applications. To the best of our knowledge, this is the first attempt in literature to establish a quantitative spatial presence assessment model by using technical parameters that are easily extracted. Experimental results demonstrate that the proposed model can reliably predict the sense of spatial presence.

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    SST-V: A Scalable Semantic Transmission Framework for Video
    LIU Chenyao, GUO Jiejie, ZHANG Yimeng, XU Wenjun, LIU Yiming
    ZTE Communications    2023, 21 (2): 70-79.   DOI: 10.12142/ZTECOM.202302010
    Abstract21)   HTML4)    PDF (1665KB)(53)       Save

    The emerging new services in the sixth generation (6G) communication system impose increasingly stringent requirements and challenges on video transmission. Semantic communications are envisioned as a promising solution to these challenges. This paper provides a highly-efficient solution to video transmission by proposing a scalable semantic transmission algorithm, named scalable semantic transmission framework for video (SST-V), which jointly considers the semantic importance and channel conditions. Specifically, a semantic importance evaluation module is designed to extract more informative semantic features according to the estimated importance level, facilitating high-efficiency semantic coding. By further considering the channel condition, a cascaded learning based scalable joint semantic-channel coding algorithm is proposed, which autonomously adapts the semantic coding and channel coding strategies to the specific signal-to-noise ratio (SNR). Simulation results show that SST-V achieves better video reconstruction performance, while significantly reducing the transmission overhead.

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    RIS-Assisted UAV-D2D Communications Exploiting Deep Reinforcement Learning
    YOU Qian, XU Qian, YANG Xin, ZHANG Tao, CHEN Ming
    ZTE Communications    2023, 21 (2): 61-69.   DOI: 10.12142/ZTECOM.202302009
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    Device-to-device (D2D) communications underlying cellular networks enabled by unmanned aerial vehicles (UAV) have been regarded as promising techniques for next-generation communications. To mitigate the strong interference caused by the line-of-sight (LoS) air-to-ground channels, we deploy a reconfigurable intelligent surface (RIS) to rebuild the wireless channels. A joint optimization problem of the transmit power of UAV, the transmit power of D2D users and the RIS phase configuration are investigated to maximize the achievable rate of D2D users while satisfying the quality of service (QoS) requirement of cellular users. Due to the high channel dynamics and the coupling among cellular users, the RIS, and the D2D users, it is challenging to find a proper solution. Thus, a RIS softmax deep double deterministic (RIS-SD3) policy gradient method is proposed, which can smooth the optimization space as well as reduce the number of local optimizations. Specifically, the SD3 algorithm maximizes the reward of the agent by training the agent to maximize the value function after the softmax operator is introduced. Simulation results show that the proposed RIS-SD3 algorithm can significantly improve the rate of the D2D users while controlling the interference to the cellular user. Moreover, the proposed RIS-SD3 algorithm has better robustness than the twin delayed deep deterministic (TD3) policy gradient algorithm in a dynamic environment.

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    The whole issue of ZTE Communications June 2023, Vol. 21 No. 2
    ZTE Communications    2023, 21 (2): 0-.  
    Abstract18)      PDF (16986KB)(49)       Save
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    Mixed Electric and Magnetic Coupling Design Based on Coupling Matrix Extraction
    XIONG Zhiang, ZHAO Ping, FAN Jiyuan, WU Zengqiang, GONG Hongwei
    ZTE Communications    2023, 21 (4): 85-90.   DOI: 10.12142/ZTECOM.202304011
    Abstract18)   HTML2)    PDF (1412KB)(9)       Save

    This paper proposes a design and fine-tuning method for mixed electric and magnetic coupling filters. It derives the quantitative relationship between the coupling coefficients (electric and magnetic coupling, i.e., EC and MC) and the linear coefficients of frequency-dependent coupling for the first time. Different from the parameter extraction technique using the bandpass circuit model, the proposed approach explicitly relatesEC and MC to the coupling matrix model. This paper provides a general theoretic framework for computer-aided design and tuning of a mixed electric and magnetic coupling filter based on coupling matrices. An example of a 7th-order coaxial combline filter design is given in the paper, verifying the practical value of the approach.

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    Perceptual Optimization for Point-Based Point Cloud Rendering
    YIN Yujie, CHEN Zhang
    ZTE Communications    2023, 21 (4): 47-53.   DOI: 10.12142/ZTECOM.202304006
    Abstract18)   HTML2)    PDF (1647KB)(43)       Save

    Point-based rendering is a common method widely used in point cloud rendering. It realizes rendering by turning the points into the base geometry. The critical step in point-based rendering is to set an appropriate rendering radius for the base geometry, usually calculated using the average Euclidean distance of the N nearest neighboring points to the rendered point. This method effectively reduces the appearance of empty spaces between points in rendering. However, it also causes the problem that the rendering radius of outlier points far away from the central region of the point cloud sequence could be large, which impacts the perceptual quality. To solve the above problem, we propose an algorithm for point-based point cloud rendering through outlier detection to optimize the perceptual quality of rendering. The algorithm determines whether the detected points are outliers using a combination of local and global geometric features. For the detected outliers, the minimum radius is used for rendering. We examine the performance of the proposed method in terms of both objective quality and perceptual quality. The experimental results show that the peak signal-to-noise ratio (PSNR) of the point cloud sequences is improved under all geometric quantization, and the PSNR improvement ratio is more evident in dense point clouds. Specifically, the PSNR of the point cloud sequences is improved by 3.6% on average compared with the original algorithm. The proposed method significantly improves the perceptual quality of the rendered point clouds and the results of ablation studies prove the feasibility and effectiveness of the proposed method.

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    Design of Raptor-Like LDPC Codes and High Throughput Decoder Towards 100 Gbit/s Throughput
    LI Hanwen, BI Ningjing, SHA Jin
    ZTE Communications    2023, 21 (3): 86-92.   DOI: 10.12142/ZTECOM.202303012
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    This paper proposes a raptor-like low-density parity-check (RL-LDPC) code design together with the corresponding decoder hardware architecture aiming at next-generation mobile communication. A new kind of protograph different from the 5G new radio (NR) LDPC basic matrix is presented, and a code construction algorithm is proposed to improve the error-correcting performance. A multi-core layered decoder architecture that supports up to 100 Gbit/s throughput is designed based on the special protograph structure.

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    Hybrid Architecture and Beamforming Optimization for Millimeter Wave Systems
    TANG Yuanqi, ZHANG Huimin, ZHENG Zheng, LI Ping, ZHU Yu
    ZTE Communications    2023, 21 (3): 93-104.   DOI: 10.12142/ZTECOM.202303013
    Abstract17)   HTML1)    PDF (4586KB)(41)       Save

    Hybrid beamforming (HBF) has become an attractive and important technology in massive multiple-input multiple-output (MIMO) millimeter-wave (mmWave) systems. There are different hybrid architectures in HBF depending on different connection strategies of the phase shifter network between antennas and radio frequency chains. This paper investigates HBF optimization with different hybrid architectures in broadband point-to-point mmWave MIMO systems. The joint hybrid architecture and beamforming optimization problem is divided into two sub-problems. First, we transform the spectral efficiency maximization problem into an equivalent weighted mean squared error minimization problem, and propose an algorithm based on the manifold optimization method for the hybrid beamformer with a fixed hybrid architecture. The overlapped subarray architecture which balances well between hardware costs and system performance is investigated. We further propose an algorithm to dynamically partition antenna subarrays and combine it with the HBF optimization algorithm. Simulation results are presented to demonstrate the performance improvement of our proposed algorithms.

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    Simulation and Modeling of Common Mode EMI Noise in Planar Transformers
    LI Wei, JI Jingkang, LIU Yuanlong, SUN Jiawei, LIN Subin
    ZTE Communications    2023, 21 (3): 105-116.   DOI: 10.12142/ZTECOM.202303014
    Abstract17)   HTML2)    PDF (3135KB)(23)       Save

    The transformer is the key circuit component of the common-mode noise current when an isolated converter is working. The high-frequency characteristics of the transformer have an important influence on the common-mode noise of the converter. Traditionally, the measurement method is used for transformer modeling, and a single lumped device is used to establish the transformer model, which cannot be predicted in the transformer design stage. Based on the transformer common-mode noise transmission mechanism, this paper derives the transformer common-mode equivalent capacitance under ideal conditions. According to the principle of experimental measurement of the network analyzer, the electromagnetic field finite element simulation software three-dimensional (3D) modeling and simulation method is used to obtain the two-port parameters of the transformer, extract the high-frequency parameters of the transformer, and establish its electromagnetic compatibility equivalent circuit model. Finally, an experimental prototype is used to verify the correctness of the model by comparing the experimental measurement results with the simulation prediction results.

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    Spatio-Temporal Context-Guided Algorithm for Lossless Point Cloud Geometry Compression
    ZHANG Huiran, DONG Zhen, WANG Mingsheng
    ZTE Communications    2023, 21 (4): 17-28.   DOI: 10.12142/ZTECOM.202304003
    Abstract17)   HTML2)    PDF (2655KB)(42)       Save

    Point cloud compression is critical to deploy 3D representation of the physical world such as 3D immersive telepresence, autonomous driving, and cultural heritage preservation. However, point cloud data are distributed irregularly and discontinuously in spatial and temporal domains, where redundant unoccupied voxels and weak correlations in 3D space make achieving efficient compression a challenging problem. In this paper, we propose a spatio-temporal context-guided algorithm for lossless point cloud geometry compression. The proposed scheme starts with dividing the point cloud into sliced layers of unit thickness along the longest axis. Then, it introduces a prediction method where both intra-frame and inter-frame point clouds are available, by determining correspondences between adjacent layers and estimating the shortest path using the travelling salesman algorithm. Finally, the few prediction residual is efficiently compressed with optimal context-guided and adaptive fast-mode arithmetic coding techniques. Experiments prove that the proposed method can effectively achieve low bit rate lossless compression of point cloud geometric information, and is suitable for 3D point cloud compression applicable to various types of scenes.

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