Top Read Articles

    Published in last 1 year |  In last 2 years |  In last 3 years |  All
    Please wait a minute...
    For Selected: Toggle Thumbnails
    Recent Developments of Transmissive Reconfigurable Intelligent Surfaces: A Review
    TANG Junwen, XU Shenheng, YANG Fan, LI Maokun
    ZTE Communications    2022, 20 (1): 21-27.   DOI: 10.12142/ZTECOM.202201004
    Abstract154)   HTML12)    PDF (1869KB)(263)       Save

    Reconfigurable intelligent surface (RIS) is considered as one of the key technologies for the next-generation mobile communication systems. The transmissive RIS is able to achieve dynamic beamforming capability while transmitting an in-band RF signal through its aperture, and has promising prospects in various practical application scenarios. This paper reviews some of the latest developments of the transmissive RIS. The approaches for transmissive RIS designs are classified and described briefly. Numerous designs with different phase resolutions, such as 1-bit, 2-bit or continuous 360° phase shifts, are presented, with detailed discussions on their operating mechanisms and transmission performances. The design solutions for various transmissive RIS elements are summarized and compared.

    Table and Figures | Reference | Related Articles | Metrics
    Dual‑Polarized RIS‑Based STBC Transmission with Polarization Coupling Analysis
    ZHOU Mingyong, CHEN Xiangyu, TANG Wankai, KE Jun Chen, JIN Shi, CHENG Qiang, CUI Tie Jun
    ZTE Communications    2022, 20 (1): 63-75.   DOI: 10.12142/ZTECOM.202201009
    Abstract141)   HTML18)    PDF (1409KB)(242)       Save

    The rapid development of the reconfigurable intelligent surface (RIS) technology has given rise to a new paradigm of wireless transmitters. At present, most research works on RIS-based transmitters focus on single-polarized RISs. In this paper, we propose a dual-polarized RIS-based transmitter, which realizes 4-transmit space-time block coding (STBC) transmission by properly partitioning RIS’s unit cells and utilizing the degree of freedom of polarization. The proposed scheme is evaluated through a prototype system that utilizes a fabricated dual-polarized phase-adjustable RIS. In particular, the polarization coupling phenomenon in each unit cell of the employed dual-polarized RIS is modeled and analyzed. The experimental results are in good agreement with the theoretical modeling and analysis results, and an initial research effort is made on characterizing the polarization coupling property in the dual-polarized RIS.

    Table and Figures | Reference | Related Articles | Metrics
    Recent Progress in Research and Development of Reconfigurable Intelligent Surface
    YUAN Yifei, GU Qi, WANG Anna, WU Dan, LI Ya
    ZTE Communications    2022, 20 (1): 3-13.   DOI: 10.12142/ZTECOM.202201002
    Abstract127)   HTML21)    PDF (2269KB)(327)       Save

    We aim to provide a comprehensive overview of the progress in research and development of the reconfigurable intelligent surface (RIS) over the last 2–3 years in this paper, especially when the RIS is used as relays in next-generation mobile networks. Major areas of research in academia are outlined, including fundamental performance, channel estimation, joint optimization with antenna precoding at base stations, propagation channel modeling and meta-material devices of RIS elements. Development in industry is surveyed from the aspects of performance potentials and issues, realistic joint optimization algorithms, control mechanisms, field trials and related activities in standardization development organizations (SDOs). Our views on how to carry out the engineering-aspect study on RIS for 6G systems are also presented, which cover the realistic performance, the comparison with other topological improvements, approaches for channel modeling, factors for designing control mechanisms and the timeline for RIS standardization.

    Table and Figures | Reference | Related Articles | Metrics
    Some Observations and Thoughts about Reconfigurable Intelligent Surface Application for 5G Evolution and 6G
    HOU Xiaolin, LI Xiang, WANG Xin, CHEN Lan, SUYAMA Satoshi
    ZTE Communications    2022, 20 (1): 14-20.   DOI: 10.12142/ZTECOM.202201003
    Abstract113)   HTML16)    PDF (1684KB)(181)       Save

    Reconfigurable intelligent surface (RIS) is one of the hottest research topics for 5G evolution and 6G. It is expected that RIS can improve the system capacity and coverage with low cost and power consumption. This paper first discusses typical applications of RIS for 5G evolution and 6G, including RIS-aided smart channels and RIS-aided mega multiple-input multiple-output (MIMO). Then, several observations from RIS trials and system-level simulations are presented, especially those on the deployment strategy and the potential performance gain of RIS for coverage enhancement. The near-field effect and a two-step dynamic RIS beamforming method are also discussed. Finally, we summarize the challenges and opportunities of the RIS technology for 5G evolution and 6G, including hardware design, system and channel modeling, algorithm design and optimization, and standardization. We also suggest a step-by-step commercialization strategy as a conclusion.

    Table and Figures | Reference | Related Articles | Metrics
    Editorial: Special Topic on Federated Learning for IoT and Edge Computing
    ZTE Communications    2022, 20 (3): 1-2.   DOI: 10.12142/ZTECOM.202203001
    Abstract87)   HTML2)    PDF (481KB)(58)       Save
    Reference | Related Articles | Metrics
    Programmable Metasurface for Simultaneously Wireless Information and Power Transfer System
    CHANG Mingyang, HAN Jiaqi, MA Xiangjin, XUE Hao, WU Xiaonan, LI Long, CUI Tiejun
    ZTE Communications    2022, 20 (2): 48-62.   DOI: 10.12142/ZTECOM.202202008
    Abstract83)   HTML4)    PDF (5135KB)(71)       Save

    Implementing self-sustainable wireless communication systems is urgent and challenging for 5G and 6G technologies. In this paper, we elaborate on a system solution using the programmable metasurface (PMS) for simultaneous wireless information and power transfers (SWIPT), offering an optimized wireless energy management network. Both transmitting and receiving sides of the proposed solution are presented in detail. On the transmitting side, employing the wireless power transfer (WPT) technique, we present versatile power conveying strategies for near-field or far-field targets, single or multiple targets, and equal or unequal power targets. On the receiving side, utilizing the wireless energy harvesting (WEH) technique, we report our work on multi-functional rectifying metasurfaces that collect the wirelessly transmitted energy and the ambient energy. More importantly, a numerical model based on the plane-wave angular spectrum method is investigated to accurately calculate the radiation fields of PMS in the Fresnel and Fraunhofer regions. With this model, the efficiencies of WPT between the transmitter and the receiver are analyzed. Finally, future research directions are discussed, and integrated PMS for wireless information and wireless power is outlined.

    Table and Figures | Reference | Related Articles | Metrics
    Markovian Cascaded Channel Estimation for RIS Aided Massive MIMO Using 1‑Bit ADCs and Oversampling
    SHAO Zhichao, YAN Wenjing, YUAN Xiaojun
    ZTE Communications    2022, 20 (1): 48-56.   DOI: 10.12142/ZTECOM.202201007
    Abstract73)   HTML8)    PDF (1115KB)(133)       Save

    A reconfigurable intelligent surface (RIS) aided massive multiple-input multiple-output (MIMO) system is considered, where the base station employs a large antenna array with low-cost and low-power 1-bit analog-to-digital converters (ADCs). To compensate for the performance loss caused by the coarse quantization, oversampling is applied at the receiver. The main challenge for the acquisition of cascaded channel state information in such a system is to handle the distortion caused by the 1-bit quantization and the sample correlation caused by oversampling. In this work, Bussgang decomposition is applied to deal with the coarse quantization, and a Markov chain is developed to characterize the banded structure of the oversampling filter. An approximate message-passing based algorithm is proposed for the estimation of the cascaded channels. Simulation results demonstrate that our proposed 1-bit systems with oversampling can approach the 2-bit systems in terms of the mean square error performance while the former consumes much less power at the receiver.

    Table and Figures | Reference | Related Articles | Metrics
    RIS: Spatial‑Wideband Effect Analysis and Off‑Grid Channel Estimation
    JIAN Mengnan, ZHANG Nan, CHEN Yijian
    ZTE Communications    2022, 20 (1): 57-62.   DOI: 10.12142/ZTECOM.202201008
    Abstract72)   HTML6)    PDF (1069KB)(97)       Save

    As a critical candidate technology for 5G-advanced and 6G, reconfigurable intelligent surfaces (RIS) have received extensive attention from academia and industry. RIS has the promising features of passiveness, reconfigurable ability, and low cost. RIS channel estimation faces the challenges of high matrix dimension, passive estimation, and spatial-wideband effect. In this article, we analyze the impact of the spatial-wideband effect on the RIS channel to account for the propagation delay across RIS elements and estimate sparse channel parameters such as angle and gain through a super-resolution compressive sensing (CS) algorithm. The simulation results explore the influence of the spatial-wideband effect on the RIS channel and verify the effectiveness of the proposed algorithm.

    Table and Figures | Reference | Related Articles | Metrics
    A Survey of Federated Learning on Non-IID Data
    HAN Xuming, GAO Minghan, WANG Limin, HE Zaobo, WANG Yanze
    ZTE Communications    2022, 20 (3): 17-26.   DOI: 10.12142/ZTECOM.202203003
    Abstract68)   HTML7)    PDF (3213KB)(46)       Save

    Federated learning (FL) is a machine learning paradigm for data silos and privacy protection,which aims to organize multiple clients for training global machine learning models without exposing data to all parties. However, when dealing with non-independently identically distributed (non-IID) client data, FL cannot obtain more satisfactory results than centrally trained machine learning and even fails to match the accuracy of the local model obtained by client training alone. To analyze and address the above issues, we survey the state-of-the-art methods in the literature related to FL on non-IID data. On this basis, a motivation-based taxonomy, which classifies these methods into two categories, including heterogeneity reducing strategies and adaptability enhancing strategies, is proposed. Moreover, the core ideas and main challenges of these methods are analyzed. Finally, we envision several promising research directions that have not been thoroughly studied, in hope of promoting research in related fields to a certain extent.

    Table and Figures | Reference | Related Articles | Metrics
    Resource Allocation for Two‑Tier RIS‑Assisted Heterogeneous NOMA Networks
    XU Yongjun, YANG Zhaohui, HUANG Chongwen, YUEN Chau, GUI Guan
    ZTE Communications    2022, 20 (1): 36-47.   DOI: 10.12142/ZTECOM.202201006
    Abstract59)   HTML1)    PDF (1761KB)(129)       Save

    Reconfigurable intelligent surface (RIS) as a promising technology has been proposed to change weak communication environments. However, most of the current resource allocation (RA) schemes have focused on RIS-assisted homogeneous networks, and there is still no open works about RA schemes of RIS-assisted heterogeneous networks (HetNets). In this paper, we design an RA scheme for a RIS-assisted HetNet with non-orthogonal multiple access to improve spectrum efficiency and transmission rates. In particular, we jointly optimize the transmit power of the small-cell base station and the phase-shift matrix of the RIS to maximize the sum rates of all small-cell users, subject to the unit modulus constraint, the minimum signal-to-interference-plus-noise ratio constraint, and the cross-tier interference constraint for protecting communication quality of microcell users. An efficient suboptimal RA scheme is proposed based on the alternating iteration approach, and successive convex approximation and logarithmic transformation approach. Simulation results verify the effectiveness of the proposed scheme in terms of data rates.

    Table and Figures | Reference | Related Articles | Metrics
    IRS‑Enabled Spectrum Sharing: Interference Modeling, Channel Estimation and Robust Passive Beamforming
    GUAN Xinrong, WU Qingqing
    ZTE Communications    2022, 20 (1): 28-35.   DOI: 10.12142/ZTECOM.202201005
    Abstract57)   HTML4)    PDF (1134KB)(90)       Save

    Intelligent reflecting surface (IRS), with its unique capability of smartly reconfiguring wireless channels, provides a new solution to improving spectrum efficiency, reducing energy consumption and saving deployment/hardware cost for future wireless networks. In this paper, IRS-enabled spectrum sharing is investigated, from the perspectives of interference modeling, efficient channel estimation and robust passive beamforming design. Specifically, we first characterize the interference in a spectrum sharing system consisting of a single primary user (PU) pair and a single secondary user (SU) pair, and extend it to the large-scale network by leveraging the Poisson point process (PPP). Then, we propose an efficient channel estimation framework based on decoupling the cascaded IRS channels. Moreover, the tradeoff between spectrum efficiency and energy efficiency is derived from the view of channel estimation accuracy. Finally, we discuss the robust passive beamforming design in presence of imperfect channel estimation and nonideal/discrete phase shifts. It is hoped that this paper provides useful guidance for unlocking the full potential of IRS for achieving efficient spectrum sharing for future wireless networks.

    Table and Figures | Reference | Related Articles | Metrics
    Metric Learning for Semantic‑Based Clothes Retrieval
    YANG Bo, GUO Caili, LI Zheng
    ZTE Communications    2022, 20 (1): 76-82.   DOI: 10.12142/ZTECOM.202201010
    Abstract54)   HTML6)    PDF (1951KB)(83)       Save

    Existing clothes retrieval methods mostly adopt binary supervision in metric learning. For each iteration, only the clothes belonging to the same instance are positive samples, and all other clothes are “indistinguishable” negative samples, which causes the following problem. The relevance between the query and candidates is only treated as relevant or irrelevant, which makes the model difficult to learn the continuous semantic similarities between clothes. Clothes that do not belong to the same instance are completely considered irrelevant and are uniformly pushed away from the query by an equal margin in the embedding space, which is not consistent with the ideal retrieval results. Motivated by this, we propose a novel method called semantic-based clothes retrieval (SCR). In SCR, we measure the semantic similarities between clothes and design a new adaptive loss based on these similarities. The margin in the proposed adaptive loss can vary with different semantic similarities between the anchor and negative samples. In this way, more coherent embedding space can be learned, where candidates with higher semantic similarities are mapped closer to the query than those with lower ones. We use Recall@K and normalized Discounted Cumulative Gain (nDCG) as evaluation metrics to conduct experiments on the DeepFashion dataset and have achieved better performance.

    Table and Figures | Reference | Related Articles | Metrics
    MSRA-Fed: A Communication-Efficient Federated Learning Method Based on Model Split and Representation Aggregate
    LIU Qinbo, JIN Zhihao, WANG Jiabo, LIU Yang, LUO Wenjian
    ZTE Communications    2022, 20 (3): 35-42.   DOI: 10.12142/ZTECOM.202203005
    Abstract53)   HTML11)    PDF (843KB)(33)       Save

    Recent years have witnessed a spurt of progress in federated learning, which can coordinate multi-participation model training while protecting the data privacy of participants. However, low communication efficiency is a bottleneck when deploying federated learning to edge computing and IoT devices due to the need to transmit a huge number of parameters during co-training. In this paper, we verify that the outputs of the last hidden layer can record the characteristics of training data. Accordingly, we propose a communication-efficient strategy based on model split and representation aggregate. Specifically, we make the client upload the outputs of the last hidden layer instead of all model parameters when participating in the aggregation, and the server distributes gradients according to the global information to revise local models. Empirical evidence from experiments verifies that our method can complete training by uploading less than one-tenth of model parameters, while preserving the usability of the model.

    Table and Figures | Reference | Related Articles | Metrics
    High-Power Simultaneous Wireless Information and Power Transfer: Injection-Locked Magnetron Technology
    YANG Bo, MITANI Tomohiko, SHINOHARA Naoki, ZHANG Huaiqing
    ZTE Communications    2022, 20 (2): 3-12.   DOI: 10.12142/ZTECOM.202202002
    Abstract52)   HTML19)    PDF (4645KB)(70)       Save

    Applications using simultaneous wireless information and power transfer (SWIPT) have increased significantly. Wireless communication technologies can be combined with the Internet of Things to develop many innovative applications using SWIPT, which is mainly based on wireless energy harvesting from electromagnetic waves used in communications. Wireless power transfer that uses magnetrons has been developed for communication technologies. Injection-locked magnetrons that can be used to facilitate high-power SWIPT for several devices are reviewed in this paper. This new technology is expected to pave the way for promoting the application of SWIPT in a wide range of fields.

    Table and Figures | Reference | Related Articles | Metrics
    Editorial: Special Topic on Reconfigurable Intelligent Surface (RIS)
    ZTE Communications    2022, 20 (1): 1-2.   DOI: 10.12142/ZTECOM.202201001
    Abstract42)   HTML23)    PDF (455KB)(79)       Save
    Reference | Related Articles | Metrics
    Symbiotic Radio Systems: Detection and Performance Analysis
    CUI Ziqi, WANG Gongpu, WANG Zhigang, AI Bo, XIAO Huahua
    ZTE Communications    2022, 20 (3): 93-98.   DOI: 10.12142/ZTECOM.202203012
    Abstract37)   HTML5)    PDF (3975KB)(29)       Save

    Symbiotic radio (SR) is an emerging green technology for the Internet of Things (IoT). One key challenge of the SR systems is to design efficient and low-complexity detectors, which is the focus of this paper. We first drive the mathematical expression of the optimal maximum-likelihood (ML) detector, and then propose a suboptimal iterative detector with low complexity. Finally, we show through numerical results that our proposed detector can obtain near-optimal bit error rate (BER) performance at a low computational cost.

    Table and Figures | Reference | Related Articles | Metrics
    Synthesis and Design of 5G Duplexer Based on Optimization Method
    WU Qingqiang, CHEN Jianzhong, WU Zengqiang, GONG Hongwei
    ZTE Communications    2022, 20 (3): 70-76.   DOI: 10.12142/ZTECOM.202203009
    Abstract34)   HTML1)    PDF (2852KB)(25)       Save

    A new optimization method is proposed to realize the synthesis of duplexers. The traditional optimization method takes all the variables of the duplexer into account, resulting in too many variables to be optimized when the order of the duplexer is too high, so it is not easy to fall into the local solution. In order to solve this problem, a new optimization strategy is proposed in this paper, that is, two-channel filters are optimized separately, which can reduce the number of optimization variables and greatly reduce the probability of results falling into local solutions. The optimization method combines the self-adaptive differential evolution algorithm (SADE) with the Levenberg-Marquardt (LM) algorithm to get a global solution more easily and accelerate the optimization speed. To verify its practical value, we design a 5G duplexer based on the proposed method. The duplexer has a large external coupling, and how to achieve a feed structure with a large coupling bandwidth at the source is also discussed. The experimental results show that the proposed optimization method can realize the synthesis of higher- order duplexers compared with the traditional methods.

    Table and Figures | Reference | Related Articles | Metrics
    Editorial: Special Topic on Simultaneous Wireless Information and Power Transfer: Technology and Practice
    ZTE Communications    2022, 20 (2): 1-2.   DOI: 10.12142/ZTECOM.202202001
    Abstract34)   HTML11)    PDF (386KB)(35)       Save
    Reference | Related Articles | Metrics
    An Overview of SWIPT Circuits and Systems
    TORRES Ricardo, MATOS Diogo, PEREIRA Felisberto, CORREIA Ricardo, CARVALHO Nuno Borges
    ZTE Communications    2022, 20 (2): 13-18.   DOI: 10.12142/ZTECOM.202202003
    Abstract33)   HTML3)    PDF (2482KB)(22)       Save

    From a circuit implementation perspective, this paper presents a brief overview of simultaneous wireless information and power transmission (SWIPT). By using zero-power batteryless wireless sensors, SWIPT mixes wireless power transmission with wireless communications to allow the truly practical implementation of the Internet of Things as well as many other applications. In this paper, technical backgrounds, problem formation, state-of-the-art solutions, circuit implementation examples, and system integrations of SWIPT are presented.

    Table and Figures | Reference | Related Articles | Metrics
    Neursafe-FL: A Reliable, Efficient, Easy-to- Use Federated Learning Framework
    TANG Bo, ZHANG Chengming, WANG Kewen, GAO Zhengguang, HAN Bingtao
    ZTE Communications    2022, 20 (3): 43-53.   DOI: 10.12142/ZTECOM.202203006
    Abstract33)   HTML3)    PDF (5160KB)(34)       Save

    Federated learning (FL) has developed rapidly in recent years as a privacy-preserving machine learning method, and it has been gradually applied to key areas involving privacy and security such as finance, medical care, and government affairs. However, the current solutions to FL rarely consider the problem of migration from centralized learning to federated learning, resulting in a high practical threshold for federated learning and low usability. Therefore, we introduce a reliable, efficient, and easy-to-use federated learning framework named Neursafe-FL. Based on the unified application program interface (API), the framework is not only compatible with mainstream machine learning frameworks, such as Tensorflow and Pytorch, but also supports further extensions, which can preserve the programming style of the original framework to lower the threshold of FL. At the same time, the design of componentization, modularization, and standardized interface makes the framework highly extensible, which meets the needs of customized requirements and FL evolution in the future. Neursafe-FL is already on Github as an open-source project1.

    Table and Figures | Reference | Related Articles | Metrics
    Toward Low-Cost Flexible Intelligent OAM in Optical Fiber Communication Networks
    YAN Baoluo, WU Qiong, SHI Hu, ZHAO Yan, JIA Yinqiu, FENG Zhenhua, CHEN Weizhang, ZHU Mo, ZHAO Zhiyong, FANG Yu, CHEN Yong
    ZTE Communications    2022, 20 (3): 54-60.   DOI: 10.12142/ZTECOM.202203007
    Abstract33)   HTML3)    PDF (3448KB)(22)       Save

    Low-cost, flexible and intelligent optical performance monitoring and management is a key enabling technology for network quality guarantee, especially in the era of explosive growth of communication capacity and network scale. However, to the best of our knowledge, it is extremely challenging to implement real-time performance monitoring and operations, administration and maintenance (OAM) in a highly complex dynamic network. In this paper, we propose an innovative optical identification (OID) scheme that can realize both performance monitoring and some advanced OAM sub-functions. The basic concepts, applications, challenges and evolution directions of this OID tool are also discussed.

    Table and Figures | Reference | Related Articles | Metrics
    Approach to Anomaly Detection in Microservice System with Multi- Source Data Streams
    ZHANG Qixun, HAN Jing, CHENG Li, ZHANG Baisheng, GONG Zican
    ZTE Communications    2022, 20 (3): 85-92.   DOI: 10.12142/ZTECOM.202203011
    Abstract33)   HTML2)    PDF (679KB)(29)       Save

    Microservices have become popular in enterprises because of their excellent scalability and timely update capabilities. However, while fine-grained modularity and service-orientation decrease the complexity of system development, the complexity of system operation and maintenance has been greatly increased, on the contrary. Multiple types of system failures occur frequently, and it is hard to detect and diagnose failures in time. Furthermore, microservices are updated frequently. Existing anomaly detection models depend on offline training and cannot adapt to the frequent updates of microservices. This paper proposes an anomaly detection approach for microservice systems with multi-source data streams. This approach realizes online model construction and online anomaly detection, and is capable of self-updating and self-adapting. Experimental results show that this approach can correctly identify 78.85% of faults of different types.

    Table and Figures | Reference | Related Articles | Metrics
    Federated Learning Based on Extremely Sparse Series Clinic Monitoring Data
    LU Feng, GU Lin, TIAN Xuehua, SONG Cheng, ZHOU Lun
    ZTE Communications    2022, 20 (3): 27-34.   DOI: 10.12142/ZTECOM.202203004
    Abstract32)   HTML2)    PDF (2499KB)(23)       Save

    Decentralized machine learning frameworks, e.g., federated learning, are emerging to facilitate learning with medical data under privacy protection. It is widely agreed that the establishment of an accurate and robust medical learning model requires a large number of continuous synchronous monitoring data of patients from various types of monitoring facilities. However, the clinic monitoring data are usually sparse and imbalanced with errors and time irregularity, leading to inaccurate risk prediction results. To address this issue, this paper designs a medical data resampling and balancing scheme for federated learning to eliminate model biases caused by sample imbalance and provide accurate disease risk prediction on multi-center medical data. Experimental results on a real-world clinical database MIMIC-IV demonstrate that the proposed method can improve AUC (the area under the receiver operating characteristic) from 50.1% to 62.8%, with a significant performance improvement of accuracy from 76.8% to 82.2%, compared to a vanilla federated learning artificial neural network (ANN). Moreover, we increase the model’s tolerance for missing data from 20% to 50% compared with a stand-alone baseline model.

    Table and Figures | Reference | Related Articles | Metrics
    A Collaborative Medical Diagnosis System Without Sharing Patient Data
    NAN Yucen, FANG Minghao, ZOU Xiaojing, DOU Yutao, Albert Y. ZOMAYA
    ZTE Communications    2022, 20 (3): 3-16.   DOI: 10.12142/ZTECOM.202203002
    Abstract32)   HTML2)    PDF (9151KB)(33)       Save

    As more medical data become digitalized, machine learning is regarded as a promising tool for constructing medical decision support systems. Even with vast medical data volumes, machine learning is still not fully exploiting its potential because the data usually sits in data silos, and privacy and security regulations restrict their access and use. To address these issues, we built a secured and explainable machine learning framework, called explainable federated XGBoost (EXPERTS), which can share valuable information among different medical institutions to improve the learning results without sharing the patients’ data. It also reveals how the machine makes a decision through eigenvalues to offer a more insightful answer to medical professionals. To study the performance, we evaluate our approach by real-world datasets, and our approach outperforms the benchmark algorithms under both federated learning and non-federated learning frameworks.

    Table and Figures | Reference | Related Articles | Metrics
    Alarm-Based Root Cause Analysis Based on Weighted Fault Propagation Topology for Distributed Information Network
    LYU Xiaomeng, CHEN Hao, WU Zhenyu, HAN Junhua, GUO Huifeng
    ZTE Communications    2022, 20 (3): 77-84.   DOI: 10.12142/ZTECOM.202203010
    Abstract27)   HTML2)    PDF (1951KB)(13)       Save

    A distributed information network with complex network structure always has a challenge of locating fault root causes. In this paper, we propose a novel root cause analysis (RCA) method by random walk on the weighted fault propagation graph. Different from other RCA methods, it mines effective features information related to root causes from offline alarms. Combined with the information, online alarms and graph relationship of network structure are used to construct a weighted graph. Thus, this approach does not require operational experience and can be widely applied in different distributed networks. The proposed method can be used in multiple fault location cases. The experiment results show the proposed approach achieves much better performance with 6% higher precision at least for root fault location, compared with three baseline methods. Besides, we explain how the optimal parameter’s value in the random walk algorithm influences RCA results.

    Table and Figures | Reference | Related Articles | Metrics
    Dynamic Power Transmission Using Common RF Feeder with Dual Supply
    DUONG Quang‑Thang, VO Quoc‑Trinh, PHAN Thuy‑Phuong, OKADA Minoru
    ZTE Communications    2022, 20 (2): 28-36.   DOI: 10.12142/ZTECOM.202202005
    Abstract25)   HTML6)    PDF (2118KB)(31)       Save

    This paper proposes the design concept of a dynamic charging system for electric vehicles using multiple transmitter coils connected to a common radio frequency (RF) feeder driven by a pair of two power supplies. Using a common RF feeder for multiple transmitter coils reduces the power electronic redundancy compared to a conventional system, where each transmitter coil is individually driven by one switched-mode power supply. Currently, wireless charging of electric vehicles is recommended to operate in the frequency range of 85 kHz and beyond. In this frequency range, the signal wavelength is shorter than about 3.5 km. Therefore, a charging pad longer than several hundred meters is subject to the standing wave effect. In such a case, the voltage significantly varies along the RF feeder, resulting in a variation in the received power level when the receiver moves. Specifically, the received power significantly deteriorates when the receiver is nearby a node of the voltage standing wave. In this paper, we employ a pair of two power sources which are electrically separated by an odd-integer number of the quarter wavelength to drive the RF feeder. As a result, the voltage standing wave generated by one power source is complemented by that of the other, leading to stable received power and transmission efficiency at all the receiver’s positions along with the charging pad. Simulation results at the 85 kHz frequency band verify the output power stabilization effect of the proposed design. It is worth noting that the proposed concept can also be applied to simultaneous wireless information and power transfer (SWIPT) for passive radio frequency identification (RFID) tags by raising the operation frequency to higher industrial, scientific and medical (ISM) bands, e.g., 13.56 MHz and employing similar modulation methods as in the current RFID technology.

    Table and Figures | Reference | Related Articles | Metrics
    A Radio‑Frequency Loop Resonator for Short‑Range Wireless Power Transmission
    WANG Xin, LI Wenbo, LU Mingyu
    ZTE Communications    2022, 20 (2): 43-47.   DOI: 10.12142/ZTECOM.202202007
    Abstract24)   HTML2)    PDF (1757KB)(19)       Save

    A microstrip loop resonator loaded with a lumped capacitor is proposed for short-range wireless power transmission applications. The overall physical dimensions of the proposed loop resonator configuration are as small as 3 cm by 3 cm. Power transmission efficiency of greater than 80% is achieved with a power transmission distance smaller than 5 mm via the strong coupling between two loop resonators around 1 GHz, as demonstrated by simulations and measurements. Experimental results also show that the power transmission performance is insensitive to various geometrical misalignments. The numerical and experimental results of this paper reveal a bandwidth of more than 50 MHz within which the power transmission efficiency is above 80%. As a result, the proposed microstrip loop resonator has the potential to accomplish efficient wireless power transmission and high-speed (higher than 10 Mbit/s) wireless communication simultaneously.

    Table and Figures | Reference | Related Articles | Metrics
    The whole issue of ZTE Communications September 2022, Vol. 20 No. 3
    ZTE Communications    2022, 20 (3): 0-.  
    Abstract24)      PDF (33705KB)(57)       Save
    Related Articles | Metrics
    Spectrum Sensing for OFDMA Using Multicarrier Covariance Matrix Aware CNN
    ZHANG Jintao, HE Zhenqing, RUI Hua, XU Xiaojing
    ZTE Communications    2022, 20 (3): 61-69.   DOI: 10.12142/ZTECOM.202203008
    Abstract24)   HTML2)    PDF (3480KB)(9)       Save

    We consider spectrum sensing problems in the orthogonal frequency division multiplexing access (OFDMA) cognitive radio scenario, where a secondary user with multiple antennas detects several consecutive subcarriers of an entire OFDM symbol occupied by multiple primary users. Specifically, an OFDM multicarrier covariance matrix convolutional neural network (CNN)-based approach is proposed for simultaneously detecting the occupancy of all OFDM subcarriers, where the multicarrier sample covariance matrix array is specially set as the input of the CNN. The proposed approach can efficiently learn the energy information and correlation information between antennas and between subcarriers to significantly improve the spectrum sensing performance. Numerical results demonstrate that the proposed method has a substantial performance advantage over the state-of-the-art spectrum sensing methods in an OFDMA scenario under the 5G new radio network.

    Table and Figures | Reference | Related Articles | Metrics
    Optimal Design of Wireless Power Transmission Systems Using Antenna Arrays
    SUN Shuyi, WEN Geyi
    ZTE Communications    2022, 20 (2): 19-27.   DOI: 10.12142/ZTECOM.202202004
    Abstract23)   HTML3)    PDF (1914KB)(32)       Save

    Three design methods for wireless power transmission (WPT) systems using antenna arrays have been investigated. The three methods, corresponding to three common application scenarios of WPT systems, are based on the method of maximum power transmission efficiency (MMPTE) between two antenna arrays. They are unconstrained MMPTE, weighted MMPTE, and constrained MMPTE. To demonstrate the optimal design process with the three methods, a WPT system operating at 2.45 GHz is designed, simulated, and fabricated, in which the transmitting (Tx) array, consisting of 36 microstrip patch elements, is configured as a square and the receiving (Rx) array, consisting of 5 patch elements, is configured as anL shape. The power transmission efficiency (PTE) is then maximized for the three application scenarios, which yields the maximum possible PTEs and the optimized distributions of excitations for both Tx and Rx arrays. The feeding networks are then built based on the optimized distributions of excitations. Simulations and experiments reveal that the unconstrained MMPTE, which corresponds to the application scenario where no radiation pattern shaping is involved, yields the highest PTE. The next highest PTE belongs to the weighted MMPTE, where the power levels at all the receiving elements are imposed to be equal. The constrained MMPTE has the lowest PTE, corresponding to the scenario in which the radiated power pattern is assumed to be flat along with the Rx array.

    Table and Figures | Reference | Related Articles | Metrics
    Security in Edge Blockchains: Attacks and Countermeasures
    CAO Yinfeng, CAO Jiannong, WANG Yuqin, WANG Kaile, LIU Xun
    ZTE Communications    2022, 20 (4): 3-14.   DOI: 10.12142/ZTECOM.202204002
    Abstract20)   HTML5)    PDF (2388KB)(27)       Save

    Edge blockchains, the blockchains running on edge computing infrastructures, have attracted a lot of attention in recent years. Thanks to data privacy, scalable computing resources, and distributed topology nature of edge computing, edge blockchains are considered promising solutions to facilitating future blockchain applications. However, edge blockchains face unique security issues caused by the deployment of vulnerable edge devices and networks, including supply chain attacks and insecure consensus offloading, which are mostly not well studied in previous literature. This paper is the first survey that discusses the attacks and countermeasures of edge blockchains. We first summarize the three-layer architecture of edge blockchains: blockchain management, blockchain consensus, and blockchain lightweight client. We then describe seven specific attacks on edge blockchain components and discuss the countermeasures. At last, we provide future research directions on securing edge blockchains. This survey will act as a guideline for researchers and developers to design and implement secure edge blockchains.

    Table and Figures | Reference | Related Articles | Metrics
    Editorial: Special Topic on Wireless Communication and Its Security: Challenges and Solutions
    ZTE Communications    2022, 20 (4): 1-2.   DOI: 10.12142/ZTECOM.202204001
    Abstract18)   HTML4)    PDF (353KB)(40)       Save
    Reference | Related Articles | Metrics
    Polarization Reconfigurable Patch Antenna for Wireless Power Transfer Related Applications
    SHEN Jun, ZHAO Tianxiang, LIU Xueguan
    ZTE Communications    2022, 20 (2): 37-42.   DOI: 10.12142/ZTECOM.202202006
    Abstract15)   HTML1)    PDF (1615KB)(22)       Save

    A polarized reconfigurable patch antenna is proposed in this paper. The proposed antenna is a dual cross-polarized patch antenna with a programmable power divider. The programmable power divider consists of two branch line couplers (BLC) and a digital phase shifter. By adjusting the phase of the phase shifter, the power ratio of the power divider can be changed, and thus the feed power to the antenna input port can be changed to reconfigure the antenna polarization. The phase-controlled power divider and the cross dual-polarized antenna are designed, fabricated and tested, and then they are combined to realize the polarized reconfigurable antenna. By moving the phase of the phase shifter, the antenna polarization is reconfigured into vertical polarization (VP), horizontal polarization (HP), and circular polarization (CP). The test is conducted at the frequency of 915 MHz, which is widely used for simultaneous wireless information and power transfer (SWIPT) in radio-frequency identification (RFID) applications. The results demonstrate that when the antenna is configured as CP, the axial ratio of the antenna is less than 3 dB, and when the antenna is configured as HP or VP, the axial ratio of the antenna exceeds 20 dB. Finally, experiments are conducted to verify the influence of antenna polarization changes on wireless power transmitting. As expected, the reconfigured antenna polarization can help improve the power transmitting efficiency.

    Table and Figures | Reference | Related Articles | Metrics
    A Unified Deep Learning Method for CSI Feedback in Massive MIMO Systems
    GAO Zhengguang, LI Lun, WU Hao, TU Xuezhen, HAN Bingtao
    ZTE Communications    2022, 20 (4): 110-115.   DOI: 10.12142/ZTECOM.202204013
    Abstract13)   HTML3)    PDF (2093KB)(5)       Save

    A unified deep learning (DL) based algorithm is proposed for channel state information (CSI) compression in massive multiple-input multiple-output (MIMO) systems. More importantly, the element filling strategy is investigated to address the problem of model redesigning and retraining for different antenna typologies in practical systems. The results show that the proposed DL-based algorithm achieves better performance than the enhanced Type Ⅱ algorithm in Release 16 of 3GPP. The proposed element filling strategy enables one-time training of a unified model to compress and reconstruct different channel state matrices in a practical MIMO system.

    Table and Figures | Reference | Related Articles | Metrics
    Broadband Sequential Load-Modulated Balanced Amplifier Using Coupler-PA Co-Design Approach
    RAN Xiongbo, DAI Zhijiang, ZHONG Kang, PANG Jingzhou, LI Mingyu
    ZTE Communications    2022, 20 (4): 62-68.   DOI: 10.12142/ZTECOM.202204008
    Abstract10)   HTML1)    PDF (3024KB)(14)       Save

    The basic theory of the sequential load-modulated balanced amplifier (SLMBA) is introduced and the working principle of its active load modulation is analyzed in this paper. In order to further improve the performance of the SLMBA, a co-designed method of the coupler and power amplifier (PA) is proposed, which is different from the traditional design of couplers. According to the back-off point and saturation point of the SLMBA, this coupler-PA co-design approach can make the working state of the coupler and three-way PA closer to the actual situation, which improves the overall performance of the SLMBA. The maximum output power ratio of the control PA and the balance PA is then determined by the preset output power back-off (OBO) of 10 dB, and the phase compensation line is determined by the trace of the load modulation impedance of the balanced PA. In order to verify the proposed method, an SLMBA operating at 1.5–2.7 GHz (57% relative bandwidth) is designed. The layout simulation results show that its saturated output powers achieve 40.7–43.7 dBm and the small signal gains are 9.7–12.4 dB. Besides, the drain efficiencies at the saturated point and 10 dB OBO point are 52.7%–73.7% and 44.9%–59.2% respectively.

    Table and Figures | Reference | Related Articles | Metrics
    Key Intrinsic Security Technologies in 6G Networks
    LU Haitao, YAN Xincheng, ZHOU Qiang, DAI Jiulong, LI Rui
    ZTE Communications    2022, 20 (4): 22-31.   DOI: 10.12142/ZTECOM.202204004
    Abstract9)   HTML1)    PDF (2487KB)(21)       Save

    Intrinsic security is a hot topic in the research of 6G network security. A revolution from the traditional “plugin-based” and “patch-based” network security protection mechanism to a self-sensing, self-adaptive and self-growing network immunity system is a general view of 6G intrinsic security in the industry. Massive connection security, physical-layer security, blockchain, and other 6G candidate intrinsic security technologies are analyzed based on 6G applications, especially hot scenarios and key technologies in the ToB (oriented to business) field.

    Table and Figures | Reference | Related Articles | Metrics
    Label Enhancement for Scene Text Detection
    MEI Junjun, GUAN Tao, TONG Junwen
    ZTE Communications    2022, 20 (4): 89-95.   DOI: 10.12142/ZTECOM.202204011
    Abstract8)   HTML2)    PDF (1431KB)(7)       Save

    Segmentation-based scene text detection has drawn a great deal of attention, as it can describe the text instance with arbitrary shapes based on its pixel-level prediction. However, most segmentation-based methods suffer from complex post-processing to separate the text instances which are close to each other, resulting in considerable time consumption during the inference procedure. A label enhancement method is proposed to construct two kinds of training labels for segmentation-based scene text detection in this paper. The label distribution learning (LDL) method is used to overcome the problem brought by pure shrunk text labels that might result in sub-optimal detection performance. The experimental results on three benchmarks demonstrate that the proposed method can consistently improve the performance without sacrificing inference speed.

    Table and Figures | Reference | Related Articles | Metrics
    Utility-Improved Key-Value Data Collection with Local Differential Privacy for Mobile Devices
    TONG Ze, DENG Bowen, ZHENG Lele, ZHANG Tao
    ZTE Communications    2022, 20 (4): 15-21.   DOI: 10.12142/ZTECOM.202204003
    Abstract7)   HTML1)    PDF (1575KB)(15)       Save

    The structure of key-value data is a typical data structure generated by mobile devices. The collection and analysis of the data from mobile devices are critical for service providers to improve service quality. Nevertheless, collecting raw data, which may contain various personal information, would lead to serious personal privacy leaks. Local differential privacy (LDP) has been proposed to protect privacy on the device side so that the server cannot obtain the raw data. However, existing mechanisms assume that all keys are equally sensitive, which cannot produce high-precision statistical results. A utility-improved data collection framework with LDP for key-value formed mobile data is proposed to solve this issue. More specifically, we divide the key-value data into sensitive and non-sensitive parts and only provide an LDP-equivalent privacy guarantee for sensitive keys and all values. We instantiate our framework by using a utility-improved key value-unary encoding (UKV-UE) mechanism based on unary encoding, with which our framework can work effectively for a large key domain. We then validate our mechanism which provides better utility and is suitable for mobile devices by evaluating it in two real datasets. Finally, some possible future research directions are envisioned.

    Table and Figures | Reference | Related Articles | Metrics
    Distributed Multi-Cell Multi-User MISO Downlink Beamforming via Deep Reinforcement Learning
    JIA Haonan, HE Zhenqing, TAN Wanlong, RUI Hua, LIN Wei
    ZTE Communications    2022, 20 (4): 69-77.   DOI: 10.12142/ZTECOM.202204009
    Abstract6)   HTML1)    PDF (2606KB)(15)       Save

    The sum rate maximization beamforming problem for a multi-cell multi-user multiple-input single-output interference channel (MISO-IC) system is considered. Conventionally, the centralized and distributed beamforming solutions to the MISO-IC system have high computational complexity and bear a heavy burden of channel state information exchange between base stations (BSs), which becomes even much worse in a large-scale antenna system. To address this, we propose a distributed deep reinforcement learning (DRL) based approach with limited information exchange. Specifically, the original beamforming problem is decomposed of the problems of beam direction design and power allocation and the costs of information exchange between BSs are significantly reduced. In particular, each BS is provided with an independent deep deterministic policy gradient network that can learn to choose the beam direction scheme and simultaneously allocate power to users. Simulation results illustrate that the proposed DRL-based approach has comparable sum rate performance with much less information exchange over the conventional distributed beamforming solutions.

    Table and Figures | Reference | Related Articles | Metrics
    A Content-Aware Bitrate Selection Method Using Multi-Step Prediction for 360-Degree Video Streaming
    GAO Nianzhen, YU Yifang, HUA Xinhai, FENG Fangzheng, JIANG Tao
    ZTE Communications    2022, 20 (4): 96-109.   DOI: 10.12142/ZTECOM.202204012
    Abstract6)   HTML1)    PDF (3795KB)(6)       Save

    A content-aware multi-step prediction control (CAMPC) algorithm is proposed to determine the bitrate of 360-degree videos, aiming to enhance the quality of experience (QoE) of users and reduce the cost of video content providers (VCP). The CAMPC algorithm first employs a neural network to generate the content richness and combines it with the current field of view (FOV) to accurately predict the probability distribution of tiles being viewed. Then, for the tiles in the predicted viewport which directly affect QoE, the CAMPC algorithm utilizes a multi-step prediction for future system states, and accordingly selects the bitrates of multiple subsequent steps, instead of an instantaneous state. Meanwhile, it controls the buffer occupancy to eliminate the impact of prediction errors. We implement CAMPC on players by building a 360-degree video streaming platform and evaluating other advanced adaptive bitrate (ABR) rules through the real network. Experimental results show that CAMPC can save 83.5% of bandwidth resources compared with the scheme that completely transmits the tiles outside the viewport with the Dynamic Adaptive Streaming over HTTP (DASH) protocol. Besides, the proposed method can improve the system utility by 62.7% and 27.6% compared with the DASH official and viewport-based rules, respectively.

    Table and Figures | Reference | Related Articles | Metrics