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    A Survey on Machine Learning Based Proactive Caching
    Stephen ANOKYE, Mohammed SEID, SUN Guolin
    ZTE Communications    2019, 17 (4): 46-55.   DOI: 10.12142/ZTECOM.201904007
    Abstract367)   HTML199)    PDF (1032KB)(397)       Save

    The world today is experiencing an enormous increase in data traffic, coupled with demand for greater quality of experience (QoE) and performance. Increasing mobile traffic leads to congestion of backhaul networks. One promising solution to this problem is the mobile edge network (MEN) and consequently mobile edge caching. In this paper, a survey of mobile edge caching using machine learning is explored. Even though a lot of work and surveys have been conducted on mobile edge caching, our efforts in this paper are rather focused on the survey of machine learning based mobile edge caching. Issues affecting edge caching, such as caching entities, caching policies and caching algorithms, are discussed. The machine learning algorithms applied to edge caching are reviewed followed by a discussion on the challenges and future works in this field. This survey shows that edge caching can reduce delay and subsequently the backhaul traffic of the network; most caching is conducted at the small base stations (SBSs) and caching at unmanned aerial vehicles (UAVs) is recently used to accommodate mobile users who dissociate from SBSs. This survey also demonstrates that machine learning approach is the state of the art and reinforcement learning is predominant.

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    Signal Detection and Channel Estimation in OTFS
    NAIKOTI Ashwitha, CHOCKALINGAM Ananthanarayanan
    ZTE Communications    2021, 19 (4): 16-33.   DOI: 10.12142/ZTECOM.202104003
    Abstract253)   HTML8)    PDF (2749KB)(494)       Save

    Orthogonal time frequency space (OTFS) modulation is a recently proposed modulation scheme that exhibits robust performance in high-Doppler environments. It is a two-dimensional modulation scheme where information symbols are multiplexed in the delay-Doppler (DD) domain. Also, the channel is viewed in the DD domain where the channel response is sparse and time-invariant for a long time. This simplifies channel estimation in the DD domain. This paper presents an overview of the state-of-the-art approaches in OTFS signal detection and DD channel estimation. We classify the signal detection approaches into three categories, namely, low-complexity linear detection, approximate maximum a posteriori (MAP) detection, and deep neural network (DNN) based detection. Similarly, we classify the DD channel estimation approaches into three categories, namely, separate pilot approach, embedded pilot approach, and superimposed pilot approach. We compile and present an overview of some of the key algorithms under these categories and illustrate their performance and complexity attributes.

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    A Case Study on Intelligent Operation System for Wireless Networks
    LIU Jianwei, YUAN Yifei, HAN Jing
    ZTE Communications    2019, 17 (4): 19-26.   DOI: 10.12142/ZTECOM.201904004
    Abstract180)   HTML186)    PDF (1189KB)(172)       Save

    The emerging fifth generation (5G) network has the potential to satisfy the rapidly growing traffic demand and promote the transformation of smartphone-centric networks into an Internet of Things (IoT) ecosystem. Due to the introduction of new communication technologies and the increased density of 5G cells, the complexity of operation and operational expenditure (OPEX) will become very challenging in 5G. Self-organizing network (SON) has been researched extensively since 2G, to cope with the similar challenge, however by predefined policies, rather than intelligent analysis. The requirement for better quality of experience and the complexity of 5G network demands call for an approach that is different from SON. In several recent studies, the combination of machine learning (ML) technology with SON has been investigated. In this paper, we focus on the intelligent operation of wireless network through ML algorithms. A comprehensive and flexible framework is proposed to achieve an intelligent operation system. Two use cases are also studied to use ML algorithms to automate the anomaly detection and fault diagnosis of key performance indicators (KPIs) in wireless networks. The effectiveness of the proposed ML algorithms is demonstrated by the real data experiments, thus encouraging the further research for intelligent wireless network operation.

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    Machine Learning Based Unmanned Aerial Vehicle Enabled Fog-Radio Aerial Vehicle Enabled Fog-Radio Access Network and Edge Computing
    Mohammed SEID, Stephen ANOKYE, SUN Guolin
    ZTE Communications    2019, 17 (4): 33-45.   DOI: 10.12142/ZTECOM.201904006
    Abstract177)   HTML79)    PDF (1257KB)(157)       Save

    The emerging unmanned aerial vehicle (UAV) technology and its applications have become part of the massive Internet of Things (mIoT) ecosystem for future cellular networks. Internet of things (IoT) devices have limited computation capacity and battery life and the cloud is not suitable for offloading IoT tasks due to the distance, latency and high energy consumption. Mobile edge computing (MEC) and fog radio access network (F-RAN) together with machine learning algorithms are an emerging approach to solving complex network problems as described above. In this paper, we suggest a new orientation with UAV enabled F-RAN architecture. This architecture adopts the decentralized deep reinforcement learning (DRL) algorithm for edge IoT devices which makes independent decisions to perform computation offloading, resource allocation, and association in the aerial to ground (A2G) network. Additionally, we summarized the works on machine learning approaches for UAV networks and MEC networks, which are related to the suggested architecture and discussed some technical challenges in the smart UAV-IoT, F-RAN 5G and Beyond 5G (6G).

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    Towards Converged Millimeter-Wave/TerahertzWireless Communication and Radar Sensing
    GAO Xiang, MUHAMMAD Saqlain, CAO Xiaoxiao, WANG Shiwei, LIU Kexin, ZHANG Hangkai, YU Xianbin
    ZTE Communications    2020, 18 (1): 73-82.   DOI: 10.12142/ZTECOM.202001011
    Abstract174)   HTML48)    PDF (1219KB)(226)       Save

    Converged communication and radar sensing systems have attained increasing attention in recent years. The development of converged radar-data systems is reviewed, with a special focus on millimeter/terahertz systems as a promising trend. Firstly, we present historical development and convergence technology concept for communication-radar systems, and highlight some emerging technologies in this area. We then provide an updated and comprehensive survey of several converged systems operating in different microwave and millimeter frequency bands, by providing some selective typical communication and radar sensing systems. In this part, we also summarize and compare the system performance in terms of maximum range/range resolution for radar mode and Bit Error Rate (BER) /wireless distance for communication mode. In the last section, the convergence of millimeter/terahertz communication-radar system is concluded by analyzing the prospect of millimeter-wave/terahertz technologies in providing ultrafast data rates and high resolution for our smart future.

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    Payload Encoding Representation from Transformer for Encrypted Traffic Classification
    HE Hongye, YANG Zhiguo, CHEN Xiangning
    ZTE Communications    2021, 19 (4): 90-97.   DOI: 10.12142/ZTECOM.202104010
    Abstract173)   HTML23)    PDF (965KB)(209)       Save

    Traffic identification becomes more important, yet more challenging as related encryption techniques are rapidly developing nowadays. Unlike recent deep learning methods that apply image processing to solve such encrypted traffic problems, in this paper, we propose a method named Payload Encoding Representation from Transformer (PERT) to perform automatic traffic feature extraction using a state-of-the-art dynamic word embedding technique. By implementing traffic classification experiments on a public encrypted traffic data set and our captured Android HTTPS traffic, we prove the proposed method can achieve an obvious better effectiveness than other compared baselines. To the best of our knowledge, this is the first time the encrypted traffic classification with the dynamic word embedding has been addressed.

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    Enabling Energy Efficiency in 5G Network
    LIU Zhuang, GAO Yin, LI Dapeng, CHEN Jiajun, HAN Jiren
    ZTE Communications    2021, 19 (1): 20-29.   DOI: 10.12142/ZTECOM.202101004
    Abstract168)   HTML4)    PDF (1356KB)(216)       Save

    The mobile Internet and Internet of Things are considered the main driving forces of 5G, as they require an ultra-dense deployment of small base stations to meet the increasing traffic demands. 5G new radio (NR) access is designed to enable denser network deployments, while leading to a significant concern about the network energy consumption. Energy consumption is a main part of network operational expense (OPEX), and base stations work as the main energy consumption equipment in the radio access network (RAN). In order to achieve RAN energy efficiency (EE), switching off cells is a strategy to reduce the energy consumption of networks during off-peak conditions. This paper introduces NR cell switching on/off schemes in 3GPP to achieve energy efficiency in 5G RAN, including intra-system energy saving (ES) scheme and inter-system ES scheme. Additionally, NR architectural features including central unit/distributed unit (CU/DU) split and dual connectivity (DC) are also considered in NR energy saving. How to apply artificial intelligence (AI) into 5G networks is a new topic in 3GPP, and we also propose a machine learning (ML) based scheme to save energy by switching off the cell selected relying on the load prediction. According to the experiment results in the real wireless environment, the ML based ES scheme can reduce more power consumption than the conventional ES scheme without load prediction.

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    A Novel 28 GHz Phased Array Antenna for 5G Mobile Communications
    LI Yezhen, REN Yongli, YANG Fan, XU Shenheng, ZHANG Jiannian
    ZTE Communications    2020, 18 (3): 20-25.   DOI: 10.12142/ZTECOM.202003004
    Abstract164)   HTML73)    PDF (3130KB)(263)       Save

    A novel phased array antenna consisting of 256 elements is presented and experimentally verified for 5G millimeter-wave wireless communications. The antenna integrated with a wave control circuit can perform real-time beam scanning by reconfiguring the phase of an antenna unit. The unit, designed at 28 GHz using a simple patch structure with one PIN diode, can be electronically controlled to generate 1 bit phase quantization. A prototype of the antenna is fabricated and measured to demonstrate the feasibility of this approach. The measurement results indicate that the antenna achieves high gain and fast beam-steering, with the scan beams within ±60° range and the maximum gain up to 21.7 dBi. Furthermore, it is also tested for wireless video transmission. In ZTE Shanghai, the antenna was used for the 5G New Radio (NR) test. The error vector magnitude (EVM) is less than 3% and the adjacent channel leakage ratio (ACLR) less than -35 dBc, which can meet 5G system requirements. Compared with the conventional phased array antenna, the proposed phased array has the advantages of low power consumption, low cost and conformal geometry. Due to these characteristics, the antenna is promising for wide applications in 5G millimeter-wave communication systems.

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    Leaky-Wave Antennas for 5G/B5G Mobile Communication Systems: A Survey
    HE Yejun, JIANG Jiachun, ZHANG Long, LI Wenting, WONG Sai-Wai, DENG Wei, CHI Baoyong
    ZTE Communications    2020, 18 (3): 3-11.   DOI: 10.12142/ZTECOM.202003002
    Abstract151)   HTML65)    PDF (3449KB)(190)       Save

    Since leaky-wave antennas (LWAs) have the advantages of high directivity, low loss and structural simplicity, LWAs are very suitable for designing millimeter-wave (mmW) antennas. The purpose of this paper is to review the latest research progress of LWAs for 5G/B5G mobile communication systems. Firstly, the conventional classification and design methods of LWAs are introduced and the effects of the phase constant and attenuation constant on the radiation characteristics are discussed. Then two types of new LWAs for 5G/B5G mobile communication systems including broadband fixed-beam LWAs and frequency-fixed beam-scanning LWAs are summarized. Finally, the challenges and future research directions of LWAs for 5G/B5G mobile communication systems are presented.

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    Enabling Intelligence at Network Edge:An Overview of Federated Learning
    YANG Howard H., ZHAO Zhongyuan, QUEK Tony Q. S.
    ZTE Communications    2020, 18 (2): 2-10.   DOI: 10.12142/ZTECOM.202002002
    Abstract150)   HTML244)    PDF (1050KB)(147)       Save

    The burgeoning advances in machine learning and wireless technologies are forging a new paradigm for future networks, which are expected to possess higher degrees of intelligence via the inference from vast dataset and being able to respond to local events in a timely manner. Due to the sheer volume of data generated by end-user devices, as well as the increasing concerns about sharing private information, a new branch of machine learning models, namely federated learning, has emerged from the intersection of artificial intelligence and edge computing. In contrast to conventional machine learning methods, federated learning brings the models directly to the device for training, where only the resultant parameters shall be sent to the edge servers. The local copies of the model on the devices bring along great advantages of eliminating network latency and preserving data privacy. Nevertheless, to make federated learning possible, one needs to tackle new challenges that require a fundamental departure from standard methods designed for distributed optimizations. In this paper, we aim to deliver a comprehensive introduction of federated learning. Specifically, we first survey the basis of federated learning, including its learning structure and the distinct features from conventional machine learning models. We then enumerate several critical issues associated with the deployment of federated learning in a wireless network, and show why and how technologies should be jointly integrated to facilitate the full implementation from different perspectives, ranging from algorithmic design, on-device training, to communication resource management. Finally, we conclude by shedding light on some potential applications and future trends.

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    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
    Abstract145)   HTML12)    PDF (1869KB)(257)       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.

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    A Survey on Low Complexity Detectors for OTFS Systems
    ZHANG Zhengquan, LIU Heng, WANG Qianli, FAN Pingzhi
    ZTE Communications    2021, 19 (4): 3-15.   DOI: 10.12142/ZTECOM.202104002
    Abstract135)   HTML11)    PDF (1427KB)(263)       Save

    The newly emerging orthogonal time frequency space (OTFS) modulation can obtain delay-Doppler diversity gain to significantly improve the system performance in high mobility wireless communication scenarios such as vehicle-to-everything (V2X), high-speed railway and unmanned aerial vehicles (UAV), by employing inverse symplectic finite Fourier transform (ISFFT) and symplectic finite Fourier transform (SFFT). However, OTFS modulation will dramatically increase system complexity, especially at the receiver side. Thus, designing low complexity OTFS receiver is a key issue for OTFS modulation to be adopted by new-generation wireless communication systems. In this paper, we review low complexity OTFS detectors and provide some insights on future researches. We firstly present the OTFS system model and basic principles, followed by an overview of OTFS detector structures, classifications and comparative discussion. We also survey the principles of OTFS detection algorithms. Furthermore, we discuss the design of hybrid OTFS and orthogonal frequency division multiplexing (OFDM) detectors in single user and multi-user multi-waveform communication systems. Finally, we address the main challenges in designing low complexity OTFS detectors and identify some future research directions.

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    Scheduling Policies for Federated Learning in Wireless Networks: An Overview
    SHI Wenqi, SUN Yuxuan, HUANG Xiufeng, ZHOU Sheng, NIU Zhisheng
    ZTE Communications    2020, 18 (2): 11-19.   DOI: 10.12142/ZTECOM.202002003
    Abstract135)   HTML81)    PDF (1466KB)(110)       Save

    Due to the increasing need for massive data analysis and machine learning model training at the network edge, as well as the rising concerns about data privacy, a new distributed training framework called federated learning (FL) has emerged and attracted much attention from both academia and industry. In FL, participating devices iteratively update the local models based on their own data and contribute to the global training by uploading model updates until the training converges. Therefore, the computation capabilities of mobile devices can be utilized and the data privacy can be preserved. However, deploying FL in resource-constrained wireless networks encounters several challenges, including the limited energy of mobile devices, weak onboard computing capability, and scarce wireless bandwidth. To address these challenges, recent solutions have been proposed to maximize the convergence rate or minimize the energy consumption under heterogeneous constraints. In this overview, we first introduce the backgrounds and fundamentals of FL. Then, the key challenges in deploying FL in wireless networks are discussed, and several existing solutions are reviewed. Finally, we highlight the open issues and future research directions in FL scheduling.

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    Machine Learning for Network Slicing Resource Management:A Comprehensive Survey
    HAN Bin, Hans D. SCHOTTEN
    ZTE Communications    2019, 17 (4): 27-32.   DOI: 10.12142/ZTECOM.201904005
    Abstract135)   HTML64)    PDF (572KB)(137)       Save

    The emerging technology of multi-tenancy network slicing is considered as an essential feature of 5G cellular networks. It provides network slices as a new type of public cloud services and therewith increases the service flexibility and enhances the network resource efficiency. Meanwhile, it raises new challenges of network resource management. A number of various methods have been proposed over the recent past years, in which machine learning and artificial intelligence techniques are widely deployed. In this article, we provide a survey to existing approaches of network slicing resource management, with a highlight on the roles played by machine learning in them.

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    Editorial: Special Topic onMachine Learning at Network Edges
    TAO Meixia, HUANG Kaibin
    ZTE Communications    2020, 18 (2): 1-1.   DOI: 10.12142/ZTECOM.202002001
    Abstract131)   HTML312)    PDF (536KB)(86)       Save
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    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
    Abstract131)   HTML18)    PDF (1409KB)(225)       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.

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    Design of Millimeter-Wave Antenna-in-Package (AiP) for 5G NR
    CHANG Su-Wei, LIN Chueh-Jen, TSAI Wen-Tsai, HUNG Tzu-Chieh, HUANG Po-Chia
    ZTE Communications    2020, 18 (3): 26-32.   DOI: 10.12142/ZTECOM.202003005
    Abstract127)   HTML74)    PDF (1429KB)(142)       Save

    For 5G new radio (NR), there are two frequency bands: Frequency Range 1 (FR?1) (low frequency) and Frequency Range 2 (FR?2) (millimeter?wave frequency). Millimeter?wave has been officially utilized in mobile applications. The wide bandwidth is the key for the millimeter-wave band. However, higher loss has become the major challenge for the wide use of this frequency range. Antenna array and beamforming technologies have been introduced to resolve the path loss and coverage problems. The key design considerations of the beamforming antenna array are low loss, compact system and small size. Antenna-in-package (AiP) has become the most attractive technology for millimeter-wave front-end system. For the design of AiP, many parameters such as RF transition, material and heat need to be considered and designed properly. The Over?the?Air (OTA) testing technology is also very critical for AiP mass production. In this paper, the detail of AiP design and new OTA testing technology are discussed and demonstrated.

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    Advanced Space Laser Communication Technology on CubeSats
    LI Li, ZHANG Xuejiao, ZHANG Jianhua, XU Changzhi, JIN Yi
    ZTE Communications    2020, 18 (4): 45-54.   DOI: 10.12142/ZTECOM.202004007
    Abstract127)   HTML16)    PDF (3658KB)(174)       Save

    The free space optical communication plays an important role in space-terrestrial integrated network due to its advantages including great improvement of data rate performance, low cost, security enhancement when compared with conventional radio frequency (RF) technology. Meanwhile, CubeSats become popular in low earth orbit (LEO) network because of the low cost, fast response and the possibility of constituting constellations and formations to execute missions that a single large satellite cannot do. However, it is a difficult task to build an optical communication link between the CubeSats. In this paper, the cutting-edge laser technology progress on the CubeSats is reviewed. The characters of laser link on the CubeSat and the key techniques in the laser communication terminal (LCT) design are demonstrated.

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    General Introduction of Non-Terrestrial Networks for New Radio
    HAN Jiren, GAO Yin
    ZTE Communications    2022, 20 (S1): 72-78.   DOI: 10.12142/ZTECOM.2022S1010
    Abstract125)   HTML11)    PDF (1266KB)(87)       Save

    In the new radio (NR) access technology, non-terrestrial networks (NTN) are introduced to meet the requirement of anywhere and anytime connections from the world market. With the introduction of NTN, the NR system is able to offer the wide-area coverage and ensure the service availability for users. In this paper, the general aspects of NTN are introduced, including the NTN architecture overview, the impact of NTN on next-generation radio access network (NG-RAN) interface functions, mobility scenarios and other NTN related issues. The current progress in 3GPP Release 17 is also provided.

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    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
    Abstract125)   HTML21)    PDF (2269KB)(325)       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.

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    Message Passing Based Detection for Orthogonal Time Frequency Space Modulation
    YUAN Zhengdao, LIU Fei, GUO Qinghua, WANG Zhongyong
    ZTE Communications    2021, 19 (4): 34-44.   DOI: 10.12142/ZTECOM.202104004
    Abstract119)   HTML5)    PDF (2103KB)(171)       Save

    The orthogonal time frequency space (OTFS) modulation has emerged as a promising modulation scheme for wireless communications in high-mobility scenarios. An efficient detector is of paramount importance to harvesting the time and frequency diversities promised by OTFS. Recently, some message passing based detectors have been developed by exploiting the features of the OTFS channel matrices. In this paper, we provide an overview of some recent message passing based OTFS detectors, compare their performance, and shed some light on potential research on the design of message passing based OTFS receivers.

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    Editorial: Special Topic on Computational Radio Intelligence: One Key for 6G Wireless
    JIANG Wei, LUO Fa-Long
    ZTE Communications    2019, 17 (4): 1-2.   DOI: 10.12142/ZTECOM.201904001
    Abstract119)   HTML167)    PDF (307KB)(112)       Save
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    Editorial: Special Topic onDomain Name and Identifier of Internet : Architecture & Systems
    Li Hui
    ZTE Communications    2020, 18 (1): 5-6.   DOI: 10.12142/ZTECOM.202001002
    Abstract116)   HTML31)    PDF (271KB)(100)       Save
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    To Learn or Not to Learn:Deep Learning Assisted Wireless Modem Design
    XUE Songyan, LI Ang, WANG Jinfei, YI Na, MA Yi, Rahim TAFAZOLLI, Terence DODGSON
    ZTE Communications    2019, 17 (4): 3-11.   DOI: 10.12142/ZTECOM.201904002
    Abstract115)   HTML151)    PDF (958KB)(83)       Save

    Deep learning is driving a radical paradigm shift in wireless communications, all the way from the application layer down to the physical layer. Despite this, there is an ongoing debate as to what additional values artificial intelligence (or machine learning) could bring to us, particularly on the physical layer design; and what penalties there may have? These questions motivate a fundamental rethinking of the wireless modem design in the artificial intelligence era. Through several physical-layer case studies, we argue for a significant role that machine learning could play, for instance in parallel error-control coding and decoding, channel equalization, interference cancellation, as well as multiuser and multiantenna detection. In addition, we discuss the fundamental bottlenecks of machine learning as well as their potential solutions in this paper.

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    Ten Reflections on 5G
    WU Hequan
    ZTE Communications    2020, 18 (1): 1-4.   DOI: 10.12142/ZTECOM.202001001
    Abstract114)   HTML167)    PDF (346KB)(97)       Save

    5G takes the concept of service-oriented architecture to replace the priority principle of network efficiency in the Internet to meet requirements of the industrial Internet and smart cities, such as high reliability and low latency. On the other hand, in order to adapt to the uncertainty of future business, 5G features the openness of services and the Internet protocols, different from the closeness of traditional telecommunication networks. Although 5G tries to have the advantages of both the Internet and telecommunication network, its realization still faces many challenges. In this paper, ten major issues concerning 5G networking and service offering are discussed.

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    End-to-End Chinese Entity Recognition Based on BERT-BiLSTM-ATT-CRF
    LI Daiyi, TU Yaofeng, ZHOU Xiangsheng, ZHANG Yangming, MA Zongmin
    ZTE Communications    2022, 20 (S1): 27-35.   DOI: 10.12142/ZTECOM.2022S1005
    Abstract109)   HTML10)    PDF (436KB)(93)       Save

    Traditional named entity recognition methods need professional domain knowledge and a large amount of human participation to extract features, as well as the Chinese named entity recognition method based on a neural network model, which brings the problem that vector representation is too singular in the process of character vector representation. To solve the above problem, we propose a Chinese named entity recognition method based on the BERT-BiLSTM-ATT-CRF model. Firstly, we use the bidirectional encoder representations from transformers (BERT) pre-training language model to obtain the semantic vector of the word according to the context information of the word; Secondly, the word vectors trained by BERT are input into the bidirectional long-term and short-term memory network embedded with attention mechanism (BiLSTM-ATT) to capture the most important semantic information in the sentence; Finally, the conditional random field (CRF) is used to learn the dependence between adjacent tags to obtain the global optimal sentence level tag sequence. The experimental results show that the proposed model achieves state-of-the-art performance on both Microsoft Research Asia (MSRA) corpus and people’s daily corpus, with F1 values of 94.77% and 95.97% respectively.

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    SVM for Constellation Shaped 8QAM PON System
    LI Zhongya, CHEN Rui, HUANG Xingang, ZHANG Junwen, NIU Wenqing, LU Qiuyi, CHI Nan
    ZTE Communications    2022, 20 (S1): 64-71.   DOI: 10.12142/ZTECOM.2022S1009
    Abstract107)   HTML1)    PDF (3357KB)(37)       Save

    Nonlinearity impairments and distortions have been bothering the bandwidth constrained passive optical network (PON) system for a long time and limiting the development of capacity in the PON system. Unlike other works concentrating on the exploration of the complex equalization algorithm, we investigate the potential of constellation shaping joint support vector machine (SVM) classification scheme. At the transmitter side, the 8 quadrature amplitude modulation (8QAM) constellation is shaped into three designs to mitigate the influence of noise and distortions in the PON channel. On the receiver side, simple multi-class linear SVM classifiers are utilized to replace complex equalization methods. Simulation results show that with the bandwidth of 25 GHz and overall bitrate of 50 Gbit/s, at 10 dBm input optical power of a 20 km standard single mode fiber (SSMF), and under a hard-decision forward error correction (FEC) threshold, transmission can be realized by employing Circular (4, 4) shaped 8QAM joint SVM classifier at the maximal power budget of 37.5 dB.

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    Kinetic Energy Harvesting Toward Battery-Free IoT: Fundamentals, Co-Design Necessity and Prospects
    LIANG Junrui, LI Xin, YANG Hailiang
    ZTE Communications    2021, 19 (1): 48-60.   DOI: 10.12142/ZTECOM.202101007
    Abstract106)   HTML9)    PDF (1567KB)(365)       Save

    Energy harvesting (EH) technology is developed with the purpose of harnessing ambient energy in different physical forms. Although the available ambient energy is usually tiny, not comparable to the centralized power generation, it brings out the convenience of on-site power generation by drawing energy from local sources, which meets the emerging power demand of long-lasting, extensively-deployed, and maintenance-free Internet of Things (IoT). Kinetic energy harvesting (KEH) is one of the most promising EH solutions toward the realization of battery-free IoT. The KEH-based battery-free IoT can be extensively deployed in the smart home, smart building, and smart city scenarios, enabling perceptivity, intelligence, and connectivity in many infrastructures. This paper gives a brief introduction to the configurations and basic principles of practical KEH-IoT systems, including their mechanical, electrical, and computing parts. Although there are already a few commercial products in some specific application markets, the understanding and practice in the co-design and optimization of a single KEH-IoT device are far from mature, let alone the conceived multiagent energy-autonomous intelligent systems. Future research and development of the KEH-IoT system beckons for more exchange and collaboration among mechanical, electrical, and computer engineers toward general design guidelines to cope with these interdisciplinary engineering problems.

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    Adaptive and Intelligent Digital Signal Processing for Improved Optical Interconnection
    SUN Lin, DU Jiangbing, HUA Feng, TANG Ningfeng, HE Zuyuan
    ZTE Communications    2020, 18 (2): 57-73.   DOI: 10.12142/ZTECOM.202002008
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    In recent years, explosively increasing data traffic has been boosting the continuous demand of high speed optical interconnection inside or among data centers, high performance computers and even consumer electronics. To pursue the improved interconnection performance of capacity, energy efficiency and simplicity, effective approaches are demonstrated including particularly advanced digital signal processing (DSP) methods. In this paper, we present a review about the enabling adaptive DSP methods for optical interconnection applications, and a detailed summary of our recent and ongoing works in this field. In brief, our works focus on dealing with the specific issues for short-reach interconnection scenarios with adaptive operation, including signal-to-noise-ratio (SNR) limitation, level nonlinearity distortion, energy efficiency consideration and the decision precision.

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    Joint User Selection and Resource Allocation for Fast Federated Edge Learning
    JIANG Zhihui, HE Yinghui, YU Guanding
    ZTE Communications    2020, 18 (2): 20-30.   DOI: 10.12142/ZTECOM.202002004
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    By periodically aggregating local learning updates from edge users, federated edge learning (FEEL) is envisioned as a promising means to reap the benefit of local rich data and protect users’ privacy. However, the scarce wireless communication resource greatly limits the number of participated users and is regarded as the main bottleneck which hinders the development of FEEL. To tackle this issue, we propose a user selection policy based on data importance for FEEL system. In order to quantify the data importance of each user, we first analyze the relationship between the loss decay and the squared norm of gradient. Then, we formulate a combinatorial optimization problem to maximize the learning efficiency by jointly considering user selection and communication resource allocation. By problem transformation and relaxation, the optimal user selection policy and resource allocation are derived, and a polynomial-time optimal algorithm is developed. Finally, we deploy two commonly used deep neural network (DNN) models for simulation. The results validate that our proposed algorithm has strong generalization ability and can attain higher learning efficiency compared with other traditional algorithms.

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    Communication-Efficient Edge AI Inference over Wireless Networks
    YANG Kai, ZHOU Yong, YANG Zhanpeng, SHI Yuanming
    ZTE Communications    2020, 18 (2): 31-39.   DOI: 10.12142/ZTECOM.202002005
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    Given the fast growth of intelligent devices, it is expected that a large number of high-stakes artificial intelligence (AI) applications, e.g., drones, autonomous cars, and tactile robots, will be deployed at the edge of wireless networks in the near future. Therefore, the intelligent communication networks will be designed to leverage advanced wireless techniques and edge computing technologies to support AI-enabled applications at various end devices with limited communication, computation, hardware and energy resources. In this article, we present the principles of efficient deployment of model inference at network edge to provide low-latency and energy-efficient AI services. This includes the wireless distributed computing framework for low-latency device distributed model inference as well as the wireless cooperative transmission strategy for energy-efficient edge cooperative model inference. The communication efficiency of edge inference systems is further improved by building up a smart radio propagation environment via intelligent reflecting surface.

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    Joint Placement and Resource Allocation for UAV-Assisted Mobile Edge Computing Networks with URLLC
    ZHANG Pengyu, XIE Lifeng, XU Jie
    ZTE Communications    2020, 18 (2): 49-56.   DOI: 10.12142/ZTECOM.202002007
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    This paper investigates an unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) network with ultra-reliable and low-latency communications (URLLC), in which a UAV acts as an aerial edge server to collect information from a set of sensors and send the processed data (e.g., command signals) to the corresponding actuators. In particular, we focus on the round-trip URLLC from the sensors to the UAV and to the actuators in the network. By considering the finite block-length codes, our objective is to minimize the maximum end-to-end packet error rate (PER) of these sensor-actuator pairs, by jointly optimizing the UAV’s placement location and transmitting power allocation, as well as the users’ block-length allocation, subject to the UAV’s sum transmitting power constraint and the total block-length constraint. Although the maximum-PER minimization problem is non-convex and difficult to be optimally solved, we obtain a high-quality solution to this problem by using the technique of alternating optimization. Numerical results show that our proposed design achieves significant performance gains over other benchmark schemes without the joint optimization.

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    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
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    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.

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    QoE Management for 5G New Radio
    ZHANG Man, LI Dapeng, LIU Zhuang, GAO Yin
    ZTE Communications    2021, 19 (3): 64-72.   DOI: 10.12142/ZTECOM.202103008
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    Quality of Experience (QoE) is used to monitor the user experience of telecommunication services, which has been studied for a long time. In universal terrestrial radio access network (UTRAN), evolved UTRAN (E-UTRA) and Long Term Evolution (LTE), QoE has also been specified for the improvement of user experience. The 5G New Radio (NR) technology is designed for providing various types of new services, and therefore operators have strong demand to continuously upgrade the 5G network to provide sufficient and good QoE for corresponding services. With new emerging 5G services, 5G QoE management collection aims at specifying the mechanism to collect the experience parameters for the multimedia telephony service for IP multimedia subsystem (IMS), multimedia broadcast and multicast service (MBMS), virtual reality (VR), etc. Taking LTE QoE as a baseline, generic NR QoE management mechanisms for activation, deactivation, configuration, and reporting of QoE measurement are introduced in this paper. Additionally, some enhanced QoE features in NR are discussed, such as radio access network (RAN) overload handling, RAN-visible QoE, per-slice QoE measurement, radio-related measurement, and QoE continuity for mobility. This paper also introduces solutions to NR QoE, which concludes the progress of NR QoE in the 3rd Generation Partnership Project (3GPP).

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    Integrated Architecture for Networkingand Industrial Internet Identity
    LU Hua, LI Xiaolu, XIE Renchao, FENG Wei
    ZTE Communications    2020, 18 (1): 24-35.   DOI: 10.12142/ZTECOM.202001005
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    Several excellent works have been done on the industrial Internet; however, some problems are still ahead, such as reliable security, heterogeneous compatibility, and system efficiency. Information-Centric Networking (ICN), an emerging paradigm for the future Internet, is expected to address the challenges of the industrial Internet to some extent. An integrated architecture for industrial network and identity resolution in the industrial Internet is proposed in this paper. A framework is also designed for the ICN-based industrial Network And Named Data Networking (NDN) based factory extranet with Software-Defined Networking (SDN). Moreover, an identity resolution architecture in the industrial Internet is proposed based on ICN paradigms with separate resolution nodes or with merging resolution and routing.

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    Multibeam Antenna Based on Butler Matrix for 3G/LTE/5G/B5G Base Station Applications
    YE Lianghua, CAO Yunfei, ZHANG Xiuyin
    ZTE Communications    2020, 18 (3): 12-19.   DOI: 10.12142/ZTECOM.202003003
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    With the rapid development of mobile communication technology and the explosion of data traffic, high capacity communication with high data transmission rate is urgently needed in densely populated areas. Since multibeam antennas are able to increase the communication capacity and support a high data transmission rate, they have attracted a lot of research interest and have been actively investigated for base station applications. In addition, since multi-beam antennas based on Butler matrix (MABBMs) have the advantages of high gain, easy design and low profile, they are suitable for base station applications. The purposes of this paper is to provide an overview of the existing MABBMs. The specifications, principles of operation, design method and implementation of MABBMs are presented. The challenge of MABBMs for 3G/LTE/5G/B5G base station applications is discussed in the end.

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    A Machine Learning Method for Prediction of Multipath Channels
    ZTE Communications    2019, 17 (4): 12-18.   DOI: 10.12142/ZTECOM.201904003
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    In this paper, a machine learning method for predicting the evolution of a mobile communication channel based on a specific type of convolutional neural network is developed and evaluated in a simulated multipath transmission scenario. The simulation and channel estimation are designed to replicate real-world scenarios and common measurements supported by reference signals in modern cellular networks. The capability of the predictor meets the requirements that a deployment of the developed method in a radio resource scheduler of a base station poses. Possible applications of the method are discussed.

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    Satellite E2E Network Slicing Based on 5G Technology
    ZHANG Jing, WEI Xiao, CHENG Junfeng, FENG Xu
    ZTE Communications    2020, 18 (4): 26-33.   DOI: 10.12142/ZTECOM.202004005
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    We investigate the design of satellite network slicing for the first time to provide customized services for the diversified applications, and propose a novel scheme for satellite end-to-end (E2E) network slicing based on 5G technology, which provides a view of common satellite network slicing and supports flexible network deployment between the satellite and the ground. Specifically, considering the limited satellite network resource and the characteristics of the satellite channel, we propose a novel satellite E2E network slicing architecture. Therein, the deployment of the network functions between the satellite and the ground is coordinately considered. Subsequently, the classification and the isolation technologies of satellite network sub-slices are proposed adaptively based on 5G technology to support resource allocation on demand. Then, we develop the management technologies for the satellite E2E network slicing including slicing key performance indicator (KPI) design, slicing deployment, and slicing management. Finally, the analysis of the challenges and future work shows the potential research in the future.

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    Study on Security of 5G and Satellite Converged Communication Network
    YAN Xincheng, TENG Huiyun, PING Li, JIANG Zhihong, ZHOU Na
    ZTE Communications    2021, 19 (4): 79-89.   DOI: 10.12142/ZTECOM.202104009
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    The 5G and satellite converged communication network (5G SCCN) is an important component of the integration of satellite-terrestrial networks, the national science, and technology major projects towards 2030. Security is the key to ensuring its operation, but at present, the research in this area has just started in our country. Based on the network characteristics and security risks, we propose the security architecture of the 5G SCCN and systematically sort out the key protection technologies and improvement directions. In particular, unique thinking on the security of lightweight data communication and design reference for the 5G SCCN network architecture is presented. It is expected to provide a piece of reference for the follow-up 5G SCCN security technology research, standard evolution, and industrialization.

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    An Improved Non-Geometrical Stochastic Model for Non-WSSUS Vehicle-to-Vehicle Channels
    HUANG Ziwei, CHENG Xiang, ZHANG Nan
    ZTE Communications    2019, 17 (4): 62-71.   DOI: 10.12142/ZTECOM.201904009
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    A novel non-geometrical stochastic model (NGSM) for non-wide sense stationary uncorrelated scattering (non-WSSUS) vehicle-to-vehicle (V2V) channels is proposed. This model is based on a conventional NGSM and employs a more accurate method to reproduce the realistic characteristics of V2V channels, which successfully extends the existing NGSM to include the line-of-sight (LoS) component. Moreover, the statistical properties of the proposed model in different scenarios, including Doppler power spectral density (PSD), power delay profile (PDP), and the tap correlation coefficient matrix are simulated and compared with those of the existing NGSM. Furthermore, the simulation results demonstrate not only the utility of the proposed model, but also the correctness of our theoretical derivations.

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