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Table of Content

    30 December 2022, Volume 20 Issue 4
    Special Topic
    Security in Edge Blockchains: Attacks and Countermeasures
    CAO Yinfeng, CAO Jiannong, WANG Yuqin, WANG Kaile, LIU Xun
    2022, 20(4):  3-14.  doi:10.12142/ZTECOM.202204002
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    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.

    Utility-Improved Key-Value Data Collection with Local Differential Privacy for Mobile Devices
    TONG Ze, DENG Bowen, ZHENG Lele, ZHANG Tao
    2022, 20(4):  15-21.  doi:10.12142/ZTECOM.202204003
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    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.

    Key Intrinsic Security Technologies in 6G Networks
    LU Haitao, YAN Xincheng, ZHOU Qiang, DAI Jiulong, LI Rui
    2022, 20(4):  22-31.  doi:10.12142/ZTECOM.202204004
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    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.

    Air-Ground Integrated Low-Energy Federated Learning for Secure 6G Communications
    WANG Pengfei, SONG Wei, SUN Geng, WEI Zongzheng, ZHANG Qiang
    2022, 20(4):  32-40.  doi:10.12142/ZTECOM.202204005
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    Federated learning (FL) is a distributed machine learning approach that could provide secure 6G communications to preserve user privacy. In 6G communications, unmanned aerial vehicles (UAVs) are widely used as FL parameter servers to collect and broadcast related parameters due to the advantages of easy deployment and high flexibility. However, the challenge of limited energy restricts the popularization of UAV-enabled FL applications. An air-ground integrated low-energy federated learning framework is proposed, which minimizes the overall energy consumption of application communication while maintaining the quality of the FL model. Specifically, a hierarchical FL framework is proposed, where base stations (BSs) aggregate model parameters updated from their surrounding users separately and send the aggregated model parameters to the server, thereby reducing the energy consumption of communication. In addition, we optimize the deployment of UAVs through a deep Q-network approach to minimize their energy consumption for transmission as well as movement, thus improving the energy efficiency of the air-ground integrated system. The evaluation results show that our proposed method can reduce the system energy consumption while maintaining the accuracy of the FL model.

    Physical Layer Security for MmWave Communications: Challenges and Solutions
    HE Miao, LI Xiangman, NI Jianbing
    2022, 20(4):  41-51.  doi:10.12142/ZTECOM.202204006
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    The mmWave communication is a promising technique to enable human commutation and a large number of machine-type communications of massive data from various non-cellphone devices like Internet of Things (IoT) devices, autonomous vehicles and remotely controlled robots. For this reason, information security, in terms of the confidentiality, integrity and availability (CIA), becomes more important in the mmWave communication than ever since. The physical layer security (PLS), which is based on the information theory and focuses on the secrecy capacity of the wiretap channel model, is a cost effective and scalable technique to protect the CIA, compared with the traditional cryptographic techniques. In this paper, the theory foundation of PLS is briefly introduced together with the typical PLS performance metrics secrecy rate and outage probability. Then, the most typical PLS techniques for mmWave are introduced, analyzed and compared, which are classified into three major categories of directional modulation (DM), artificial noise (AN), and directional precoding (DPC). Finally, several mmWave PLS research problems are briefly discussed, including the low-complexity DM weight vector codebook construction, impact of phase shifter (PS) with finite precision on PLS, and DM-based communications for multiple target receivers.

    Review
    Autonomous Network Technology Innovation in Digital and Intelligent Era
    DUAN Xiangyang, KANG Honghui, ZHANG Jianjian
    2022, 20(4):  52-61.  doi:10.12142/ZTECOM.202204007
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    The issues of wireless communication network autonomy, the definition of capability level and the concept of AI-native solution based on the integration of the information communication data technology (ICDT) are first introduced in this paper. A series of innovative technologies proposed by ZTE Corporation, such as an autonomous evolution network and intelligent orchestration network, are then analyzed. These technologies are developed to realize the evolution of wireless networks to Level-4 and Level-5 intelligent networks. It is expected that the future AI-native intelligent network system will be built based on innovative technologies such as digital twins, intent-based networking, and the data plane and intelligent plane. These new technical paradigms will promote the development of intelligent B5G and 6G networks.

    Research Paper
    Broadband Sequential Load-Modulated Balanced Amplifier Using Coupler-PA Co-Design Approach
    RAN Xiongbo, DAI Zhijiang, ZHONG Kang, PANG Jingzhou, LI Mingyu
    2022, 20(4):  62-68.  doi:10.12142/ZTECOM.202204008
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    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.

    Distributed Multi-Cell Multi-User MISO Downlink Beamforming via Deep Reinforcement Learning
    JIA Haonan, HE Zhenqing, TAN Wanlong, RUI Hua, LIN Wei
    2022, 20(4):  69-77.  doi:10.12142/ZTECOM.202204009
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    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.

    Predictive Scheme for Mixed Transmission in Time-Sensitive Networking
    LI Zonghui, YANG Siqi, YU Jinghai, HE Fei, SHI Qingjiang
    2022, 20(4):  78-88.  doi:10.12142/ZTECOM.202204010
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    Time-sensitive networking (TSN) is an important research area for updating the infrastructure of industrial Internet of Things. As a product of the integration of the operation technology (OT) and the information technology (IT), it meets the real-time and deterministic nature of industrial control and is compatible with Ethernet to support the mixed transmission of industrial control data and Ethernet data. This paper systematically summarizes and analyzes the shortcomings of the current mixed transmission technologies of the bursty flows and the periodic flows. To conquer these shortages, we propose a predictive mixed-transmission scheme of the bursty flows and the periodic flows. The core idea is to use the predictability of time-triggered transmission of TSN to further reduce bandwidth loss of the previous mixed-transmission methods. This paper formalizes the probabilistic model of the predictive mixed transmission mechanism and proves that the proposed mechanism can effectively reduce the loss of bandwidth. Finally, based on the formalized probabilistic model, we simulate the bandwidth loss of the proposed mechanism. The results demonstrate that compared with the previous mixed-transmission method, the bandwidth loss of the proposed mechanism achieves a 79.48% reduction on average.

    Label Enhancement for Scene Text Detection
    MEI Junjun, GUAN Tao, TONG Junwen
    2022, 20(4):  89-95.  doi:10.12142/ZTECOM.202204011
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    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.

    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
    2022, 20(4):  96-109.  doi:10.12142/ZTECOM.202204012
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    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.

    A Unified Deep Learning Method for CSI Feedback in Massive MIMO Systems
    GAO Zhengguang, LI Lun, WU Hao, TU Xuezhen, HAN Bingtao
    2022, 20(4):  110-115.  doi:10.12142/ZTECOM.202204013
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    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.

    Reconfigurable Intelligent Surface (RIS)
    2022, 20(4):  116-118. 
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