Table of Content

    25 March 2021, Volume 19 Issue 1
    Special Topic
    Editorial: Special Topic on Energy Consumption Challenges and Prospects on B5G Communication Systems
    2021, 19(1):  1-1.  doi:10.12142/ZTECOM.202101001
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    Saving Energy for Wireless Transmission: An Important Revelation from Shannon Formula
    ZHU Jinkang, ZHAO Ming
    2021, 19(1):  2-10.  doi:10.12142/ZTECOM.202101002
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    The reduction of power consumption is important for wireless communications and networks. To develop the energy-saving technologies for future wireless transmissions and networks, this paper presents two basic study points: 1) The multiple events are merged into a single event; 2) the high-order mode is changed to the low-order mode. For this reason, we seek that multiple events in wireless transmission links are fused into a single event from Shannon formulas. We also analyze the relationship between the information modulation and the error correction, and give a fusion structure of error-corrected modulation. The energy-saving performance of the error-corrected modulation method is further analyzed through comparison with the traditional methods of modulation plus error correction. The results of numerical analysis demonstrate the wireless energy saving methods for wireless systems based on Shannon formulas are the achievable efficient schemes.

    Efficient Network Slicing with Dynamic Resource Allocation
    JI Hong, ZHANG Tianxiang, ZHANG Kai, WANG Wanyuan, WU Weiwei
    2021, 19(1):  11-19.  doi:10.12142/ZTECOM.202101003
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    With the rapid development of wireless network technologies and the growing demand for a high quality of service (QoS), the effective management of network resources has attracted a lot of attention. For example, in a practical scenario, when a network shock occurs, a batch of affected flows needs to be rerouted to respond to the network shock to bring the entire network deployment back to the optimal state, and in the process of rerouting a batch of flows, the entire response time needs to be as short as possible. Specifically, we reduce the time consumed for routing by slicing, but the routing success rate after slicing is reduced compared with the unsliced case. In this context, we propose a two-stage dynamic network resource allocation framework that first makes decisions on the slices to which flows are assigned, and coordinates resources among slices to ensure a comparable routing success rate as in the unsliced case, while taking advantage of the time efficiency gains from slicing.

    Enabling Energy Efficiency in 5G Network
    LIU Zhuang, GAO Yin, LI Dapeng, CHEN Jiajun, HAN Jiren
    2021, 19(1):  20-29.  doi:10.12142/ZTECOM.202101004
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    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.

    Cluster Head Selection Algorithm for UAV Assisted Clustered IoT Network Utilizing Blockchain
    LIN Xinhua, ZHANG Jing, LI Qiang
    2021, 19(1):  30-38.  doi:10.12142/ZTECOM.202101005
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    To guarantee the security of Internet of Things (IoT) devices, the blockchain technology is often applied to clustered IoT networks. However, cluster heads (CHs) need to undertake additional control tasks. For battery-powered IoT devices, the conventional CH selection algorithm is limited. Based on the above problem, an unmanned aerial vehicle (UAV) network assisted clustered IoT system is proposed, and a corresponding UAV CH selection algorithm is designed. In this scheme, UAVs are selected as CHs to serve IoT clusters. The proposed CH selection algorithm considers the maximal transmit power, residual energy and distance information of UAVs, which can greatly extend the working life of IoT clusters. Through Monte Carlo simulation, the key performance indexes of the system, including energy consumption, average secrecy rate and the maximal number of data packets received by the base station (BS), are evaluated. The simulation results show that the proposed algorithm has great advantages compared with the existing CH selection algorithms.

    Green Air-Ground Integrated Heterogeneous Network in 6G Era
    WU Huici, LI Hanjie, TAO Xiaofeng
    2021, 19(1):  39-47.  doi:10.12142/ZTECOM.202101006
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    The research of three-dimensional integrated communication technology plays a key role in achieving the ubiquitous connectivity, ultra-high data rates, and emergency communications in the sixth generation (6G) networks. Aerial networking provides a promising solution to flexible, scalable, low-cost and reliable coverage for wireless devices. The integration of aerial network and terrestrial network has been an inevitable paradigm in the 6G era. However, energy-efficient communications and networking among aerial network and terrestrial network face great challenges. This paper is dedicated to discussing green communications of the air-ground integrated heterogeneous network (AGIHN). We first provide a brief introduction to the characteristics of AGIHN in 6G networks. Further, we analyze the challenges of green AGIHN from the aspects of green terrestrial networks and green aerial networks. Finally, several solutions to and key technologies of the green AGIHN are discussed.

    Kinetic Energy Harvesting Toward Battery-Free IoT: Fundamentals, Co-Design Necessity and Prospects
    LIANG Junrui, LI Xin, YANG Hailiang
    2021, 19(1):  48-60.  doi:10.12142/ZTECOM.202101007
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    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.

    Next Generation Semantic and Spatial Joint Perception
    ZHU Fang
    2021, 19(1):  61-71.  doi:10.12142/ZTECOM.202101008
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    Efficient perception of the real world is a long-standing effort of computer vision. Modern visual computing techniques have succeeded in attaching semantic labels to thousands of daily objects and reconstructing dense depth maps of complex scenes. However, simultaneous semantic and spatial joint perception, so-called dense 3D semantic mapping, estimating the 3D geometry of a scene and attaching semantic labels to the geometry, remains a challenging problem that, if solved, would make structured vision understanding and editing more widely accessible. Concurrently, progress in computer vision and machine learning has motivated us to pursue the capability of understanding and digitally reconstructing the surrounding world. Neural metric-semantic understanding is a new and rapidly emerging field that combines differentiable machine learning techniques with physical knowledge from computer vision, e.g., the integration of visual-inertial simultaneous localization and mapping (SLAM), mesh reconstruction, and semantic understanding. In this paper, we attempt to summarize the recent trends and applications of neural metric-semantic understanding. Starting with an overview of the underlying computer vision and machine learning concepts, we discuss critical aspects of such perception approaches. Specifically, our emphasis is on fully leveraging the joint semantic and 3D information. Later on, many important applications of the perception capability such as novel view synthesis and semantic augmented reality (AR) contents manipulation are also presented. Finally, we conclude with a discussion of the technical implications of the technology under a 5G edge computing scenario.

    Research Paper
    Integrating Coarse Granularity Part-Level Features with Supervised Global-Level Features for Person Re-Identification
    CAO Jiahao, MAO Xiaofei, LI Dongfang, ZHENG Qingfang, JIA Xia
    2021, 19(1):  72-81.  doi:10.12142/ZTECOM.202101009
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    Person re-identification (Re-ID) has achieved great progress in recent years. However, person Re-ID methods are still suffering from body part missing and occlusion problems, which makes the learned representations less reliable. In this paper, we propose a robust coarse granularity part-level network (CGPN) for person Re-ID, which extracts robust regional features and integrates supervised global features for pedestrian images. CGPN gains two-fold benefit toward higher accuracy for person Re-ID. On one hand, CGPN learns to extract effective regional features for pedestrian images. On the other hand, compared with extracting global features directly by backbone network, CGPN learns to extract more accurate global features with a supervision strategy. The single model trained on three Re-ID datasets achieves state-of-the-art performances. Especially on CUHK03, the most challenging Re-ID dataset, we obtain a top result of Rank-1/mean average precision (mAP)=87.1%/83.6% without re-ranking.

    Adaptability Analysis of Fluctuating Traffic for IP Switching and Optical Switching
    LIAN Meng, GU Rentao, JI Yuefeng, WANG Dajiang, LI Hongbiao
    2021, 19(1):  82-90.  doi:10.12142/ZTECOM.202101010
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    The technological development of smart devices and Internet of Things (IoT) has brought ever-larger bandwidth and fluctuating traffic to existing networks. The analysis of network capital expenditure (CAPEX) is extremely important and plays a fundamental role in further network optimizing. In this paper, an adaptability analysis is raised for IP switching and optical transport network (OTN) switching in CAPEX when the service bandwidth is fluctuating violently. This paper establishes a multi-layer network architecture through Clos network model and discusses impacts of maximum allowable blocking rate and service bandwidth standard deviation on CAPEX of IP network and OTN network to find CAPEX demarcation point in different situations. As simulation results show, when the bandwidth deviation mean rate is 0.3 and the maximum allowable blocking rate is 0.01, the hardware cost of OTN switching will exceed IP switching as the average bandwidth is greater than 6 100 Mbit/s. When the service bandwidth fluctuation is severe, the hardware cost of OTN switching will increase and exceed IP switching as the single port rate is allowed in optical switching. The increasing of maximum allowable blocking rate can decrease hardware cost of OTN switching. Finally, it is found that Flex Ethernet (FlexE) can be used to decrease CAPEX of OTN switching greatly at this time.