Design Framework of Unsourced Multiple Access for 6G Massive IoT

In this paper,ambient IoT is used as a typical use case of massive connections for the sixth generation(6G)mobile communications where we derive the performance requirements to facilitate the evaluation of technical solutions.A rather complete design of unsourced multiple access is proposed in which two key parts:a compressed sensing module for active user detection,and a sparse interleaver-division multiple access(SIDMA)module are simulated side by side on a same platform at balanced signal to noise ratio(SNR)operating points.With a proper combination of compressed sensing matrix,a convolutional encoder,receiver algorithms,the simulated performance results appear superior to the state-of-the-art benchmark,yet with relatively less complicated processing.

System Integration of Terrestrial Mobile Communication and Satellite Communication——The Trends, Challenges and Key Technologies in B5G and 6G

Mobile communication standards have been developed into a new era of B5G and 6G.In recent years,low earth orbit(LEO)satellites and space Internet have become hot topics.The integrated satellite and terrestrial systems have been widely discussed by industries and academics,and even are expected to be applied in those huge constellations in construction.This paper points out the trends of two stages towards system integration of the terrestrial mobile communication and the satellite communications:to be compatible with 5G,and to be integrated within 6G.Based on analysis of the challenges of both stages,key technologies are thereafter analyzed in detail,covering both air interface currently discussed in 3GPP for B5G and also novel network architecture and related transmission technologies toward future 6G.

Hybrid NOMA Based MIMO Offloading for Mobile Edge Computing in 6G Networks

Non-orthogonal multiple access (NOMA), multiple-input multiple-output (MIMO) and mobile edge computing (MEC) are prominent technologies to meet high data rate demand in the sixth generation (6G) communication networks. In this paper, we aim to minimize the transmission delay in the MIMO-MEC in order to improve the spectral efficiency, energy efficiency, and data rate of MEC offloading. Dinkelbach transform and generalized singular value decomposition (GSVD) method are used to solve the delay minimization problem. Analytical results are provided to evaluate the performance of the proposed Hybrid-NOMA-MIMO-MEC system. Simulation results reveal that the H-NOMA-MIMO-MEC system can achieve better delay performance and lower energy consumption compared to OMA.

6G Mobile Networks: Emerging Technologies and Applications

Modern society has been widely benefiting from the advances in wireless technology. During the past decade, extensive research efforts have been dedicated to develop the fifth-generation (5G) wireless mobile networks. This resulted in enabling technologies for the three generic connectivity types in 5G (broadband, massive Internet-of-things connectivity and ultra-reliable low latency communication) as well as their coexistence.

Edge-Coordinated Energy-Efficient Video Analytics for Digital Twin in 6G

Camera networks are essential to constructing fast and accurate mapping between virtual and physical space for digital twin.In this paper,with the aim of developing energy-efficient digital twin in 6G,we investigate real-time video analytics based on cameras mounted on mobile devices with edge coordination.This problem is challenging because 1)mobile devices are with limited battery life and lightweight computation capability,and 2)the captured video frames of mobile devices are continuous changing,which makes the corresponding tasks arrival uncertain.To achieve energy-efficient video analytics in digital twin,by taking energy consumption,analytics accuracy,and latency into consideration,we formulate a deep reinforcement learning based mobile device and edge coordination video analytics framework,which can utilized digital twin models to achieve joint offloading decision and configuration selection.The edge nodes help to collect the information on network topology and task arrival.Extensive simulation results demonstrate that our proposed framework outperforms the benchmarks on accuracy improvement and energy and latency reduction.

Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability,availability,and ultra-low latency.The requirements of such networks are the main challenges that can be handled using a range of recent technologies,including multi-access edge computing(MEC),artificial intelligence(AI),millimeterwave communications(mmWave),and software-defined networking.Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service(QoS).This article considers developing a complex MEC structure for fifth and sixth-generation(5G/6G)cellular networks.Furthermore,we propose a seamless migration technique for complex edge computing structures.The developed migration scheme enables services to adapt to the required load on the radio channels.The proposed algorithm is analyzed for various use cases,and a test bench has been developed to emulate the operator’s infrastructure.The obtained results are introduced and discussed.

QoS-Aware Offloading Based on Communication-Computation Resource Coordination for 6G Edge Intelligence

Driven by the demands of diverse artificial intelligence(AI)-enabled application,Mobile Edge Computing(MEC)is considered one of the key technologies for 6G edge intelligence.In this paper,we consider a serial task model and design a quality of service(QoS)-aware task offloading via communication-computation resource coordination for multi-user MEC systems,which can mitigate the I/O interference brought by resource reuse among virtual machines.Then we construct the system utility measuring QoS based on application latency and user devices’energy consumption.We also propose a heuristic offloading algorithm to maximize the system utility function with the constraints of task priority and I/O interference.Simulation results demonstrate the proposed algorithm’s significant advantages in terms of task completion time,terminal energy consumption and system resource utilization.

Space/Air Covert Communications:Potentials,Scenarios,and Key Technologies

Space/air communications have been envisioned as an essential part of the next-generation mobile communication networks for providing highquality global connectivity. However, the inherent broadcasting nature of wireless propagation environment and the broad coverage pose severe threats to the protection of private data. Emerging covert communications provides a promising solution to achieve robust communication security. Aiming at facilitating the practical implementation of covert communications in space/air networks, we present a tutorial overview of its potentials, scenarios, and key technologies. Specifically, first, the commonly used covertness constraint model, covert performance metrics, and potential application scenarios are briefly introduced. Then, several efficient methods that introduce uncertainty into the covert system are thoroughly summarized, followed by several critical enabling technologies, including joint resource allocation and deployment/trajectory design, multi-antenna and beamforming techniques, reconfigurable intelligent surface(RIS), and artificial intelligence algorithms. Finally, we highlight some open issues for future investigation.

6G技术中的空中接入网:进展与展望

空中接入网(aerial access network, AAN)具有抗毁性强、覆盖范围广、视距传播概率高的特点,与地面网络相结合能够缓解流量负载压力,提供全时空的连接与服务,可有效助力第六代移动通信网络(6th generation mobile networks, 6G)“万物智联”愿景的实现.因此,在分析AAN中的卫星通信和高空平台(high altitude platform station, HAPS)技术研究进展的基础上,研究了卫星通信在数据接入方面的应用以及HAPS在一体化网络架构中的作用,探究了卫星与HAPS两种技术对未来网络的影响以及进一步优化二者的潜在方法.最后,对卫星接入的数据碰撞问题和HAPS的缓存问题进行讨论分析,并从上述问题出发展望了卫星通信和HAPS技术的未来发展方向.

The Analysis on the Candidate Frequency Bands of Future Mobile Communication Systems

Appropriate candidate frequency bands are extremely important for the development of future 5G systems. In this work, the researches on 5G spectrum around the world are summarized. Then the potential candidate frequency bands for 5G systems are investigated based on practical utilization of spectrum in China. For spectrum below 6GHz, the feasibility of possible frequency bands for 5G system are analyzed, which mainly come from 2G/3G/4G spectrum re-farming, the spectrum identified by footnotes for IMT systems in Regulations on the Radio Frequency Allocation of China, and potential candidate bands from WRC-15 Agenda Item 1.1. Moreover, propagation characteristics of WRC-15 candidate frequency bands proposed by China are measured and modeled to verify their effectiveness. For spectrum above 6GHz, the potential candidate frequency bands for 5G systems are selected based on the preliminary analysis of spectrum allocation, allotment and the current usage in China. Suggestions are provided for further studies on 5G spectrum.

Towards Intelligent Reflecting Surface Empowered 6G Terahertz Communications: A Survey

Terahertz(THz)communications have been widely envisioned as a promising enabler to provide adequate bandwidth and achieve ultra-high data rates for sixth generation(6G)wireless networks.In order to mitigate blockage vulnerability caused by serious propagation attenuation and poor diffraction of THz waves,an intelligent reflecting surface(IRS),which manipulates the propagation of incident electromagnetic waves in a programmable manner by adjusting the phase shifts of passive reflecting elements,is proposed to create smart radio environments,improve spectrum efficiency and enhance coverage capability.Firstly,some prospective application scenarios driven by the IRS empowered THz communications are introduced,including wireless mobile communications,secure communications,unmanned aerial vehicle(UAV)scenario,mobile edge computing(MEC)scenario and THz localization scenario.Then,we discuss the enabling technologies employed by the IRS empowered THz system,involving hardware design,channel estimation,capacity optimization,beam control,resource allocation and robustness design.Moreover,the arising challenges and open problems encountered in the future IRS empowered THz communications are also highlighted.Concretely,these emerging problems possibly originate from channel modeling,new material exploration,experimental IRS testbeds and intensive deployment.Ultimately,the combination of THz communications and IRS is capable of accelerating the development of 6G wireless networks.

Business Ecosystem Strategies of Mobile Network Operators in the 3G Era

This paper applies the perspective of business ecosystem to mobile communications industry,trying to help mobile network operators improve their strategies in the era of the third generation mobile communications(3G).According to the definition of the business ecosystem,the ecosystem structure of mobile network operators is analyzed.As an important hub in the ecosystem,mobile network operators are advised to take a keystone strategy.The key points of the strategy are summarized.Finally,suggestions for Chinese mobile network operators are given based on the analysis.

Spatio-Temporal Cellular Network Traffic Prediction Using Multi-Task Deep Learning for AI-Enabled 6G

Spatio-temporal cellular network traffic prediction at wide-area level plays an important role in resource reconfiguration,traffic scheduling and intrusion detection,thus potentially supporting connected intelligence of the sixth generation of mobile communications technology(6G).However,the existing studies just focus on the spatio-temporal modeling of traffic data of single network service,such as short message,call,or Internet.It is not conducive to accurate prediction of traffic data,characterised by diverse network service,spatio-temporality and supersize volume.To address this issue,a novel multi-task deep learning framework is developed for citywide cellular network traffic prediction.Functionally,this framework mainly consists of a dual modular feature sharing layer and a multi-task learning layer(DMFS-MT).The former aims at mining long-term spatio-temporal dependencies and local spatio-temporal fluctuation trends in data,respectively,via a new combination of convolutional gated recurrent unit(ConvGRU)and 3-dimensional convolutional neural network(3D-CNN).For the latter,each task is performed for predicting service-specific traffic data based on a fully connected network.On the real-world Telecom Italia dataset,simulation results demonstrate the effectiveness of our proposal through prediction performance measure,spatial pattern comparison and statistical distribution verification.

DDoS Detection for 6G Internet of Things: Spatial-Temporal Trust Model and New Architecture

With the rapid development of the sixth generation(6G)network and Internet of Things(IoT),it has become extremely challenging to efficiently detect and prevent the distributed denial of service(DDoS)attacks originating from IoT devices.In this paper we propose an innovative trust model for IoT devices to prevent potential DDoS attacks by evaluating their trustworthiness,which can be deployed in the access network of 6G IoT.Based on historical communication behaviors,this model combines spatial trust and temporal trust values to comprehensively characterize the normal behavior patterns of IoT devices,thereby effectively distinguishing attack traffic.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method has advantages in terms of both accuracy and efficiency in identifying attack flows.

Metaheuristic Based Data Gathering Scheme for Clustered UAVs in 6G Communication Network

The sixth-generation(6G)wireless communication networks are anticipated in integrating aerial,terrestrial,and maritime communication into a robust system to accomplish trustworthy,quick,and low latency needs.It enables to achieve maximum throughput and delay for several applications.Besides,the evolution of 6G leads to the design of unmanned aerial vehicles(UAVs)in providing inexpensive and effective solutions in various application areas such as healthcare,environment monitoring,and so on.In the UAV network,effective data collection with restricted energy capacity poses a major issue to achieving high quality network communication.It can be addressed by the use of clustering techniques forUAVs in 6G networks.In this aspect,this study develops a novel metaheuristic based energy efficient data gathering scheme for clustered unmanned aerial vehicles(MEEDG-CUAV).The proposed MEEDG-CUAV technique intends in partitioning the UAV networks into various clusters and assign a cluster head(CH)to reduce the overall energy utilization.Besides,the quantum chaotic butterfly optimization algorithm(QCBOA)with a fitness function is derived to choose CHs and construct clusters.The experimental validation of the MEEDG-CUAV technique occurs utilizing benchmark dataset and the experimental results highlighted the better performance over the other state of art techniques interms of different measures.

MIMO-Terahertz in 6G Nano-Communications:Channel Modeling and Analysis

With the development of wireless mobile communication technology,the demand for wireless communication rate and frequency increases year by year.Existing wireless mobile communication frequency tends to be saturated,which demands for new solutions.Terahertz(THz)communication has great potential for the future mobile communications(Beyond 5G),and is also an important technique for the high data rate transmission in spatial information network.THz communication has great application prospects in military-civilian integration and coordinated development.In China,important breakthroughs have been achieved for the key techniques of THz high data rate communications,which is practically keeping up with the most advanced technological level in the world.Therefore,further intensifying efforts on the development of THz communication have the strategic importance for China in leading the development of future wireless communication techniques and the standardization process of Beyond 5G.This paper analyzes the performance of the MIMO channel in the Terahertz(THz)band and a discrete mathematical method is used to propose a novel channel model.Then,a channel capacity model is proposed by the combination of path loss and molecular absorption in the THz band based on the CSI at the receiver.Simulation results show that the integration of MIMO in the THz band gives better data rate and channel capacity as compared with a single channel.

Cloud-Assisted Distributed Edge Brains for Multi-Cell Joint Beamforming Optimization for 6G

In 5G networks,optimization of antenna beam weights of base stations has become the key application of AI for network optimization.For 6G,higher frequency bands and much denser cells are expected,and the importance of automatic and accurate beamforming assisted by AI will become more prominent.In existing network,servers are“patched”to network equipment to act as a centralized brain for model training and inference leading to high transmission overhead,large inference latency and potential risks of data security.Decentralized architectures have been proposed to achieve flexible parameter configuration and fast local response,but it is inefficient in collecting and sharing global information among base stations.In this paper,we propose a novel solution based on a collaborative cloud edge architecture for multi-cell joint beamforming optimization.We analyze the performance and costs of the proposed solution with two other architectural solutions by simulation.Compared with the centralized solution,our solution improves prediction accuracy by 24.66%,and reduces storage cost by 83.82%.Compared with the decentralized solution,our solution improves prediction accuracy by 68.26%,and improves coverage performance by 0.4 dB.At last,the future research work is prospected.

Compact 5G Vivaldi Tapered Slot Filtering Antenna with Enhanced Bandwidth

Compact fifth-generation(5G)low-frequency band filtering antennas(filtennas)with stable directive radiation patterns,improved bandwidth(BW),and gain are designed,fabricated,and tested in this research.The proposed filtennas are achieved by combining the predesigned compact 5G(5.975–7.125 GHz)third-order uniform and non-uniform transmission line hairpin bandpass filters(UTL and NTL HPBFs)with the compact ultrawide band Vivaldi tapered slot antenna(UWB VTSA)in one module.The objective of this integration is to enhance the performance of 5.975–7.125GHz filtennas which will be suitable for modern mobile communication applications by exploiting the benefits of UWB VTSA.Based on NTL HPBF,more space is provided to add the direct current(DC)biassing circuits in cognitive radio networks(CRNs)for frequency reconfigurable applications.To overcome the mismatch between HPBFs and VTSA,detailed parametric studies are presented.Computer simulation technology(CST)software is used for the simulation in this study.Good measured S11 appeared to be<−13 and<−10.54 dB at 5.48–7.73 and 5.9–7.98GHz with peak realized gains of 6.37 and 6.27 dBi,for VTSA with UTL and NTL HPBFs,respectively which outperforms the predesigned filters.Validation is carried out by comparing the measured and simulated results.

地震台站无线远程监控及3G备用信道方案实现

介绍了一稳定可靠的地震台站无线远程监控及3G备用信道方案的实现,其是基于GSM(全球移动通信系统)网络3G(第三代移动通信技术)和SMS(短信业务)的方案。当有线信道出现故障时,中心人员可通过SMS(短信业务)指令对其控制切换并启动无线备用信道,当有线信道恢复后,通过指令关闭无线信道并切换至有线信道进行地震数据传输。具有通过3G无线传输测震数据的功能。

基于4G的机器人与云平台无线数据传输系统设计

将机器人采集到的温度、湿度和位置等数据信息传送至云平台服务器,便于云端数据分析、机器人数据共享以及人机信息交互等。本文应用第4代移动通信技术(the 4th generation mobile communication technology,4G)无线传输技术,设计的机器人与云平台无线数据传输系统主要包括硬件和软件实现2个部分,硬件方面机器人通过32位微控制器(STMicroelectronics32,STM32)嵌入式系统与4G模块组合,将传感器采集到的数据信息,通过Internet网络传输到云平台服务器中,软件部分运用C语言编程通过网络透传模式(transmission control protocol/user datagram protocol, TCP/UDP)实现无线通信过程,最终实验验证了机器人与云平台服务器之间的双向无线数据传输功能。