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.

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.

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.

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.

Terahertz Band: Lighting up Next-Generation Wireless Communications

With the explosion of wireless data rates,the terahertz(THz)band(0.1–10 THz)is envisioned as a promising candidate to break the existing bandwidth bottleneck and satisfy the ever-increasing capacity demand.The THz wireless communications feature a number of attractive properties,such as potential terabit-per-second capacity and high energy efficiency.In this paper,an overview on the state-of-the-art THz communications is studied,with a special focus on key technologies of THz transceivers and THz communication systems.The recent progress on both electronic and photonic THz transmitters are presented,and then the THz receivers operating in direct-and heterodyne reception modes are individually surveyed.Based on the THz transceiver schemes,three kinds of THz wireless communication systems are reviewed,including solid-state electronic systems,photonics-assisted systems and all-photonics systems.The prospective key enabling technologies,corresponding challenges and research directions for lighting up high-speed THz communication systems are discussed as well.

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.

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.

Native intelligence for 6G mobile network: technical challenges,architecture and key features

The application of the artificial intelligence(AI) technology in the 5 th generation mobile communication system(5 G) networks promotes the development of the mobile communication network and its application in vertical industries, however, the application models of "patching" and "plug-in" have hindered the effect of AI applications. Meanwhile, the application of AI in all walks of life puts forward requirements for new capabilities of the future network, such as distributed training, real-time collaborative inference, local data processing, etc., which require "native intelligence design" in future networks. This paper discusses the requirements of native intelligence in the 6 th generation mobile communication system(6 G) networks from the perspectives of 5 G intelligent network challenges and the "ubiquitous intelligence" vision of 6 G, and analyzes the technical challenges of the AI workflows in its lifecycle and the AI as a service(AIaaS) in cloud network. The progress and deficiencies of the current research on AI functional architecture in various industry organizations are summarized. The end-to-end functional architecture for native AI for 6 G network and its three key technical characteristics are proposed: quality of AI services(QoAIS) based AI service orchestration for its full lifecycle, deep integration of native AI computing and communication, and integration of native AI and digital twin network. The directions of future research are also prospected.

Intellicise communication system: model-driven semantic communications

As one of the critical technologies for the 6 th generation mobile communication system(6 G) mobile communication systems, artificial intelligence(AI) technology will provide complete automation for connecting the virtual and physical worlds. In order to construct the future ubiquitous intelligent network, people are beginning to rethink how mobile communication systems transmit and exploit intelligent information. This paper proposes a new communication paradigm, called the Intellicise communication system: model-driven semantic communication. Intellicise communication system is built on top of the traditional communication system and innovatively adds a new feature dimension on top of the traditional source coding, which enables the communication system to evolve from the traditional transmission of bit to the transmission of "model". Like the semantic base(Seb) for semantic communication, the model is considered as the new feature obtained from the joint source-channel coding. The sink node can re-construct the original signal based on the received model and the encoded sequence. In addition, the performance evaluation metrics and the implementation details of the Intellicise communication system are discussed in this paper. Finally, preliminary results of model-driven image transmission in the Intellicise communication system are presented.

Reconfigurable Intelligent Surfaces for 6G:Nine Fundamental Issues and One Critical Problem

Thanks to the recent advances in metamaterials,Reconfigurable Intelligent Surface(RIS)has emergedas a promising technology for future 6G wireless communications.Benefiting from its high array gain,low cost,and low power consumption,RISs are expected to greatly enlarge signal coverage,improve system capacity,andincrease energy efficiency.In this article,we systematically overview the emerging RIS technology with the focuson its key basics,nine fundamental issues,and one critical problem.Specifically,we first explain the RIS basics,including its working principles,hardware structures,and potential benefits for communications.Based on thesebasics,nine fundamental issues of RISs,such as“What’s the differences between RISs and massive MIMO?”and“Is RIS really intelligent?”,are explicitly addressed to elaborate its technical features,distinguish it from existingtechnologies,and clarify some misunderstandings in the literature.Then,one critical problem of RISs is revealedthat,due to the“multiplicative fading”effect,existing passive RISs can hardly achieve visible performance gains inmany communication scenarios with strong direct links.To address this critical problem,a potential solution calledactive RISs is introduced,and its effectiveness is demonstrated by numerical simulations.

A Joint Algorithm for Resource Allocation in D2D 5G Wireless Networks

With the rapid development of Internet technology,users have an increasing demand for data.The continuous popularization of traffic-intensive applications such as high-definition video,3D visualization,and cloud computing has promoted the rapid evolution of the communications industry.In order to cope with the huge traffic demand of today’s users,5G networks must be fast,flexible,reliable and sustainable.Based on these research backgrounds,the academic community has proposed D2D communication.The main feature of D2D communication is that it enables direct communication between devices,thereby effectively improve resource utilization and reduce the dependence on base stations,so it can effectively improve the throughput of multimedia data.One of the most considerable factor which affects the performance of D2D communication is the co-channel interference which results due to the multiplexing of multiple D2D user using the same channel resource of the cellular user.To solve this problem,this paper proposes a joint algorithm time scheduling and power control.The main idea is to effectively maximize the number of allocated resources in each scheduling period with satisfied quality of service requirements.The constraint problem is decomposed into time scheduling and power control subproblems.The power control subproblem has the characteristics of mixed-integer linear programming of NP-hard.Therefore,we proposed a gradual power control method.The time scheduling subproblem belongs to the NP-hard problem having convex-cordinality,therefore,we proposed a heuristic scheme to optimize resource allocation.Simulation results show that the proposed algorithm effectively improved the resource allocation and overcome the co-channel interference as compared with existing algorithms.

Aerial edge computing for 6G

In the 6 th generation mobile communication system(6 G) era, a large number of delay-sensitive and computation-intensive applications impose great pressure on resource-constrained Internet of things(IoT) devices. Aerial edge computing is envisioned as a promising and cost-effective solution, especially in hostile environments without terrestrial infrastructures. Therefore, this paper focuses on integrating aerial edge computing into 6 G for providing ubiquitous computing services for IoT devices. This paper first presents the layered network architecture of aerial edge computing for 6 G. The benefits, potential applications, and design challenges are also discussed in detail. Next, several key techniques like unmanned aerial vehicle(UAV) deployment, operation mode, offloading mode, caching policy, and resource management are highlighted to present how to integrated aerial edge computing into 6 G. Then, the joint UAV deployment optimization and computation offloading method is designed to minimize the computing delay for a typical aerial edge computing network. Numerical results reveal the significant delay reduction of the proposed method compared with the other benchmark methods. Finally, several open issues for aerial edge computing in 6 G are elaborated to provide some guidance for future research.