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.

Federated Learning for 6G Communications:Challenges,Methods,and Future Directions

As the 5G communication networks are being widely deployed worldwide,both industry and academia have started to move beyond 5G and explore 6G communications.It is generally believed that 6G will be established on ubiquitous Artificial Intelligence(AI)to achieve data-driven Machine Learning(ML)solutions in heterogeneous and massive-scale networks.However,traditional ML techniques require centralized data collection and processing by a central server,which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy concerns.Federated learning,as an emerging distributed AI approach with privacy preservation nature,is particularly attractive for various wireless applications,especially being treated as one of the vital solutions to achieve ubiquitous AI in 6G.In this article,we first introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G.We then describe key technical challenges,the corresponding federated learning methods,and open problems for future research on federated learning in the context of 6G communications.

Milestones of Wireless Communication Networks and Technology Prospect of Next Generation(6G)

Since around 1980,a new generation of wireless technology has arisen approximately every 10 years.First-generation(1G)and secondgeneration(2G)began with voice and eventually introduced more and more data in third-generation(3G)and became highly popular in the fourthgeneration(4G).To increase the data rate along with low latency and mass connectivity the fifth-generation(5G)networks are being installed from 2020.However,the 5G technology will not be able to fulfill the data demand at the end of this decade.Therefore,it is expected that 6G communication networks will rise,providing better services through the implementation of new enabling technologies and allowing users to connect everywhere.6G technology would not be confined to cellular communications networks,but would also comply with non-terrestrial communication system requirements,such as satellite communication.The ultimate objectives of this work are to address the major challenges of the evolution of cellular communication networks and to discourse the recent growth of the industry based on the key scopes of application and challenges.The main areas of research topics are summarized into(i)major 6G wireless networkmilestones;(ii)key performance indicators;(iii)future new applications;and(iv)potential fields of research,challenges,and open issues.

Air-Ground Integrated Low-Energy Federated Learning for Secure 6G Communications

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 populariza⁃tion of UAV-enabled FL applications.An airground 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 deploy⁃ment of UAVs through a deep Q-network approach to minimize their energy consumption for transmission as well as movement,thus improv⁃ing the energy efficiency of the airground integrated system.The evaluation results show that our proposed method can reduce the system en⁃ergy consumption while maintaining the accuracy of the FL model.

Toward 6G Communication Networks:Terahertz Frequency Challenges and Open Research Issues

Future networks communication scenarios by the 2030s will include notable applications are three-dimensional(3D)calls,haptics communications,unmanned mobility,tele-operated driving,bio-internet of things,and the Nanointernet of things.Unlike the current scenario in which megahertz bandwidth are sufficient to drive the audio and video components of user applications,the future networks of the 2030s will require bandwidths in several gigahertzes(GHz)(from tens of gigahertz to 1 terahertz[THz])to perform optimally.Based on the current radio frequency allocation chart,it is not possible to obtain such a wide contiguous radio spectrum below 90 GHz(0.09 THz).Interestingly,these contiguous blocks of radio spectrum are readily available in the higher electromagnetic spectrum,specifically in the Terahertz(THz)frequency band.The major contribution of this study is discussing the substantial issues and key features of THz waves,which include(i)key features and significance of THz frequency;(ii)recent regulatory;(iii)the most promising applications;and(iv)possible open research issues.These research topics were deeply investigated with the aim of providing a specific,synopsis,and encompassing conclusion.Thus,this article will be as a catalyst towards exploring new frontiers for future networks of the 2030s.

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.

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.

Intelligent Decision Making Framework for 6G Network

Sixth Generation(6G)wireless communication network has been expected to provide global coverage,enhanced spectral efficiency,and AI(Artificial Intelligence)-native intelligence,etc.To meet these requirements,the computational concept of Decision-Making of cognition intelligence,its implementation framework adapting to foreseen innovations on networks and services,and its empirical evaluations are key techniques to guarantee the generationagnostic intelligence evolution of wireless communication networks.In this paper,we propose an Intelligent Decision Making(IDM)framework,acting as the role of network brain,based on Reinforcement Learning modelling philosophy to empower autonomous intelligence evolution capability to 6G network.Besides,usage scenarios and simulation demonstrate the generality and efficiency of IDM.We hope that some of the ideas of IDM will assist the research of 6G network in a new or different light.

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.

Improved Multi-party Quantum Key Agreement with Four-qubit Cluster States

Quantum key agreement is a promising key establishing protocol that can play a significant role in securing 5G/6G communication networks.Recently,Liu et al.(Quantum Information Processing 18(8):1-10,2019)proposed a multi-party quantum key agreement protocol based on four-qubit cluster states was proposed.The aim of their protocol is to agree on a shared secret key among multiple remote participants.Liu et al.employed four-qubit cluster states to be the quantum resources and the X operation to securely share a secret key.In addition,Liu et al.’s protocol guarantees that each participant makes an equal contribution to the final key.The authors also claimed that the proposed protocol is secure against participant attack and dishonest participants cannot generate the final shared key alone.However,we show here that Liu et al.protocol is insecure against a collusive attack,where dishonest participants can retrieve the private inputs of a trustworthy participant without being caught.Additionally,the corresponding modifications are presented to address these security flaws in Liu et al.’s protocol.

Simultaneously transmitting and reflecting(STAR)RISs for 6G:fundamentals,recent advances,and future directions

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces(STAR-RISs)have been attracting significant attention in both academia and industry for their advantages of achieving 360°coverage and enhanced degrees-of-freedom.This article first identifies the fundamentals of STAR-RIS,by discussing the hardware models,channel models,and signal models.Then,three representative categorizing approaches for STAR-RISs are introduced from the phase-shift,directional,and energy consumption perspectives.Furthermore,the beamforming design of STAR-RISs is investigated for both independent and coupled phase-shift cases.As a recent advance,a general optimization framework,which has high compatibility and provable optimality regardless of the application scenarios,is proposed.As a further advance,several promising applications are discussed to demonstrate the potential benefits of applying STAR-RISs in sixth-generation wireless communication.Lastly,a few future directions and research opportunities are highlighted.

Ergodic sum rate for uplink NOMA transmission in satellite-aerial-ground integrated networks

The application of Non-Orthogonal Multiple Access(NOMA) technology into satelliteaerial-ground integrated networks can meet the requirements of ultra-high rate and massive connectivity for the Sixth-Generation(6G) communication systems. We consider an uplink NOMA scenario for such a satellite-aerial-ground integrated network where multiple users communicate with satellite under the help of an Unmanned Aerial Vehicle(UAV) as an aerial relay equipped with a phased array. Supposing that buffer-aided decode-and-forward protocol is adopted at the UAV relay, we first formulate an optimization problem to maximize Ergodic Sum Rate(ESR) of the considered system subject to individual power constraint and quality-of-service constraint of each user.Then, with known imperfect channel state information of each user, we propose a joint power allocation and robust Beam Forming(BF) iterative algorithm to maximize ESR for the user-to-UAV link. Besides, to take the advantages of Free-Space Optical(FSO) and millimeter Wave(mmWave)communications, we present a switch-based hybrid FSO/mmWave scheme and a robust BF algorithm for the UAV-to-satellite link to achieve higher rate. Moreover, a closed-form ESR expression is derived. Finally, the effectiveness and correctness of the proposed solutions are verified by numerical simulations, and the performance evaluation results show that the proposed solutions not only achieve performance enhancement and robustness, but also outperform the orthogonal multiple access significantly.