Channel-Feedback-Free Transmission for Downlink FD-RAN:A Radio Map Based Complex-Valued Precoding Network Approach

As the demand for high-quality services proliferates,an innovative network architecture,the fully-decoupled RAN(FD-RAN),has emerged for more flexible spectrum resource utilization and lower network costs.However,with the decoupling of uplink base stations and downlink base stations in FDRAN,the traditional transmission mechanism,which relies on real-time channel feedback,is not suitable as the receiver is not able to feedback accurate and timely channel state information to the transmitter.This paper proposes a novel transmission scheme without relying on physical layer channel feedback.Specifically,we design a radio map based complex-valued precoding network(RMCPNet)model,which outputs the base station precoding based on user location.RMCPNet comprises multiple subnets,with each subnet responsible for extracting unique modal features from diverse input modalities.Furthermore,the multimodal embeddings derived from these distinct subnets are integrated within the information fusion layer,culminating in a unified representation.We also develop a specific RMCPNet training algorithm that employs the negative spectral efficiency as the loss function.We evaluate the performance of the proposed scheme on the public DeepMIMO dataset and show that RMCPNet can achieve 16%and 76%performance improvements over the conventional real-valued neural network and statistical codebook approach,respectively.

UltraStar:A Lightweight Simulator of Ultra-Dense LEO Satellite Constellation Networking for 6G

The mega-constellation network has gained significant attention recently due to its great potential in providing ubiquitous and high-capacity connectivity in sixth-generation(6G)wireless communication systems.However,the high dynamics of network topology and large scale of mega-constellation pose new challenges to the constellation simulation and performance evaluation.In this paper,we introduce UltraStar,a lightweight network simulator,which aims to facilitate the complicated simulation for the emerging mega-constellation of unprecedented scale.Particularly,a systematic and extensible architecture is proposed,where the joint requirement for network simulation,quantitative evaluation,data statistics and visualization is fully considered.For characterizing the network,we make lightweight abstractions of physical entities and models,which contain basic representatives of networking nodes,structures and protocol stacks.Then,to consider the high dynamics of Walker constellations,we give a two-stage topology maintenance method for constellation initialization and orbit prediction.Further,based on the discrete event simulation(DES)theory,a new set of discrete events is specifically designed for basic network processes,so as to maintain network state changes over time.Finally,taking the first-generation Starlink of 11927 low earth orbit(LEO)satellites as an example,we use UltraStar to fully evaluate its network performance for different deployment stages,such as characteristics of constellation topology,performance of end-to-end service and effects of network-wide traffic interaction.The simulation results not only demonstrate its superior performance,but also verify the effectiveness of UltraStar.

Blockchain for Transparent Data Management Toward 6G

The wealth of user data acts as a fuel for network intelligence toward the sixth generation wireless networks(6G).Due to data heterogeneity and dynamics,decentralized data management(DM)is desirable for achieving transparent data operations across network domains,and blockchain can be a promising solution.However,the increasing data volume and stringent data privacy-preservation requirements in 6G bring significantly technical challenge to balance transparency,efficiency,and privacy requirements in decentralized blockchain-based DM.In this paper,we investigate blockchain solutions to address the challenge.First,we explore the consensus protocols and scalability mechanisms in blockchains and discuss the roles of DM stakeholders in blockchain architectures.Second,we investigate the authentication and authorization requirements for DM stakeholders.Third,we categorize DM privacy requirements and study blockchain-based mechanisms for collaborative data processing.Subsequently,we present research issues and potential solutions for blockchain-based DM toward 6G from these three perspectives.Finally,we conclude this paper and discuss future research directions.

Editorial for the Special Issue on 6G Requirements,Vision,and Enabling Technologies

As the commercialization process of the fifth-generation(5G)communication systems accelerates,research on the sixth genera-tion(6G)is being placed on the agenda in academic and industrial communities all over the world.6G systems are expected to fur-ther enhance the performance of 5G systems and continue to pen-etrate into all aspects of society,promoting the technological integration of communications with other disciplines,such as arti-ficial intelligence,materials science,and biology.Therefore,a spe-cial issue on the development of the future 6G is both timely and valuable.This special issue contains eight papers detailing recent cutting-edge research achievements from the perspectives of 6G requirements,visions,and enabling technologies.

Dynamic Spectrum Control-Assisted Secure and Efficient Transmission Scheme in Heterogeneous Cellular Networks

Heterogeneous cellular networks(HCNs)are envisioned as a promising architecture to provide seamless wireless coverage and increase network capacity.However,the densified multi-tier network architecture introduces excessive intra-and cross-tier interference and makes HCNs vulnerable to eavesdropping attacks.In this article,a dynamic spectrum control(DSC)-assisted transmission scheme is proposed for HCNs to strengthen network security and increase the network capacity.Specifically,the proposed DSC-assisted transmission scheme leverages the idea of block cryptography to generate sequence families,which represent the transmission decisions,by performing iterative and orthogonal sequence transformations.Based on the sequence families,multiple users can dynamically occupy different frequency slots for data transmission simultaneously.In addition,the collision probability of the data transmission is analyzed,which results in closed-form expressions of the reliable transmission probability and the secrecy probability.Then,the upper and lower bounds of network capacity are further derived with given requirements on the reliable and secure transmission probabilities.Simulation results demonstrate that the proposed DSC-assisted scheme can outperform the benchmark scheme in terms of security performance.Finally,the impacts of key factors in the proposed DSC-assisted scheme on the network capacity and security are evaluated and discussed.

Learning-Based Joint Resource Slicing and Scheduling in Space-Terrestrial Integrated Vehicular Networks

In this paper,we investigate the resource slicing and scheduling problem in the space-terrestrial integrated vehicular networks to support both delay-sensitive services(DSSs)and delay-tolerant services(DTSs).Resource slicing and scheduling are to allocate spectrum resources to different slices and determine user association and bandwidth allocation for individual vehicles.To accommodate the dynamic network conditions,we first formulate a joint resource slicing and scheduling(JRSS)problem to minimize the long-term system cost,including the DSS requirement violation cost,DTS delay cost,and slice reconfiguration cost.Since resource slicing and scheduling decisions are interdependent with different timescales,we decompose the JRSS problem into a large-timescale resource slicing subproblem and a small-timescale resource scheduling subproblem.We propose a two-layered reinforcement learning(RL)-based JRSS scheme to find the solutions to the subproblems.In the resource slicing layer,spectrum resources are pre-allocated to different slices via a proximal policy optimization-based RL algorithm.In the resource scheduling layer,spectrum resources in each slice are scheduled to individual vehicles based on dynamic network conditions and service requirements via matching-based algorithms.We conduct extensive trace-driven experiments to demonstrate that the proposed scheme can effectively reduce the system cost while satisfying service quality requirements.

Secure and Privacy-Preserving Decision Tree Classification with Lower Complexity

As a widely-used machine-learning classifier,a decision tree model can be trained and deployed at a service provider to provide classification services for clients,e.g.,remote diagnostics.To address privacy concerns regarding the sensitive information in these services(i.e.,the clients’inputs,model parameters,and classification results),we propose a privacy-preserving decision tree classification scheme(PDTC)in this paper.Specifically,we first tailor an additively homomorphic encryption primitive and a secret sharing technique to design a new secure two-party comparison protocol,where the numeric inputs of each party can be privately compared as a whole instead of doing that in a bit-by-bit manner.Then,based on the comparison protocol,we exploit the structure of the decision tree to construct PDTC,where the input of a client and the model parameters of a service provider are concealed from the counterparty and the classification result is only revealed to the client.A formal simulation-based security model and the security proof demonstrate that PDTC achieves desirable security properties.In addition,performance evaluation shows that PDTC achieves a lower communication and computation overhead compared with existing schemes.

Cybertwin-Assisted Mode Selection in Ultra-Dense LEO Integrated Satellite-Terrestrial Network

Ultra-dense low earth orbit(LEO)integrated satellite-terrestrial network(ULISTN)has become an emerging paradigm to support massive access of Internet of things(IoT)in beyond fifth generation mobile networks(B5G).In ULISTN,there are two communication modes:cellular mode and satellite mode,where IoT users assessing terrestrial small base stations(TSBSs)and terrestrial-satellite terminals(TSTs)respectively.However,how to optimize the network performance and guarantee self-interests of the operator and IoT users in ULISTN is a challenging issue.In this paper,we propose a cybertwin-assisted joint mode selection and dynamic pricing(JMSDP)scheme for effective network management in ULISTN,where cybertwin serves as the intelligent agent.In JMSDP,the operator determines optimal access prices of TSBSs and TSTs,while each user selects the access mode according to access prices.Specifically,the operator conducts the Stackelberg game aiming at maximizing average throughput depending on the mode selection results of IoT users.Meanwhile,IoT users as followers adopt the evolutionary game to choose an access mode based on the access prices provided by the operator.Simulation results show that the proposed JMSDP can improve the average throughput and reduce the delay effectively,comparing with random access(RA)and maximum rate access.

Digital Twin-Empowered Network Planning for Multi-Tier Computing

In this paper,we design a resource management scheme to support stateful applications,which will be prevalent in sixth generation(6G)networks.Different from stateless applications,stateful applications require context data while executing computing tasks from user terminals(UTs).Using a multi-tier computing paradigm with servers deployed at the core network,gateways,and base stations to support stateful applications,we aim to optimize long-term resource reservation by jointly minimizing the usage of computing,storage,and communication resources and the cost of reconfiguring resource reservation.The coupling among different resources and the impact of UT mobility create challenges in resource management.To address the challenges,we develop digital twin(DT)empowered network planning with two elements,i.e.,multi-resource reservation and resource reservation reconfiguration.First,DTs are designed for collecting UT status data,based on which UTs are grouped according to their mobility patterns.Second,an algorithm is proposed to customize resource reservation for different groups to satisfy their different resource demands.Last,a Meta-learning-based approach is developed to reconfigure resource reservation for balancing the network resource usage and the reconfiguration cost.Simulation results demonstrate that the proposed DTempowered network planning outperforms benchmark frameworks by using less resources and incurring lower reconfiguration costs.