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| 1 | Wireless channel feature extraction via GMM and CNN in the tomographic channel model显示文摘Wireless channel modeling has always been one of the most fundamental highlights of the wireless communication research.The performance of new advanced models and technologies heavily depends on the accuracy of the wireless CSI(Channel State Information).This study examined the randomness of the wireless channel parameters based on the characteristics of the radio propagation environment.The diversity of the statistical properties of wireless channel parameters inspired us to introduce the concept of the tomographic channel model.With this model,the static part of the CSI can be extracted from the huge amount of existing CSI data of previous measurements,which can be de ned as the wireless channel feature.In the proposed scheme for obtaining CSI with the tomographic channel model,the GMM(Gaussian Mixture Model)is applied to acquire the distribution of the wireless channel parameters,and the CNN(Convolutional Neural Network)is applied to automatically distinguish di erent wireless channels.The wireless channel feature information can be stored oine to guide the design of pilot symbols and save pilot resources.The numerical results based on actual measurements demonstrated the clear diversity of the statistical properties of wireless channel parameters and that the proposed scheme can extract the wireless channel feature automatically with fewer pilot resources.Thus,computing and storage resources can be exchanged for the nite and precious spectrum resource. | Haihan Li Yunzhou Li Shidong Zhou Jing Wang | 2017 | Journal of Communications and Information Networks2017，2，1： | 6 |
| 2 | Energy Optimization for Cellular-Connected Multi-UAV Mobile Edge Computing Systems with Multi-Access Schemes显示文摘In this paper,a cellular-connected unmanned aerial vehicle(UAV)mobile edge computing system is studied where several UAVs are associated to a terrestrial base station(TBS)for computation offloading.To compute the large amount of data bits,a part of computation task is migrated to TBS and the other part is locally handled at UAVs.Our goal is to minimize the total energy consumption of all UAVs by jointly adjusting the bit allocation,power allocation,resource partitioning as well as UAV trajectory under TBS’s energy budget.For deeply comprehending the impact of multi-UAV access strategy on the system performance,four access schemes in the uplink transmission is considered,i.e.,time division multiple access,orthogonal frequency division multiple access,one-by-one access and non-orthogonal multiple access.The involved problems under different access schemes are all formulated in non-convex forms,which are difficult to be tackled optimally.To solve this class of problem,the successive convex approximation technique is employed to obtain the suboptimal solutions.The numerical results show that the proposed scheme save significant energy consumption compared with the benchmark schemes. | Meng Hua Yongming Huang Yi Wang Qingqing Wu Haibo Dai Lyuxi Yang | 2018 | Journal of Communications and Information Networks2018，3，4： | 6 |
| 3 | Tensor-Based Low-Complexity Channel Estimation for mmWave Massive MIMO-OTFS Systems显示文摘Orthogonal time frequency space(OTFS)modulation,collaborated with millimeter-wave(mmWave)massive multiple-input-multiple-output(MIMO),is a promising technology for next generation wireless communications in high mobility scenarios.However,one of the main challenges for mmWave massive MIMO-OTFS systems is the enormous computational complexity of channel estimation incurred by the huge OTFS symbol size and the large number of antennas.To address this issue,in this paper,a tensor-based orthogonal matching pursuit(OMP)channel estimation algorithm is proposed by exploiting the channel sparsity in the delay-Doppler-angle domain.In particular,we firstly propose a novel pilot design for the OTFS symbol structure in the frequency-time domain.Then,based on the proposed pilot structure,we formulate the channel estimation as a sparse signal recovery problem,and the tensor decomposition and parallel support detection are introduced into the tensor-based OMP algorithm to reduce the signal processing dimension significantly.Numerical simulations are performed to verify the superiority and the robustness of the proposed tensor-based OMP algorithm. | Xianda Wu Shaodan Ma Xi Yang | 2020 | Journal of Communications and Information Networks2020，5，3： | 5 |
| 4 | Large Intelligent Surface/Antennas (LISA): Making Reflective Radios Smart显示文摘Large intelligent surface/antennas(LISA),a two-dimensional artificial structure with a large number of reflective-surface/antenna elements,is a promising reflective radio technology to construct programmable wireless environments in a smart way.Specifically,each element of the LISA adjusts the reflection of the incident electromagnetic waves with unnatural properties,such as negative refraction,perfect absorption,and anomalous reflection,thus the wireless environments can be software-defined according to various design objectives.In this paper,we introduce the reflective radio basics,including backscattering principles,backscatter communication,reflective relay,the fundamentals and implementations of LISA technology.Then,we present an overview of the state-of-the-art research on emerging applications of LISA-aided wireless networks.Finally,the limitations,challenges,and open issues associated with LISA for future wireless applications are discussed. | Ying-Chang Liang Ruizhe Long Qianqian Zhang Jie Chen Hei Victor Cheng Huayan Guo | 2019 | Journal of Communications and Information Networks2019，4，2： | 5 |
| 5 | Deep Learning-Based Spectrum Sensing in Space-Air-Ground Integrated Networks显示文摘To complement terrestrial connections,the space-air-ground integrated network(SAGIN)has been proposed to provide wide-area connections with improved quality of experience(QoE).Spectrum management is an important issue in SAGIN due to the explosive proliferation of wireless devices and services.While the progress on enabling dynamic spectrum access shows promise in advancing increased spectrum sharing,the issue of reliable spectrum sensing under low signal-to-noise ratio(SNR)remains one of the key challenges faced by the spectrum management.As artificial intelligence can provide wireless networks intelligence through learning and data mining,deep learning-based spectrum sensing is proposed in order to improve the spectrum sensing performance,where a deep neural network-based detection framework is built to extract features in a data-driven way based on the covariance matrix of the received signal.To eliminate the impact of noise uncertainty,a blind threshold setting scheme is proposed without using the system prior information.Numerical analyses on simulated and real-world signals show that the detection performance of the proposed scheme is improved under a low SNR regime. | Ruifan Liu Yuan Ma Xingjian Zhang Yue Gao | 2021 | Journal of Communications and Information Networks2021，6，1： | 5 |
| 6 | Joint Communication and Computation Optimization for Wireless Powered Mobile Edge Computing with D2D Offloading显示文摘This paper studies a wireless powered mobile edge computing(MEC)system with device-to-device(D2D)-enabled task offloading.In this system,a set of distributed multi-antenna energy transmitters(ETs)use collaborative energy beamforming to wirelessly charge multiple users.By using the harvested energy,the actively computing user nodes can offload their computation tasks to nearby idle users(as helper nodes)via D2D communication links for self-sustainable remote computing.We consider the frequency division multiple access(FDMA)protocol,such that the D2D communications of different user-helper pairs are implemented over orthogonal frequency bands.Furthermore,we focus on a particular time block for task execution,which is divided into three slots for computation task offloading,remote computing,and result downloading,respectively,at different user-helper pairs.Under this setup,we jointly optimize the collaborative energy beamforming at ETs,the communication and computation resource allocation at users and helpers,and the user-helper pairing,so as to maximize the sum computation rate(i.e.,the number of task input-bits executed over this block)of the users,subject to individual energy neutrality constraints at both users and helpers.First,we consider the computation rate maximization problem under any given user-helper pairs,for which an efficient solution is proposed by using the techniques of alternating optimization and convex optimization.Next,we develop the optimal user-helper pairing scheme based on exhaustive search and a low-complexity scheme based on greedy selection.Numerical results show that the proposed design significantly improves the sum computation rate at users,as compared to benchmark schemes without such joint optimization. | Dixiao Wu Feng Wang Xiaowen Cao Jie Xu | 2019 | Journal of Communications and Information Networks2019，4，4： | 5 |
| 7 | What Is Semantic Communication?A View on Conveying Meaning in the Era of Machine Intelligence显示文摘In the 1940s,Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel.Guided by this fundamental work,the main theme of wireless system design up until the fifth generation(5G)was the data rate maximization.In Shannon’s theory,the semantic aspect and meaning of messages were treated as largely irrelevant to communication.The classic theory started to reveal its limitations in the modern era of machine intelligence,consisting of the synergy between Internet-of-things(IoT)and artificial intelligence(AI).By broadening the scope of the classic communication-theoretic framework,in this article,we present a view of semantic communication(SemCom)and conveying meaning through the communication systems.We address three communication modalities:human-to-human(H2H),human-to-machine(H2M),and machine-to-machine(M2M)communications.The latter two represent the paradigm shift in communication and computing,and define the main theme of this article.H2M SemCom refers to semantic techniques for conveying meanings understandable not only by humans but also by machines so that they can have interaction and“dialogue”.On the other hand,M2M SemCom refers to effective techniques for efficiently connecting multiple machines such that they can effectively execute a specific computation task in a wireless network.The first part of this article focuses on introducing the SemCom principles including encoding,layered system architecture,and two design approaches:1)layer-coupling design;and 2)end-to-end design using a neural network.The second part focuses on the discussion of specific techniques for different application areas of H2M SemCom[including human and AI symbiosis,recommendation,human sensing and care,and virtual reality(VR)/augmented reality(AR)]and M2M SemCom(including distributed learning,split inference,distributed consensus,and machine-vision cameras).Finally,we discuss the approach for designing SemCom systems based on knowledge graphs.We believe that this comprehensive introduction will provide a useful guide into the emerging area of SemCom that is expected to play an important role in sixth generation(6G)featuring connected intelligence and integrated sensing,computing,communication,and control. | Qiao Lan Dingzhu Wen Zezhong Zhang Qunsong Zeng Xu Chen Petar Popovski Kaibin Huang | 2021 | Journal of Communications and Information Networks2021，6，4： | 4 |
| 8 | Wireless big data:transforming heterogeneous networks to smart networks显示文摘In HetNets(Heterogeneous Networks),each network is allocated with xed spectrum resource and provides service to its assigned users using speci c RAT(Radio Access Technology).Due to the high dynamics of load distribution among di erent networks,simply optimizing the performance of individual network can hardly meet the demands from the dramatically increasing access devices,the consequent upsurge of data trac,and dynamic user QoE(Quality-of-Experience).The deployment of smart networks,which are supported by SRA(Smart Resource Allocation)among di erent networks and CUA(Cognitive User Access)among di erent users,is deemed a promising solution to these challenges.In this paper,we propose a frame-work to transform HetNets to smart networks by leveraging WBD(Wireless Big Data),CR(Cognitive Radio)and NFV(Network Function Virtualization)techniques.CR and NFV support resource slicing in spectrum,physical layers,and network layers,while WBD is used to design intelligent mechanisms for resource mapping and trac prediction through powerful AI(Arti cial Intelligence)methods.We analyze the characteristics of WBD and review possible AI methods to be utilized in smart networks.In particular,the potential of WBD is revealed through high level view on SRA,which intelligently maps radio and network resources to each network for meeting the dynamic trac demand,as well as CUA,which allows mobile users to access the best available network with manageable cost,yet achieving target QoS(Quality-of-Service)or QoE. | Yudi Huang Junjie Tan Ying-Chang Liang | 2017 | Journal of Communications and Information Networks2017，2，1： | 4 |
| 9 | Architecture and critical technologies of space information networks显示文摘Focusing on its main requirements and challenges and by analyzing the characteristics of different space platforms,an overall architecture for space information networks is proposed based on national strategic planning and the present development status of associated technologies.Furthermore,the core scientific problems that need to be solved are expounded.In addition,the primary considerations and a preliminary integrated demonstration environment for verification of key technologies are presented. | YU Quan WANG Jingchao BAI Lin | 2016 | Journal of Communications and Information Networks2016，1，3： | 4 |
| 10 | Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design显示文摘This paper studies a federated edge learning system,in which an edge server coordinates a set of edge devices to train a shared machine learning(ML)model based on their locally distributed data samples.During the distributed training,we exploit the joint communication and computation design for improving the system energy efficiency,in which both the communication resource allocation for global ML-parameters aggregation and the computation resource allocation for locally updating ML-parameters are jointly optimized.In particular,we consider two transmission protocols for edge devices to upload ML-parameters to edge server,based on the non-orthogonal multiple access(NOMA)and time division multiple access(TDMA),respectively.Under both protocols,we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy,by jointly optimizing the transmission power and rates at edge devices for uploading ML-parameters and their central processing unit(CPU)frequencies for local update.We propose efficient algorithms to solve the formulated energy minimization problems by using the techniques from convex optimization.Numerical results show that as compared to other benchmark schemes,our proposed joint communication and computation design significantly can improve the energy efficiency of the federated edge learning system,by properly balancing the energy tradeoff between communication and computation. | Xiaopeng Mo Jie Xu | 2021 | Journal of Communications and Information Networks2021，6，2： | 3 |
| 11 | Learning-Based Joint Resource Slicing and Scheduling in Space-Terrestrial Integrated Vehicular Networks显示文摘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. | Huaqing Wu Jiayin Chen Conghao Zhou Junling Li Xuemin(Sherman)Shen | 2021 | Journal of Communications and Information Networks2021，6，3： | 3 |
| 12 | Data scheme-based wireless channel modeling method: motivation, principle and performance显示文摘In recent years,data mining and machine learning technologies have made great progress driven by enormous volumes of data.Meanwhile,the wireless-channel measurement data appears large in volume because of the large-scale antenna numbers,increased bandwidth,and versatile application scenarios.With powerful data mining and machine learning methods and large volumes of data,we can extract valuable and hidden rules from the wireless channel.Motivated by this,we propose a channel-modeling method using PCA in this paper.Its principle is to utilize the features and structures extracted from the CIR data collected by measurements,and then model the wireless channel of the targeted measurement scenario.In addition,a noise removing method using a BP neural network is designed for the proposed model,which can recognize and remove the noise of the polluted CIR accurately.The performance of the proposed scheme is investigated with the actual measured CIR data,and its superiority is verified. | Xiaochuan Ma Jianhua Zhang Yuxiang Zhang Zhanyu Ma | 2017 | Journal of Communications and Information Networks2017，2，3： | 3 |
| 13 | Joint Computation Offloading and Resource Allocation for Mobile-Edge Computing Assisted Ultra-Dense Networks显示文摘Mobile-edge computing(MEC),enabling to offload computing tasks on mobile devices towards edge servers,can reduce the terminals cost.However,a single MEC sever usually has limited computing capabilities,which can not meet a large number of terminals’requirements.In this paper,we consider an ultra-dense networks(UDN)scenario where the macro base stations(MBSs)are assisted by MEC severs.In particular,we first construct system model for MEC assisted UDN,and build the system overhead minimization.Next,in order to solve the problem,we transform the problem into three sub-problems,i.e.,offloading strategies subproblem,channel assignments subproblem,and power allocation subproblem.Then,employing joint offloading and resource allocation algorithms,we obtain the optimal joint strategy for the MEC assisted UDNs.Finally,simulations are conducted to evaluate the performance of our proposed algorithms.Numerical results show that obtained algorithms can effectively reduce the energy consumption of the system and improve the overall performance of the system. | Ya Gao Haoran Zhang Fei Yu Yujie Xia Yongpeng Shi | 2022 | Journal of Communications and Information Networks2022，7，1： | 3 |
| 14 | Radio Resource Allocation for Bidirectional Offloading in Space-Air-Ground Integrated Vehicular Network显示文摘Aerial platforms and edge servers have been recognized as two promising building blocks to improve the quality of service(QoS)in space-air-ground integrated vehicular networks(SAGIN).Communication intensive tasks can be offloaded to aerial platforms via broadcasting,while computation intensive tasks can be offloaded to ground edge servers.However,the key issues including how to allocate radio resources and how to determine the task offloading strategy for the two types of tasks,are yet to be solved.In this paper,the joint optimization of radio resource allocation and bidirectional offloading configuration is investigated.To deal with the non-convex nature of the original problem,we decouple it into a two-step optimization problem.In the first step,we optimize the bidirectional offloading configuration in the case of the radio resource allocation known in advance,which is proved to be a convex optimization problem.In the second step,we optimize the radio resource allocation through a brute-force search method.We use queuing theories to analyze the average delay of the two tasks with respect to the broadcasting capacity and task arrival rate.The offloading strategies with closed-form expressions of communication intensive tasks are proposed.We then propose a heuristic algorithm which is shown to perform better than interior point algorithm in simulations.The numerical results also demonstrate that the aerial platforms and edge servers can significantly reduce the average delay of the tasks under different network conditions. | Guangchao Wang Sheng Zhou Zhisheng Niu | 2019 | Journal of Communications and Information Networks2019，4，4： | 3 |
| 15 | A hybrid policy for fault tolerant load balancing in grid computing environments显示文摘 | Jasma Balasangameshwara Nedunchezhian Raju | 2011 | Journal of Network and Computer Applications2011，，1： | 3 |
| 16 | Multi-Antenna UAV Data Harvesting:Joint Trajectory and Communication Optimization显示文摘Unmanned aerial vehicle(UAV)-enabled communication is a promising technology to extend coverage and enhance throughput for traditional terres-trial wireless communication systems.In this paper,we consider a UAV-enabled wireless sensor network,where a multi-antenna UAV is dispatched to collect data from a group of sensor nodes(SNs).The objective is to maximize the minimum data collection rate from all SNs via jointly optimizing their transmission scheduling and power allocations as well as the trajectory of the UAV,subject to the practical constraints on the maximum transmit power of the SNs and the maximum speed of the UAV.The formulated optimization problem is challenging to solve as it involves non-convex constraints and discrete-value variables.To draw useful insight,we first consider the special case of the formulated problem by ignoring the UAV speed constraint and optimally solve it based on the Lagrange duality method.It is shown that for this relaxed problem,the UAV should hover above a finite number of optimal locations with different durations in general.Next,we address the general case of the formulated problem where the UAV speed constraint is considered and propose a traveling salesman problem-based trajec-tory initialization,where the UAV sequentially visits the locations obtained in the relaxed problem with minimumflying time.Given this initial trajectory,we thenfind the corresponding transmission scheduling and power alloca-tions of the SNs and further optimize the UAV trajectory by applying the block coordinate descent and successive convex approximation techniques.Finally,numerical results are provided to illustrate the spectrum and energy efficiency gains of the proposed scheme for multi-antenna UAV data harvesting,as compared to benchmark schemes. | Jingwei Zhang Yong Zeng Rui Zhang | 2020 | Journal of Communications and Information Networks2020，5，1： | 3 |
| 17 | A Novel Multi-Modal OAM Vortex Electromagnetic Wave Microstrip Array Antenna显示文摘In this paper,we combine the circular polarization technology and orbital angular momentum(OAM)into the array antenna design for the first time,achieve free switch of the different topological charges in the array by using high-speed radio frequency(RF)switch technology.We arrange microstrip patch antenna elements equidistantly along the circumference to form eight elements multi-modal OAM vortex electromagnetic wave microstrip array antenna.It can generate elec-tromagnetic waves with dual characteristics of circular polarization and multi-modal vortex OAM(where OAM mode values are l=0,l=±1,l=±2,l=±3).Through simulation,we find mutual coupling between the radiating elements is small relatively,and increasing the number of array elements can not only improve the beam quality,but also generate electromagnetic waves with a higher order of OAM modes.Antenna model can meet the basic demands of ordinary array antenna,and also confirm the practicality of this circular polarized microstrip antenna array model. | Xuehong Sun Jianfeng Shao Boya Dan Qiang Li | 2019 | Journal of Communications and Information Networks2019，4，4： | 3 |
| 18 | A survey of routing techniques for satellite networks显示文摘Satellite networks have many advantages over traditional terrestrial networks.However,it is very difficult to design a satellite network with excellent performance.The paper briefly summarizes some existing satellite network routing technologies from the perspective of both single-layer and multilayer satellite constellations,and focuses on the main ideas,characteristics,and existing problems of these routing technologies.For single-layer satellite networks,two routing strategies are discussed,virtual node strategy and virtual topology strategy.Moreover,considering the deficiency of existing multilayer satellite network routing,we discuss the topic invulnerability.Finally,the challenges and problems faced by the satellite network are analyzed and the trend of future development is predicted. | QI Xiaogang MA Jiulong WU Dan LIU Lifang HU Shaolin | 2016 | Journal of Communications and Information Networks2016，1，4： | 3 |
| 19 | Atmospheric Ducting Effect in Wireless Communications:Challenges and Opportunities显示文摘Atmospheric ducting has a significant impact on electromagnetic wave propagation.Radio signals that are trapped and guided by the atmospheric duct can travel a much longer distance over the horizon with lower attenuation since the signal power does not spread isotropically through the atmosphere.Atmospheric ducting brings both challenges and opportunities to wireless communications.On one hand,the signals propagating in the atmospheric duct may interfere with a receiver far away as remote co-channel interference.On the other hand,a point-to-point link can be established directly through the atmospheric duct to enable beyond line-of-sight communications.In this article,the formation of the atmospheric duct and its effects on radio wave propagation are first overviewed.Then solutions and standardization activities in the 3rd Generation Partnership Project(3GPP)to mitigate atmospheric duct induced remote interference are presented.Finally,the applications and design challenges of atmospheric duct enabled beyond line-of-sight communications are reviewed and future research directions are suggested. | Fangfang Liu Jiaxi Pan Xiangwei Zhou Geoffrey Ye Li | 2021 | Journal of Communications and Information Networks2021，6，2： | 2 |
| 20 | Apply agent to build grid service management显示文摘 | Li Chunlin Li Layuan | 2003 | Journal of Network and Computer Applications2003，26，4： | 2 |
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