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China Communications
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V2I Based Environment Perception for Autonomous Vehicles at Intersections
Xuting Duan, Hang Jiang, Daxin Tian, Tianyuan Zou, Jianshan Zhou, Yue Cao
In recent years, autonomous driving technology has made good progress, but the non-cooperative intelligence of vehicle for autonomous driving still has many technica...
 
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  2021, 18(7)  
 
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V2I Based Environment Perception for Autonomous Vehicles at Intersections Hot!
Xuting Duan, Hang Jiang, Daxin Tian, Tianyuan Zou, Jianshan Zhou, Yue Cao
China Communications, 2021, 18(7): 1-12
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In recent years, autonomous driving technology has made good progress, but the non-cooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving challenges. V2I (Vehicle-to-Infrastructure) communication is a potential solution to enable cooperative intelligence of vehicles and roads. In this paper, the RGB-PVRCNN, an environment perception framework, is proposed to improve the environmental awareness of autonomous vehicles at intersections by leveraging V2I communication technology. This framework integrates vision feature based on PVRCNN. The normal distributions transform(NDT) point cloud registration algorithm is deployed both on onboard and roadside to obtain the position of the autonomous vehicles and to build the local map objects detected by roadside multi-sensor system are sent back to autonomous vehicles to enhance the perception ability of autonomous vehicles for benefiting path planning and traffic efficiency at the intersection. The field-testing results show that our method can effectively extend the environmental perception ability and range of autonomous vehicles at the intersection and outperform the PointPillar algorithm and the VoxelRCNN algorithm in detection accuracy.
Machine Learning-Based Radio Access Technology Selection in the Internet of Moving Things
Ramon Sanchez-Iborra, Luis Bernal-Escobedo, Jose Santa
China Communications, 2021, 18(7): 13-24
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The Internet of Moving Things (IoMT) takes a step further with respect to traditional static IoT deployments. In this line, the integration of new eco-friendly mobility devices such as scooters or bicycles within the Cooperative-Intelligent Transportation Systems (C-ITS) and smart city ecosystems is crucial to provide novel services. To this end, a range of communication technologies is available, such as cellular, vehicular WiFi or Low-Power Wide-Area Network (LPWAN); however, none of them can fully cover energy consumption and Quality of Service (QoS) requirements. Thus, we propose a Decision Support System (DSS), based on supervised Machine Learning (ML) classification, for selecting the most adequate transmission interface to send a certain message in a multi-Radio Access Technology (RAT) set up. Different ML algorithms have been explored taking into account computing and energy constraints of IoMT end-devices and traffic type. Besides, a real implementation of a decision tree-based DSS for micro-controller units is presented and evaluated. The attained results demonstrate the validity of the proposal, saving energy in communication tasks as well as satisfying QoS requirements of certain urgent messages. The footprint of the real implementation on an Arduino Uno is 444 bytes and it can be executed in around 50 μs.
A Joint Power and Bandwidth Allocation Method Based on Deep Reinforcement Learning for V2V Communications in 5G
Xin Hu, Sujie Xu, Libing Wang, Yin Wang, Zhijun Liu, Lexi Xu, You Li, Weidong Wang
China Communications, 2021, 18(7): 25-35
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Vehicular communications have recently attracted great interest due to their potential to improve the intelligence of the transportation system. When maintaining the high reliability and low latency in the vehicle-to-vehicle (V2V) links as well as large capacity in the vehicle-to-infrastructure (V2I) links, it is essential to flexibility allocate the radio resource to satisfy the different requirements in the V2V communication. This paper proposes a new radio resources allocation system for V2V communications based on the proximal strategy optimization method. In this radio resources allocation framework, a vehicle or V2V link that is designed as an agent. And through interacting with the environment, it can learn the optimal policy based on the strategy gradient and make the decision to select the optimal sub-band and the transmitted power level. Because the proposed method can output continuous actions and multi-dimensional actions, it greatly reduces the implementation complexity of large-scale communication scenarios. The simulation results indicate that the allocation method proposed in this paper can meet the latency constraints and the requested capacity of V2V links under the premise of minimizing the interference to vehicle-to-infrastructure communications.
CSI Intelligent Feedback for Massive MIMO Systems in V2I Scenarios
Shiyi Wang, Yong Liao
China Communications, 2021, 18(7): 36-43
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With the rapid development of the Internet of vehicles (IoV), vehicle to everything (V2X) has strict requirements for ultra-reliable and low latency communications (URLLC), and massive multi-input multi-output (MIMO) channel state information (CSI) feedback can effectively support URLLC communication in 5G vehicle to infrastructure (V2I) scenarios. Existing research applies deep learning (DL) to CSI feedback, but most of its algorithms are based on low-speed outdoor or indoor environments and assume that the feedback link is perfect. However, the actual channel still has the influence of additive noise and nonlinear effects, especially in the high-speed V2I scene, the channel characteristics are more complex and time-varying. In response to the above problems, this paper proposes a CSI intelligent feedback network model for V2I scenarios, named residual mix-net (RM-Net). The network learns the channel characteristics in the V2I scenario at the vehicle user (User Equipment, UE), compresses the CSI and sends it to the channel; the roadside base station (Base Station, BS) receives the data and learns the compressed data characteristics, and then restore the original CSI. The system simulation results show that the RM-Net training speed is fast, requires fewer training samples, and its performance is significantly better than the existing DL-based CSI feedback algorithm. It can learn channel characteristics in high-speed mobile V2I scenarios and overcome the influence of additive noise. At the same time, the network still has good performance under high compression ratio and low signal-to-noise ratio (SNR).
Better Platooning toward Autonomous Driving: Inter-Vehicle Communications with Directional Antenna
Xiaoyan Wang, Diquan Wang, Nobuhiro Ariyasu, Masahiro Umehira
China Communications, 2021, 18(7): 44-57
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Recently, in the researches on vehicular Internet-of-Things (IoT), platooning have received lots of attentions due to its potential to improve the fuel efficiency and driving experience. Platoon is a group of vehicles that act as smart agents, they travel collaboratively by following the leading human-driven vehicle. A vehicle in the platoon utilizes radar and wireless communication to share important information to other vehicles in the same platoon such as speed and acceleration, to realize the safe and efficient driving. The quality of wireless communication is of great importance to manage and maintain the platoons. However, in a scenario that a large number of vehicles exist, communication delay and packet loss caused by channel congestion may endanger the safe inter-vehicle distance. In this paper, we introduce inter-vehicle communication with directional antenna into platooning. By extensive simulations, we evaluate the packet delay and inter-vehicle distance in both normal driving and braking scenarios, and verify the usefulness of directional antenna in platooning for vehicular IoT.
Reinforcement Learning Based Dynamic Spectrum Access in Cognitive Internet of Vehicles
Xin Liu, Can Sun, Mu Zhou, Bin Lin, Yuto Lim
China Communications, 2021, 18(7): 58-68
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Cognitive Internet of Vehicles (CIoV) can improve spectrum utilization by accessing the spectrum licensed to primary user (PU) under the premise of not disturbing the PU's transmissions. However, the traditional static spectrum access makes the CIoV unable to adapt to the various spectrum environments. In this paper, a reinforcement learning based dynamic spectrum access scheme is proposed to improve the transmission performance of the CIoV in the licensed spectrum, and avoid causing harmful interference to the PU. The frame structure of the CIoV is separated into sensing period and access period, whereby the CIoV can optimize the transmission parameters in the access period according to the spectrum decisions in the sensing period. Considering both detection probability and false alarm probability, a Q-learning based spectrum access algorithm is proposed for the CIoV to intelligently select the optimal channel, bandwidth and transmit power under the dynamic spectrum states and various spectrum sensing performance. The simulations have shown that compared with the traditional non-learning spectrum access algorithm, the proposed Q-learning algorithm can effectively improve the spectral efficiency and throughput of the CIoV as well as decrease the interference power to the PU.
MADCR: Mobility Aware Dynamic Clustering-Based Routing Protocol in Internet of Vehicles
Sankar Sennan, Somula Ramasubbareddy, Sathiyabhama Balasubramaniyam, Anand Nayyar, Chaker Abdelaziz Kerrache, Muhammad Bilal
China Communications, 2021, 18(7): 69-85
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Internet of Vehicles (IoV) is an evolution of the Internet of Things (IoT) to improve the capabilities of vehicular ad -hoc networks (VANETs) in intelligence transport systems. The network topology in IoV paradigm is highly dynamic. Clustering is one of the promising solutions to maintain the route stability in the dynamic network. However, existing algorithms consume a considerable amount of time in the cluster head (CH) selection process. Thus, this study proposes a mobility aware dynamic clustering -based routing (MADCR) protocol in IoV to maximize the lifespan of networks and reduce the end -to -end delay of vehicles. The MADCR protocol consists of cluster formation and CH selection processes. A cluster is formed on the basis of Euclidean distance. The CH is then chosen using the mayfly optimization algorithm (MOA). The CH subsequently receives vehicle data and forwards such data to the Road Side Unit (RSU). The performance of the MADCR protocol is compared with that ofAnt Colony Optimization (ACO), Comprehensive Learning Particle Swarm Optimization (CLPSO), and Clustering Algorithm for Internet of Vehicles based on Dragonfly Optimizer (CAVDO). The proposed MADCR protocol decreases the end-to-end delay by 5-80 ms and increases the packet delivery ratio by 5%-15%.
CHRT: Clustering-Based Hybrid Re-Routing System for Traffic Congestion Avoidance
Jie Huo, Xiangming Wen, Luning Liu, Luhan Wang, Meiling Li, Zhaoming Lu
China Communications, 2021, 18(7): 86-102
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Re-routing system has become an important technology to improve traffic efficiency. The traditional re-routing schemes do not consider the dynamic characteristics of urban traffic, making the planned routes unable to cope with the changing traffic conditions. Based on real-time traffic information, it is challenging to dynamically re-route connected vehicles to alleviate traffic congestion. Moreover, how to obtain global traffic information while reducing communication costs and improving travel efficiency poses a challenge to the re-routing system. To deal with these challenges, this paper proposes CHRT, a clustering-based hybrid re-routing system for traffic congestion avoidance. CHRT develops a multi-layer hybrid architecture. The central server accesses the global view of traffic, and the distributed part is composed of vehicles divided into clusters to reduce latency and communication overhead. Then, a clustering-based priority mechanism is proposed, which sets priorities for clusters based on real-time traffic information to avoid secondary congestion. Furthermore, to plan the optimal routes for vehicles while alleviating global traffic congestion, this paper presents a multi-metric re-routing algorithm. Through extensive simulations based on the SUMO traffic simulator, CHRT reduces vehicle traveling time, fuel consumption, and CO2 emissions compared to other systems. In addition, CHRT globally alleviates traffic congestion and improves traffic efficiency.
A Novel Improved Artificial Bee Colony and Blockchain-Based Secure Clustering Routing Scheme for FANET
Liang Zhao, Muhammad Bin Saif, Ammar Hawbani, Geyong Min, Su Peng, Na Lin
China Communications, 2021, 18(7): 103-116
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Flying Ad hoc Network (FANET) has drawn significant consideration due to its rapid advancements and extensive use in civil applications. However, the characteristics of FANET including high mobility, limited resources, and distributed nature, have posed a new challenge to develop a secure and efficient routing scheme for FANET. To overcome these challenges, this paper proposes a novel cluster based secure routing scheme, which aims to solve the routing and data security problem of FANET. In this scheme, the optimal cluster head selection is based on residual energy, online time, reputation, blockchain transactions, mobility, and connectivity by using Improved Artificial Bee Colony Optimization (IABC). The proposed IABC utilizes two different search equations for employee bee and onlooker bee to enhance convergence rate and exploitation abilities. Further, a lightweight blockchain consensus algorithm, AI-Proof of Witness Consensus Algorithm (AI-PoWCA) is proposed, which utilizes the optimal cluster head for mining. In AI-PoWCA, the concept of the witness for block verification is also involved to make the proposed scheme resource efficient and highly resilient against 51% attack. Simulation results demonstrate that the proposed scheme outperforms its counterparts and achieves up to 90% packet delivery ratio, lowest end-to-end delay, highest throughput, resilience against security attacks, and superior in block processing time.
A Federated Bidirectional Connection Broad Learning Scheme for Secure Data Sharing in Internet of Vehicles
Xiaoming Yuan, Jiahui Chen, Ning Zhang, Xiaojie Fang, Didi Liu
China Communications, 2021, 18(7): 117-133
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Data sharing in Internet of Vehicles (IoV) makes it possible to provide personalized services for users by service providers in Intelligent Transportation Systems (ITS). As IoV is a multi-user mobile scenario, the reliability and efficiency of data sharing need to be further enhanced. Federated learning allows the server to exchange parameters without obtaining private data from clients so that the privacy is protected. Broad learning system is a novel artificial intelligence technology that can improve training efficiency of data set. Thus, we propose a federated bidirectional connection broad learning scheme (FeBBLS) to solve the data sharing issues. Firstly, we adopt the bidirectional connection broad learning system (BiBLS) model to train data set in vehicular nodes. The server aggregates the collected parameters of BiBLS from vehicular nodes through the federated broad learning system (FedBLS) algorithm. Moreover, we propose a clustering FedBLS algorithm to offload the data sharing into clusters for improving the aggregation capability of the model. Some simulation results show our scheme can improve the efficiency and prediction accuracy of data sharing and protect the privacy of data sharing.
Deep Reinforcement Learning-Based URLLC-Aware Task Offloading in Collaborative Vehicular Networks
Chao Pan, Zhao Wang, Zhenyu Zhou, Xincheng Ren
China Communications, 2021, 18(7): 134-146
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Collaborative vehicular networks is a key enabler to meet the stringent ultra-reliable and low-latency communications (URLLC) requirements. A user vehicle (UV) dynamically optimizes task offloading by exploiting its collaborations with edge servers and vehicular fog servers (VFSs). However, the optimization of task offloading in highly dynamic collaborative vehicular networks faces several challenges such as URLLC guaranteeing, incomplete information, and dimensionality curse. In this paper, we first characterize URLLC in terms of queuing delay bound violation and high-order statistics of excess backlogs. Then, a Deep Reinforcement lEarning-based URLLC-Aware task offloading algorithM named DREAM is proposed to maximize the throughput of the UVs while satisfying the URLLC constraints in a best-effort way. Compared with existing task offloading algorithms, DREAM achieves superior performance in throughput, queuing delay, and URLLC.
Game Theoretical Secure Wireless Communication for UAV-assisted Vehicular Internet of Things
Bo Liu, Zhou Su, Qichao Xu
China Communications, 2021, 18(7): 147-157
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With the ever-expanding applications of vehicles and the development of wireless communication technology, the burgeoning unmanned aerial vehicle (UAV) assisted vehicular internet of things (UVIoTs) has emerged, where the ground vehicles can experience more efficient wireless services by employing UAVs as a temporary mobile base station. However, due to the diversity of UAVs, there exist UAVs such as jammers to degenerate the performance of wireless communication between the normal UAVs and vehicles. To solve above the problem, in this paper, we propose a game based secure data transmission scheme in UVIoTs. Specifically, we exploit the offensive and defensive game to model the interactions between the normal UAVs and jammers. Here, the strategy of the normal UAV is to determine whether to transmit data, while that of the jammer is whether to interfere. We then formulate two optimization problems, i.e., maximizing the both utilities of UAVs and jammers. Afterwards, we exploit the backward induction method to analyze the proposed countermeasures and finally solve the optimal solution. Lastly, the simulation results show that the proposed scheme can improve the wireless communication performance under the attacks of jammers compared with conventional schemes.
AIRIS: Artificial Intelligence Enhanced Signal Processing in Reconfigurable Intelligent Surface Communications
Shun Zhang, Muye Li, Mengnan Jian, Yajun Zhao, Feifei Gao
China Communications, 2021, 18(7): 158-171
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Reconfigurable intelligent surface (RIS) is an emerging meta-surface that can provide additional communications links through reflecting the signals, and has been recognized as a strong candidate of 6G mobile communications systems. Meanwhile, it has been recently admitted that implementing artificial intelligence (AI) into RIS communications will extensively benefit the reconfiguration capacity and enhance the robustness to complicated transmission environments. Besides the conventional model-driven approaches, AI can also deal with the existing signal processing problems in a data-driven manner via digging the inherent characteristic from the real data. Hence, AI is particularly suitable for the signal processing problems over RIS networks under unideal scenarios like modeling mismatching, insufficient resource, hardware impairment, as well as dynamical transmissions. As one of the earliest survey papers, we will introduce the merging of AI and RIS, called AIRIS, over various signal processing topics, including environmental sensing, channel acquisition, beamforming design, and resource scheduling, etc. We will also discuss the challenges of AIRIS and present some interesting future directions.
BER Analysis of NOMA with Max-Min Relay Selection
Meng Shen, Zihao Huang, Xianfu Lei, Lisheng Fan
China Communications, 2021, 18(7): 172-182
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In contrast with orthogonal multiple access (OMA) technologies, non-orthogonal multiple access (NOMA) technology can achieve higher spectral efficiency and support more users simultaneously. Multiple relays can be deployed in NOMA networks, in order to combat fading and thus increase the transmission reliability. In the existing works on cooperative NOMA networks with multiple relays, the decoding error at the relays and the imperfect successive interference cancellation (SIC) are not taken into account simultaneously. In this work, we consider the bit error rate (BER) analysis of relay-aided NOMA networks, where max-min relay selection is employed. Particularly, closed-form BER expressions are derived for NOMA networks with Rayleigh fading, in the existence of the relay decoding error and imperfect SIC. The asymptotic BER performance is then obtained to provide some useful insights into the system design such as the diversity order. Finally, simulation results illustrate the correctness of the derived expressions. The presented results reveal the effects of imperfect SIC and relay decoding error on the performance of cooperative NOMA networks.
Deep Learning Based User Grouping for FD-MIMO Systems Exploiting Statistical Channel State Information
Shupeng Ji, Qisheng Wang, Shiyu Wu, Jiachen Tian, Xiao Li, Wenjin Wang
China Communications, 2021, 18(7): 183-196
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The joint spatial division and multiplexing (JSDM) is a two-phase precoding scheme for massive multiple-input-multiple-output (MIMO) system under frequency division duplex (FDD) mode to reduce the amount of channel state information (CSI) feedback. To apply this scheme, users need to be partitioned into groups so that users in the same group have similar channel covariance eigenvectors while users in different groups have almost orthogonal eigenvectors. In this paper, taking the clustered user model into account, we consider the user grouping of JSDM for the downlink massive MIMO system with uniform planar antenna array (UPA) at base station (BS). A deep learning based user grouping algorithm is proposed to improve the efficiency of the user grouping process. The proposed grouping algorithm transfers the statistical CSI of all users into a picture, and utilizes the deep learning enabled objective detection model you look only once (YOLO) to divide the users into different groups rapidly. Simulation results show that the proposed user grouping scheme can achieve higher sum rate with less time delay.
Information-Defined Networks: A Communication Network Approach for Network Studies
Wenjie Jia, Tao Jiang
China Communications, 2021, 18(7): 197-210
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The research of complex networks facilitates the progress of various disciplines, including biology, chemistry, social science, computer, and communication engineering. Recently, it is popular to utilize complex networks to study the communication networks, such as designing efficient routing strategies and robust communication networks. However, exploiting the advantages of communication networks to investigate networks in various disciplines beyond telecommunications is still in infancy. Because of this situation, this paper proposes an information-defined network (IDN) framework by which a complex network can be abstracted as a communication network associated with multiple intelligent agents. Specifically, each component and dynamic process in this framework can be defined by information. We show that the IDN framework promotes the research of unsolved problems in the current complex network field, especially for detecting new interaction types in real-world networks.
CLORKE-SFS: Certificateless One-Round Key Exchange Protocol with Strong Forward Security in Limited Communication Scenarios
Xiaowei Li, Dengqi Yang, Benhui Chen, Yuqing Zhang
China Communications, 2021, 18(7): 211-222
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Certificateless one-round key exchange (CL-ORKE) protocols enable each participant to share a common key with only one round of communication which greatly saves communication cost. CL-ORKE protocols can be applied to scenarios with limited communication, such as space communication. Although CL-ORKE protocols have been researched for years, lots of them only consider what secrets can be compromised but ignore the time when the secrets have been corrupted. In CL-ORKE protocols, the reveal of the long-term key attacks can be divided into two different attacks according to the time of the long-term key revealed: the attack to weak Forward Security (wFS) and the attack to strong Forward Security (sFS). Many CLKE protocols did not take into account the sFS property or considered sFS as wFS. In this paper, we first propose a new security model for CL-ORKE protocols which considers the sFS property as well as the Ephemeral Key Reveal attack. Then, we give a CL-ORKE protocol which is called CLORKE-SFS. CLORKE-SFS is provably secure under the proposed model provided the Elliptic Curve Computational Diffie-Hellman (ECCDH) and the Bilinear Computational Diffie-Hellman problem (BCDH) assumption hold. The security model and the protocol may give inspiration for constructing one-round key exchange protocols with perfect forward security in certificateless scenarios.
An Intelligent Scheme for Slot Reservation in Vehicular Ad Hoc Networks
Surjeet, Priyanka Bhardwaj, Raghavendra Pal, Nishu Gupta
China Communications, 2021, 18(7): 223-235
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Vehicular ad hoc network is a solution for increasing road traffic demand. Non-safety messages are sent during the service channel interval. The slots during which the messages are sent are not decided prior to the transmission. If the reservation of slots is done during the control channel interval, then the non-safety messages can be transmitted without any collision and thus the network performance can be improved. Further, to improve the network performance, the safety packets can be scheduled in the queue according to the time remaining for which sender and receiver are in the range of each other. This work proposes and evaluates the performance of safety message scheduling and infotainment message reservation through a MAC protocol SSIR-MAC to ensure network stability by transmitting beacons without any collision. The safety messages are queued according to their deadline and the slots for the transmission of non-safety packets are reserved during the control channel itself. Further, a hybrid queue is proposed to decrease the delay of enqueue and dequeue operations. Evaluation through extensive simulation results demonstrates the strength of SSIR-MAC. Comparisons are made with IEEE 802.11p standard and with two existing protocols which are relevant to the proposed work.
Joint Computing and Communication Resource Allocation for Satellite Communication Networks with Edge Computing
Shanghong Zhang, Gaofeng Cui, Yating Long, Weidong Wang
China Communications, 2021, 18(7): 236-252
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Benefit from the enhanced onboard processing capacities and high-speed satellite-terrestrial links, satellite edge computing has been regarded as a promising technique to facilitate the execution of the computation-intensive applications for satellite communication networks (SCNs). By deploying edge computing servers in satellite and gateway stations, SCNs can achieve significant performance gains of the computing capacities at the expense of extending the dimensions and complexity of resource management. Therefore, in this paper, we investigate the joint computing and communication resource management problem for SCNs to minimize the execution latency of the computation-intensive applications, while two different satellite edge computing scenarios and local execution are considered. Furthermore, the joint computing and communication resource allocation problem for the computation-intensive services is formulated as a mixed-integer programming problem. A game-theoretic and many-to-one matching theory-based scheme (JCCRA-GM) is proposed to achieve an approximate optimal solution. Numerical results show that the proposed method with low complexity can achieve almost the same weight-sum latency as the Brute-force method.
Energy Model for UAV Communications: Experimental Validation and Model Generalization
Ning Gao, Yong Zeng, Jian Wang, Di Wu, Chaoyue Zhang, Qingheng Song, Jachen Qian, Shi Jin
China Communications, 2021, 18(7): 253-264
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Wireless communication involving unmanned aerial vehicles (UAVs) is expected to play an important role in future wireless networks. However, different from conventional terrestrial communication systems, UAVs typically have rather limited onboard energy on one hand, and require additional flying energy consumption on the other hand. This renders energy-efficient UAV communication with smart energy expenditure of paramount importance. In this paper, via extensive flight experiments, we aim to firstly validate the recently derived theoretical energy model for rotary-wing UAVs, and then develop a general model for those complicated flight scenarios where rigorous theoretical model derivation is quite challenging, if not impossible. Specifically, we first investigate how UAV power consumption varies with its flying speed for the simplest straight-and-level flight. With about 12,000 valid power-speed data points collected, we first apply the model-based curve fitting to obtain the modelling parameters based on the theoretical closed-form energy model in the existing literature. In addition, in order to exclude the potential bias caused by the theoretical energy model, the obtained measurement data is also trained using a model-free deep neural network. It is found that the obtained curve from both methods can match quite well with the theoretical energy model. Next, we further extend the study to arbitrary 2-dimensional (2-D) flight, where, to our best knowledge, no rigorous theoretical derivation is available for the closed-form energy model as a function of its flying speed, direction, and acceleration. To fill the gap, we first propose a heuristic energy model for these more complicated cases, and then provide experimental validation based on the measurement results for circular level flight.
Joint Topology Construction and Power Adjustment for UAV Networks: A Deep Reinforcement Learning Based Approach
Wenjun, Xu, Huangchun Lei, Jin Shang
China Communications, 2021, 18(7): 265-283
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In this paper, we investigate a backhaul framework jointly considering topology construction and power adjustment for self-organizing UAV networks. To enhance the backhaul rate with limited information exchange and avoid malicious power competition, we propose a deep reinforcement learning (DRL) based method to construct the backhaul framework where each UAV distributedly makes decisions. First, we decompose the backhaul framework into three submodules, i.e., transmission target selection (TS), total power control (PC), and multi-channel power allocation (PA). Then, the three submodules are solved by heterogeneous DRL algorithms with tailored rewards to regulate UAVs' behaviors. In particular, TS is solved by deep-Q learning to construct topology with less relay and guarantee the backhaul rate. PC and PA are solved by deep deterministic policy gradient to match the traffic requirement with proper fine-grained transmission power. As a result, the malicious power competition is alleviated, and the backhaul rate is further enhanced. Simulation results show that the proposed framework effectively achieves system-level and all-around performance gain compared with DQL and max-min method, i.e., higher backhaul rate, lower transmission power, and fewer hop.
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