Main Conference
Track 4 – Emerging Technologies, Architectures and Services
Session T4-S10
Crowdsourcing and Incentive Mechanism
Conference
10:40 AM
—
12:10 PM KST
Local
May 27 Wed, 6:40 PM
—
8:10 PM PDT
Microscopic Traffic Monitoring and Data Collection Cloud Platform Based on Aerial Video
Guan-Wen Chen, Tzu-Chuan Yeh, Ching-Yu Liu and Tsì-Uí İk (National Chiao Tung University, Taiwan)
0
Real-time traffic video streaming, such as roadside surveillance and aerial video, has been widely used in traffic monitoring nowadays. However, most of the traditional traffic data collection methods lack mobility that can only collect macroscopic data. In this paper, an intelligent traffic monitoring system based on an open source cooperative platform called SAGE2 was developed. Based on the integrated big screen TV wall of SAGE2, a map-based aerial traffic video streaming management interface was designed. In the image pre-processing section, it provides functions such as lens distortion removal, top view projection transforms, and video stabilization; simulate video streaming to provide instant and long-term micro-flow data collection. Micro-traffic flow data provides high-resolution information both in time and space which can be used to analyze the driving behavior of individuals and the public. Combined with the lane level map, it can provide a variety of visual vehicle flow presentations, such as intersection traffic distribution that can also be used to develop an innovative application in the future.
Blockchain-Enabled Computing Resource Trading: A Deep Reinforcement Learning Approach
Zixuan Xie and Run Wu (Sun Yat-sen University, China); Miao Hu (Sun Yat-Sen University, China); Haibo Tian (Sun Yat Sen University, China)
1
Driven by the vision of the Internet of Things (IoT) under the fifth-generation (5G) wireless network, computing resource trading attracts numerous attention from both academia and industry. Prior works mainly focus on the design of auction mechanisms to implement pricing and resource allocation. However, it is still a challenging problem because of the following three aspects: 1) How to ensure that the auction mechanism runs fairly? An auction mechanism is vulnerable and questionable since the auctioneer may fail the orders matching operation or collude with a few peers. 2) It's hard to assign the computing resources of providers to customers and guarantee reasonable rewards for each participator. 3) How to make bidding strategies for each participator? Each participator has its willingness to sell/buy, which are time-variant and private. To address the above issues, we build a blockchain-enabled computing resource trading system that takes both pricing and bidding strategies into consideration, on which providers and customers can trade computing resources securely, safely and willingly. Next, we formulate a decision-making problem in the continuous double auction (CDA) to maximize their payoffs. Then, we propose a universal model-free Deep Reinforcement Learning (DRL) framework for both computing resource providers and customers. We conduct extensive experiments to evaluate the performance of our DRL framework. Simulation results show that our solution outperforms others in both static and dynamic scenarios. Our DRL framework can achieve higher rewards than others by at least 35%. Furthermore, the average trading price from our DRL framework is less volatile than that from the compared methods. The DRL framework promotes trading and brings larger trading quantities, thus resulting in higher social welfare by at least 25% than the compared schemes.
Rating-aware Pre-cache and Incentive Mechanism Design in Data Offloading
Yiting Luo and Fen Hou (University of Macau, Macao); Bin Lin (Dalian Maritime University, China); Guanghua Yang (Jinan University, China)
0
Pre-caching popular contents in advance at the network edge such as base stations is a promising method to improve the service quality by reducing the transmission cost and network congestion. In this paper, by jointly considering the mobile users' rating on different contents into the incentive mechanism design, we proposed a rating-aware incentive mechanism for efficiently selecting some BSs to pre-cache the popular contents. The proposed mechanism can achieve higher performance compared with other existing methods in terms of the social welfare. In specific, the proposed mechanism can improve the achieved social welfare by 11.75% and 9.97% compared with the mechanism of random caching and the caching based on bid price, respectively. In addition, the proposed mechanism satisfies the nice properties of individual rationality and truthfulness.
Incentive Mechanism Design for Mobile Data Rewards using Multi-Dimensional Contract
Zehui Xiong (Nanyang Technological University, Singapore); Wei Yang Bryan Lim (Nanyang Technological University & Alibaba Group, Singapore); Jiawen Kang and Dusit Niyato (Nanyang Technological University, Singapore); Ping Wang (York University, Canada); Chunyan Miao (Nanyang Technological University, Singapore)
1
Mobile data rewards is now leading a new economic trend in wireless networks, where the operators stimulate mobile users to view ads with data rewards and ask for corresponding payments from advertisers. Yet, due to the uncertain nature of users' preferences, it is always challenging for the advertiser to find the best choice of data rewards to attain an optimum balance between ad revenue and rewards spent. In this paper, we develop a general contract-theoretic framework to address the problem of data rewards design in a realistic asymmetric information scenario, where each user is associated with multi-dimensional private information. Specifically, we model the interplay between the advertiser and users by using a multi-dimensional contract design approach, and theoretically analyze optimal data rewarding schemes. To ensure global incentive compatibility, we convert the multi-dimensional contract problem into an equivalent one-dimensional contract problem. Necessary and sufficient conditions for an optimal and feasible contract are then derived to provide incentives for engagement of users in data rewarding scheme. We leverage numerical results to evaluate the performance of the designed multi-dimensional contract for data rewarding scheme.
VCG-QCP:A Reverse Pricing Mechanism Based on VCG and Quality All-pay for Collaborative Crowdsourcing
Lifei Hao, Bing Jia, Jingbin Liu, Baoqi Huang and Wuyungerile Li (Inner Mongolia University, China)
0
With the rapid development of the Internet and combined with outsourcing, a new paradigm - crowdsourcing which shines brilliantly as a new labor mode. However, the existing pricing strategies for crowdsourcing tasks have several undesirable problems, e.g., no universal pricing model, not meeting the multiple requirements of users, pricing rely too much on decision makers, etc., which bring an unreasonable allocation of task rewards so as to make the pricing results subjective and uncontrollable. Therefore, this paper proposes a reverse pricing mechanism based on VCG and quality all-pay for collaborative crowdsourcing (VCG-QCP). The actual crowdsourcing scenario is considered with VCG mechanism, and the concept of quality all-pay is introduced to evaluate the work quality of workers who might perform the task. Then a general reverse pricing model is established by mathematical modeling, and the pricing algorithm is designed based on this model. Simulations show that the proposed method can achieve higher algorithm efficiency, higher task completion quality, a reasonable balance of benefits between employers and workers, and ensuring the truthfulness of workers' bidding.
Session Chair
Won-Yong Shin (Yonsei University, Korea)
Session T4-S9
Recognition and Prediction
Conference
10:40 AM
—
12:10 PM KST
Local
May 27 Wed, 6:40 PM
—
8:10 PM PDT
Performance Evaluation of Driving Behavior Identification Models through CAN-BUS Data
Mozhgan Nasr Azadani and Azzedine Boukerche (University of Ottawa, Canada)
0
Modern cars can collect several hundreds of sensor data through the controller area network (CAN) bus technology that provides almost real-time information about the car, the surrounding environment, and the driver. These data can be later processed and analyzed to offer efficient solutions and insights for human behavior analysis and further applied in a variety of fields such as accident prevention, driver identification, driving models design, and vehicle energy consumption. By analyzing and identifying unique driving behavior, we can distinguish drivers, which can be helpful in driver profiling and security of the cars (anti-theft systems). In this paper, we evaluate the performance of data-driven end-to-end models designed for driving behavior identification. We present a critical analysis of the principles considered in designing the models. Moreover, various data-driven deep learning and machine learning models are implemented and the cross-validation results are presented employing the naturalistic driving dataset.
PathExtractor: A Path-Semantic extraction Algorithm for Mobility Prediction
Zhuang Su, Ying Wang and Zhengwei Lyu (Beijing University of Posts and Telecommunications, China)
0
In recent years, mobility prediction has attracted much attention. Prediction methods include two steps, extracting spatial-temporal features to convert the trajectory to location sequences and constructing a model to make further predictions. Traditional methods often define the location as grids or points of interest (POIs) in mining spatial-temporal features. But these methods may not perform well in prediction because of losing detailed information of trajectories. Thus, a novel location semantics is necessary to compress detailed trajectories. In this paper, a path semantics extraction method, PathExtractor was proposed to extract typical paths and build path sequences, which contains complete information of trajectories. Furthermore, to verify that path sequences can effectively express movement patterns, the prediction is performed by constructing a recurrent neural network model. Finally, in order to evaluate the application value of path semantics, path similarity is used as performance indicator, and experiments prove the accuracy of path prediction and geographical precision higher than others.
Detection of Suspicious Objects Concealed by Walking Pedestrians Using WiFi
Bao Zhou, Zuona Chen, Ziyuan Gong and Rui Zhou (University of Electronic Science and Technology of China, China)
0
Security is of vital importance in public places. Detection of suspicious objects such as metal and liquid often requires dedicated and expensive equipment, preventing its wide deployment. This paper proposes a pervasive device-free method to detect suspicious objects concealed by walking pedestrians using WiFi Channel State Information (CSI). By analyzing the different variations of subcarrier amplitude caused by different materials, the proposed method is able to detect suspicious objects such as metal and liquid concealed by pedestrians, when they walk through the transmission link of the WiFi transmitter and receiver. The proposed method employs Convolutional Neural Network (CNN) to classify suspicious objects, on which majority voting is applied to vote for the final result, in order to improve the detection accuracy for walking pedestrians. Evaluations show that the proposed method with majority voting achieve the detection accuracy of 93.3% for metal and liquid concealed by walking pedestrians, 95.6% for exposed metal and liquid carried by walking pedestrians, and 100% for metal and liquid carried by standing pedestrians.
Human Motion Patterns Recognition based on RSS and Support Vector Machines
Sameer Ahmad Bhat and Abolfazl Mehbodniya (Kuwait College of Science and Technology, Kuwait); Ahmed Elsayed Al Wakeel (KCST University, Kuwait); Julian L Webber (Osaka University & Advanced Telecommunications Research Institute International, Japan); Khalid Albegain (Kuwait College of Science and Technology, Kuwait)
0
In this paper, we propose a novel received signal strength (RSS) and machine learning (ML) based system for recognizing human motion walking patterns. Our proposed system eliminates the need to attach sensors on a human body, thereby enabling a non-invasive monitoring system. The proposed system model exploits the potential of a single channel RSS in precisely identifying unique human motion patterns in indoor environments and implements a support vector machine (SVM) algorithm for higher pattern detection accuracy. We validate our proposed model by developing a testbed setup based on state-of-the-art Software Defined Radios (SDRs) and provide a comparative analysis of machine learning models used in the patterns classification process. Our study results reveal that unique walking patterns embedded within RSS and with machine learning classifier, can precisely help in identifying human motion patterns with detection accuracy of approximately 99 percent. The study results impact research scholars actively engaged in developing human motion recognition systems, intrusion detection systems, or healthcare monitoring systems, and in those developing innovative and efficient techniques for monitoring and control systems.
Location-Free CSI Based Activity Recognition With Angle Difference of Arrival
Yanan Li (Beijing University of Posts and Telecommunications, China); Ting Jiang (Beijing University of Posts & Telecommunications, China); Xue Ding and Yangyang Wang (Beijing University of Posts and Telecommunications, China)
0
Device-free activity recognition is an indispensable technology in Human-Computer Interaction (HCI). The activity recognition system based on WiFi signals relying on the wide coverage of WiFi makes HCI more convenient. The previous research on WiFi-based activity recognition system has achieved high recognition accuracy. While the challenge that activity recognition is limited to fixed location and complex background, remains unresolved. In this paper, we propose a location-free activity recognition system which leverages fine-grained channel state information (CSI) to recognize same activities regardless of different locations and background. With CSI recorded in the Network Interface Card (NIC), Angle Difference of Arrival (ADoA) is reckoned to eliminate the location and background information, which is only consistent with the activity tendency. Then the Principal Component Analysis (PCA) method is utilized to reduce the dimension and followed by curve smoothing to make the signal more smoother. Furthermore, Bidirectional Long Short-Term Memory (BiLSTM) network is selected as ideal training machine to deal with issues that are highly correlated with time series. We use two commercial wireless network cards in the typical life scene, and finally achieve 93.7% of recognition accuracy.
Session Chair
Hyun Jong Yang (UNIST, Korea)
Session T4-S11
Low Power and IoT
Conference
2:00 PM
—
3:30 PM KST
Local
May 27 Wed, 10:00 PM
—
11:30 PM PDT
NB-IoT Micro-Operator for Smart Campus: Performance and Lessons Learned in 5GTN
Rumana Yasmin (University of Oulu, Finland); Konstantin Mikhaylov (University of Oulu & Solmu Technologies OY, Finland); Muhammad Arif (University of Oulu, Finland); Ari T. Pouttu (Centre for Wireless Communications University of Oulu, Finland); Ville Niemelä (Centre for Wireless Communications, Finland); Olli Liinamaa (Centre for Wireless Communications University of Oulu, Finland)
1
In recent years, many new radio-based connectivity solutions for the Internet of Things (IoT) have been proposed. At the same time, development towards the 6G has brought on the stage the new business concepts. One of them is the concept of the micro-operator and implies the local entities to act as the telecom infrastructure owner and provider in their premises. In the current paper, we discuss the deployment and report the practical performance of a single-cell NB-IoT deployed as a part of the 5G Test Network (5GTN) and controlled by a smart-campus micro-operator. The practical measurements reported in the paper have been carried in the University of Oulu within a huge interconnected indoor environment with the total floor area of 188 600 m2. Our results demonstrate that the NB-IoT technology is a viable connectivity solution for various non-critical machine-based applications deployed indoors, highlight the practical performance of this technology, and reveal some practical specifics and challenges for acting as an IoT micro-operator.
Collaborative Learning Model for Cyberattack Detection Systems in IoT Industry 4.0
Tran Viet Khoa (VNU University of Engineering and Technology, Vietnam); Yuris Mulya Saputra (University of Technology Sydney, Australia & Universitas Gadjah Mada, Indonesia); Hoang Thai Dinh (University of Technology Sydney (UTS), Australia); Nguyen Linh Trung (Vietnam National University, Hanoi, Vietnam); Diep N. Nguyen (University of Technology Sydney, Australia); Nguyen Viet Ha (VNU Ha Noi, Vietnam); Eryk Dutkiewicz (University of Technology Sydney, Australia)
2
Although the development of IoT Industry 4.0 has brought breakthrough achievements in many sectors, e.g., manufacturing, healthcare, and agriculture, it also raises many security issues to human beings due to a huge of emerging cybersecurity threats recently. In this paper, we propose a novel collaborative learning-based intrusion detection system which can be efficiently implemented in IoT Industry 4.0. In the system under consideration, we develop smart "filters" which can be deployed at the IoT gateways to promptly detect and prevent cyberattacks. In particular, each filter uses the collected data in its network to train its cyberattack detection model based on the deep learning algorithm. After that, the trained model will be shared with other IoT gateways to improve the accuracy in detecting intrusions in the whole system. In this way, not only the detection accuracy is improved, but our proposed system also can significantly reduce the information disclosure as well as network traffic in exchanging data among the IoT gateways. Through thorough simulations on real datasets, we show that the performance obtained by our proposed method can outperform those of the conventional machine learning methods.
An Efficient Downlink Receiver Design for NB-IoT
Shutao Zhang, Shiying Zeng and Fenglin Ye (Sun Yat-sen University, China); Ruibo Tang (China Electronics Technology Group Corporation No. 7 Research Institute, China); Peiran Wu and Minghua Xia (Sun Yat-sen University, China)
2
As of the specification Release 13 completed by the 3rd Generation Partnership Project (3GPP) in June 2016, narrowband Internet-of-Things (NB-IoT) has attracted great attention in both academia and industry. Some new features were further specified in subsequent Releases 14 and 15. In light of these specifications, efficient downlink receiver design is critical to the implementation of NB-IoT, due to the strictly limited hardware resources at a receiver. Conforming to Release 15, this paper develops an efficient downlink receiver by jointly accounting for the synchronization, channel estimation and soft combination for repetitive transmissions. Simulation results demonstrate that both the detection probability for the narrowband primary synchronization signal (NPSS) and the block error rate (BLER) for the narrowband physical downlink sharing channel satisfy the benchmarks designated by 3GPP.
An Experimental Performance Evaluation of Bluetooth Mesh Technology for monitoring Applications
Eduardo De Leon and Majid Nabi (Eindhoven University of Technology, The Netherlands)
0
The introduction of Bluetooth Low Energy (BLE) in 2010 provided constrained devices with a wireless point-to- point communication standard. It facilitates the creation of pico-nets and reducing product development time and cost. It is until 2017 that the Bluetooth special interest group releases the Mesh Profile allowing a multi-hop interconnection through BLE's advertisements. Being a relatively new technology, this paper aims to experimentally evaluate its performance and investigate the limits of the technology in terms of data delivery capacity in monitoring applications. Several experiments are performed by deploying a number of BLE nodes in an office environment, making a multi-hop network. The performance of the network in terms of packet delivery to a base station is measured in each experiment. Moreover, experiments including mobile nodes are carried out under the multi-hop setup to test the behaviour of the protocol when some nodes move around. The experimental results show that the relay nodes impose critical limitations for message delivery in multi-hop networks, limiting the usage of the BLM technology for many monitoring applications.
Extending BLE Beacon Lifetime by a Novel Neural Network-driven Framework
Kang Eun Jeon, James She and Tat Yuen Simon Wong (Hong Kong University of Science and Technology, Hong Kong)
0
Bluetooth Low Energy (BLE) beacon networks are a popular infrastructure for IoT and smart city applications due to their scalability and affordability, as well as the proliferation of Bluetooth-enabled devices. However, BLE beacon networks suffer from short battery lifetime, inducing additional maintenance costs. Previous works have tackled this problem by proposing a more energy-efficient BLE beacon firmware that will change its operating configuration based on user existence information. However, previous efforts could not adapt to varying user traffic conditions and therefore was impractical. To address this issue, this paper proposes a novel neural network-driven framework, User-P, that extends beacon lifetime by changing its operating configuration by predicting user traffic conditions. Furthermore, the paper also presents a novel machine learning method tailored for user traffic prediction. Last but not least, the effectiveness of the proposed framework and methods are proven through a set of simulations. The simulation results show that the proposed framework can extend the beacon lifetime by 180% in comparison to that of the state-of-the-art techniques.
Session Chair
Joohyun Lee (Hanyang University, Korea)
Session T4-S12
Communications with UAVs
Conference
2:00 PM
—
3:30 PM KST
Local
May 27 Wed, 10:00 PM
—
11:30 PM PDT
Power Limited Ultra-Reliable and Low-Latency Communication in UAV-Enabled IoT Networks
Kanghua Chen, Ying Wang, Zixuan Fei and Xue Wang (Beijing University of Posts and Telecommunications, China)
0
Ultra-reliable and low-latency communication (URLLC) is proposed as one of the three key services of 5G for Internet of Things (IoT), especially for mission-critical applications. This paper investigates the minimum power of devices in uplink in IoT networks for URLLC. Unmanned aerial vehicles (UAVs) are utilized to assistant the IoT system because they have flexible deployment and high probability to establish line-of-sight (LoS) communication links. First, we formulate a minimum average transmit power problem under the constraints of latency and reliability in modern industry. The deployment of UAVs and device association need to be jointly optimized, making the problem non-linear and non-convex. Then the block error probability which characterizes the reliability is derived under finite blocklength regime and an iteration algorithm is proposed. Additionally, the minimum average transmit power of IoT devices in URLLC is also calculated by deploying different number of UAVs. Simulation results are presented to show that the transmit power can be greatly reduced by appropriately deploying more UAVs or relaxing the tolerance of latency.
Joint Optimization of UAV Trajectory and User Scheduling Based on NOMA Technology
Xuemeng Wu, Zaixue Wei, Zhenqiao Cheng and Xin Zhang (Beijing University of Posts and Telecommunications, China)
0
Unmanned aerial vehicles (UAVs) have been widely used in the past few decades due to their high maneuverability. This paper studies a downlink UAV air-ground wireless network based on non-orthogonal multiple access (NOMA) technology, where a UAV is deployed as an aerial base station to provide periodic service for a group of users. With the purpose of maximizing the minimum sum rate of system in the limited time, an iterative algorithm for jointly optimizing user scheduling and UAV trajectory is proposed. In order to find the optimal users to communicate with, we present a method of user partitioning based on k-Means clustering algorithm and a strategy for selecting subset to schedule on the basis of NOMA technology. The maximum sum rate of edge users in the NOMA-based UAV communication system is also considered. The simulation results are provided to show the optimal UAV trajectories with different objective functions, and to indicate that the NOMA-based UAV system provides the benefits of achieving a better air-ground communication compared to OMA scheme and other benchmark schemes.
Enhancing Cellular Communications for UAVs via Intelligent Reflective Surface
Dong Ma (University of New South Wales, Australia); Ming Ding (Data 61, Australia); Mahbub Hassan (University of New South Wales, Australia)
0
Intelligent reflective surfaces (IRSs) capable of reconfiguring their electromagnetic absorption and reflection properties in real-time are offering unprecedented opportunities to enhance wireless communication experience in challenging environments. In this paper, we analyze the potential of IRS in enhancing cellular communications for UAVs, which currently suffers from poor signal strength due to the down-tilt of base station antennas optimized to serve ground users. We consider deployment of IRS on building walls, which can be remotely configured by cellular base stations to coherently direct the reflected radio waves towards specific UAVs in order to increase their received signal strengths. Using the recently released 3GPP ground-to-air channel models, we analyze the signal gains at UAVs due to the IRS deployments as a function of UAV height as well as various IRS parameters including size, altitude, and distance from base station. Our analysis suggests that even with a small IRS, we can achieve significant signal gain for UAVs flying above the cellular base station. We also find that the maximum gain can be achieved by optimizing the location of IRS including its altitude and distance to BS.
UAV-based Air-to-Ground Channel Modeling for Diverse Environments
Muhammad Usaid Akram, Usama Saeed and Syed Ali Hassan (National University of Sciences and Technology, Pakistan); Haejoon Jung (Incheon National University, Korea (South))
0
In recent years, unmanned aerial vehicles (UAVs) have been deployed in a range of new applications such as remote surveillance, package delivery and relief operations. The existing scenario of next-generation communications systems envisions the use of UAVs as low altitude platforms (LAPs) as one of the enabling technologies of next-gen networks. Telecom operators have been exploring low-altitude UAV-based communications solutions for on-demand deployment. The emerging possibilities of UAVs in air-to-ground (AG) communication necessitate accurate channel models in order to facilitate the design and implementation of such AG links. However, the propagation channels of Pakistan and in general the South Asian region have not been as of yet widely investigated. In this paper, a comprehensive study is presented on the air-to-ground channel parameters along with details of measurement campaigns as well as the limitations of this work and future research directions.
Optimizing Transmission and Propulsion Powers for Flying Base Stations
Mohammadsaleh Nikooroo and Zdenek Becvar (Czech Technical University in Prague, Czech Republic)
0
Unmanned aerial vehicles acting as flying base stations (FlyBSs) have been considered as an efficient tool to enhance capacity of mobile networks and to facilitate communication in emergency cases. The enhancement provided by such network necessitates a dynamic positioning of the FlyBSs with respect to the users. Despite that, the power consumption of the FlyBS remains an important issue to be addressed due to limitations on the capacity of FlyBS's batteries. In this paper, we propose a novel solution combining a transmission power control and the positioning of the FlyBS in order to ensure quality of service to the users while minimizing total consumed power of the FlyBS. We derive a closed-form solution for joint transmission and propulsion power optimization in a single future step. Moreover, we also provide a numerical method to solve the joint propulsion and transmission power optimization problem when a realistic (i.e. inaccurate) prediction of the users' movement is available. According to the simulations, the proposed scheme brings up to 26% of total FlyBS's power saving compared to existing solutions.
Session Chair
Han Seung Jang (Chonnam National University, Korea)
Session T4-S13
Hybrid Satellite Networks
Conference
4:00 PM
—
5:30 PM KST
Local
May 28 Thu, 12:00 AM
—
1:30 AM PDT
Content Delivery for High-Speed Railway via Integrated Terrestrial-Satellite Networks
Xinmu Wang, Hewu Li, Wenbing Yao, Tianming Lan and Qian Wu (Tsinghua University, China)
0
The rapid development of high-speed railway (HSR) system draws great attention and challenges the current broadband Internet access with high mobility. Though LTE-A networks can provide 100 Mbps throughput within a cell for a train speeding up to 350 km/h, the frequent handovers and service disruptions remain to be handled. This problem is particularly prominent for the delivery of a large volume of contents which demands high throughput and continuous connections. Besides the terrestrial cellular system, a satellite can also provide wireless broadband access with less frequent handovers. Recent advances in low earth orbit (LEO) satellite networks also prove to be practical for content delivery with acceptable delay. Therefore, we present a solution based on the integrated terrestrial-satellite network (ITSN) for high throughput and continuous connectivity. Multipath TCP (MPTCP) protocol is adopted to support multi-bearer communications and we apply network coding to further optimize the performance of MPTCP. Considering the mobility patterns of the HSR and satellite, we propose a scheduling and resource allocation mechanism with the prediction of the handovers and channel situation information (CSI). Cache assisted femto cells are implemented to aggregate the traffic demands and proactively cache the requested contents. Numerical results demonstrate that our solution well resists to the dynamic network conditions and improves the network performance and content delivery efficiency.
Switching Algorithm Based On Monte Carlo-Markov Decision Under Space-Air-Ground Integrated Network
Zhuoran Zhou, Ke Wang, Zhongliang Deng and Wenliang Lin (Beijing University of Posts and Telecommunications, China); Yun Liu (The 54th Research Institute of China Electronics Technology Group Corporation, China)
0
As one of the next-generation mobile network visions, the Space-Air-Ground integrated network is an inevitable trend of the future network, and research on heterogeneous network switching algorithms under the Space-Air-Ground integrated network becomes more important. Existing heterogeneous network switching algorithms usually use fixed weights of attribute to make decisions, but when the single or multiple attributes of multiple networks are too different, users will be connected to the same network. In the Space-Air-Ground integrated network, large differences in networks between heterogeneous networks, such as delay, will result in excessive load on a single network. In this paper, we proposed the Monte Carlo-Markov decision process (MC-MDP) algorithm to balance the network load of multiple networks. It can dynamically adjust the access networks of users in the system while considering the user's service requirements and network differences. Monte Carlo method is used to improve the convergence speed of the Markov decision process (MDP) algorithm. Numerical results confirm the MC-MDP can improve the bandwidth resource utilization efficiency of the heterogeneous network and the convergence speed of the MDP algorithm.
How Capacity is Influenced by Ultra-dense LEO Topology in Multi-terminal Satellite Systems?
Ruoqi Deng (Peking University, China); Boya Di (Imperial College London, United Kingdom (Great Britain) & Peking University, China); Lingyang Song (Peking University, China)
0
In this paper, we consider an uplink ultra-dense LEO-based multi-terminal satellite system where each ground terminal station connects to multiple satellites for data transmission. Benefited from the dense satellite constellation, high channel capacity can be achieved via the spatial diversity of multiple satellites. To evaluate the multi-satellite channel capacity performance, we first derive the lower bound and upper bound of the channel capacity in uplink LEO-based multi-terminal systems with multiple single-antenna satellites. Based on the capacity bounds, we theoretically prove that the capacity grows almost linearly with the number of satellites, and there exists an optimal LEO satellite distribution to achieve the maximum capacity of the system. We then illustrate that such statements also hold for the multi-antenna satellite case where the upper bound of the channel capacity and the lower bound of the achievable rate after receive beamforming are derived separately. Simulation results verify our theoretical analysis.
Patch Antenna Arrays Beam Steering for Enhanced LEO Nanosatellite Communications
Néstor J. Hernández Marcano and Hannes Bartle (Aarhus University, Denmark); Rune Hylsberg Jacobsen (Aarhus University & Electrical and Computer Engineering, Denmark)
3
Given the growing demand of high-performance communication solutions on high-constraint Low Earth Orbit (LEO) small satellites, in this work we propose a set of designs of patch antenna arrays for CubeSats in the X band that are suitable for satellite-to-ground and Inter-Satellite Link (ISL) communications for LEO. In our analysis we consider the unit cell geometry as well as the array design as parameters. The parameter space is evaluated using the Finite Element Method (FEM) analysis software CST Microwave Studio. We transfer the designs evaluated in CST to AGI Systems ToolKit (STK) to evaluate the influence of each parameter on the link budget for different passes and attitude noise conditions. Our results show that it is possible to achieve a 12-15 dB gain in the link budget for the given scenarios. We also observe that such antenna arrays can provide satisfactory attitude inaccuracy compensation with a phase shifter quantization as low as 2 bits.
Collaborative Transmission in Hybrid Satellite-Terrestrial Networks: Design and Implementation
Yinan Jia and Jiaxin Zhang (Beijing University of Posts and Telecommunications, China); Peng Wang and Liangjingrong Liu (Beijing University of Post and Telecommunications, China); Xing Zhang and Wenbo Wang (Beijing University of Posts and Telecommunications, China)
0
With the rapid development of 5G technology, there is an increasing demand of high-definition (HD) video service, so that efficient content transmission is expected to guaranteed the quality of experience of users. However, traditional terrestrial networks can hardly support this kind of service due to the limited coverage and capability especially in scene of remote area or peak hours of hotspots. Under the support of High Throughput Satellite, satellite can serve as a supplement in hybrid satellite-terrestrial networks (HSTN) to provide various of services. Specifically, aggregation in packet level for collaborative transmission between satellite and terrestrial networks is in direction of development which should be reconsidered. In this paper, a classic SDN-aware HSTN architecture is adopted to capture content information of the system and make strategy dynamically for efficient distribution of content in finer scale. Key technologies, including tag method, path selection strategy and reordering scheme, are proposed to achieve collaborative transmission of HD videos. Finally, complete implementation of a prototype, a Hardware In the Loop (HIL) platform with the SITL module of OPNET, is built to illustrate the feasibility and effectiveness of the proposed solutions. Numerical results show that collaborative transmission in HSTN can effectively realize link aggregation, which has great significance for complex conditions in future networks.
Underlay Cognitive Hybrid Satellite-Terrestrial Networks with Cooperative-NOMA
Vibhum Singh, Vinay Bankey and Prabhat Kumar Upadhyay (Indian Institute of Technology Indore, India)
1
In this paper, we investigate the performance of an underlay cognitive hybrid satellite-terrestrial network comprising a primary satellite source with its terrestrial receiver and the secondary transmitter (ST) with its pre-paired users on the ground. Herein, the ST employs a cooperative non-orthogonal multiple access (C-NOMA) scheme in which a nearby NOMA user acts as a relay and detects and forwards the information of the far-away NOMA user during the cooperation phase. Further, the widely adopted shadowed-Rician fading and Nakagami-m fading models are considered for the pertinent hybrid channels. For this overall set-up, we obtain a novel closed-form expression for the outage probability of secondary network in the presence of primary interference power constraint imposed by the adjacent primary satellite network. Finally, our analytical findings are corroborated through various numerical and simulation results.
Session Chair
Hyoil Kim (Ulsan National Institute of Science and Technology (UNIST), Korea)
Session T4-S14
mmWave and Optical Wireless
Conference
4:00 PM
—
5:30 PM KST
Local
May 28 Thu, 12:00 AM
—
1:30 AM PDT
Performance Evaluation of Spectrum Sharing in mmWave Cellular Networks using Ray-Tracing
Constantinos Vrontos (University of Bristol, United Kingdom (Great Britain)); Federico Boccardi (Ofcom, United Kingdom (Great Britain)); Simon Armour and Evangelos Mellios (University of Bristol, United Kingdom (Great Britain)); Joe Butler (Ofcom, United Kingdom (Great Britain))
1
In order to meet the expectations of future generation mobile networks, the mmwave spectrum has been considered along with new spectrum utilization methods that will ensure wider bandwidths and improved spectrum utilization efficiency. Spectrum sharing enables mobile operators to share all available resources, at anytime and anywhere. By doing so, there is a significant increase in the inter-operator interference. At the same time, the propagation characteristics of mmwave frequencies and the deployment of directional antenna beamforming can contribute towards lower interference and higher SINR levels. This paper investigates the performance of a multi-operator network that uses spectrum sharing and how it compares to the traditional exclusive license model. While
previous works on this topic considered more theoretic assumptions and methodologies, this paper looks at this problem from a more realistic perspective. It employs a channel model obtained by ray tracing a real world environment and utilizes detailed antenna array modelling based on measurements of a real patch antenna. Our simulation results for a multi-operator mmwave mobile network show that spectrum sharing always outperforms the exclusive license model. Given that extended simulations for higher network layers were not implemented, the purpose of this paper is to provide an accurate comparison
between the performances of the models under investigation and not to test their actual performance. Spectrum sharing proved to be beneficial even for the lowest SINR users without employing any coordination techniques or other interference mitigation mechanisms.
previous works on this topic considered more theoretic assumptions and methodologies, this paper looks at this problem from a more realistic perspective. It employs a channel model obtained by ray tracing a real world environment and utilizes detailed antenna array modelling based on measurements of a real patch antenna. Our simulation results for a multi-operator mmwave mobile network show that spectrum sharing always outperforms the exclusive license model. Given that extended simulations for higher network layers were not implemented, the purpose of this paper is to provide an accurate comparison
between the performances of the models under investigation and not to test their actual performance. Spectrum sharing proved to be beneficial even for the lowest SINR users without employing any coordination techniques or other interference mitigation mechanisms.
Studies of Flatness of LiFi Channel for IEEE 802.11bb
Ardimas Andi Purwita (University of Edinburgh, United Kingdom (Great Britain)); Harald Haas (The University of Edinburgh, United Kingdom (Great Britain))
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A task group named IEEE 802.11 Light Communications Amendment - Task Group "bb" (TGbb) was established in July 2018. By bringing light-fidelity (LiFi) technology into the WiFi ecosystem, LiFi can take advantage of the globally recognized WiFi brand, while also improving its capability due to the fact that LiFi does not interfere with WiFi. Early discussions in the task group focused on the physical (PHY) layer. There are two major proposals for the PHY layer. The first one is to use the existing IEEE 802.11 chipsets with LiFi analog front-ends. This is done by means of the frequency up and down-conversions and adding a DC bias. The second proposal is to redefine a whole PHY layer and optimize it by means of adopting adaptive bit loading in order to combat the low-pass filter characteristics of the non-line- of-sight wireless optical channels. Each approach has advantages in terms of the low-entry barrier to the mass market and better performance, respectively. The root question in determining the common mode PHY between the two approaches is how frequent LiFi encounters flat channels. That is, if the channel is flat, then the gain of the adaptive bit loading is not significant. Therefore, this paper aims to investigate the flatness of many samples from the reference channel models defined in the TGbb. We find that the majority of the channels are flat if the signal bandwidth is 20 MHz.
IQ-WDM for IEEE 802.11bb-based LiFi
Ardimas Andi Purwita (University of Edinburgh, United Kingdom (Great Britain)); Harald Haas (The University of Edinburgh, United Kingdom (Great Britain))
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In July 2018, a new IEEE task group focusing on light communications was formed, namely IEEE 802.11bb (TGbb). The primary motivation of this task group is to standardize mobile, networked light communications, i.e., light fidelity (LiFi). At the time of writing, discussions in TGbb still focus on the physical (PHY) layer, and recently a common mode PHY has been agreed. The common mode PHY is defined based on the frequency upconversion of the signal outputs of existing WiFi chipsets. In addition, a DC bias is used to enable the intensity-modulation and direct-detection (IM/DD) over a light emitting diode (LED). In this paper, we compare it with another method, namely in-phase and quadrature wavelength division multiplexing (IQ-WDM). IQ-WDM refers to a method where both I and Q baseband signal from existing WiFi chipsets are transmitted independently at different wavelengths. A comprehensive comparison is done, and our error performance results indicate that IQ-WDM significantly outperforms the frequency upconversion method by 6 dB. We also show that IQ-WDM is robust againsts IQ imbalance.
A Novel E-band Testbed for Polarization MIMO-OFDM Systems with Wideband IQ Imbalance Compensation
Daisuke Uchida, Tamio Kawaguchi, Daiki Yoda and Makoto Sano (Toshiba Corporation, Japan); Koji Akita (Toshiba Corp, Japan); Magnus Sandell (Toshiba TRL, United Kingdom (Great Britain)); Evgeny Tsimbalo (Telecommunications Research Laboratory of Toshiba Research Europe Ltd., United Kingdom (Great Britain)); Seifallah Jardak (Toshiba Research Europe Limited, United Kingdom (Great Britain)); Ichiro Seto (Toshiba corporation, Japan)
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This talk does not have an abstract.
SoftFG: A Dynamic Load Balancer for Soft Reconfiguration of Wireless Data Centers
Amer AlGhadhban (KAUST, Saudi Arabia); Abdulkadir Celik (King Abdullah University of Science & Technology, Saudi Arabia); Basem Shihada (KAUST, Saudi Arabia); Mohamed-Slim Alouini (King Abdullah University of Science and Technology (KAUST), Saudi Arabia)
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In this paper, we investigate the soft-reconfiguration of optical wireless data centers (WDCs). In the considered physical topology, edge top-of-rack (ToR) switches in the leaf layer are inter-connected with core switches in the spine layer via wavelength division multiplexing (WDM) based free-space optical (FSO) links. We propose an agile load balancing (LB) solution, namely SoftFG, to cope with the dynamically changing link load variations and the low-utilization time intervals within the wireless data centers (DCs). SoftFG executes flow grooming (FG) and soft reconfigurations on the virtual topology depending upon the fine-grain network statistics. Unlike the long-term LBs, SoftFG offloads large flows of congested paths onto underutilized links without making any hardware reconfiguration on path capacity and routes. Flows can be offloaded to other wavelengths within the same FSO link (i.e., intra-link), to other FSO links (i.e., inter-link), or within/across topologies (i.e., intra/inter topology). To do so, SoftFG ensures clear visibility on network paths, early congestion detection, and fast-accurate reaction to reroute offloaded flows onto underutilized wavelengths or links. Therefore, SoftFG is designed as a kernel module installed on the virtual switches/hypervisor. The module collects flow statistics based on a source-destination collaborative scheme and records them in flow and path information tables. SoftFG accordingly makes quick decisions on offloading and reroutes flows with high accuracy. Emulation results show that SoftFG delivers about 12 χ and 17 χ faster flow completion time (FCT) than LetFlow and CONGA LBs, respectively.
Session Chair
Hoon Lee (Pukyong National University, Korea)