Workshops and Tutorials

Session TUT-01: Age of Information as a New Data Freshness Metric in the IoT Era: From Theory to Implementation Session TUT-02: Privacy and Security in Federated Learning Session TUT-03: Wireless Communications with Intelligent Reflecting Surface Session TUT-04: Sparsity Modulation for mmWave, Terahertz and Optical Wireless Communication Session TUT-05: Information Freshness for IoT Networks: From Concept to Application Session TUT-06: Cell-Free Massive MIMO Systems: A New Next-Generation Paradigm Session TUT-07: Reconfigurable Intelligent Surface for 6G: Communication, Sensing, and Localization
Session WS1-1: Session 1 Session WS1-2: Session 2
Session WS2-1: Session 1 Session WS2-2: Session 2
Session WS3-1: Session 1 Session WS3-2: Session 2 Session WS3-3: Session 3 Session WS3-4: Session 4
Session WS4-1: Session 1 Session WS4-2: Session 2 Session WS4-3: Session 3
Session WS5-1: Session 1 Session WS5-2: Session 2
Session WS6-1: Session 1 Session WS6-2: Session 2

Network Softwarization for Internet of Things

Session WS3-1

Session 1

Conference
9:00 AM — 10:30 AM CST
Local
Aug 8 Sat, 9:00 PM — 10:30 PM EDT

Edge Intelligence for Vehicular Communication, Computation and Caching

Lei Liu ([email protected] ), Xidian University

0
This talk does not have an abstract.

Some Key Technologies in Mobile Edge Computing

Jianbo Du ([email protected]), Xi'an University of Posts and Telecommunications.

0
This talk does not have an abstract.
Session Chair

Haotong Cao

Session WS3-2

Session 2

Conference
11:00 AM — 12:30 PM CST
Local
Aug 8 Sat, 11:00 PM — 12:30 AM EDT

Service Function Chain Embedding Framework for NFV-Enabled IoT Application

Yue Hu (China Mobile Communications Group Jiangsu Co., Ltd., China); Sijia Lou, Shengchen Wu and Longxiang Yang (Nanjing University of Posts and Telecommunications, China)

1
It is becoming more and more difficult to manage data and resources simultaneously and efficiently in IoT systems. In recent years, network function virtualization (NFV) technology emerges, regarded as one most promising candidate towards efficient resource allocation for IoT application. By adopting NFV scheme, each IoT service, represented by a service function chain (SFC), can be composed of an ordered set of virtual network functions (VNFs). However, existing researchers concentrate on finding optimal or near-optimal embedding solution per SFC, incurring much more computation time. Considering the dynamic nature of IoT service, it is necessary to find feasible and efficient SFC embedding solution as soon as possible. Hence, in this paper, we propose one SFC embedding framework for IoT application. The framework for SFC embedding is labeled as SFC-E-IoT. Each elements in SFC-E-IoT framework is discussed in this paper. When given one SFC, our SFC-E-IoT will find the feasible SFC embedding solution, fulfilling its computing, storage and networking resources. We conduct the simulation work so as to validate the SFC-E-IoT performance.

Performance Analysis of Floodlight and Ryu SDN Controllers under Mininet Simulator

Yanzhen Li (State Grid Qingdao Power Supply Company, China); Xiaobo Guo (The 54th Institute of CETC, China); Xue Pang (China University of Petroleum (East China) Qingdao, China); Bo Peng and Xiaoyue Li (State Grid Qingdao Power Supply Company, China); Peiying Zhang (China University of Petroleum, China)

1
As the traditional network architecture can not meet the requirements of network users for high-quality network services, software defined network (SDN) has been proposed as a new network architecture. SDN divides the data plane and control plane in the network equipment, so that the controller can schedule the data in the network flexibly through openflow protocol. Floodlight and Ryu are two typical SDN controllers proposed for different purposes. In this paper, we simulate two kinds of controllers, floodlight and Ryu, and compare their bandwidth and delay performance under different network topology types. The experimental results show that in all kinds of topology, the Floodlight controller has higher bandwidth and lower latency than Ryu controller, so the Floodlight controller has better performance.

GAPG: a Heuristic Greedy Algorithm for Grouping Storage Scheme in Blockchain

Jinsen Dai, Dapeng Li, Rui Jiang and Xiaoming Wang (Nanjing University of Posts and Telecommunications, China); Youyun Xu (Nanjing University of Posts and Telecommunications & Shanghai Jioatong University, China)

0
The decentralization and immutability of blockchain make it have a wide application prospect, however, as the number of transactions increases, the storage cost of each node will also increase. So blockchain will be facing the bottleneck of insufficient scalability. Consensus Unit(CU) is one of grouping storage scheme for improving the scalability of blockchain, which organizes different nodes into one cluster and allocates all the blocks of the whole chain to the nodes in each cluster. In this paper, we propose an optimization problem that assigning blockchain data to nodes and minimized the total query cost, this is a variant of generalized basic probability assignment problem, so we propose a heuristic algorithm named GAPG to solve this problem. Numerical results demonstrate that the proposed algorithm is superior to random assignment and greedy algorithms for clusters with a large number of nodes.
Session Chair

Haotong Cao

Session WS3-3

Session 3

Conference
2:00 PM — 3:30 PM CST
Local
Aug 9 Sun, 2:00 AM — 3:30 AM EDT

A Practical Dynamic Clustering Scheme Using Spectral Clustering in Ultra Dense Network

Yanxia Liang (Xi'an University of Post and Telecommunication, China); Xin Liu (Xi'an Eurasia University, China); Jing Jiang (Xi'an University of Posts and Telecommunications, China); Jianbo Du (Xi'an University of Posts & Telecommunications, China); Changyin Sun (College of XiЎЇan Post and Communication, China); Yongbin Xie (Xi'an University of Posts & Telecommunications, China)

0
Coordinated Multiple-Point (CoMP) technology is one of the most important technologies in ultra-dense networks (UDN). Effective CoMP clustering algorithm can provide additional gain for system performance, such as higher system throughput and spectral efficiency (SE) for cell-edge users. This paper, a novel dynamic clustering scheme based on spectral clustering in graph theory for CoMP-user is presented to maximize system SE and cell-edge users' throughput. Our proposed scheme mainly consists of weight design, graph construction and spectral clustering that can achieve good performance and low complexity. Simulation results show that the proposed algorithm yields significant gains of SE and throughput and low running time compared with some existing clustering schemes.

Nested Beam Selection Algorithm in Beamspace Millimeter wave Massive MIMO System

Hua He and Jing Jiang (Xi'an University of Posts and Telecommunications, China); Yanxia Liang (Xi'an University of Post and Telecommunication, China)

0
The beamspace millimeter wave (mm-wave) Massive MIMO system uses lens array antenna for transmission, which utilizes mm-wave sparsity to reduce the number of radio frequency (RF) links, and without obvious system performance degradation. In order to reduce the inter-user interference and the waste of radio frequency chain, a nested beam selection algorithm is proposed. The nested beam selection algorithm divides the process of beam selection into several nested layers. In each nested layer, the algorithm tries its best to make user select its optimal beam, and avoiding the inter-user interference. The simulation results show that the proposed algorithm can achieve higher system sum rate than the random beam selection and optimal beam selection.

Resource Allocation for Virtualized Wireless Networks with Mobile Edge Computing

Xiaozhen Zhu (Nanjing University of Posts and Telecommunications & Jiangsu Key Laboratory of Wireless Communications, China); Longxiang Yang (Nanjing University of Posts and Telecommunications, China)

0
This paper introduced a strategy to efficiently allocate resources of virtualized wireless network and studied the application of MEC and ICN.

Latency Optimization for Mobile Edge Computing Based Proximity Detection in Road Networks

Yunlong Song (Beijing University of Posts and Telecommunications Xitucheng Campus & Beijing University of Posts and Telecommunications, China); Yaqiong Liu and Guochu Shou (Beijing University of Posts and Telecommunications, China); Hu Yihong (Beijing University of Posts and Telecommunications Xitucheng Campus & Beijing University of Posts and Telecommunications, China)

0
In recent years, continuous breakthroughs in 5G and artificial intelligence technology have promoted the development of autonomous driving technology. In the actual application scenario of autonomous driving, the mobile user needs to find which mobile users are in proximity with him/her in the road network in real time, referred to as the problem of proximity detection in road networks. However, in the case of limited storage resources and computing resources at the mobile devices, how to calculate the proximity relationship between users in a short time at the mobile devices becomes a tough issue. Therefore, in this paper, we propose a computation offloading scheme in the scenario of proximity detection based on mobile edge computing (MEC), and formulate a communication latency optimization problem. To the formulated latency minimization problem in the computational offloading scheme, we use a convex optimization solution, namely, SLSQP, which significantly optimizes the communication latency. Simulation results demonstrate that our solution can solve the latency optimization problem effectively and efficiently.
Session Chair

Jianbo Du

Session WS3-4

Session 4

Conference
4:00 PM — 5:30 PM CST
Local
Aug 9 Sun, 4:00 AM — 5:30 AM EDT

SDN-enabled Congestion Control Coordination and Coverage Class Adaptation in 5G NB-IoT Networks

Shangjing Lin, Jianguo Yu and Yuanxiang Chen (Beijing University of Posts and Telecommunications, China); Jin Tian and Ji Ma (Jinling Institute of Technology, China)

0
In massive and densely deployed 5G NB-IoT networks, centralized managements are computationally prohibitive and limit the network scalability. As a result, the auto-integration and self-management capabilities are required in the 5G NBIoT architectural to realize their full potential. In this paper, we construct a novel analytical model to estimate the random access performance of 5G NB-IoT network from an individual perspective. Then, based on the individual-based analytical model, we propose to decouple random access control management in 5G NB-IoT networks into coverage class adaptation and congestion control coordination, so that the critical issues of traffic burstiness and network scalability can be tackled. In particular, the random access control is progressively coordinated through a centralized SDN controller, and the coverage class adaptation can be achieved gradually through IoT devices under the guidance of the SDN controller. As a result, bursty traffics are equally dispersed among coverage classes, and the requirement of network control on complexity can be substantially alleviated, simplifying access control management and enhancing network scalability in a massive deployed NB-IoT networks. Case studies show that our SDN-enabled network control is effective for timely load balance.

Power Control for Multi-UAV Location-aware Wireless Powered Communication Networks

Jiansong Miao and Pengjie Wang (Beijing University of Posts and Telecommunications, China)

0
Unmanned aerial vehicle (UAV) enabled wireless powered communication network (WPCN) is attracting a lot of attention for the energy consumption problem of UAV communication system. In this paper, we consider a multi-UAV location-aware wireless powered communication network, where multiple UAVs monitor in the air along the scheduled flight trajectory and transmit monitoring data to micro base stations (mBSs) with the harvested energy. Our target is to maximize the achievable sum rate of all UAVs over each time slot in the uplink communication.

In the system, the distance between mBSs and UAVs varies with the location change of flying UAVs over time. Thus, we optimize the mBSs' transmit power allocation based on the location- aware technique. To solve the non-convex problem, an optimal power control scheme is proposed by applying the successive alternate optimization (SAO) algorithm based on successive convex approximation (SCA) technique. Extensive simulation results show that the proposed algorithm achieves significant throughput gains compared to other schemes.

Motivational Game-Theoretic Vehicle-to-Vehicle Energy Trading in the Smart Grid

Xinyi Ye and Yan Qi Zhang (Nanjing University of Posts and Telecommunications, China); Yiyang Ni (Jiangsu Second Normal University, China); Qin Wang (Nanjing University of Posts and Telecommunications, China); Yan Chen (Chinese Academy of Engineering, China)

0
With the emergence and popularity of electric vehicles (EVs), more and more people begin to choose to use EVs. However, due to the increasing number of EVs, the insufficient number and limited range of charging stations, and the lack of battery power, EVs cannot be charged on the road. The energy problem has held back the development of EVs. Vehicle-to-Vehicle (V2V) energy trading is seen as a potential solution to the problem. However, some problems and challenges remain in the current V2V energy trading scenarios. In order to solve the problem of selfish behavior among vehicles, we propose an incentive Stackelberg game model to facilitate energy trading among EVs. The numerical results show that the overall utilities of buyers and sellers can be maximized by the incentive model.

A New Method of Human Gesture Recognition Using Wi-Fi Signals Based on XGBoost

Xue Ding (Beijing University of Posts and Telecommunications, China); Ting Jiang (Beijing University of Posts & Telecommunications, China); Wenling Xue and Zhiwei Li (Beijing University of Posts and Telecommunications, China); Yi Zhong (Beijing Institute of Technology, China)

0
Human gesture recognition has drawn widespread attention for its great application value in both the Internet of Things (IoT) and Human-Computer Interaction (HCI). Although most of the existing approaches have achieved promising effect, they rely on deep learning method enabled by a large number of samples. In this paper, a gesture recognition method based on the eXtreme Gradient Boosting (XGBoost) classification model is proposed to achieve gesture identification without too many samples and features. Meanwhile, it can maintain the recognition accuracy as well as the recognition speed. We collected six predefined dynamic gestures samples and conducted extensive experiments to evaluate its performance. The results demonstrate that our method can achieve an average recognition accuracy of 94.55% when ten features are used and average accuracy of 91.75% when two suitable features are selected. Comparing with the traditional classification algorithms, the method presented in this paper has a great balance among performance, recognition speed, and the number of features of the gestures.
Session Chair

Qin Wang

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