Workshops and Tutorials

Session HWS1-S1: 5G and Beyond Technology-enabled Remote Health Systems I Session HWS1-S2: 5G and Beyond Technology-enabled Remote Health Systems II
Session HWS2-S1: Invited Keynote Speakers - Part 1 Session HWS2-S2: Aerial Communications in 5G and Beyond Networks Session HWS2-S3: Invited Keynote Speakers - Part 2 Session HWS2-S4: Panel Discussion
Session HWS5-S1: Invited Keynote Presenations Session HWS5-S2: Full Duplex Radio Technologies
Session FWS7-S1: Invited Keynote Presentation Session FWS7-S2: Distributed Sensing and Localization Session FWS7-S3: IoT Applications
Session FWS6-S1: Caching in Heterogenous Wireless Network Session FWS6-S2: Coded Caching at Network Edge Session FWS6-S3: Intellignet Edge Computing Session FWS6-S4: Enabling the Network Edge Intelligence
Session HWS10-Opening: Opening Session Session HWS10-Keynote: Keynote Session HWS10-S1: Smart Spectrum 1 Session HWS10-S2: Smart Spectrum 2
Session FWS8-S1: Invited Keynote Speaker - 1 Session FWS8-S2: Session 1: Open-RAN Infrastructure Session FWS8-S3: Invited Keynote Speaker - 2 Session FWS8-S4: Invited Keynote Speaker - 3 Session FWS8-S5: Session 2: Open-RAN Modelling and Analysis Session FWS8-S6: Invited Keynote Speaker - 4 Session FWS8-S7: Session 3: Open-RAN Intelligent ML/AI Evaluations
Session FWS9-S1: Mobile Wireless Networks Session FWS9-S2: Future Networking Technologies Session FWS9-S3: Intelligent Communication Technologies Session FWS9-S4: Communication Techniques
Session FWS3-S1: Keynotes and Industry Activity Session FWS3-S2: Measurements, RFIC, and Optical Methods Session FWS3-S3: Metasurface, Lens, and OAM Session FWS3-S4: Channel Sounder, Antenna Array, and MIMO
Session TUT01: Tutorial: Federated Learning at the Network Edge: Fundamentals, Key Technologies, and Future Trends Session TUT02: Tutorial: Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Networks Session TUT03: Tutorial: Machine Learning for Future Wireless Networks Session TUT05: Tutorial: Moving Towards Zero-Touch Automation, A Key Enabler for 6G: The Challenges & Opportunities Session TUT06: Tutorial: B5G: A New Frontier for Non-Orthogonal Multiple Access Session TUT04: Tutorial: Wireless Transmission for Advanced Internet of Things: A Unifying Data-Oriented Approach Session TUT07: Tutorial: UAV Communications in 5G and Beyond: Integration of Sensing, Control, and Learning Session TUT08: Tutorial: Orthogonal Time Frequency Space (OTFS) Modulation and Applications Session TUT09: Tutorial: URLLC for 5G and Beyond: Physical, MAC and Network Design and Solutions Session TUT10: Tutorial: NOMA-Based Random Access for Massive MTC in 5G
Session FWS4-S1: Access and Core Technologies: Part I Session FWS4-S2: Access and Core Technologies: Part II Session FWS4-S3: Architecture, Business Model and Services

5G and Beyond Technology-enabled Remote Health Systems

Session HWS1-S1

5G and Beyond Technology-enabled Remote Health Systems I

Conference
9:00 AM — 10:30 AM KST
Local
May 24 Sun, 5:00 PM — 6:30 PM PDT

Keynote: How 5G will affect healthcare?

Haesik Kim (VTT Research Center, Finland)

0
This talk does not have an abstract.

Random Access Preamble Design and Detection for 5G Remote Health via Satellite Communications

Teng Sun (Xi'an University of Posts & Telecommunications, China); Li Zhen (Xi'an University of Posts and Telecommunications, China); Guangyue Lu (Xi’an University of Posts & Telecommunications, China); Keping Yu (Waseda University, Japan)

2
This paper deals with the crucial issue of random access (RA) preamble design and detection for 5G remote health systems based on satellite communication. In consideration of the characteristics of satellite environment and system compatibility, a long preamble sequence is first constructed by cascading multiple different root Zadoff-Chu (ZC) sequences with large subcarrier interval in time domain. Then, we further present a multiple sequence joint correlation (MSJC) based timing detection scheme to estimate the value of timing advance (TA) in one step. By flexibly adjusting the number of ZC sequences involved in correlation operation, the proposed method not only is capable of effective mitigation of noise, but also can achieve the robustness to large carrier frequency offsets (CFOs). Simulation results consist with mathematical analysis, and demonstrate that the proposed method significantly improves performance in terms of timing mean square error (MSE), compared with the previous methods.

Modulation Division Based User Grouping transmission in Massive SIMO Systems

Linxin Zhang, Jingjie Zong, Gangtao Han, Shuangzhi Li and Xiaomin Mu (Zhengzhou University, China)

0
A novel user grouping transmission scheme for the massive SIMO system is proposed for 5G health applications in this paper, where the base station (BS) equipped with a large number of antennas. In the proposed scheme, users are divided into different groups, and orthogonal pilot sequences are arranged to different groups, while different users in the same group use the same pilot sequence and a sum-constellation is uniquely divided into a group of sub-constellations for the users in the same group. Orthogonal pilots are utilized to eliminate the interference between different groups, and the modulation division method is used to cancel the interference inner the group. Finally, simulation results demonstrate that our proposed transmission scheme and the user grouping method perform very well.
Session Chair

Di Zhang (Zhengzhou Univerisity, P.R. China)

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Session HWS1-S2

5G and Beyond Technology-enabled Remote Health Systems II

Conference
10:45 AM — 12:15 PM KST
Local
May 24 Sun, 6:45 PM — 8:15 PM PDT

Prioritizing Health Care Data Traffic in a Congested IoT Cloud Network

Sara Beitelspacher, Kedir Mamo Besher, Mohammed Zamshed Ali and Mohammad Mubashir (UTD, USA)

1
While routing through the Internet of Things (IoT) network, conventional healthcare data packets do not get any special priority routing treatment. IoT is facing performance issues in packet data transmission due to network congestion; with the conventional packet routing process, there is no guarantee that the patients' health data will be properly routed to doctors on time. This leaves the remote medical treatment at risk with possible threat to patients' lives. In this paper, we studied the current healthcare packet transmission process in IoT and its performance issues in congested IoT networks, then proposed a solution to prioritize healthcare data routing in congested IoT cloud networks.

Our proposed system adds a healthcare data identifier in IP packet headers at the sensor level, modifies QoS software at the network router level, and prioritizes healthcare data packet routing based on the healthcare data identifier seen at router QoS. Test data shows promising results that may significantly benefit remote medical diagnostic process by saving time, cost, and more importantly, human lives. The analysis also opens up further avenues for research.

Uplink Pilot Power Allocation for MA-MIMO-HetNet Remote Health Systems

Yabo Guo and Zhengyu Zhu (Zhengzhou University, China); Xinhua Lu (Zhengzhou University & Institute of Nanyang Technology, China); Zhongyong Wang and Wanming Hao (Zhengzhou University, China); Ali Kashif Bashir (Manchester Metropolitan University, United Kingdom (Great Britain))

1
In this paper, we study the uplink pilot transmit power allocation of information communication technology (ICT)-assisted remote health system with massive multi-input multi-output (MA-MIMO) and heterogeneous cellular network (HetNet), which is so crucial to effectively mitigate the interference for improving the system performance that more patients can get high-quality service in remote areas simultaneously. We propose a pilot transmit power allocation method which is inspired by Water-Filling (WF) algorithm to increase the system capacity and obtain the effect of pilot transmit power in MA-MIMO-HetNet remote health system. Simulation results demonstrate that the system uplink capacity can be improved significantly by the proposed scheme and illustrate the influence of different total pilot transmit power on system performance, while imposing a scarcely increased complexity.

Glioma segmentation strategies in 5G teleradiology

Xiangchuan Gao, Lei Ma and Jin Jin (Zhengzhou University, China); Junmin Li (The First Affiliated Hospital of Zhengzhou University, China); Zhenxia Ma (ZhengZhou University, China); Yunkai Zhai (Zhengzhou University, China); Xingwang Li (Henan Polytechnic University, China)

0
as an essential part of the telemedicine system, teleradiology not only realizes mutual recognition of image diagnosis among medical institutions but also ensures the quality of medical image diagnosis. In this paper, an edge computing (EC) driven 5G teleradiology framework is proposed to segment glioma accurately. In our framework, through the high-speed transmission and sharing mechanism of medical image data under 5G condition, the high-definition Magnetic resonance imaging (MRI) images data of glioma patients are delivered to the edge server for data augmentation and training. Three typical data augmentation methods, i.e., traditional geometric transformation, DCGAN, and CycleGAN, are used to study the impact of the proportions of synthetic images generated by the above data augmentation methods to original MRI images on the performance of glioma segmentation. It is observed that training with different proportions leads to different segmentation results. Based on this phenomenon, an efficient data augmentation strategy is established, which exploits the optimal proportions of synthetic and original data to yield the best glioma segmentation results in terms of sensitivity, specificity, accuracy, and Dice score. Experiments on the BraTS dataset show that the proposed data augmentation strategy improves the average Dice score of Dense U-net compared to the conventional data augmentation method. With the aid of our EC driven 5G teleradiology framework, accurate glioma segmentation results can be acquired rapidly at the terminal equipment to facilitate the online diagnosis.

Multi-channel Lightweight Convolutional Neural Network for Remote Myocardial Infarction Monitoring

Yangjie Cao, Tingting Wei and Nan Lin (Zhengzhou University, China); Di Zhang (Zhengzhou Univerisity, China); Joel J. P. C. Rodrigues (Federal University of Piauí (UFPI), Brazil & Instituto de Telecomunicações, Portugal)

0
Remote Myocardial Infarction (RMI) monitoring uses electronic devices to detect the electrocardiogram changes and inform the doctor in emergency conditions, which is an effective solution to save the patient's life. In this paper, we propose the Multi-Channel Lightweight CNN (MCL-CNN), which combines electrocardiogram signals from four leads (v2, v3, v5 and aVL) to detect the Anterior MI (AMI). Its multi-channel design allows the convolution of each lead to be independent of each other, and allowing them to find the filter that best suits them. In addition, constructing a lightweight network using different convolutional combinations in the MCL-CNN model, which makes the network has certain advantages in computing runtime parameters and more suitable for mobile devices. Meanwhile, we use balanced cross entropy to solve the problem of dataset class imbalance. These strategies make the MCL-CNN suitable for multi-lead ECG processing. Experimental results using public ECG datasets obtained from the PTB diagnostic database demonstrate that MCL-CNN's accuracy is 96.65%.
Session Chair

Di Zhang (Zhengzhou Univerisity, P.R. China)

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