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

Open-RAN: Open Road to Next Generation Mobile Networks

Session FWS8-S1

Invited Keynote Speaker - 1

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

Keynote: Bringing Cloud-Native Principles to Radio Access Networks

Kaustubh Joshi (AT&T Labs, USA)

6
This talk does not have an abstract.
Session Chair

Dr. Intaik Park (Samsung Electronics, Korea (South)), Mr. Avinash Bhat (Samsung R&D India-Bangalore)

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Session FWS8-S2

Session 1: Open-RAN Infrastructure

Conference
9:45 AM — 11:00 AM KST
Local
May 24 Sun, 5:45 PM — 7:00 PM PDT

RAN Slice Selection Mechanism Based on Satisfaction Degree

Xuanzhi Chen, Yuliang Tang and Mingyu Zhang (Xiamen University, China)

1
Recently, with the development of Network Function Virtualization (NFV) and Software Defined Network (SDN), network slicing has been introduced as a key enabler to accommodate diversified services. However, there is no direct study on related topic of RAN network slice and its selection. Therefore, this paper proposes an Radio Access Network (RAN) slice selection mechanism based on transmission rate, delay and blocking rate. The goal of our design is to ensure the user's highest acquisition and cost efficiency while ensuring the user's service. In order to maximize the utilization of network resources. The optimization problem is proposed with the greatest Satisfaction Degree of the global network, and the problem is proved to be a 0-1 knapsack problem. The optimal solution is obtained by the dynamic programming algorithm. Finally, the simulation results show that the RAN slice selection mechanism proposed in this paper is better than reference mechanism.

MEC Enabled Cell Selection for Micro-operators based 5G Open Network Deployment

Sridharan Natarajan (Samsung Research Institute Bangalore & IIITB, India); Tarun Khandelwal and Mohit Mittal (Samsung R&D Institute India Bangalore Private Limited, India)

0
Heterogeneous network (HetNet) involves multiple frequency bands and a combination of macro and micro cells. 5G network needs to support various services and propagation properties of the spectrum used for 5G. Hence, the nature of 5G network deployment is heterogeneous. Cell selection in HetNet consumes significant power of the user equipment. It may lead to errors and can result in inefficient cell throughput. Therefore, we need to have efficient cell selection algorithm, which can optimize the transmission power of the mobile and achieve maximum throughput. In this paper, we propose a novel method of enabling the 5G UE to select the right cell based on Mobile Edge Computing (MEC) framework to achieve maximum throughput with better power efficiency. Numerical simulation of our proposed model shows that the throughput and power efficiency is improved by 29% as compared with the conventional scheme.

Optimized Controller Placement for Soft Handover in Virtualized 5G Network

Deborsi Basu (Indian Institute of Technology, Kharagpur & IEEE Student Member, India); Abhishek Jain (Indian Institute of Technology, Kharagpur, India); Raja Datta (Indian Institute of Technology Kharagpur, India); Uttam Ghosh (Vanderbilt University, USA)

1
Softwarized Network Function Virtualization (sNFV) is driving the next generation 5G tele-communication networks to fulfil all the real time service demands of mobile users. Seamless connectivity with ultra low latency (ULL) in high mobility scenarios is one of the most necessary and challenging areas where researchers are working intensively. Thanks to Network Function Softwarization that helps to optimize the user data plane and control plane characteristics very easily with the help of Software Defined Networking (SDN). Inside a 5G network architecture the Serving GateWays (SGW) and Packet Data GateWays (PGW) are responsible for handing over the control functions (e.g signalling and tunnel creation) from one controller to another. SDN brakes SGW to make S/PGW-C at control plane and S/PGW-U at data plane. The UE traffic
forwarding decisions are taken at S/PGW-C and further it instructs the S/PGW-U to execute the required data transferring operations. Dynamic up-gradations for uninterrupted signal flow are done at SGW-C using virtual machines running
on a federated cloud. In this paper, we focus on a unique and novel SGW Controller Placement Problem (CPP) where a trade off has been made in between the controller load and handover frequency within a restricted network latency
constrain. The problem formulations are done based on a Mixed Integer Liner Optimization Programming model. The Pareto optimal algorithmic solution shows the suitable control plane distributions of SGW-C satisfying all the necessary objectives.

Method and System for reduction of insignificant KPI data in a heterogeneous RAN and Core network

Abhishek Chaturvedi (Samsung Research & Development Institute, India)

0
This paper describes a technique to reduce insignificant KPI data generated by NEs like RAN/CN or EMS/NMS. The aim of this technique is to utilize network bandwidth (i.e. control plane traffic load) and storage space in EMS/OSS efficiently. Realizing this aim, helps operators of EMS/OSS identify network KPI deterioration in near real time. Using this technique also reduces loss of critical events in lieu of network bandwidth loaded with heavy KPI data. Control plane systems in 4G as well as 5G network can both use this general technique. The disclosed technique utilizes a parameter called as KPI delta, defined for each KPI to determine significance of a KPI data. Further, this technique proposes systems, on how to use KPI delta to reduce the KPI data. Then various methods to derive or calculate KPI delta are described, namely unsupervised learning (K-means clustering) method, statistical analysis (Douglas Peucker algorithm) method and a rudimentary method (keeping KPI delta as zero). Performance evaluation of these three methods shows that unsupervised learning (K-means clustering) gives better results compared to other two methods for KPI data files: size improvement by 0.81% and 3.81%; data points improvement by 3.02% and 14.62%.
Session Chair

Prof. Nawab Muhammad Faseeh Qureshi (Sungkyunkwan University, Korea (South)), Dr. Sukhdeep Singh (Samsung R&D India-Bangalore)

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Session FWS8-S3

Invited Keynote Speaker - 2

Conference
11:00 AM — 12:00 PM KST
Local
May 24 Sun, 7:00 PM — 8:00 PM PDT

Keynote: How Open Are Open Standards

Satish Jamadagni (Reliance Jio, India)

1
This talk does not have an abstract.

Keynote: Evolution in RAN and xHAUL Architectures for 5G

Subodh Gajare (Cisco, India)

1
This talk does not have an abstract.
Session Chair

Dr. Intaik Park (Samsung Electronics, Korea (South)), Mr. Avinash Bhat (Samsung R&D India-Bangalore)

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Session FWS8-S4

Invited Keynote Speaker - 3

Conference
1:30 PM — 2:00 PM KST
Local
May 24 Sun, 9:30 PM — 10:00 PM PDT

Keynote: An Application of Grassmann Clustering in Massive MIMO

Harpreet Singh Dhillon (Virginia Tech, USA)

2
This talk does not have an abstract.
Session Chair

Dr. Intaik Park (Samsung Electronics, Korea (South)), Mr. Avinash Bhat (Samsung R&D India-Bangalore)

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Session FWS8-S5

Session 2: Open-RAN Modelling and Analysis

Conference
2:00 PM — 3:15 PM KST
Local
May 24 Sun, 10:00 PM — 11:15 PM PDT

A Neural Network for Estimating CQI in 5G Communication Systems

Satya Kumar Vankayala (Samsung R&D Institute Bangalore, India); Konchady Gautam Shenoy (Indian Institute of Science (IISC), India)

0
Channel Quality Indicator (CQI) is a key parameter in communication system design that encodes the state of the channel. With this information, a base station (BS) can adjust the Quality of Service that would best suit the channel at that time and place, thereby facilitating communications. As it is
counterproductive to request CQI from all users, it is preferable to estimate it in some cases. This paper studies the current CQI request-estimation paradigm and proposes a neural network based solution to attain the best of both worlds. We show that the neural network based architecture outperforms the legacy based system. With Radio Access Network (RAN) architectures being
virtualized, we can expect a lot of baseband processing to be offloaded to the Cloud in proximity of the base station site. The Edge Cloud is expected to have large computational capabilities, which can suitably host our Neural Network based solution.

Anomaly Detection in Mobile Networks

Anish Nediyanchath (Samsung R&D Institute India - Bangalore, India); Chirag Singh (Samsung R&D Institute-India, Bangalore, India); Harman Jit Singh (Samsung Research Institute Bangalore, India); Himanshu Mangla (Samsung R&D Institute, Bangalore, India); Karan Mangla (Samsung R&D Institute India - Bangalore, India); Manoj Kiran Sakhala (Samsung Electronics & Samsung R&D Institute Bangalore, India); Saravanan Balasubramanian (Samsung R&D Institute India - Bangalore, India); Seema Pareek (Samsung Research Bangalore, India); Shwetha S (Samsung R&D Institute India - Bangalore, India)

1
With the widespread usage of 4G technologies and the upcoming promise of 5G networks, there is a strong need for increased network performance and reliability. However, as these networks become bigger and faster, so does their complexity. Currently, network operators detect most of the network failures manually. This is a very time consuming and tedious task for them, oftentimes taking up to several hours. Thereby arises a need for an automated Anomaly Detection and Correction system. Such a system would be a step towards the ultimate goal of a cognitive self-organizing network. We here take the case of a mobile network with hundreds of key performance indicators, which generates huge amount of network logs every hour. Since user behavior has patterns in usage, e.g. weekdays network traffic will be higher than weekend's traffic near office areas, we analyze a Time Series (TS) Decomposition based approach, which takes into consideration of trends and seasonality in data. We also explore the use of a seasonal auto-regressive technique, SARIMA, for anomaly detection. Assuming that an anomalous behaviour is continuous in time, we evaluate a recurrent encoder-decoder based approach, MSCRED for Anomalous Window Detection. We do this analysis to find the KPI and the respective network element, whose behavior is abnormal. Our results show that while Time Series Decomposition outperforms SARIMA over single point anomaly detection, MSCRED significantly performs well in predicting anomalous time windows.

Open5G: A Software-Defined Networking Protocol for 5G Multi-RAT Wireless Networks

Pradnya Kiri Taksande (IIT Bombay, India); Pranav Jha (Indian Institute of Technology Bombay, India); Abhay Karandikar and Prasanna Chaporkar (IIT Bombay, India)

0
With the recent advancements in mobile networks, Software-Defined Networking (SDN) has been introduced in 5G architecture, where the control-plane functions are separated from the data-plane functions. This has led to the introduction
of a number of different nodes in 5G architecture, requiring several different interfaces for communication between each other. Moreover, only 5G RAN nodes have been sub-divided into control-plane and data-plane nodes, leaving the other Radio
Access Technologies (RATs) untouched. Further, Wireless Local Area Network (WLAN) is not included in 5G, even though it is commonly used worldwide by network operators and users alike. We introduce an SDN-based multi-RAT RAN (SMRAN) where the control-plane of RAN nodes belonging to multiple RATs is segregated from their data-planes, and Open5G protocol to communicate between control-plane and data-plane of SMRAN. Open5G is based on OpenFlow, which is a commonly used protocol applicable to SDN-based wired networks. With the Open5G protocol, the entire network can be controlled by an open interface, thus, bringing flexibility and simplicity in network interactions.

Dimension Expansion of OFDM System for the Spectral Efficiency Improvement

Changyoung An and Heung-Gyoon Ryu (Chungbuk National University, Korea (South))

0
In this paper, we analyze the spectral efficiency of three-dimensional orthogonal frequency division multiplexing (3D-OFDM) and multidimensional OFDM (MD-OFDM) systems. 3D-OFDM can improve the performance of the system by mapping symbols to three-dimensional space. Moreover, the expansion of the dimension further improves this property. However, these systems consume additional time resources to expand the dimension, so the benefits of performance improvement can be offset. In this paper, we analyze these characteristics in terms of spectral efficiency. Simulation results show that MD-OFDM can improve system performance by improving symbol error rate (SER) performance as dimension is increased. However, when bit error rate (BER) performance considering time resource and bit efficiency is evaluated, performance is almost unchanged even if dimension is increased. That is, MD-OFDM including 3D-OFDM has spectral efficiency similar to that of conventional OFDM systems in a typical additive white Gaussian noise (AWGN) environment. However, as the dimension is extended, the Euclidean distance between adjacent symbols increases, which could be used for various signal processing such as trellis based channel coding to improve the system.
Session Chair

Prof. Nawab Muhammad Faseeh Qureshi (Sungkyunkwan University, Kores (South)), Dr. Sukhdeep Singh (Samsung R&D India-Bangalore)

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Session FWS8-S6

Invited Keynote Speaker - 4

Conference
3:45 PM — 4:15 PM KST
Local
May 24 Sun, 11:45 PM — 12:15 AM PDT

Keynote: TBA

Renuka Bhalerao (Facebook, USA)

0
This talk does not have an abstract.
Session Chair

Dr. Intaik Park (Samsung Electronics, Korea (South)), Mr. Avinash Bhat (Samsung R&D India-Bangalore)

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Session FWS8-S7

Session 3: Open-RAN Intelligent ML/AI Evaluations

Conference
4:30 PM — 6:00 PM KST
Local
May 25 Mon, 12:30 AM — 2:00 AM PDT

The Evolution of Radio Access Network Towards Open-RAN: Challenges And Opportunities

Sameer Kumar Singh (IIT Ropar, Rupnagar, India); Rohit Singh and Brijesh Kumbhani (IIT Ropar, India)

0
The coexistence of massive Internet of Things (IoT) network and modern technologies (e.g., high speed gaming and self driving vehicles) requires a versatile network which can provide support to all such applications. Since the Quality of Service (QoS) requirement of each application is different from one another, the existing Radio Access Network(RAN) is unable to support such diverse applications. Consequently, Open Radio Access Network(O-RAN) is being considered as the most viable solution for next generation RAN. In this paper, we present
the evolution of RAN along with the possible architecture and features of the most promising next generation RAN (i.e., ORAN). This work mainly discusses architectural and functional advancement of the RAN in each generation. In addition, we discuss various challenges associated with O-RAN implementation and possible opportunities created with the advent by O-RAN.

RAN Resource Slicing and Sharing with NOMA for Latency Reduction in Uplink URLLC Networks

Nadia Jaya (Bangladesh University of Engineering and Technology, Bangladesh); Md. Farhad Hossain (Bangladesh University of Engineering and Technology (BUET), Bangladesh)

1
A major feature of 5G, ultra reliable low latency communication (URLLC) network aims to enable mission critical services with stringent latency constraints. On the other hand, non-orthogonal multiple access scheme (NOMA), a prime candidate for 5G multiple access scheme, utilizes the received power differences to allow several users to share the same resource blocks (RBs), increasing the throughput, while negatively affecting successive interference cancellation (SIC) decoding latency of certain users. Therefore, this paper proposes two different NOMA-based user clustering techniques for uplink transmissions aiming to reduce latency for time stringent services. Users are clustered and RBs are assigned among the users in a way to achieve the target latency constraints of users. Changes in latency and throughputs are thoroughly investigated for the validation of the proposed techniques.

Data-driven Semi-supervised Anomaly Detection using Real-World Call Data Record

Shan Jaffry (Dongguan University of Technology & IEEE ComSoc, China); Syed Faraz Hasan (Massey University, New Zealand); Syed Tariq Shah (Sungkyunkwan University, Korea (South) & Balochistan University of Information Technology, Engineering, and Management Sciences, Pakistan)

1
The beyond 5G (B5G) networks are expected to provide ubiquitous, ultra-reliable low latency connectivity to cellular users. Maintaining this stringent B5G performance requirement will be a challenging task for cellular service providers.
A key factor that may affect network performance will be anomalies such as sleeping cells or congestion due to high traffic volumes. In the worst cases, these anomalies may cause a partial or complete network outage.
Traditional outage management techniques, such as drive-testing, may prove unsuitable in the B5G era as they are time-consuming and costly. These outdated mechanisms are also unable to provide real-time data analysis.
Hence future networks will rely on data-driven self-organizing networks (SON) with self-healing capabilities to detect anomalies. Machine learning will be an essential component of such systems.
Motivated by this argument, in this paper we have proposed a semi-supervised learning algorithm to detect anomaly using real-world Spatio-temporal call data records (CDRs). We will demonstrate that our proposed algorithm can detect anomalies with high accuracy. The CDR is collected for the entire city of Milan, Italy in the form of spatial grids.
We will demonstrate that once trained using the single-cell grid record, our model can accurately predict anomalies for the neighboring grids as well.

ONAP based Pro-active Access Discovery and Selection for 5G Networks

Rahul Banerji, Naman Gupta, Suman Kumar and Sukhdeep Singh (Samsung R&D India - Bangalore, India); Seungil Yoon (Samsung Electronics, Korea (South)); Avinash Bhat (Samsung R&D, India); Bharat J.R. Sahu (ITER, Siksha `O' Anusandhan University, India)

0
This paper enhances the functionality of analytics and policy framework of ONAP to solve the problem of access discovery and selection of radio access networks, which is currently manually configured by the operators. We propose to automate the policy creation and handle it dynamically, based on current and historic data collected from various network nodes based on three main services (open to accommodate new services) of 5G i.e. Enhanced mobile broadband (eMBB), Ultra-reliable Low Latency communication (URLLC) and Massive Machine type communication (mMTC). Learning, analysis and prediction of the real and non-real time data can help to form dynamic access discovery and selection policies on the go with help of ONAP's DCAE (Data Collection, Analytics, and Events) and policy framework. To verify the effectiveness of our proposal we created a test bed with different access points and blended SOC with ONAP. Based on three different use cases we collected real time and non-real time traffic traces for our SOC to form dynamic policies with the help of DCAE and policy framework for selection of best available gNBs or eNBs. We compare our proposal with legacy networks and existing research works in the literature.
Session Chair

Prof. Nawab Muhammad Faseeh Qureshi (Sungkyunkwan University, Korea (South)), Dr. Sukhdeep Singh (Samsung R&D India-Bangalore)

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