Workshops

Session AidTSP-TS1: AidTSP Technical Session I Session AidTSP-TS2: AidTSP Technical Session II Session AidTSP-TS3: AidTSP Technical Session III Session AidTSP-TS4: AidTSP Technical Session IV
Session AoI-Open: Opening Session Session AoI-Key: Keynote Session AoI-SCH: Scheduling and Optimization for AoI Session AoI-CBrk: Virtual Coffee Break Session AoI-BEY: Beyond AoI Metrics
Session CNERT-O: Opening Session Session CNERT-K: Keynote Session CNERT-P: Panel Session CNERT-S1: Session 1, Experimentation Enabling Capabilities Session CNERT-S2: Session 2, Services and network design Session CNERT-S3: Session 3, Wireless Session CNERT-D: Demo Session
Session DeepWireless-OS: Opening Session Session DeepWireless-KS1: Keynote Session 1 Session DeepWireless-TS1: Technical Session 1 - Deep Learning for Wireless Security Session DeepWireless-TS2: Technical Session 2 - Deep Learning for Wireless Applications Session DeepWireless-KS2: Keynote Session 2 Session DeepWireless-TS3: Technical Session 3 - Deep Learning for 5G Communications Session DeepWireless-CS: Closing Session
Session DroneCom-TS1: Technical Session I Session DroneCom-KS1: Keynote Session I Session DroneCom-TS2: Technical Session II Session DroneCom-TS3: Technical Session III Session DroneCom-KS2: Keynote Session II Session DroneCom-TS4: Technical Session IV
Session FOGML-S1: Opening Remarks Session FOGML-S2: Federated Learning Session FOGML-S3: Distributed Learning and Edge Computing
Session GI-00: Session#0 - Opening Session with Invited Keynote Session GI-01: Session#1 - Applying Machine Learning to Next Generation Internet Session GI-02: Session#2 - Performance Optimization of Next Generation Internet
Session ICCN-KS1: Security in Wireless Body Area Networks Session ICCN-B1: Virtual Coffee Break Session ICCN-S1: Cloud and Edge Computing Session ICCN-S2: Cloud and Edge Security Session ICCN-B2: Virtual Lunch Break Session ICCN-S3: Cloud and Edge Computing Session ICCN-S4: Cloud and Edge Computing
Session MobiSec-S1: AI & Security (I) Session MobiSec-K: Keynote: AI for Cybersecurity and Security of AI Session MobiSec-S2: AI & Security (II) Session MobiSec-S3: Mobile & Edge Security Session MobiSec-S4: Communication Security
Session NG-OPERA-OS: Opening Session Session NG-OPERA-KS: Keynote Session Session NG-OPERA-PS: Panel Session Session NG-OPERA-Break-1: Break Session NG-OPERA-TS1: Technical Session 1: Machine Learning and Resource Optimization for 6G RANs Session NG-OPERA-Lunch: Lunch Session NG-OPERA-TS2: Technical Session 2: System-level Solutions for Open RANs Session NG-OPERA-Break-2: Break Session NG-OPERA-TS3: Technical Session 3: Non-terrestrial Integration and Posters Session NG-OPERA-ACS: Awards and Closing
Session NetSciQCom-S1: Keynote Talk Session NetSciQCom-S2: Contributed Talks
Session PerAI-6G-Opening: Opening Session Session PerAI-6G-Keynote-1: Keynote Session 1 Session PerAI-6G-Keynote-2: Keynote Session 2 Session PerAI-6G-Session-1: Session 1: AI applications in 6G Session PerAI-6G-Session-2: Session 2: Distributed Learning in 6G Session PerAI-6G-Session-3: Session 3: Digital Twin in 6G Session PerAI-6G-Session-4: Session 4: Resource Management in 6G Session PerAI-6G-Closing: Closing Session
Session WISARN-O: Opening Session WISARN-K1: Keynote Session WISARN-CB: Virtual Coffee Break Session WISARN-S1: Technical Session
Session Wireless-Sec-I: Wireless Security I Session Wireless-Sec-II: Wireless Security II

Session NG-OPERA-OS

Opening Session

Conference
8:00 AM — 8:15 AM EDT
Local
May 20 Sat, 5:00 AM — 5:15 AM PDT
Session Chair

Nakjung Choi (Nokia Bell Labs, USA)

Session NG-OPERA-KS

Keynote Session

Conference
8:15 AM — 9:00 AM EDT
Local
May 20 Sat, 5:15 AM — 6:00 AM PDT

Perspectives from the O-RAN next Generation Research Group (nGRG) on 6G

Hannu Flinck (Nokia, Finland)

0
TBD
Speaker
Speaker biography is not available.
Session Chair

Nakjung Choi (Nokia Bell Labs, USA)

Session NG-OPERA-PS

Panel Session

Conference
9:00 AM — 10:00 AM EDT
Local
May 20 Sat, 6:00 AM — 7:00 AM PDT

Panel discussion on the evolution of programmable cellular networks towards 6G

David M. Gutierrez Estevez (Samsung R&D Institute, UK), Gerald Karam (Nokia Bell Labs, USA), Simona Marinova (Bell Canada, Canada), Navid Nikaein (EURECOM, France), and Srinivas Shakkottai (Texas A&M University, USA)

0
TBD
Speaker
Speaker biography is not available.
Session Chair

Ahan Kak (Nokia Bell Labs, USA)

Session NG-OPERA-Break-1

Break

Conference
10:00 AM — 10:30 AM EDT
Local
May 20 Sat, 7:00 AM — 7:30 AM PDT

Session NG-OPERA-TS1

Technical Session 1: Machine Learning and Resource Optimization for 6G RANs

Conference
10:30 AM — 12:00 PM EDT
Local
May 20 Sat, 7:30 AM — 9:00 AM PDT

Developing xApps for Rogue Base Station Detection in SDR-Enabled O-RAN

Jun-Hong Huang (National Taiwan University of Science and Technology, Taiwan), Shin-Ming Cheng (National Taiwan University of Science and Technology & Academia Sinica, Taiwan), Rafael Kaliski (National Sun Yat-Sen University, Taiwan), and Cheng-Feng Hung (National Taiwan University of Science and Technology, Taiwan)

1
In order to support the diverse requirements of 5G communications, a multitude of RAN components are required. To enable multiple vendor support for 5G, each of whom can independently choose components, Open-RAN (O-RAN) defined a set of standards to which the components must adhere. In addition, O-RAN defines the management elements used to manage each component to secure the 5G networks. While the proposed architecture can manage both 4G and 5G environments, including 5G NSA (Non-Standalone), it inherently suffers from the same vulnerabilities found in 4G LTE. Consequently, an attacker can use unprotected signaling and a low-cost Software Defined Radio (SDR) to launch rogue base station (RBS) attacks on the user equipment (UE), even in O-RAN architectures. In this paper, we consider the stability of signals collected from high-quality operational BSs versus cheap RBSs. Using signal stability features, we develop a machine learning (ML) based RBS detector located on the UE. With the aid of an O-RAN xAPP, ML models can be retrained using the data collected from multiple UEs, and the updated model can be delivered to UEs to enable higher detection accuracy. We conduct extensive experiments by implementing an RBS using a USRP B210, enabling O-RAN using E-Release, and data collected from operational BSs. Moreover, the detector is implemented as an Android APP, which realizes the connection to the O-RAN xAPP. The experimental results show that our detector can achieve more than 99% accuracy, precision, recall, and F1 score.
Speaker Cheng-Feng Hung(National Taiwan University of Science and Technology)

Cheng-Feng Hung received his B.S. degree in information technology and applications the college of science and engineering from the National Quemoy University, Kinmen, Taiwan, in 2019. He is currently a Ph.D. candidate in the Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei. He visited the Warsaw University of Technology in 2022. His research interests is mobile network security.

Intelligent Cellular Traffic Prediction in Open-RAN Based on Cross-Domain Data Fusion

Zihao Xiong, Kaisa Zhang, Gang Chuai, Xu Yang, and Yichao Xu (Beijing University of Posts and Telecommunications, China)

0
Open Radio Access Network (Open-RAN) is considered as a new RAN paradigm and has become a hotspot for operators. Accurate traffic prediction models can be deployed on Open-RAN as a critical component of implementing intelligent communication system. The communication indicators describing the network status are diverse, with independent significances and complex correlations. Adequate capture of correlations among these features will help establish a more reasonable and accurate prediction model. However, traditional prediction methods rarely research the joint capture of spatial, temporal and feature dependencies. Consider the problem, this paper proposes a comprehensive prediction system based on cross-domain data fusion. Firstly, the spatial topological relationship among base stations is mined by correlation analysis algorithm and constructed into a graph structure. Then, a novel neural network for cellular traffic prediction is designed by combining Graph Attention Network (GAT) and Gated Recurrent Unit (GRU) algorithms, where GRU is used to capture temporal dependence, and GAT captures spatial and feature dependencies. Finally, experimental results on real-world dataset verify the superiority of the proposed model, and the ability to mine spatial-temporal-feature correlation.
Speaker Zihao Xiong (Beijing University of Posts and Telecommunications, China)


Time-varying Real-time Online Multi-Resource Allocation for Scaling the Slices and VNF Isolation

Akbar Majidi (Trinity College Dublin, Ireland), Nazila Jahanbakhsh (Shanghai Jiao Tong University, China & Islamic Azad University, Iran), and Siobhan Clarke (Trinity College Dublin, Ireland)

0
The infrastructure of mobile networks in 5G will offer various services in the form of network slices that can be deployed and implemented in a highly customizable manner. A real dynamic network with time-varying network utility has not been considered in previous works. In this paper, we examine the multi-resource allocation problem for network slicing in an online manner where the utility functions change over time. To solve the problems as mentioned above, we present Metis, a first systematic solution. Metis is an online network slice resource allocation framework that combines the time-varying property of the network utility function given the bandwidth and processing capacity constraints with the virtual network functions isolation requirements. As a result, we aim to maximize the cumulative network utility over time. Utilizing state-of-the-art concave optimization methods, we formulate the multi-resource allocation problem. To the best of our knowledge, this is the first work investigating an online method for multi-resources allocation for network slicing in a real-time network. Metis can proveably converge to the optimal solution, and the experiment results show a steady state behavior for Metis which converges in dynamic network settings.
Speaker Akbar Majidi

Akbar Majidi received the Ph.D. degree from the Department of Computer Science at Shanghai Jiao Tong University, in 2020. As a Post-Doctoral Researcher, he has worked at University College Cork in Ireland, Czech Technical University in Prague, and Munster Technological University in Ireland. He is currently working as a Research Fellow with the CONNECT/Enable Research Program at Trinity College Dublin (TCD), The University of Dublin, Ireland. He has published more than 20 papers in high-ranked conferences and proceedings, such as IEEE ICNP, ACM ICPP, GLOBECOM, LCN, and ICPADS.


He has also published in well-archived international journals, such as IEEE/ACM TRANSACTIONS ON NETWORKING, IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, Journal of Network and Computer Applications, and Computer Communications. His research interests include DCNs, SDN/NFV with network slicing, and machine learning systems for congestion control.

Sum-Rate Maximization for Active RIS-Aided Downlink RSMA System

Xinhao Li (Beijing University of Posts and Telecommunications, China), Tao Wang (Beijing University of Posts and Telecommunications, China), Haonan Tong (Beijing University of Posts and Telecommunications, China), Zhaohui Yang (Zhejiang University, China), Yijie Mao (ShanghaiTech University, China), and Changchuan Yin (Beijing University of Posts and Telecommunications, China)

1
In this paper, the problem of sum-rate maximization for an active reconfigurable intelligent surface (RIS) assisted downlink rate-splitting multiple access (RSMA) transmission system is studied. In the considered model, the active RIS is deployed to overcome severe power attenuation, which is caused by the cumulative product of RIS incidence path loss and the reflection path loss. Since the active RIS can adjust both the phase and the amplitude of the incident signal simultaneously, the RIS control scheme requires delicate design to improve RSMA communication performance. To address this issue, a sum-rate maximization problem is formulated to jointly optimize the beamforming vectors, rate allocation vector, and RIS precoding matrix. To solve this non-convex sum-rate maximization problem, an iterative algorithm based on fractional programming (FP) and quadratic constraint quadratic programming (QCQP) is proposed. In particular, the proposed algorithm firstly decomposes the original problem into two subproblems, namely, 1) beamforming and rate allocation optimization and 2) active RIS precoding optimization. The corresponding variables of the two subproblems are optimized through sequential convex approximation (SCA) and block coordinate descent (BCD), respectively. Numerical results show that the proposed active RIS-aided RSMA system could increase the sum-rate by up to 45% over the conventional passive RIS-aided RSMA system with the same energy consumption.
Speaker Xinhao Li (Beijing University of Posts and Telecommunications, China)

Xinhao Li received the B.S. degree from the College of Electronic Science and Engineering, Jilin University, Changchun, China. He is now pursuing the M.S. degree in the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China. His current research interests include reconfigurable intelligent surface (RIS) and signal processing.

Session Chair

Tao Han (New Jersey Institute of Technology, USA)

Session NG-OPERA-Lunch

Lunch

Conference
12:00 PM — 2:00 PM EDT
Local
May 20 Sat, 9:00 AM — 11:00 AM PDT

Session NG-OPERA-TS2

Technical Session 2: System-level Solutions for Open RANs

Conference
2:00 PM — 3:30 PM EDT
Local
May 20 Sat, 11:00 AM — 12:30 PM PDT

Positioning and Dynamic Tracking under 3GPP Cellular Network on GNURadio Testbed

Shunyu Chang (Beijing University of Posts and Telecommunications, China), Zhaoming Lu (Beijing University of Posts and Telecommunications, China), Xinghe Chu (Beijing University of Posts and Telecommunications, China), Di Zhang (Zhengzhou University, China), Luhan Wang (Beijing University of Posts and Telecommunications, China), and Xiangming Wen (Beijing University of Posts and Telecommunications, China)

0
This paper addresses high precision 5G positioning and dynamic tracking, which has risen increasingly attention from industry and academia recently. The recent works show that the position of user equipment (UE) could be estimated based on reference signals (e.g. PRS) in 5G on real testbed. However, these works failed to exploit the positional information behind the consecutive nature of users' moving, which could enhance the positioning accuracy by exploiting dynamic tracking architectures. In this paper, we extract the angle of arrival (AoA) and time of arrival (ToA) under 3GPP physical layer protocol, and then introduce a dynamic tracking method to improve the accuracy of AoA and ToA estimation. The performance of the proposed method was evaluated on GNURadio. The results show that the AoA and ToA could be precisely extracted in the real propagation channel on the premise of guaranteeing the communication performance. And in the case of the same bandwidth and number of antennas, compared with traditional base station (BS) tracking method, our dynamic tracking could reduce tracking error by 35.11% during the 115s tracking process.
Speaker Shunyu Chang(Beijing University of Posts and Telecommunications)


Demystifying the Near-real Time RIC: Architecture, Operations, and Benchmarking Insights

Huu-Trung Thieu, Van-Quan Pham, Ahan Kak, and Nakjung Choi (Nokia Bell Labs, USA)

0
Widely expected to serve as a blueprint for the upcoming sixth generation of cellular networks, the Open Radio Access Network (Open RAN) architecture is ushering in a new era of programmable control and assurance for the telecommunications industry. Central to Open RAN's themes of softwarization and programmability is the Near-real Time RAN Intelligent Controller (near-RT RIC). Operating on a timescale of greater than 10 ms, the near-RT RIC is proving indispensable for key use cases relating to traffic engineering, resource optimization, infrastructure sharing, and service assurance. However, despite its critical role, the near-RT RIC is still largely considered a black box, with existing literature providing little to no insight regarding the RIC's internal architecture, operations, and performance. With a view to addressing this gap in the prior art, this paper provides a comprehensive overview of the near-RT RIC, detailing both the RIC's architecture as well as delivering scalability-related insights through a detailed set of performance benchmarks. Furthermore, for the first time in the literature, this paper also provides unique operational input regarding the RIC.
Speaker Huu Trung THIEU (Nokia Bell Labs)

Huu-Trung Thieu (male) received a MS degree in Mobile network and Telecommunication System engineering from the IMT Atlantique, Brest, France. He is currently a Member of Technical Staff in the Mobile Networks Systems Department within the Network Systems and Security Research Lab at Bell Labs. His current research interests include 6G network systems, software-defined networking, machine learning operations, 5G/6G E2E slicing architecture, and network service automation. 

5GECO: A Cross-domain Intelligent Neutral Host Architecture for 5G and Beyond

Joao Francisco Nunes Pinheiro (University of Antwerpen & IMEC, Belgium), Chia-Yu Chang (Nokia Bell Labs, Belgium), Tom Collins (Citymesh, Belgium), Eric Smekens (Citymesh, Belgium), Revaz Berozashvili (Accelleran, Belgium), Adnan Shahid (Gent University - imec, Belgium), Danny De Vleeschauwer (Nokia Bell Labs, Belgium), Paola Soto (Universiteit Antwerpen - imec, Belgium), Ingrid Moerman (Ghent University - imec, Belgium), Johann Marquez-Barja (University of Antwerpen & imec, Belgium), Jens Buysse (Citymesh, Belgium), Miguel Camelo (University of Antwerp - imec, Belgium)

0
Radio Access Network (RAN) openness is a vision for 5G and beyond to avoid unnecessary vendor lock-in effects and introduce new business models. To expand the Open RAN (O-RAN) potentials, such as real-time control and data-driven intelligence, we propose a cross-domain 5GECO network architecture to let flourish the new Intelligent Neutral Host (INH) model in the value chain. Also, to realize this new model, we consider the role of a INH Service provider as having to define a Service Level Agreement (SLA) with its customers, called tenants, and a scheme to map said SLA into 5G technology (termed the 5GECO multiplet) is elaborated. It is noted that this 5GECO architecture considers sharing, control, and orchestration across both the RAN and the Transport Network (TN) domains. Finally, three key challenges are identified for an INH Service Provider, along with a preliminary solution analysis, ranging from Radio Resource Management (RRM) optimization, cross-domain lowlatency, and flexible virtualized resource scalability.
Speaker Joao Francisco Nunes Pinheiro (IDLab - University of Antwerp)

Joao Pinheiro is a PhD candidate at the University of Antwerp within the research group IDLab of imec, located in Belgium. His main research topic is 5G communications with emphasis on Core and RAN deployment and sharing.

O-RAN and RIC compliant solutions for Next Generation Networks

Marco Silva (University of Coimbra, Portugal), Joao Pedro Fonseca (Capgemini Enginnering, Portugal), David Perez Abreu (University of Coimbra, Portugal), Paulo Martins (Capgemini Engineering, Portugal), Paulo Duarte (Capgemini Engineering, Portugal), Raul Barbosa (Capgemini Engineering, Portugal), Bruno Mendes (University of Aveiro, Portugal), Joao Silva (Capgemini Engineering, Portugal), Adriano Goes (Capgemini Engineering, Portugal), Marco Araujo (Capgemini Engineering, Portugal), Bruno Sousa (University of Coimbra, Portugal), Marilia Curado (University of Coimbra, Portugal), and Jose Santos (University of Coimbra, Portugal)

1
The Radio Access Network (RAN) is undergoing deep changes in the transition to beyond 5G systems. The Open-Radio Access Network (O-RAN) alliance aims to split the RAN architecture and support heterogeneity. At the same time, Open-Source Software (OSS) solutions like OpenAirInterface and srsRAN initiatives attempt to incorporate several stakeholders. This work proposes an End-to-End (E2E) orchestration framework using OSS solutions that are O-RAN compliant. A high-level architecture is presented focused on the RAN Intelligent Controller (RIC): the connection between Near-Real Time RAN Intelligent Controller (Near-RT RIC) with Service Management and Orchestration via the A1 interface and the connection with O-RAN Network Functions (E2 Nodes) via the E2 interface. The proposed architecture was validated on an experimental prototype. The main results compare state of the art OSS solutions status for deploying Near-RT RIC and RAN network functions. Our findings focused on the RAN functions interoperability with the RIC.
Speaker Marco Silva (University of Coimbra, Portugal)

Marco Silva received the master’s degree in electrical and computer engineering (specialization in Computers) from the University of Coimbra, Coimbra, Portugal, in 2020, where he is currently pursuing the Ph.D. degree.

After his bachelor’s degree, he joined a research project with the Centre for Informatics and Systems, University of Coimbra in the field of intelligent sensing for 5G platforms, having been a Researcher on the Project P2020 MobiWise. As a Ph.D. student, he was a Researcher on the Project POWER: Empowering a Digital Future. Most recently, he has been a Researcher on the Project P2020 OREOS: Orchestration and Resource optimization for reliable and low-latency Services. His research interests involve intelligent control mechanisms in communication networks, energy efficiency, and 5G networks.

Session Chair

Mikel Irazabal (OpenAirInterface Software Alliance, France)

Session NG-OPERA-Break-2

Break

Conference
3:30 PM — 4:00 PM EDT
Local
May 20 Sat, 12:30 PM — 1:00 PM PDT

Session NG-OPERA-TS3

Technical Session 3: Non-terrestrial Integration and Posters

Conference
4:00 PM — 5:30 PM EDT
Local
May 20 Sat, 1:00 PM — 2:30 PM PDT

SPELS: Scalable and Programmable Testbed for Evaluating LEO Satellite Swarm Communications

Venkata Srirama Rohit Kantheti (North Carolina State University, USA), Shih-Chun Lin (North Carolina State University, USA), and Liang C. Chu (Lockheed Martin Space Systems Company, USA)

0
Low earth orbit (LEO) satellite communications promise next-generation mobile networks with seamless connectivity to rural, remote, and inaccessible areas. Notably, due to low-cost deployment and quick turn-around times in production, proliferated LEOs deployed and orchestrated as a swarm of satellites can support ultra-broad transmissions for the ever-evolving communications and aid current wireless network infrastructure. This paper introduces a scalable and programmable OTA (over-the-air) testbed, called SPELS, to provide a real-time architectural implementation of satellite swarm systems and demonstrate the testbed's effectiveness in online swarm communications. First, the in-lab SPELS testbed is established with COTS (commercial off-the-shelf) software-defined radios, a high-performance host computer, and wireless softwarization. Accordingly, the latest AI-enabled wireless communications and real-time signal processing constraints can be easily realized upon various frontends by decoupling radio swarm networks' control and data planes. Furthermore, based on the designed infrastructure, an end-to-end module is proposed for timely and resilient satellite swarm communications. This module consists of swarm-MRC, an optimal swarm combining technique, and a 5G-compliant deep learning-based LDPC scheme. Experimental evaluations validate the superiority of our swarm combiner with learning-enabled channel coding for online frontend operations, thus facilitating LEO swarm readiness.
Speaker Venkata Srirama Rohit Kantheti (NC State University)

Venkata Srirama Rohit Kantheti (K V S Rohit) received his B.Tech in Electronics and Instrumentation Engineering and M.Tech in VLSI Design and Embedded Systems from National Institute of Technology, Rourkela, India, as part of a 5-year Integrated Dual Degree in 2016. He is currently pursuing a PhD degree with the Department of Electrical and Computer Engineering at NC State University since 2021.

SFC Deployment in Space-Air-Ground Integrated Networks Based on Matching Game

Yilu Cao (Nanjing University of Aeronautics and Astronautics, China), Ziye Jia (Nanjing University of Aeronautics and Astronautics, China), Chao Dong (Nanjing University of Aeronautics and Astronautics, China), Yanting Wang (Northwesten Polytechnical University, China), Jiahao You (Nanjing University of Aeronautics and Astronautics, China), and Qihui Wu (Nanjing University of Aeronautics and Astronautics, China)

0
The space-air-ground integrated network (SAGIN) is dynamic and flexible, which can support transmitting data in environments lacking ground communication facilities. However, the nodes of SAGIN are heterogeneous and it is intractable to share the resources to provide multiple services. Therefore, in this paper, we consider using network function virtualization technology to handle the problem of agile resource allocation. In particular, the service function chains (SFCs) are constructed to deploy multiple virtual network functions of different tasks. To depict the dynamic model of SAGIN, we propose the reconfigurable time extension graph. Then, an optimization problem is formulated to maximize the number of completed tasks, i.e., the successful deployed SFC. It is a mixed integer linear programming problem, which is hard to solve in limited time complexity. Hence, we transform it as a many-to-one two-sided matching game problem. Then, we design a Gale-Shapley based algorithm. Finally, via abundant simulations, it is verified that the designed algorithm can effectively deploy SFCs with efficient resource utilization.
Speaker Yilu Cao (Nanjing University of Aeronautics and Astronautics, China)

Yilu Cao is a postgraduate student at the Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Nanjing University of Aeronautics and Astronautics, China. Her research content is the network function virtualization in the space-air-ground integrated network.

Detection and mitigation of indirect conflicts between xApps in Open Radio Access Networks

Cezary Adamczyk and Adrian Kliks (Poznan University of Technology, Poland)

0
In Open Radio Access Networks, the Conflict Mitigation component, which is part of the Near-RT RIC, aims to detect and resolve any conflicts between xApp decisions. In this paper, we propose a universal method for detecting and resolving of indirect conflicts between xApps. Its efficiency is validated by extensive computer simulations. Our results demonstrate that, in the considered scenario, the mean bitrate satisfaction of users increases by 2%, while the number of radio link failures and ping-pong handovers in the network is significantly reduced.
Speaker Cezary Adamczyk

Cezary Adamczyk is a PhD student at Poznan University of Technology, conducting research in the field of AI/ML utilization in open radio access network radio resource optimization. He works as an OSS Solutions Architect in international telecommunication projects. His prior research work related to O-RAN includes a paper proposing a novel ML algorithm for optimizing radio resources utilization and an article on a conflict mitigation framework for the Near Real-Time RAN Intelligent Controller.

Implementing an Open 5G Standalone Testbed: Challenges and Lessons Learnt

Maryam Amini, Ahmed El-Ashmawy, and Catherine Rosenberg (University of Waterloo, Canada)

0
We are working towards the implementation of a functional open 5G standalone (SA) multi-cell testbed. This poster aims at presenting our current progress, i.e., a fully functional single 5G SA cell, as well as the challenges that we have faced so far together with the lessons learnt. We have uploaded a recorded video of our demo at https://bit.ly/3KZPXZW. A glimpse of the next steps is given in the conclusion.
Speaker Maryam Amini (University of Waterloo)

Maryam is pursuing her Ph.D. in electrical and computer engineering at the Univeristy of Waterloo, Ontario, Canada. Her current research involves 5G networks and next-generation radio access networks (NG-RANs). She received both her B.S. and M.S. in computer engineering at the Iran University of Science and Technology.

Towards a Scalable 5G RAN Central Unit

Cuidi Wei (George Washington University, USA), Ahan Kak (Nokia Bell Labs, USA), Nakjung Choi (Nokia Bell Labs, USA), and Timothy Wood (George Washington University, USA)

0
The radio access network (RAN) connects mobile users to the core network. As the complexity of the RAN has increased with the shift towards 5G and beyond, the need for flexible deployment options has become a key requirement, as evidenced by the rise of disaggregated RAN architectures. Since a disaggregated RAN deployment may utilize a single central unit (CU) for multiple distributed units (DUs), it is critical that the CU not become a performance bottleneck in terms of the aggregate traffic that can be processed. To that end, in this work we focus on improving the scalability of the CU by addressing the performance limitations in a reference open source solution, the OpenAirInterface (OAI) 5G platform. We demonstrate a roughly 5X improvement in scalability by using the Data Plane Development Kit (DPDK) to provide a kernel bypass solution for the OAI 5G CU. While this provides a significant performance boost, we believe that even greater improvements may be possible with further architecture refinements or the use of accelerator platforms like programmable network cards or switches.
Speaker Cuidi Wei (George Washington University)

I'm a phd student from George Washington University who focuses on the distributed systems and networking.

Signaling Storm Detection in IIoT Network based on the Open RAN Architecture

Marcin Hoffmann and Pawel Kryszkiewicz (Poznan University of Technology and Rimedo Labs, Poland)

0
The Industrial Internet of Things devices due to their low cost and complexity are exposed to being hacked and utilized to attack the network infrastructure causing a so-called Signaling Storm. In this paper, we propose to utilize the Open Radio Access Network (O-RAN) architecture, to monitor the control plane messages in order to detect the activity of adversaries at its early stage.
Speaker Marcin Hoffmann

Marcin Hoffmann received the M.Sc. degree (Hons.) in electronics and telecommunication from Poznań University of Technology, in 2019, where he is currently pursuing a Ph.D. degree with the Institute of Radiocommunications. He is gaining scientific experience by being involved in both national and international research projects. His research interests include the utilization of machine learning and location-dependent information for the purpose of network management. In addition to that Marcin works on massive MIMO and advanced beamforming techniques. His scientific articles are published in top journals like IEEE Transactions on Intelligent Transportation Systems or IEEE Access. In addition to that he is an R&D engineer at Rimedo Labs working on O-RAN software development solutions and spectrum sharing-related projects.

Session Chair

Qiang Liu (University of Nebraska-Lincoln, USA)

Session NG-OPERA-ACS

Awards and Closing

Conference
5:30 PM — 6:00 PM EDT
Local
May 20 Sat, 2:30 PM — 3:00 PM PDT
Session Chair

Tao Han (New Jersey Institute of Technology, USA)


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