WOS1: 5G NR and Beyond

Tuesday, 16 June 2020, 12:15-14:30 CEST, Recommended re-viewing, https://www.youtube.com/playlist?list=PLjQu6nB1DfNBfvzf2WRa8Ob3qwcryxaHQ

Tuesday, 16 June 2020, 12:15-17:00 CEST, Non-Live interaction (Chat),  link sent only to Registered people

 

Multi-antenna Multi-user Clustering for Relay Aided Cellular Massive-MIMO Systems

Ghadir Mostafa (The German University in Cairo, Egypt); Engy Aly Maher (German University in Cairo, Egypt); Ahmed E. El-Mahdy (The German University in Cairo, Egypt)
In this paper, we scrutinize on the enhancement of the sum rate of multi-antenna active users by implementing user clustering to utilize the standby users at a close proximity. A massive antenna array at the BS and maximum-ratio transmission(MRT) linear precoding is implemented in order to realize a massive multiple-input-multiple-output (MIMO) system. Numerical results show an improvement in the users’ spectral efficiency (SE) due to the mitigation of the interference from external cells when using massive-MIMO. Additionally, the residual self-loop interference at the standby users has been proven to have a significant degradation impact on the relayed link and hence the total SE.

 

A Study on a New Type of DDoS Attack Against 5G Ultra-Reliable and Low-Latency Communications

Cheng-Yeh Chen, Guo-Liang Hung and Hung-Yun Hsieh (National Taiwan University, Taiwan)
5G New Radio (NR) allows enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) to coexist in the physical layer for better resource utilization. To enable the coexistence while satisfying the QoS requirement of URLLC, 3GPP introduces the cancellation mechanism that allows a URLLC UE to preempt the transmission of eMBB UEs. In this paper, we show that the 3GPP design to provide stringent QoS guarantee for URLLC may become a threat to interfere both eMBB and URLLC via highly synchronized low- volume DDoS. We dissect potential vulnerability for both eMBB and URLLC UEs in the uplink from the 3GPP standards. An attack model is investigated and evaluated through system-level simulations. We find that synchronization among compromised URLLC UEs could be leveraged by the attacker to amplify the overall impact on both eMBB throughput and URLLC latency, even when the number of compromised UEs is small.

 

Synchronization in 5G: a Bayesian Approach

Meysam Goodarzi (Humboldt University of Berlin & IHP – Leibniz-Institut für Innovative Mikroelektronik, Germany); Darko Cvetkovski (Humboldt University of Berlin, Germany); Nebojsa Maletic and Jesús Gutiérrez (IHP – Leibniz-Institut für Innovative Mikroelektronik, Germany); Eckhard Grass (IHP & Humboldt-University Berlin, Germany)
In this work, we propose a hybrid approach to synchronize large scale networks. In particular, we introduce the use of Kalman Filtering (KF) along with time-stamps generated by the Precision Time Protocol (PTP) for pairwise node synchronization. Furthermore, we investigate the merit of Factor Graph (FG) along with Belief Propagation (BP) algorithm in achieving high precision end-to-end network synchronization. Finally, we present the idea of dividing the large-scale network into local synchronization domains, for each a suitable sync algorithm is utilized. The simulation results indicate that, despite the simplifications in the hybrid approach, the error in the offset estimation remains in the order of sub-5 ns.

 

Machine-Learning Based Traffic Forecasting for Resource Management in C-RAN

Rolando Guerra-Gómez (Universitat Politécnica de Catalunya (UPC), Spain); Silvia Ruiz Boqué (UPC, Spain); Mario Garcia-Lozano (Universitat Politècnica de Catalunya, Spain); Joan Olmos (Universitat Politecnica de Catalunya, Spain)
The assumption of a fixed computational capacity at the Baseband Unit (BBU) pools in a Cloud Radio Access Network (C-RAN) deployment results in underutilized resources or unsatisfied users depending on traffic requirements. In this paper a new strategy to predict the required resources based on Machine Learning techniques is proposed and analysed. Support Vector Machine (SVM), Time-Delay Neural Network (TDNN), and Long Short-Term Memory (LSTM) have been tested and
compared to select the best predicting approach. Instead of using a regular synthetic scenario a realistic dense cell deployment over Vienna city is used to validate the results. Authors show that the proposed solution reduces the unused resources average by 96 %.

 

Real-Time Demonstration of ARoF Fronthaul for High-Bandwidth mm-Wave 5G NR Signal Transmission over Multi-Core Fiber

Simon Rommel and Bruno Cimoli (Eindhoven University of Technology, The Netherlands); Evangelos Grivas (Eulambia, Greece); Delphin Dodane (Thales Research and Technology, France); Alvaro Morales (Eindhoven University of Technology, The Netherlands); Evangelos Pikasis (Eulambia Advanced Technologies Ltd, Greece); Jerome Bourderionnet (Theales Research and Technology, France); Gilles Feugnet (Thales Research and Technology, France); Juliana Barros Carvalho (Institute for Photonic Integration & Eindhoven University of Technology, The Netherlands); Michail Katsikis (Intracom Telecom, Greece); Konstantinos Ntontin (University of Barcelona, Spain); Dimitrios S. Kritharidis (Intracom Telecom, Greece); Izabela Spaleniak and Paul Mitchell (Optoscribe, United Kingdom (Great Britain)); Mykhaylo Dubov (Aston University, United Kingdom (Great Britain)); Idelfonso Tafur Monroy (Eindhoven University of Technology, The Netherlands)
This paper presents an experimental demonstration of analog radio-over-fiber (ARoF) fronthaul for high-bandwidth, high-capacity millimeter wave (mm-wave) extended fifth generation mobile network (5G) new radio (NR) signals over an optical distribution network with optical space division multiplexing (SDM). ARoF is shown to alleviate fronthaul capacity bottlenecks, transporting an 800 MHz wide extended 5G NR signal and allowing to maintain full centralization in a centralized radio access network (C-RAN). The proposed ARoF fronthaul architecture features a transmitter that generates the ARoF signal and an optical signal carrying a reference local oscillator (LO) employed for downconversion at the remote unit (RU) from a single radio frequency (RF) reference at the central office (CO). An SDM based RAN with 7-core multi-core fiber (MCF) allows parallel transport of the uplink ARoF signal and reference LO at the same wavelength over separate cores. Transmission of an 800 MHz wide extended 5G NR fronthaul signal over 7-core MCF is shown with full real-time processing, achieving 1.4 Gbit/s with BER < 3.8e−3 and thus below the limit for hard-decision forward error correction (FEC) with 7 % overhead. Downconversion at the RU is performed electrically with the remote-fed LO provided by the CO.