Publications

@inproceedings{Ma2020a,
title = {Characterization for High-Speed Railway Channel enabling Smart Rail Mobility at 22.6 GHz},
author = {Lei Ma and Ke Guan and Dong Yan and Danping He and Bo Ai and Junhyeong Kim and Heesang Chung},
doi = {10.1109/WCNC45663.2020.9120474},
issn = {1558-2612},
year = {2020},
date = {2020-05-01},
booktitle = {2020 IEEE Wireless Communications and Networking Conference (WCNC)},
pages = {1-6},
abstract = {The millimeter wave (mmWave) communication with large bandwidth is a key enabler for both the fifth-generation mobile communication system (5G) and smart rail mobility. Thus, in order to provide realistic channel fundamental, the wireless channel at 22.6 GHz is characterized for a typical high-speed railway (HSR) environment in this paper. After importing the three-dimensional environment model of a typical HSR scenario into a self-developed high-performance cloud-computing Ray-Tracing platform \textendash CloudRT, extensive raytracing simulations are realized. Based on the results, the HSR channel characteristics are extracted and analyzed, considering the extra loss of various weather conditions. The results of this paper can help for the design and evaluation for the HSR communication systems enabling smart rail mobility.},
keywords = {5G, High-speed railway, mmWave, radio propagation, ray-tracing, smart rail mobility},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Giuseppi2020b,
title = {Network Selection in 5G Networks Based on Markov Games and Friend-or-Foe Reinforcement Learning},
author = {Alessandro Giuseppi and Emanuele De Santis and Francesco Delli Priscoli and Seok Ho Won and Taesang Choi and Antonio Pietrabissa},
doi = {10.1109/WCNCW48565.2020.9124723},
year = {2020},
date = {2020-04-01},
booktitle = {2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)},
pages = {1-5},
abstract = {This paper presents a control solution for the optimal network selection problem in 5G heterogeneous networks. The control logic proposed is based on multi-agent Friend-or-Foe Q-Learning, allowing the design of a distributed control architecture that sees the various access points compete for the allocation of the connection requests. Numerical simulations validate conceptually the approach, developed in the scope of the EU-Korea project 5G-ALLSTAR},
keywords = {5G, Markov Games, Multi-Agent Reinforcement Learning, Network Selection},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{cassiau:hal-02864733,
title = {Satellite and terrestrial multi-connectivity for 5G: making spectrum sharing possible},
author = {Nicolas Cassiau and Gosan Noh and Stephan Jaeckel and Leszek Raschkowski and Jean-Michel Houssin and Laurent Combelles and Marjorie Thary and Junhyeong Kim and Jean-Baptiste Dor\'{e} and Marc Laugeois},
url = {https://hal.archives-ouvertes.fr/hal-02864733},
year = {2020},
date = {2020-01-01},
booktitle = {IEEE Wireless Communications and Networking Conference (WCNC 2020)},
address = {Seoul, South Korea},
abstract = {This paper reports the first results of the 5G-ALLSTAR project [1] aiming at providing solutions and enablers for spectrum sharing in a 5G cellular and satellite multi-connectivity context. First, we present an exhaustive study of the frequency bands eligible for these systems in the short and medium term. A ray-tracing based and a geometry-based stochastic channel models developed in the project are then described. These models can be used to simulate systems involving terrestrial and non-terrestrial networks. We then describe three different ways investigated in the project for managing interference: signal processing (hardware implementation of a 5G New Radio compatible physical layer), beamforming (steering and switching beams in order to avoid the interference while preserving the spectral efficiency) and radio resource management (tool designed for joint optimization of satellite and terrestrial resource sharing).},
keywords = {5G, multi-connectivity, satellite, spectrum sharing},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Giuseppi2020c,
title = {Traffic steering and network selection in 5G networks based on Reinforcement Learning},
author = {Alessandro Giuseppi and Antonio Pietrabissa and Francesco Liberati and Roberto German\`{a} and Francesco Delli Priscoli},
year = {2020},
date = {2020-01-01},
booktitle = {European Control Conference 2020},
keywords = {5G, Network Selection, reinforcement learning, traffic steering},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}

@inproceedings{Jaeckel2021,
title = {A 5G-NR Satellite Extension for the QuaDRiGa Channel Model},
author = {Stephan Jaeckel and Leszek Raschkowski and Lars Thiele},
year = {2020},
date = {2020-01-01},
booktitle = {IEEE 93rd Vehicular Technology Conference: VTC2021-Spring},
keywords = {5G, channel model, QuaDRiGa, satellite},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}

@inproceedings{Choi2020,
title = {Management and Orchestration Architecture for Integrated Access of Satellite and Terrestrial in 5G},
author = {Taesang Choi and Seok Ho Won and Alessandro Giuseppi and Antonio Pietrabissa and Sungoh Kwon},
doi = {10.1109/ICOIN48656.2020.9016484},
issn = {1976-7684},
year = {2020},
date = {2020-01-01},
booktitle = {2020 International Conference on Information Networking (ICOIN)},
pages = {40-45},
abstract = {Multi-RAT access network, or heterogeneous access network, is considered to be the key enabling technology to satisfy the 5G requirements, such as high data rate, ultra-low latency and reliability. To make efficient use of all the available network resources, various research activities on multi-connectivity have been proposed to simultaneously connect, steer, and orchestrate across multiple different radio access technologies. Standardization of the management and orchestration of multi-connectivity environment, however, has just been initiated, thus further research and development is required. This paper proposes a novel management and orchestration architecture for integrated access of satellite and terrestrial in 5G. It especially focuses on the traffic steering and load-balancing of heterogeneous multi-RAT access environment.},
keywords = {5G, Computer architecture, heterogeneous network, high data rate, integrated access, load-balancing, management and orchestration, management standardization, mobility management (mobile radio), multi-connectivity, multiple different radio access technologies, network resources, orchestration architecture, QoS/QoE management, Quality of experience, Quality of service, radio access networks, Rats, reliability, satellite, telecommunication traffic, traffic steering, ultra-low latency},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Kim2019a,
title = {A Study on Frequency Planning of MN System for 5G Vehicular Communications},
author = {Junhyeong Kim and Sung-Woo Choi and Gosan Noh and Heesang Chung and Ilgyu Kim},
doi = {10.1109/ICTC46691.2019.8939787},
issn = {2162-1233},
year = {2019},
date = {2019-10-01},
booktitle = {2019 International Conference on Information and Communication Technology Convergence (ICTC)},
pages = {1442-1445},
abstract = {Recently in Korea, a research project has been launched to develop Moving Network (MN) system, which is a millimeter-wave (mmWave)-band vehicular communications system aiming to provide public transportation (e.g., city buses, express buses) with broadband mobile wireless backhaul (MWB). The MN system is designed to operate in Flexible Access Common Spectrum (FACS), which is the unlicensed band of 22-23.6 GHz that has been designated by the Korean government, thereby allowing onboard passengers to use Gigabit Wi-Fi for free. Although it is possible to utilize a very high bandwidth of 1.6 GHz in FACS, it is necessary to investigate the proper frequency planning (FP) for MN system that can effectively mitigate inter-cell interference (ICI) so as to optimize the system performance. For this reason, in this paper, we investigate three different FP strategies for MN system and conduct a simple performance evaluation. Simulation results show that as inter-site distance (ISD) gets closer, the reverse frequency reuse (R-FR)-based FP achieves better signal-to-interference-plus-noise ratio (SINR) and capacity performances than the other FPs.},
keywords = {5G, frequency planning, frequency reuse, Moving Network, vehicular communications},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Lisi2019,
title = {Multi-Connectivity in 5G terrestrial-Satellite Networks: the 5G-ALLSTAR Solution},
author = {Federico Lisi and Giacinto Losquadro and Andrea Tortorelli and Antonio Ornatelli and Manuel Donsante},
url = {https://arxiv.org/abs/2004.00368
http://proceedings.kaconf.org/papers/2019/ka17_2.pdf},
year = {2019},
date = {2019-01-01},
booktitle = {Ka and Broadband Communications, Navigation and Earth Observation Conference},
journal = {arXiv preprint arXiv:2004.00368},
abstract = {The 5G-ALLSTAR project is aimed at integrating Terrestrial and Satellite Networks for satisfying the highly challenging and demanding requirements of the 5G use cases. The integration of the two networks is a key feature to assure the service continuity in challenging communication situations (e.g., emergency cases, marine, railway, etc.) by avoiding service interruptions. The 5G-ALLSTAR project proposes to develop Multi-Connectivity (MC) solutions in order to guarantee network reliability and improve the throughput and latency for each connection between User Equipment (UE) and network. In the 5G-ALLSTAR vision, we divide the gNB in two entities: 1) gNB-CU (Centralized Unit) and 2) gNB-DU (Distributed Unit) The gNB-CU integrates an innovative Traffic Flow Control algorithm able to optimize the network resources by coordinating the controlled gNB-DUs resources, while implementing MC solutions. The MC permits to connect each UE with simultaneous multiple access points (even different radio access technologies). This solution leads to have independent gNB-DU/s that contain the RLC, MAC and PHY layers. The 5G-ALLSTAR MC algorithms offer advanced functionalities to RRC layer (in the gNB-CU) that is, in turn, able to set up the SDAP, the PDCP and the lower layers in gNB-DU. In this regard, the AI-based MC algorithms, implemented in gNB-CU, by considering the network performances in the UE surrounding environment as well as the UE QoS requirements, will dynamically select the most promising access points able to guarantee the fulfilment of the requirements also enabling the optimal traffic splitting to cope with the connection reliability. In this paper, we present also an innovative AI-based framework, included within the Traffic Flow Control, able to address the MC objectives, by implementing a Reinforcement Learning algorithm in charge of solving the network control problem.},
keywords = {5G, multi-connectivity, satellite},
pubstate = {published},
tppubtype = {inproceedings}
}