Ioannis D. Moscholios
Dept. of Informatics and Telecommunications, Univ. of Peloponnese
Low earth orbit mobile satellite systems (LEO-MSS) are ideally suited for providing multiservice real time applications to a diverse population in large geographical areas. Compared to geostationary MSS (GEO-MSS), their requirements in terms of transmit power and transmission delays are lower at the cost of introducing frequent beam handovers (that occur due to the high speed of LEO satellites) to in-service mobile users (MUs) during their lifetime in the system.
To assure quality of service (QoS) in the complicated multirate traffic environment of contemporary LEO-MSS it is essential to develop QoS mechanisms, with efficient and fast QoS assessment, that: i) provide access to the necessary bandwidth needed by the services of the MUs, ii) ensure fairness among different "competing" mobile services/applications and iii) reduce handover failures for in-service MUs.
Considering call-level traffic in a LEO-MSS which accommodates different service-classes with different QoS requirements, such a QoS mechanism is a channel sharing policy, since it affects call-level performance measures, like call blocking probabilities (CBP) and handover failure probabilities. The QoS assessment of LEO-MSS under a channel sharing policy can be accomplished through teletraffic loss or queueing models.The teletraffic models presented in this talk have as a springboard either the Erlang B or C formula or the Kaufman-Roberts recursive formula, used to accurately determine the link occupancy distribution and consequently CBP in the so called Erlang Multirate Loss Model (EMLM). In the EMLM, calls of different service-classes have fixed bandwidth requirements, arrive to a link of certain bandwidth capacity according to a Poisson process, and compete for the available link bandwidth under the complete sharing policy (a new call is accepted in the link if its required bandwidth is available - otherwise the call is blocked and lost). The recursive calculation of the link occupancy distribution leads to the efficient determination of the various performance indexes in the EMLM.
Ioannis D. Moscholios was born in Athens, Greece, in 1976. He received the Dipl.-Eng. degree in Electrical & Computer Engineering from the University of Patras, Patras, Greece, in 1999, the M.Sc. degree in Spacecraft Technology & Satellite Communications from the University College London, UK, in 2000 and the Ph.D. degree in Electrical & Computer Engineering from the University of Patras, in 2005. From 2005 to 2009 he was a Research Associate at the Wire Communications Laboratory, Dept. of Electrical & Computer Engineering, University of Patras. From 2009 to 2013 he was a Lecturer in the Dept. of Telecommunications Science and Technology, University of Peloponnese, Tripolis, Greece. From 2013 to 2018 he was an Assistant Professor in the Dept. of Informatics & Telecommunications, University of Peloponnese, Tripolis, Greece. Currently, he is an Assοciate Professor in the Dept. of Informatics & Telecommunications, University of Peloponnese, Tripolis, Greece. His research interests include teletraffic engineering, simulation and performance analysis of communication networks. He has published 1 book and over 160 papers in international journals/ conferences. He has served as a Guest Editor in: a) IET Communications, b) IET Networks and c) Mobile Information Systems. He has also served as an Associate Editor in IEICE Transactions on Communications. He also serves in the TPC of IEEE ICC and IEEE Globecom (Communication QoS, Reliability and Modeling). He is an IARIA Fellow and a member of the Technical Chamber of Greece (TEE).
Department of Photonics Engineering, Technical University of Denmark
Artificial intelligence has been successfully applied to address some of the society’s biggest and most complex challenges from financial marked analysis to healthcare. Machine learning, a well-known and mature branch of artificial intelligence, provides the theory and the tools to learn from existing data. Recently, there has been an increasing amount of research applying machine learning techniques in the field of optical communication networks. Some examples are quality of transmission estimation, optical amplifiers control, modulation format recognition, and optical performance monitoring. This talk will give a brief overview of machine learning applications in optical communication systems. Then, it will present the latest results achieved by DTU and Politecnico Di Torino groups regarding the design, modeling and optimization of Raman amplifiers using machine learning techniques.
Darko Zibar received the M.Sc. degree in telecommunication and the Ph.D. degree in optical communications from the Technical University of Denmark, in 2004 and 2007, respectively. He was a Visiting Researcher with the Optoelectronic Research Group (Prof. John E. Bowers), University of California, Santa Barbara, CA, USA, in 2006 and 2008, where he worked on coherent receivers for analog optical links. In 2009, he was a Visiting Researcher with Nokia-Siemens Networks, where he worked on clock recovery techniques for polarization multiplexed systems. He is currently Associate Professor at DTU Fotonik, Technical University of Denmark and the group leader of Machine Learning In Photonics Systems (M-LiPS) group. His research efforts are currently focused on the application of machine learning methods to optical communication, ultra-sensitive amplitude and phase detection and optical fibre sensing systems. He is a recipient of Young Researcher Award by University of Erlangen-Nurnberg, in 2016, for his contributions to applications of machine learning techniques to optical technologies. He was a part of the team that won the HORIZON 2020 prize for breaking the optical transmission barriers. In 2017, he was granted European Research Council (ERC) Consolidator Grant where the focus is on the demonstration of nonlinear-distortion free optical communication systems by employing modulation of eigenvalues.
INEA S.A., Poznan, Poland
INEA owns large GPON and XGS-PON network which covers approximately 350 000 households in the area of Wielkopolska region. Using that infrastructure, INEA offers Fiber-To-The-Home (FTTH) connection with symmetrical data transfer speeds of 1Gbps and 10 Gbps. The symmetrical link allows INEA customers not only to benefit from the content available on Internet but also to publish their own material or use services with high demand for uplink bandwidth. In this presentation we will share information about the performance of this network and its utilisation and other operational parameters. This analysis will demonstrate the type of work operator needs to continuously carry in order to maintain high capacity, reliability, and scalability needed to deliver enhanced ultra-broadband services required now and in the future.
Karol Kowalik is a telecommunications engineer, holding a PhD from Dublin City University in Ireland. He is the Technology Development Manager at INEA S.A. where he participates in many technological projects covering wide spectrum of technologies such as GPON, DOCSIS, WiFi, WiMAX. His research interests include networking, switching, routing, network management, wireless and wired access. He has a number of publications in these areas.
IEICE Information and Communication Technology Forum 2019