WOS1: Wireless, Optical and Satellite Networks I

Wednesday, 9 June 2021, 16:00-17:30, Zoom Room

Session Chair: Ronald Raulefs (DLR, Germany)

Satellite and Cellular Networks Integration – A System Overview

Gilles Charbit (MediaTek Inc, United Kingdom (Great Britain)); Kader Medles (MediaTek Inc., United Kingdom (Great Britain)); Pradeep Jose (MediaTek, United Kingdom (Great Britain)); Debby Lin (MediaTek Inc., Taiwan); Xc Zhu (MediaTek, United Kingdom (Great Britain)); I-Kang Fu (MediaTek Inc., Taiwan)
Using the same radio interface for both cellular (i.e. terrestrial) and satellite (i.e. non-terrestrial) networks can potentially provide huge benefit by leveraging cellular ecosystem to bring satellite communications into consumer markets. By integration of cellular and satellite networks can provide ubiquitous coverage from indoor, urban to rural areas around the world. It is especially important for IoT applications since machine distribution is much different than human distribution. From a long-term perspective, the spectrum allocation for terrestrial network (TN) and non-terrestrial network (NTN) may be harmonized to achieve better spectrum utilization if cellular and satellite networks can be well integrated. This paper provides an overview of the system design challenges and potential solutions to extend the cellular radio technologies on New Radio Mobile Broadband (NR MBB) and Narrow-Band Internet of Things (NB-IoT) to support satellite communications. The design target is not only for the ongoing 3GPP NTN standardization but also for future radio interface designs toward 6G.

Weighted Secrecy Coverage Analysis and the Impact of Friendly Jamming over UAV-Enabled Networks

Xavier Alejandro Flores Cabezas, Diana Pamela Moya Osorio and Matti Latva-aho (University of Oulu, Finland)
In 5G and beyond networks, Unmanned Aerial Vehicles (UAV) are an attractive solution to enhance the secrecy of a wireless systems by exploiting their predominant LOS links and spacial manoeuvrability to introduce a friendly jamming. In this work, we investigate the impact of two cooperative UAV-based jammers on the secrecy performance of a ground wireless wiretap channel by considering secrecy-area related metrics, the jamming coverage and jamming efficiency. Moreover, we propose a hybrid metric, the so-called Weighted Secrecy Coverage (WSC) that can be used as a metric for gaining insights on the optimal deployments of the UAV jammers to provide the best exploration of jamming signals. For evaluating these metrics, we derive a closed-form position-based metric, the secrecy improvement, and propose an analogous computationally simpler metric. Our simulations show that a balanced power allocation between the two UAVs leads to the best performances, as well as a symmetrical positioning behind the line of sight between the legitimate transmitter and receiver. Moreover, there exist an optimal UAV height for the jammers. Finally, we propose a sub-optimal and simpler problem for the maximisation of the WSC.

Asynchronous Time-Sensitive Networking for Industrial Networks

Jonathan Prados-Garzon, Lorena Chinchilla-Romero, Pablo Ameigeiras, Pablo Muñoz and Juan M. Lopez-Soler (University of Granada, Spain)
Time-Sensitive Networking (TSN) is expected to be a cornerstone in tomorrow’s industrial networks. That is because of its ability to provide deterministic quality-of-service in terms of delay, jitter, and scalability. Moreover, it enables more scalable, more affordable, and easier to manage and operate networks compared to current industrial networks, which are based on Industrial Ethernet. In this article, we evaluate the maximum capacity of the asynchronous TSN networks to accommodate industrial traffic flows. To that end, we formally formulate the flow allocation problem in the mentioned networks as a convex mixed-integer non-linear program. To the best of the authors’ knowledge, neither the maximum utilization of the asynchronous TSN networks nor the formulation of the flow allocation problem in those networks have been previously addressed in the literature. The results show that the network topology and the traffic matrix highly impact on the link utilization.

Mobility for Cellular-Connected UAVs: Challenges for the Network Provider

Erika Fonseca (CONNECT Research Centre, Trinity College Dublin, Ireland); Boris Galkin (Trinity College Dublin, Ireland); Marvin Kelly (Dense Air Ltd., Ireland); Luiz DaSilva (Virginia Tech, USA & Trinity College Dublin, Ireland); Ivana Dusparic (Trinity College Dublin, Ireland)
Unmanned Aerial Vehicle (UAV) technology is becoming more prevalent and more diverse in its application. 5G and beyond networks must enable UAV connectivity. This will require the network operator to consider this new type of user in the planning and operation of the network. This work presents the challenges an operator will encounter and should consider in the future as UAVs become users of the network. We analyse the 3GPP specifications, the existing research literature, and a publicly available UAV connectivity dataset, to describe the challenges. We classify these challenges into network planning and network optimisation categories. We discuss the challenge of planning network coverage when considering coverage for flying users and the PCI collision and confusion issues that can be aggravated by these users. In discussing network optimisation challenges, we introduce Automatic Neighbouring Relation (ANR) and handover challenges, specifically the number of neighbours in the Neighbour Relation Table (NRT), and their potential deletion and block-listing, the frequent number of handovers and the possibility that the UAV disconnects because of handover issues. We discuss possible approaches to address the presented challenges and use a real-world dataset to support our findings about these challenges and their importance.

Hierarchical Multi-Objective Deep Reinforcement Learning for Packet Duplication in Multi-Connectivity for URLLC

Qiyang Zhao (Nokia Bell Labs, France); Stefano Paris (Nokia Bell Labs & Université Paris Descartes, France); Teemu Veijalainen (Nokia Bell Labs, Finland); Samad Ali (University of Oulu, Finland)
In this paper, machine learning solutions have been investigated to improve the decision of packet duplication in a multi-connectivity cellular network to optimize the satisfaction of delay and reliability in 5G. A multi-agent deep reinforcement learning scheme with sequential actor-critic model has been developed to improve the decision of packet duplication from observations of radio environment including channel state, interference and load. A multi-objective reward function has been developed to minimize the transmission delay, error rate and maximize satisfaction of the URLLC targets. System-level simulations have been carried out in a heterogeneous network by utilizing dual connectivity between macro and small cells. Our deep reinforcement learning scheme is shown to prioritize packet duplication to the UE where it gains from lower queueing and interference. Comparing with standard 5G multi-connectivity, it reduces the overall packet error rate and delay, with increased satisfaction rate of URLLC targets. Furthermore, it improves the network throughput and resource efficiency in dynamic user traffic with lower redundancy.

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