Now showing 1 - 7 of 7
  • Publication
    Development of the Ground Segment Communication System for the EIRSAT-1 CubeSat
    The Educational Irish Research Satellite (EIRSAT-1) is a student-led project to design, build and test Ireland’s first satellite. As part of the development, a ground segment (GS) has also been designed alongside the spacecraft. The ground segment will support two-way communications with the spacecraft throughout the mission. Communication with the satellite will occur in the very high frequency (VHF) and the ultra high frequency (UHF) bands for the uplink and downlink respectively. Different modulation schemes have been implemented for both uplink and downlink as part of the GS system. Uplink incorporates an Audio Frequency Shift-Keying (AFSK) scheme, while downlink incorporates a Gaussian Minimum Shift-Keying (GMSK) scheme. In order for the spacecraft to successfully receive a telecommand (TC) transmitted from the ground station, a framing protocol is required. AX.25 was selected as the data link layer protocol. A hardware terminal node controller (TNC) executes both the AX.25 framing and the AFSK modulation. Keep It Simple Stupid (KISS) framing software was developed to allow data to be accepted by the TNC. A software defined radio (SDR) approach has been chosen for the downlink. GNURadio is software that allows flowcharts to be built to undertake the required signal processing of the received signal, the demodulation of the signal and the decoding of data. This paper provides a detailed account of the software developed for the ground segment communication system. A review of the AX.25 and KISS framing protocols is presented. The GNURadio flowcharts that handle the signal processing and data decoding are broken down and each constituent is explained. To ensure the reliability and robustness of the system, a suite of tests was undertaken, the results of which are also presented.
  • Publication
    Over the Sea UAV Based Communication
    Unmanned Aerial Vehicle (UAV) aided wireless networks have been recently envisioned as a solution to provide a reliable, low latency cellular link for search and rescue operations over the sea. We propose three different network architectures, based on the technology deployed on the UAV: a flying relay, a flying Base Station (BS) and a flying Remote Radio Head (RRH). We describe the challenges and highlight the benefits of the proposed architectures from the perspective of search and rescue operations over the sea. We compare the performance in term of data rate and latency, analyzing different solutions to provide a Backhaul (BH)/Fronthaul (FH) link for long coverage over the sea. Results show that a system architecture is not outperforming over the others. A cost function is thus indicated as a tool to find a suboptimal solution.
      546Scopus© Citations 5
  • Publication
    Resilience of airborne networks
    Networked flying platforms can be used to provide cellular coverage and capacity. Given that 5G and beyond networks are expected to be always available and highly reliable, resilience and reliability of these networks must be investigated. This paper introduces the specific features of airborne networks that influence their resilience. We then discuss how machine learning and blockchain technologies can enhance the resilience of networked flying platforms.
      526Scopus© Citations 6
  • Publication
    Saving Lives at Sea with UAV-assisted Wireless Networks
    In this paper, we investigate traits and tradeoffs of a system combining Unmanned Aerial Vehicle (UAV)s with Base Station (BS) or Cloud Radio Access Networks (C-RAN) for extending the terrestrial wireless coverage over the sea in emergency situations. Results for an over the sea deployment link budget show the trade-off between power consumption and throughput to meet the Search and Rescue targets.
      392Scopus© Citations 2
  • Publication
    Wireless Communication with Unmanned Aerial Vehicles : Design Tradeoffs and Machine Learning Techniques
    (University College Dublin. School of Electrical and Electronic Engineering, 2022) ;
    Unmanned Aerial Vehicles (UAVs) are expanding rapidly in a wide range of wireless network applications. UAVs have inherent mobility, agility, ability to form Line of Sight (LoS) radio links in highly dynamic environments that are promising features in all their potential application scenarios. UAVs are poised to become a key platform in wireless systems and are expected to play an important part as cellular-connected User Equipments (UEs). In addition, UAV can overcome the static nature of ground Base Stations (BSs) deployment to cater to certain Fifth Generation (5G) use cases, such as surging traffic demands in hotspots (stadium, event centres), as well as their availability in emergencies (e.g. natural disasters), where the infrastructure could itself be compromised. UAVs require continuous and reliable connectivity, efficient network and well-designed path planning to fulfil their potential to support future wireless communication. This thesis investigates the interaction between conventional mathematical methods and Machine Learning (ML) techniques, particularly Reinforcement Learning (RL), as a promising synergy for solving various UAV challenges, resulting in increased reliability, throughput and robustness. The first part of this thesis explores the performance of the ground to air link to a UAV-Base Station (BS)/Remote Radio Head (RRH). Stochastic geometry allows us to perform an outage analysis of the Fronthaul (FH) link in an urban scenario for different UAV heights and blockages. In addition, we evaluate the ability of the Backhaul (BH)-FH link to satisfy a target rate on the access link. We extend the evaluation to a Search and Rescue scenario over the sea through computer-based simulations. The results of these two studies show the potential bottlenecks for the ground to air BH/FH and offer insights on how to improve the resilience and create a reliable high capacity ground to air link. To this aim, in the second part of this dissertation, we explore the use of Machine Learning (ML) techniques, especially Deep Reinforcement Learning (DRL). The complex task of UAV trajectory design in communication scenarios is generally a non-convex optimization problem. DRL is a framework able to tackle and automate it. We apply DRL to jointly optimize the UAV flying time and the communication performance of the ground to air link. Initially, we solve the communication-aware trajectory design by proposing 3D trajectory and band switch DRL algorithms. Thus, we consider a novel Lyapunov-based Double DQN (DDQN) to solve the trajectory design while ensuring the satisfaction of the connectivity constraint on the ground to air link. While the RL paradigm offers many advantages for solving optimization problems in UAV communication, some practical challenges remain. The ML model training in UAV communication is a costly and time consuming process requiring a high amount of data. Thus, in the last part of this thesis, we address this issue taking advantage of Transfer Learning (TL) approach. In the Transfer Learning (TL) algorithm developed in this dissertation, the agent utilizes a teacher policy. The teacher trajectory policy, previously trained at sub-6 GHz trajectory, is the solution to a connectivity-aware trajectory probilem. Later, the teacher policy’s knowledge gained in a previous domain at sub-6 GHz is transferred into the new domain at millimeter Wave (mmWave) to considerably reduce the demand for expensive UAV flights and data collection. In the last chapter, we discuss possible future research directions and open challenges, such as the computation load distribution and the integration of UAVs with satellite network. The overall contribution of this thesis is the investigation of practical constraints of UAV communication in wireless communication and to propose relative solutions to make UAV communication more reliable and feasible in real scenarios.
  • Publication
    Development and Validation of the Operations Procedures and Manual for a 2U CubeSat, EIRSAT-1, with Three Novel Payloads
    The CubeSat standard, relatively short launch timescale, and orders of magnitude difference in cost in comparison to large scale missions, has allowed universities and smaller institutions to develop space missions. The Educational Irish Research Satellite (EIRSAT-1) is a 2U CubeSat being developed in University College Dublin (UCD) as part of the second round of the European Space Agency (ESA) Education Office’s Fly Your Satellite! (FYS) Programme. EIRSAT-1 is a student-led project to build, test, launch and operate Ireland’s first satellite. CubeSats typically use commercial off-the-shelf (COTS) components to facilitate new teams in developing a satellite on a rapid timescale. While some of the EIRSAT-1 subsystems are COTS procured from AAC Clyde Space, EIRSAT-1 has three novel experiments on-board which have been developed in UCD. The spacecraft’s Antenna Deployment Module has also been designed and built in-house. The on-board computer (OBC), procured from AAC Clyde Space, has been adapted to interface with these novel hardware components, accompanied by in-house developed software and firmware. All of these innovative subsystems complicate the CubeSat functionality making it essential to document and rigorously test the operations procedures for EIRSAT-1. In preparation for launch with these novel spacecraft subsystems, the EIRSAT-1 Operations Manual is being developed and incrementally verified. The Operations Manual contains the procedures to command and control the satellite, account for nominal and non-nominal scenarios and guide the operator in determining the cause of any anomalies observed during the mission and facilitate recovery. A series of operations development tests (ODTs) have been designed and conducted for a robust verification process. Each procedure is written up by a member of the EIRSAT-1 Operations Team in the EIRSAT-1 Operations Manual format. During an ODT, an in-flight scenario is considered in which the procedure under test is required. The procedure is then followed by a team member who has not been involved in the procedure development process. The feedback from these tests and from the operators is used to improve the procedures and continually update the Operations Manual. This paper will present the approach to operations development used by the EIRSAT-1 team and discuss the lessons learned for CubeSat operations development, testing and pre-flight verification.
  • Publication
    Deep Reinforcement Learning for Dynamic Band Switch in Cellular-Connected UAV
    The choice of the transmitting frequency to provide cellular-connected Unmanned Aerial Vehicle (UAV) reliable connectivity and mobility support introduce several challenges. Conventional sub-6 GHz networks are optimized for ground Users (UEs). Operating at the millimeter Wave (mmWave) band would provide high-capacity but highly intermittent links. To reach the destination while minimizing a weighted function of traveling time and number of radio failures, we propose in this paper a UAV joint trajectory and band switch approach. By leveraging Double Deep Q-Learning we develop two different approaches to learn a trajectory besides managing the band switch. A first blind approach switches the band along the trajectory anytime the UAV-UE throughput is below a predefined threshold. In addition, we propose a smart approach for simultaneous learning-based path planning of UAV and band switch. The two approaches are compared with an optimal band switch strategy in terms of radio failure and band switches for different thresholds. Results reveal that the smart approach is able in a high threshold regime to reduce the number of radio failures and band switches while reaching the desired destination.
      138Scopus© Citations 2