Analytical Channel Model and Link Design Optimization for Ground-to-HAP Free-Space Optical Communication Networks

Integrating high altitude platforms (HAPs) and free-space optical (FSO) communications is a promising solution to establish high data rate aerial links for the next-generation wireless networks. However, practical limitations such as pointing errors and angle-of-arrival (AOA) fluctuations of the optical beam due to the orientation deviations of hovering HAPs make it challenging to implement HAP-based FSO links. For a ground-to-HAP FSO link, tractable, closed-form statistical channel models are derived in this paper to simplify the optimal design of such systems. The proposed models include the combined effects of atmospheric turbulence regimes (i.e., log-normal and gamma-gamma), pointing error induced geometrical loss, pointing jitter variance caused by beam wander, detector aperture size, beam-width, and AOA fluctuations of the received optical beam. The analytical expressions are corroborated by performing Monte-Carlo simulations. Furthermore, closed-form expressions for the outage probability of the considered link under different turbulence regimes are derived. Detailed analysis is carried out to optimize the transmitted laser beam and the field-of-view of the receiver for minimizing outage probability under different channel conditions. The obtained analytical results can be applied to finding the optimal parameter values and designing ground-to-HAP FSO links without resorting to time-consuming simulations.Comment: 11 pages, 13 figures, accepted on May 22, 2020, for publication in IEEE/OSA Journal of Lightwave Technolog

Joint Access and Backhaul Resource Management in Satellite-Drone Networks: A Competitive Market Approach

In this paper, the problem of user association and resource allocation is studied for an integrated satellite-drone network (ISDN). In the considered model, drone base stations (DBSs) provide downlink connectivity, supplementally, to ground users whose demand cannot be satisfied by terrestrial small cell base stations (SBSs). Meanwhile, a satellite system and a set of terrestrial macrocell base stations (MBSs) are used to provide resources for backhaul connectivity for both DBSs and SBSs. For this scenario, one must jointly consider resource management over satellite-DBS/SBS backhaul links, MBS-DBS/SBS terrestrial backhaul links, and DBS/SBS-user radio access links as well as user association with DBSs and SBSs. This joint user association and resource allocation problem is modeled using a competitive market setting in which the transmission data is considered as a good that is being exchanged between users, DBSs, and SBSs that act as "buyers", and DBSs, SBSs, MBSs, and the satellite that act as "sellers". In this market, the quality-of-service (QoS) is used to capture the quality of the data transmission (defined as good), while the energy consumption the buyers use for data transmission is the cost of exchanging a good. According to the quality of goods, sellers in the market propose quotations to the buyers to sell their goods, while the buyers purchase the goods based on the quotation. The buyers profit from the difference between the earned QoS and the charged price, while the sellers profit from the difference between earned price and the energy spent for data transmission. The buyers and sellers in the market seek to reach a Walrasian equilibrium, at which all the goods are sold, and each of the devices' profit is maximized. A heavy ball based iterative algorithm is proposed to compute the Walrasian equilibrium of the formulated market

Data-Driven Network Management for Next-Generation Wireless Networks

With the commercialization and maturity of the fifth-generation (5G) wireless networks, the next-generation wireless network (NGWN) is envisioned to provide seamless connectivity for mobile user terminals (MUTs) and to support a wide range of new applications with stringent quality of service (QoS) requirements. In the NGWN, the network architecture will be highly heterogeneous due to the integration of terrestrial networks, satellite networks, and aerial networks formed by unmanned aerial vehicles (UAVs), and the network environment becomes highly dynamic because of the mobility of MUTs and the spatiotemporal variation of service demands. In order to provide high-quality services in such dynamic and heterogeneous networks, flexible, fine-grained, and adaptive network management will be essential. Recent advancements in deep learning (DL) and digital twins (DTs) have made it possible to enable data-driven solutions to support network management in the NGWN. DL methods can solve network management problems by leveraging data instead of explicit mathematical models, and DTs can facilitate DL methods by providing extensive data based on the full digital representations created for individual MUTs. Data-driven solutions that integrates DL and DT can address complicated network management problems and explore implicit network characteristics to adapt to dynamic network environments in the NGWN. However, the design of data-driven network management solutions in the NGWN meets several technical challenges: 1) how the NGWN can be configured to support multiple services with different spatiotemporal service demands while simultaneously satisfying their different QoS requirements; 2) how the multi-dimensional network resources are proactively reserved to support MUTs with different mobility patterns in a resource-efficient manner; and 3) how the heterogeneous NGWN components, including base stations (BSs), satellites, and UAVs, jointly coordinate their network resources to support dynamic service demands, etc. In this thesis, we develop efficient data-driven network management strategies in two stages, i.e., long-term network planning and real-time network operation, to address the above challenges in the NGWN. Firstly, we investigate planning-stage network configuration to satisfy different service requirements for communication services. We consider a two-tier network with one macro BS and multiple small BSs, which supports communication services with different spatiotemporal data traffic distributions. The objective is to maximize the energy efficiency of BSs by jointly configuring downlink transmission power and communication coverage for each BS. To achieve this objective, we first design a network planning scheme with flexible binary slice zooming, dual time-scale planning, and grid-based network planning. The scheme allows flexibility to differentiate the communication coverage and downlink transmission power of the same BS for different services while improving the temporal and spatial granularity of network planning. We formulate a combinatorial optimization problem in which communication coverage management and power control are mutually dependent. To solve the problem, we propose a data-driven method with two steps: 1) we propose an unsupervised-learning-assisted approach to determine the communication coverage of BSs; and 2) we derive a closed-form solution for power control. Secondly, we investigate planning-stage resource reservation for a compute-intensive service to support MUTs with different mobility patterns. The MUTs can offload their computing tasks to the computing servers deployed at the core networks, gateways, and BSs. Each computing server requires both computing and storage resources to execute computing tasks. The objective is to optimize long-term resource reservation by jointly minimizing the usage of computing, storage, and communication resources and the cost from re-configuring resource reservation. To this end, we develop a data-driven network planning scheme with two elements, i.e., multi-resource reservation and resource reservation re-configuration. First, DTs are designed for collecting MUT status data, based on which MUTs are grouped according to their mobility patterns. Then, an optimization algorithm is proposed to customize resource reservation for different groups to satisfy their different resource demands. Last, a meta-learning-based approach is proposed to re-configure resource reservation for balancing the network resource usage and the re-configuration cost. Thirdly, we investigate operation-stage computing resource allocation in a space-air-ground integrated network (SAGIN). A UAV is deployed to fly around MUTs and collect their computing tasks, while scheduling the collected computing tasks to be processed at the UAV locally or offloaded to the nearby BSs or the remote satellite. The energy budget of the UAV, intermittent connectivity between the UAV and BSs, and dynamic computing task arrival pose challenges in computing task scheduling. The objective is to design a real-time computing task scheduling policy for minimizing the delay of computing task offloading and processing in the SAGIN. To achieve the objective, we first formulate the on-line computing scheduling in the dynamic network environment as a constrained Markov decision process. Then, we develop a risk-sensitive reinforcement learning approach in which a risk value is used to represent energy consumption that exceeds the budget. By balancing the risk value and the reward from delay minimization, the UAV can explore the task scheduling policy to minimize task offloading and processing delay while satisfying the UAV energy constraint. Extensive simulation have been conducted to demonstrate that the proposed data-driven network management approach for the NGWN can achieve flexible BS configuration for multiple communication services, fine-grained multi-dimensional resource reservation for a compute-intensive service, and adaptive computing resource allocation in the dynamic SAGIN. The schemes developed in the thesis are valuable to the data-driven network planning and operation in the NGWN

Design, Modelling and Analysis of Satcoms for UAV operations

Η ανάγκη για άμεση συνεισφορά, απόκριση και ακρίβεια των αποτελεσμάτων οδήγησε στην είσοδο των drones και ιδιαίτερα των μη επανδρωμένων εναέριων οχημάτων (UAV) ως νέα τεχνολογικά οχήματα. Ωστόσο, η ενσωμάτωση ενός τόσο κολοσσιαίου τεχνολογικού αποκτήματος δεν είναι καθόλου εύκολη υπόθεση. Πολλές απαιτήσεις εμφανίζονται σε διάφορους τομείς όπως τηλεπικοινωνίες, προβλήματα ωφέλιμου φορτίου που πρέπει να φέρει το UAV και σχέδια λειτουργίας, απαιτήσεις που πρέπει να πληρούνται για την αποφυγή προβλημάτων ασφάλειας, αποφυγή σύγκρουσης, ασταθείς συνδέσεις και άλλα. Σκοπός της παρούσας διπλωματικής εργασίας είναι να μελετήσει όσο το δυνατόν καλύτερα και αποτελεσματικότερα τη συμβολή των δορυφορικών επικοινωνιών στην επίτευξη αξιόπιστων και ανθεκτικών επιχειρήσεων Μη Επανδρωμένων Αεροσκαφών (UAV). Θα παρουσιαστεί μια ανάλυση τριών επιπέδων που θα αφορά τον Σχεδιασμό, τη Μοντελοποίηση και την Ανάλυση δορυφορικών επικοινωνιών σε συνδυασμό με λειτουργίες UAV με τρόπο που η αποτελεσματικότητα της ζεύξης θα είναι μέγιστη εφικτή. Σε τελική ανάλυση, θα πραγματοποιηθεί ένα πείραμα που θα συζητηθούν τα αποτελέσματα και οι παράμετροι που χρησιμοποιούνται προκειμένου να υπολογιστεί η αποτελεσματικότητα του προϋπολογισμού των ζεύξεων. Είναι σημαντικό να γνωρίζουμε ότι οι κυψελοειδείς επικοινωνίες έχουν παίξει μέχρι στιγμής τον πιο σημαντικό και ακριβή ρόλο τόσο στις επίγειες όσο και στις αεροπορικές επικοινωνίες. Αυτό πρόκειται να αλλάξει καθώς οι δορυφόροι υπόσχονται χαρακτηριστικά που δεν μπορούν να ανταγωνιστούν τα επίγεια δίκτυα, με αποτέλεσμα την ενοποίηση των UAV με τις δορυφορικές επικοινωνίες. Ωστόσο, τα πράγματα είναι ασαφή και οι κίνδυνοι που ενέχουν είτε από την άποψη των προσωπικών δεδομένων είτε από την ασφάλεια και την υγεία μπορούν να λειτουργήσουν ως εμπόδιο στην ανάπτυξη και την αναβάθμιση των επικοινωνιών.The need for immediate contribution, response, and accuracy of results has led to the entry of drones and especially Unmanned Aerial Vehicles (UAVs) as new technological vehicles. However, the integration of such a colossal technological acquisition is by no means an easy task. Many requirements appear in various areas such as telecommunications, payload problems that the UAV must carry, and operations plans, requirements that must be met to avoid safety issues, collision avoidance, unstable connections, and so more. The purpose of this thesis is to study as best as possible and most effectively the contribution of satellite communications to achieve reliable and durable Unmanned Aerial Vehicles (UAVs) operations. A three-level analysis will be presented which will concern the Design, Modeling, and Analysis of satellite communications in combination with UAV operations in a way where efficiency of the link will be maximum. After all, an experiment will take place that results and parameters used will be discussed in order to compute the efficiency of the link budget. It is important to know that cellular communications have so far played the most important and accurate role in both terrestrial and air communications. This is about to change as satellites promise features that cannot compete with terrestrial networks, resulting in the integration of UAVs with satellite communications. However, things are unclear, and the risks posed either from the point of view of personal data or from safety and health can act as an obstacle in developing and upgrading communications