Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Machine Learning for Unmanned Aerial System (UAS) Networking

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS

A cell outage management framework for dense heterogeneous networks

In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner

A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.Comment: Accepted by IEEE JSA

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Producción CientíficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT

Optimization of Mobility Parameters using Fuzzy Logic and Reinforcement Learning in Self-Organizing Networks

In this thesis, several optimization techniques for next-generation wireless networks are proposed to solve different problems in the field of Self-Organizing Networks and heterogeneous networks. The common basis of these problems is that network parameters are automatically tuned to deal with the specific problem. As the set of network parameters is extremely large, this work mainly focuses on parameters involved in mobility management. In addition, the proposed self-tuning schemes are based on Fuzzy Logic Controllers (FLC), whose potential lies in the capability to express the knowledge in a similar way to the human perception and reasoning. In addition, in those cases in which a mathematical approach has been required to optimize the behavior of the FLC, the selected solution has been Reinforcement Learning, since this methodology is especially appropriate for learning from interaction, which becomes essential in complex systems such as wireless networks. Taking this into account, firstly, a new Mobility Load Balancing (MLB) scheme is proposed to solve persistent congestion problems in next-generation wireless networks, in particular, due to an uneven spatial traffic distribution, which typically leads to an inefficient usage of resources. A key feature of the proposed algorithm is that not only the parameters are optimized, but also the parameter tuning strategy. Secondly, a novel MLB algorithm for enterprise femtocells scenarios is proposed. Such scenarios are characterized by the lack of a thorough deployment of these low-cost nodes, meaning that a more efficient use of radio resources can be achieved by applying effective MLB schemes. As in the previous problem, the optimization of the self-tuning process is also studied in this case. Thirdly, a new self-tuning algorithm for Mobility Robustness Optimization (MRO) is proposed. This study includes the impact of context factors such as the system load and user speed, as well as a proposal for coordination between the designed MLB and MRO functions. Fourthly, a novel self-tuning algorithm for Traffic Steering (TS) in heterogeneous networks is proposed. The main features of the proposed algorithm are the flexibility to support different operator policies and the adaptation capability to network variations. Finally, with the aim of validating the proposed techniques, a dynamic system-level simulator for Long-Term Evolution (LTE) networks has been designed

The integration of device-to-device communication in future cellular systems

The usage of mobile data services over cellular spectrum has been dramatically increased in the last decade. The increment led to an explosive growth in user’s booming data demands over the cellular spectrum band. However, the current technologies have a limitation in the allocated spectrum resources, compared to the data demands. This leads to insufficient throughput using the current technologies in the next few years. Improving the throughput with user’s data demands necessitates finding an efficient approach to offload data booming. Device-to-Device (D2D) communication has been proposed as an unconventional mobile paradigm with a scalable manner to offload the mobile data traffic of the local peer-to-peer mobile users by sharing the resources the cellular networks without traversing the base stations. Applying such paradigm increases the spectrum utilization which improves the total throughput in a given cell. However, many issues negatively influence the performance of D2D communication over the cellular spectrum band such as interference from the cellular users, the low activity of the cellular users over the allocated resources in time which reduces spectrum utilization and the dynamic cellular environment which impacts the link performance of D2D communication and may leads to not meet the desired quality of service requirements of the data services. Solving the aforementioned issues requires: i) developing the appropriate access paradigms of the D2D communication to meet the desired quality of service requirements of mobile data services; ii) increasing the spectrum utilization of licensed band by enabling unlicensed users to invest the spectrum holes; and iii) developing the link adaptation processes to overcome the dynamic behavior of the cellular system and improve the throughput in D2D communication links. This dissertation presents the aforementioned solutions for the D2D communication which improve the total throughput in the cell. The dissertation has three main contributions: i) position-based hybrid access paradigm for D2D communication; ii) hybrid access paradigm for unlicensed peer-to-peer users (unlicensed D2D communication); and iii) an algorithm for link adaptation of unlicensed D2D communication. First, the thesis develops a position-based model for maximizing the throughput of D2D communication using different access paradigms. This model defines the regions in the cell in which the D2D communication can be performed with the desired QoS. Then, a position-based hybrid access paradigm is presented which selects a given access paradigm used by D2D pairs in order to improve the total throughput in the cell. The proposed access paradigms are evaluated using numerical simulations and the results show improvements in the total throughput and the number of satisfied D2D communications in the cell, compared to recent access paradigms. Second, an integration of cognitive radio technology with the unlicensed D2D pairs to apply dynamic spectrum access is presented. The recent access paradigms of cognitive radio and their achievable throughput are studied. Then, a position-based hybrid access paradigm is introduced to increase the regions of unlicensed D2D communication. The evaluation of the proposed access paradigm is performed using numerical simulations. The results show improvement in the throughput over the cell and the area used by unlicensed D2D communications, compared to recent access paradigms. Third, the dynamic behavior of the cellular environment and its interaction with the unlicensed D2D communication is studied. One possible solution presented applying artificial intelligence technique as a cognitive engine to perform link adaptation efficiently. Based on this study, a Self-Organized Link Adaptation (SOLinA) algorithm is presented to adapt the link of unlicensed D2D communication autonomously and determine the link configuration which improves the throughput of the system. The evaluation of the proposed algorithm is performed using simulations of unlicensed D2D communication within dynamic cellular environment. The results of the simulations show that SOLinA outperforms the previous work in the throughput under different separations between the unlicensed D2D communication, different cellular system requirements and at different user’s positions in the cell.In der letzten Dekade nahm der Einsatz mobile Datendienste in zellularen Netzen stark zu und führte zu einem exponentiellen Anstieg der Nutzerdaten. Die aktuellen Technologien weisen jedoch, bezogen auf das geforderte Datenvolumen, Einschränkungen auf, die auch in den kommenden Jahren zu einem unzureichenden Durchsatz führen werden. Die Erhöhung des Durchsatzes für Nutzerdaten erfordert die Suche nach einem effizienten Ansatz um das steigende Datenvolumen zu bewältigen. Ein möglicher Ansatz ist Device-to-Device (D2D) Kommunikation als ein unkonventionelles, mobiles Paradigma, das hohen Datenverkehr skalierbar auf lokale Peer-to -Peer- Anwender überträgt. Dabei werden die Ressourcen zwischen D2D- und zellularen Nutzern aufgeteilt, wobei der D2DDatenverkehr direkt zwischen Endgeräten, d.h. ohne Einsatz der Basis Station erfolgt. Dadurch kann das Frequenzspektrum effizienter genutzt, und somit der Gesamtdurchsatz der Zelle erhöht werden. Zugleich wirken sich mehrere Faktoren negativ auf den Durchsatz der D2D-Kommunikation im zellularen Spektralband aus, wie z. B. die Interferenz zellularer Endgeräte, die weiterhin direkt mit der Basisstation kommunizieren. Zusätzlich reduziert geringe Aktivität der zellularen Endgeräte über die zugewiesenen Ressourcen die Spektraleffizienz, mit negativer Auswirkung auf den Durchsatz der D2DVerbinding. Das kann dazu führen, dass die gewünschte Qualität des Datendiensts nicht erreicht wird. Die Lösung der oben genannten Probleme erfordert: i) die Entwicklung entsprechender Zugriffsmechanismen für die D2D-Kommunikation, um die gewünschte Qualität der Service-Anforderungen mobiler Datendienst zu erreichen; ii) die Auslastung des Funkspektrums des lizenzierten Bands zu erhöhen, indem nicht lizenzierten Nutzern temporär freie Bereiche des Spektrums verwenden; und iii) die Entwicklung eines Prozesses zur Link-Adaption unter Berücksichtigung des dynamischen Verhaltens des zellularen Systems, so dass sich der Durchsatz der D2D-Verbindung erhöht . Die vorliegende Arbeit stellt die oben genannten Lösungen für die D2D-Kommunikation, die den Gesamtdurchsatz in der Zelle erhöhen sollen, vor. Die Hauptbeiträge sind: i) ein positionsbasierter, hybrider Zugriffsmechanismus für die D2D-Kommunikation; ii) ein hybrider Zugriffsmechanismus für unlizenzierte Peer-to-Peer Nutzer (unlizenzierte D2D-Kommunikation); und iii) ein Algorithmus zur Link-Adaption der unlizenzierten D2D-Kommunikation. Zuerst wird in dieser Arbeit ein positions-basiertes Modell entwickelt, um den Durchsatz der D2D-Kommunikation zu maximieren, wobei unterschiedliche Zugriffsmechanismen eingesetzt werden. Im Modell werden die Regionen in der Zelle definiert, in der die D2D-Kommunikation mit der gewünschten Service-Qualität (QoS) durchgeführt werden kann. Anschließend wird ein positionsbasierter, hybrider Zugriffsmechanismus vorgestellt, der beim Einsatz für ein D2D-Paar zur Erhöhung des Gesamtumsatzes der Zelle führt. Die vorgeschlagenen Zugriffsmechanismen werden durch numerische Simulationen evaluiert. Die Ergebnisse zeigen Verbesserungen im Gesamtdurchsatz und der Anzahl der zufriedenen D2D-Kommunikationen in der Zelle. Als nächster Schritt wird kognitive Funktechnik (Cognitive Radio) in das unlizenzierte D2D-Paar integriert, wodurch ein dynamischer Zugriff auf das Funkspektrum erreicht wird. Die neu eingeführten Zugriffsmechanismen und der jeweilig erreichbare Durchsatz werden untersucht. Als Ergebnis wird ein positionsbasierter, hybrider Zugriffsmechanismus eingeführt, um die Regionen mit unlizenzierter D2D-Kommunikation zu vergrößern. Zur Evaluierung des vorgeschlagenen Zugriffsmechanismus werden numerische Simulationen eingesetzt. Die erzielten Ergebnisse weisen Verbesserungen im Durchsatz, sowohl innerhalb der Zelle, als auch in der unlizenzierten D2D-Kommunikation, nach. Als letzter Schritt wird das dynamische Verhalten der zellularen Umgebung, sowie ihrer Interaktion mit der unlizenzierten D2D-Kommunikation untersucht. Als eine mögliche Lösung wird der Einsatz künstlicher Intelligenz als eine kognitive Maschine vorgestellt, um die Funkverbindung effizient zu adaptieren. Auf Basis der Studie wird der Algorithmus „Self-Organized Link Adaptation” (SOLinA) vorgestellt, um die Funkverbindung der unlizenzierten D2D-Kommunikation autonom anzupassen und die Verbindungskonfiguration so anzupassen, dass der Systemdurchsatz erhöht wird. Evaluiert wird der vorgeschlagene Algorithmus durch Simulationen der unlizenzierten D2D-Kommumikation innerhalb einer dynamischen zellularen Umgebung. Die neuesten Simulationsergebnisse zeigen, dass SOLinA die zuvor erzielten Ergebnisse beim Durchsatz, sowohl für unterschiedliche Konstellationen der unlizenzierten D2D-Kommunikation, als auch für unterschiedliche Anforderungen an das zellulare System und für variierende Positionen der Nutzer in der Zelle übertrifft

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture