Software-Driven and Virtualized Architectures for Scalable 5G Networks

In this dissertation, we argue that it is essential to rearchitect 4G cellular core networks–sitting between the Internet and the radio access network–to meet the scalability, performance, and flexibility requirements of 5G networks. Today, there is a growing consensus among operators and research community that software-defined networking (SDN), network function virtualization (NFV), and mobile edge computing (MEC) paradigms will be the key ingredients of the next-generation cellular networks. Motivated by these trends, we design and optimize three core network architectures, SoftMoW, SoftBox, and SkyCore, for different network scales, objectives, and conditions. SoftMoW provides global control over nationwide core networks with the ultimate goal of enabling new routing and mobility optimizations. SoftBox attempts to enhance policy enforcement in statewide core networks to enable low-latency, signaling-efficient, and customized services for mobile devices. Sky- Core is aimed at realizing a compact core network for citywide UAV-based radio networks that are going to serve first responders in the future. Network slicing techniques make it possible to deploy these solutions on the same infrastructure in parallel. To better support mobility and provide verifiable security, these architectures can use an addressing scheme that separates network locations and identities with self-certifying, flat and non-aggregatable address components. To benefit the proposed architectures, we designed a high-speed and memory-efficient router, called Caesar, for this type of addressing schemePHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146130/1/moradi_1.pd

Multi-UAV Integrated Internet of Things System for Generating Safe Map in Post-Disaster

芝浦工業大学2019年

Design of Ad Hoc Wireless Mesh Networks Formed by Unmanned Aerial Vehicles with Advanced Mechanical Automation

Ad hoc wireless mesh networks formed by unmanned aerial vehicles (UAVs) equipped with wireless transceivers (access points (APs)) are increasingly being touted as being able to provide a flexible "on-the-fly" communications infrastructure that can collect and transmit sensor data from sensors in remote, wilderness, or disaster-hit areas. Recent advances in the mechanical automation of UAVs have resulted in separable APs and replaceable batteries that can be carried by UAVs and placed at arbitrary locations in the field. These advanced mechanized UAV mesh networks pose interesting questions in terms of the design of the network architecture and the optimal UAV scheduling algorithms. This paper studies a range of network architectures that depend on the mechanized automation (AP separation and battery replacement) capabilities of UAVs and proposes heuristic UAV scheduling algorithms for each network architecture, which are benchmarked against optimal designs.Comment: 12 page

Self-Evolving Integrated Vertical Heterogeneous Networks

6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a wide range of services and use cases while dealing with network complexity efficiently. Such enhanced network agility will require developing a self-evolving capability in designing both the network architecture and resource management to intelligently utilize resources, reduce operational costs, and achieve the coveted quality of service (QoS). To enable this capability, the necessity of considering an integrated vertical heterogeneous network (VHetNet) architecture appears to be inevitable due to its high inherent agility. Moreover, employing an intelligent framework is another crucial requirement for self-evolving networks to deal with real-time network optimization problems. Hence, in this work, to provide a better insight on network architecture design in support of self-evolving networks, we highlight the merits of integrated VHetNet architecture while proposing an intelligent framework for self-evolving integrated vertical heterogeneous networks (SEI-VHetNets). The impact of the challenges associated with SEI-VHetNet architecture, on network management is also studied considering a generalized network model. Furthermore, the current literature on network management of integrated VHetNets along with the recent advancements in artificial intelligence (AI)/machine learning (ML) solutions are discussed. Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are identified. Finally, the potential future research directions for advancing the autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig