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

Framework for Content Distribution over Wireless LANs

Wireless LAN (also called as Wi-Fi) is dominantly considered as the most pervasive technology for Intent access. Due to the low-cost of chipsets and support for high data rates, Wi-Fi has become a universal solution for ever-increasing application space which includes, video streaming, content delivery, emergency communication, vehicular communication and Internet-of-Things (IoT). Wireless LAN technology is defined by the IEEE 802.11 standard. The 802.11 standard has been amended several times over the last two decades, to incorporate the requirement of future applications. The 802.11 based Wi-Fi networks are infrastructure networks in which devices communicate through an access point. However, in 2010, Wi-Fi Alliance has released a specification to standardize direct communication in Wi-Fi networks. The technology is called Wi-Fi Direct. Wi-Fi Direct after 9 years of its release is still used for very basic services (connectivity, file transfer etc.), despite the potential to support a wide range of applications. The reason behind the limited inception of Wi-Fi Direct is some inherent shortcomings that limit its performance in dense networks. These include the issues related to topology design, such as non-optimal group formation, Group Owner selection problem, clustering in dense networks and coping with device mobility in dynamic networks. Furthermore, Wi-Fi networks also face challenges to meet the growing number of Wi Fi users. The next generation of Wi-Fi networks is characterized as ultra-dense networks where the topology changes frequently which directly affects the network performance. The dynamic nature of such networks challenges the operators to design and make optimum planifications. In this dissertation, we propose solutions to the aforementioned problems. We contributed to the existing Wi-Fi Direct technology by enhancing the group formation process. The proposed group formation scheme is backwards-compatible and incorporates role selection based on the device's capabilities to improve network performance. Optimum clustering scheme using mixed integer programming is proposed to design efficient topologies in fixed dense networks, which improves network throughput and reduces packet loss ratio. A novel architecture using Unmanned Aeriel Vehicles (UAVs) in Wi-Fi Direct networks is proposed for dynamic networks. In ultra-dense, highly dynamic topologies, we propose cognitive networks using machine-learning algorithms to predict the network changes ahead of time and self-configuring the network

A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research

5G and beyond networks

This chapter investigates the Network Layer aspects that will characterize the merger of the cellular paradigm and the IoT architectures, in the context of the evolution towards 5G-and-beyond, including some promising emerging services as Unmanned Aerial Vehicles or Base Stations, and V2X communications