A survey of self organisation in future cellular networks

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

Learning and Reasoning Strategies for User Association in Ultra-dense Small Cell Vehicular Networks

Recent vehicular ad hoc networks research has been focusing on providing intelligent transportation services by employing information and communication technologies on road transport. It has been understood that advanced demands such as reliable connectivity, high user throughput, and ultra-low latency required by these services cannot be met using traditional communication technologies. Consequently, this thesis reports on the application of artificial intelligence to user association as a technology enabler in ultra-dense small cell vehicular networks. In particular, the work focuses on mitigating mobility-related concerns and networking issues at different mobility levels by employing diverse heuristic as well as reinforcement learning (RL) methods. Firstly, driven by rapid fluctuations in the network topology and the radio environment, a conventional, three-step sequence user association policy is designed to highlight and explore the impact of vehicle speed and different performance indicators on network quality of service (QoS) and user experience. Secondly, inspired by control-theoretic models and dynamic programming, a real-time controlled feedback user association approach is proposed. The algorithm adapts to the changing vehicular environment by employing derived network performance information as a heuristic, resulting in improved network performance. Thirdly, a sequence of novel RL based user association algorithms are developed that employ variable learning rate, variable rewards function and adaptation of the control feedback framework to improve the initial and steady-state learning performance. Furthermore, to accelerate the learning process and enhance the adaptability and robustness of the developed RL algorithms, heuristically accelerated RL and case-based transfer learning methods are employed. A comprehensive, two-tier, event-based, system level simulator which is an integration of a dynamic vehicular network, a highway, and an ultra-dense small cell network is developed. The model has enabled the analysis of user mobility effects on the network performance across different mobility levels as well as served as a firm foundation for the evaluation of the empirical properties of the investigated approaches

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

Efficient spectrum occupancy prediction exploiting multidimensional correlations through composite 2D-LSTM models

In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. Studies have demonstrated that usage of the spectrum has a high correlation over multidimensions, which includes time, frequency, and space. Accordingly, recent literature uses tensor-based methods to exploit the multidimensional spectrum correlation. However, these methods share two main drawbacks. First, they are computationally complex. Second, they need to re-train the overall model when no information is received from any base station for any reason. Different than the existing works, this paper proposes a method for dividing the multidimensional correlation exploitation problem into a set of smaller sub-problems. This division is achieved through composite two-dimensional (2D)-long short-term memory (LSTM) models. Extensive experimental results reveal a high detection performance with more robustness and less complexity attained by the proposed method. The real-world measurements provided by one of the leading mobile network operators in Turkey validate these results

Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G

Non-Terrestrial Networks (NTN) are expected to be a critical component of 6th Generation (6G) networks, providing ubiquitous, continuous, and scalable services. Satellites emerge as the primary enabler for NTN, leveraging their extensive coverage, stable orbits, scalability, and adherence to international regulations. However, satellite-based NTN presents unique challenges, including long propagation delay, high Doppler shift, frequent handovers, spectrum sharing complexities, and intricate beam and resource allocation, among others. The integration of NTNs into existing terrestrial networks in 6G introduces a range of novel challenges, including task offloading, network routing, network slicing, and many more. To tackle all these obstacles, this paper proposes Artificial Intelligence (AI) as a promising solution, harnessing its ability to capture intricate correlations among diverse network parameters. We begin by providing a comprehensive background on NTN and AI, highlighting the potential of AI techniques in addressing various NTN challenges. Next, we present an overview of existing works, emphasizing AI as an enabling tool for satellite-based NTN, and explore potential research directions. Furthermore, we discuss ongoing research efforts that aim to enable AI in satellite-based NTN through software-defined implementations, while also discussing the associated challenges. Finally, we conclude by providing insights and recommendations for enabling AI-driven satellite-based NTN in future 6G networks.Comment: 40 pages, 19 Figure, 10 Tables, Surve