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

Self-organization for 5G and beyond mobile networks using reinforcement learning

The next generations of mobile networks 5G and beyond, must overcome current networks limitations as well as improve network performance. Some of the requirements envisioned for future mobile networks are: addressing the massive growth required in coverage, capacity and traffic; providing better quality of service and experience to end users; supporting ultra high data rates and reliability; ensuring latency as low as one millisecond, among others. Thus, in order for future networks to enable all of these stringent requirements, a promising concept has emerged, self organising networks (SONs). SONs consist of making mobile networks more adaptive and autonomous and are divided in three main branches, depending on their use-cases, namely: self-configuration, self-optimisation, and self-healing. SON is a very promising and broad concept, and in order to enable it, more intelligence needs to be embedded in the mobile network. As such, one possible solution is the utilisation of machine learning (ML) algorithms. ML has many branches, such as supervised, unsupervised and Reinforcement Learning (RL), and all can be used in different SON use-cases. The objectives of this thesis are to explore different RL techniques in the context of SONs, more specifically in self-optimization use-cases. First, the use-case of user-cell association in future heterogeneous networks is analysed and optimised. This scenario considers not only Radio Access Network (RAN) constraints, but also in terms of the backhaul. Based on this, a distributed solution utilizing RL is proposed and compared with other state-of-the-art methods. Results show that the proposed RL algorithm outperforms current ones and is able to achieve better user satisfaction, while minimizing the number of users in outage. Another objective of this thesis is the evaluation of Unmanned Aerial vehicles (UAVs) to optimize cellular networks. It is envisioned that UAVs can be utilized in different SON use-cases and integrated with RL algorithms to determine their optimal 3D positions in space according to network constraints. As such, two different mobile network scenarios are analysed, one emergency and a pop-up network. The emergency scenario considers that a major natural disaster destroyed most of the ground network infrastructure and the goal is to provide coverage to the highest number of users possible using UAVs as access points. The second scenario simulates an event happening in a city and, because of the ground network congestion, network capacity needs to be enhanced by the deployment of aerial base stations. For both scenarios different types of RL algorithms are considered and their complexity and convergence are analysed. In both cases it is shown that UAVs coupled with RL are capable of solving network issues in an efficient and quick manner. Thus, due to its ability to learn from interaction with an environment and from previous experience, without knowing the dynamics of the environment, or relying on previously collected data, RL is considered as a promising solution to enable SON

Our Mythical Hope

Classical Antiquity is a particularly important field in terms of “Hope studies” […]. For centuries, the ancient tradition, and classical mythology in particular, has been a common reference point for whole hosts of creators of culture, across many parts of the world, and with the new media and globalization only increasing its impact. Thus, in our research at this stage, we have decided to study how the authors of literary and audiovisual texts for youth make use of the ancient myths to support their young protagonists (and readers or viewers) in crucial moments of their existence, on their road into adulthood, and in those dark hours when it seems that life is about to shatter and fade away. However, if Hope is summoned in time, the crisis can be overcome and the protagonist grows stronger, with a powerful uplifting message for the public. […] Owing to this, we get a chance to remain true to our ideas, to keep faith in our dreams, and, when the decisive moment comes, to choose not hatred but love, not darkness but light. Katarzyna Marciniak, University of Warsaw, From the introductory chapte

Our Mythical Hope

Classical Antiquity is a particularly important field in terms of “Hope studies” […]. For centuries, the ancient tradition, and classical mythology in particular, has been a common reference point for whole hosts of creators of culture, across many parts of the world, and with the new media and globalization only increasing its impact. Thus, in our research at this stage, we have decided to study how the authors of literary and audiovisual texts for youth make use of the ancient myths to support their young protagonists (and readers or viewers) in crucial moments of their existence, on their road into adulthood, and in those dark hours when it seems that life is about to shatter and fade away. However, if Hope is summoned in time, the crisis can be overcome and the protagonist grows stronger, with a powerful uplifting message for the public. […] Owing to this, we get a chance to remain true to our ideas, to keep faith in our dreams, and, when the decisive moment comes, to choose not hatred but love, not darkness but light. Katarzyna Marciniak, University of Warsaw, From the introductory chapte

Bowdoin Orient v.135, no.1-25 (2005-2006)

https://digitalcommons.bowdoin.edu/bowdoinorient-2000s/1006/thumbnail.jp