Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks

Forecasting with high accuracy the volume of data traffic that mobile users will consume is becoming increasingly important for precision traffic engineering, demand-aware network resource allocation, as well as public transportation. Measurements collection in dense urban deployments is however complex and expensive, and the post-processing required to make predictions is highly non-trivial, given the intricate spatio-temporal variability of mobile traffic due to user mobility. To overcome these challenges, in this paper we harness the exceptional feature extraction abilities of deep learning and propose a Spatio-Temporal neural Network (STN) architecture purposely designed for precise network-wide mobile traffic forecasting. We present a mechanism that fine tunes the STN and enables its operation with only limited ground truth observations. We then introduce a Double STN technique (D-STN), which uniquely combines the STN predictions with historical statistics, thereby making faithful long-term mobile traffic projections. Experiments we conduct with real-world mobile traffic data sets, collected over 60 days in both urban and rural areas, demonstrate that the proposed (D-)STN schemes perform up to 10-hour long predictions with remarkable accuracy, irrespective of the time of day when they are triggered. Specifically, our solutions achieve up to 61% smaller prediction errors as compared to widely used forecasting approaches, while operating with up to 600 times shorter measurement intervals

5G Dimensioning And Optimization Through Use Analysis Of A Real Scenario

Mobile networks have become essential to our daily communications. The growth of mobile traffic and users has increased exponentially in recent years, with increasing demands on throughput and latency. To handle this growing traffic, a scaling strategy that guarantees quality of service over time is essential. This thesis proposes the dimensioning of a mobile network based on a real 4G scenario, using techniques such as the implementation of new carriers and 5G technology. It also proposes the dynamic implementation of Cloud RAN, assigning the location of BBU pools according to network characteristics

A review of the use of artificial intelligence methods in infrastructure systems

The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the growth of digitalisation and has the potential to enable the ‘system of systems’ approach required in increasingly complex infrastructure systems. This paper reviews the extent to which research in economic infrastructure sectors has engaged with fields of AI, to investigate the specific AI methods chosen and the purposes to which they have been applied both within and across sectors. Machine learning is found to dominate the research in this field, with methods such as artificial neural networks, support vector machines, and random forests among the most popular. The automated reasoning technique of fuzzy logic has also seen widespread use, due to its ability to incorporate uncertainties in input variables. Across the infrastructure sectors of energy, water and wastewater, transport, and telecommunications, the main purposes to which AI has been applied are network provision, forecasting, routing, maintenance and security, and network quality management. The data-driven nature of AI offers significant flexibility, and work has been conducted across a range of network sizes and at different temporal and geographic scales. However, there remains a lack of integration of planning and policy concerns, such as stakeholder engagement and quantitative feasibility assessment, and the majority of research focuses on a specific type of infrastructure, with an absence of work beyond individual economic sectors. To enable solutions to be implemented into real-world infrastructure systems, research will need to move away from a siloed perspective and adopt a more interdisciplinary perspective that considers the increasing interconnectedness of these systems

Machine Learning-based Orchestration Solutions for Future Slicing-Enabled Mobile Networks

The fifth generation mobile networks (5G) will incorporate novel technologies such as network programmability and virtualization enabled by Software-Defined Networking (SDN) and Network Function Virtualization (NFV) paradigms, which have recently attracted major interest from both academic and industrial stakeholders. Building on these concepts, Network Slicing raised as the main driver of a novel business model where mobile operators may open, i.e., “slice”, their infrastructure to new business players and offer independent, isolated and self-contained sets of network functions and physical/virtual resources tailored to specific services requirements. While Network Slicing has the potential to increase the revenue sources of service providers, it involves a number of technical challenges that must be carefully addressed. End-to-end (E2E) network slices encompass time and spectrum resources in the radio access network (RAN), transport resources on the fronthauling/backhauling links, and computing and storage resources at core and edge data centers. Additionally, the vertical service requirements’ heterogeneity (e.g., high throughput, low latency, high reliability) exacerbates the need for novel orchestration solutions able to manage end-to-end network slice resources across different domains, while satisfying stringent service level agreements and specific traffic requirements. An end-to-end network slicing orchestration solution shall i) admit network slice requests such that the overall system revenues are maximized, ii) provide the required resources across different network domains to fulfill the Service Level Agreements (SLAs) iii) dynamically adapt the resource allocation based on the real-time traffic load, endusers’ mobility and instantaneous wireless channel statistics. Certainly, a mobile network represents a fast-changing scenario characterized by complex spatio-temporal relationship connecting end-users’ traffic demand with social activities and economy. Legacy models that aim at providing dynamic resource allocation based on traditional traffic demand forecasting techniques fail to capture these important aspects. To close this gap, machine learning-aided solutions are quickly arising as promising technologies to sustain, in a scalable manner, the set of operations required by the network slicing context. How to implement such resource allocation schemes among slices, while trying to make the most efficient use of the networking resources composing the mobile infrastructure, are key problems underlying the network slicing paradigm, which will be addressed in this thesis

Energy efficiency with quality of service constraints in heterogenous networks

The Fifth Generation (5G) cellular network is a new technology that is driven by the demand of high data usage, large number of wireless devices and better Quality of Service (QoS). An important challenge for 5G is the energy consumption which is causing the wireless communication networks to be one of the main contributors of the global worming. Thus, energy efficiency becomes an important aspect in designing the wireless communication networks. In this thesis, we study different approaches for Energy Efficient (EE) operation of Small Base Stations (SBSs) in Heterogenous wireless Networks (HetNets). First, we focus on enhancing energy efficiency in heterogenous networks, where Macro BSs (MBSs) and SBSs co-exist, by presenting a sleeping strategy. In the sleeping strategy SBSs serving few or no users are turned off and their users and resources are offloaded to neighboring SBSs. However, adapting the sleeping strategy will affect the lifetime of the electronics of the SBSs, due to the frequent change of power level between turning ON and OFF the SBS. Therefore, in order to maximize energy savings, we formulate an optimization problem that provides an optimal user association and SBSs sleeping strategy for the entire network, while minimizing the total numbers of the switching of SBSs ON and OFF. Furthermore, an other approach consider is the deactivated SBSs are equipped with two power sources, a harvested energy (HE) source and a grid power source, where first the SBS will use its available HE to serve the associated users. Then, the SBS will request any shortage of its energy from other active or deactivated SBSs which have surplus of HE. Finally, if there is still shortage in energy, the SBS will use the power drawn from the grid. However, since the formulated problem is a Mixed Integer NonLinear Problem (MINLP), Generalized Bender Decomposition (GBD) is proposed to decompose the problem into two subproblems. Moreover, a new heuristic approach is proposed to provide a computational efficient algorithm to solve and optimize the user association and energy harvesting of the system model. A new UEs’ prediction method is introduced to provide a future information for the model and to apply an accurate designing parameters. This method is based on a combined approach of Non-linear Autoregressive with External input(NARX) and probabilistic Latent semantic Analysis (pLSA) to provide accurate prediction for multiple steps. Therefore, we consider integrating the powerful Machine Learning (ML) techniques to provide solution of the system model with less computation demands. Thus, we introduced an efficient less complex approach that is based on synthetically generating data from the optimization problem and employing it to train and configure an Artificial Neural Network. An extensive simulation results are presented to show the effectiveness of our approaches in comparison to the optimal results