A Multi-Service Oriented Multiple-Access Scheme for Next-Generation Mobile Networks

One of the key requirements for fifth-generation (5G) cellular networks is their ability to handle densely connected devices with different quality of service (QoS) requirements. In this article, we present multi-service oriented multiple access (MOMA), an integrated access scheme for massive connections with diverse QoS profiles and/or traffic patterns originating from both handheld devices and machine-to-machine (M2M) transmissions. MOMA is based on a) stablishing separate classes of users based on relevant criteria that go beyond the simple handheld/M2M split, b) class dependent hierarchical spreading of the data signal and c) a mix of multiuser and single-user detection schemes at the receiver. Practical implementations of the MOMA principle are provided for base stations (BSs) that are equipped with a large number of antenna elements. Finally, it is shown that such a massive-multiple-input-multiple-output (MIMO) scenario enables the achievement of all the benefits of MOMA even with a simple receiver structure that allows to concentrate the receiver complexity where effectively needed.Comment: 6 pages, 3 figures, accepted to the European Conference on Networks and Communications (EuCNC 2016

SymbioCity: Smart Cities for Smarter Networks

The "Smart City" (SC) concept revolves around the idea of embodying cutting-edge ICT solutions in the very fabric of future cities, in order to offer new and better services to citizens while lowering the city management costs, both in monetary, social, and environmental terms. In this framework, communication technologies are perceived as subservient to the SC services, providing the means to collect and process the data needed to make the services function. In this paper, we propose a new vision in which technology and SC services are designed to take advantage of each other in a symbiotic manner. According to this new paradigm, which we call "SymbioCity", SC services can indeed be exploited to improve the performance of the same communication systems that provide them with data. Suggestive examples of this symbiotic ecosystem are discussed in the paper. The dissertation is then substantiated in a proof-of-concept case study, where we show how the traffic monitoring service provided by the London Smart City initiative can be used to predict the density of users in a certain zone and optimize the cellular service in that area.Comment: 14 pages, submitted for publication to ETT Transactions on Emerging Telecommunications Technologie

Reliable machine-to-machine multicast services with multi-radio cooperative retransmissions

The final publication is available at Springer via http://dx.doi.org/10.1007/s11036-015-0575-6The 3GPP is working towards the definition of service requirements and technical solutions to provide support for energy-efficient Machine Type Communications (MTC) in the forthcoming generations of cellular networks. One of the envisioned solutions consists in applying group management policies to clusters of devices in order to reduce control signaling and improve upon energy efficiency, e.g., multicast Over-The-Air (OTA) firmware updates. In this paper, a Multi-Radio Cooperative Retransmission Scheme is proposed to efficiently carry out multicast transmissions in MTC networks, reducing both control signaling and improving energy-efficiency. The proposal can be executed in networks composed by devices equipped with multiple radio interfaces which enable them to connect to both a cellular access network, e.g., LTE, and a short-range MTC area network, e.g., Low-Power Wi-Fi or ZigBee, as foreseen by the MTC architecture defined by ETSI. The main idea is to carry out retransmissions over the M2M area network upon error in the main cellular link. This yields a reduction in both the traffic load over the cellular link and the energy consumption of the devices. Computer-based simulations with ns-3 have been conducted to analyze the performance of the proposed scheme in terms of energy consumption and assess its superior performance compared to non-cooperative retransmission schemes, thus validating its suitability for energy-constrained MTC applications.Peer ReviewedPostprint (author's final draft

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

A role-based software architecture to support mobile service computing in IoT scenarios

The interaction among components of an IoT-based system usually requires using low latency or real time for message delivery, depending on the application needs and the quality of the communication links among the components. Moreover, in some cases, this interaction should consider the use of communication links with poor or uncertain Quality of Service (QoS). Research efforts in communication support for IoT scenarios have overlooked the challenge of providing real-time interaction support in unstable links, making these systems use dedicated networks that are expensive and usually limited in terms of physical coverage and robustness. This paper presents an alternative to address such a communication challenge, through the use of a model that allows soft real-time interaction among components of an IoT-based system. The behavior of the proposed model was validated using state machine theory, opening an opportunity to explore a whole new branch of smart distributed solutions and to extend the state-of-the-art and the-state-of-the-practice in this particular IoT study scenario.Peer ReviewedPostprint (published version

Models and Methods for Network Selection and Balancing in Heterogeneous Scenarios

The outbreak of 5G technologies for wireless communications can be considered a response to the need for widespread coverage, in terms of connectivity and bandwidth, to guarantee broadband services, such as streaming or on-demand programs offered by the main television networks or new generation services based on augmented and virtual reality (AR / VR). The purpose of the study conducted for this thesis aims to solve two of the main problems that will occur with the outbreak of 5G, that is, the search for the best possible connectivity, in order to offer users the resources necessary to take advantage of the new generation services, and multicast as required by the eMBMS. The aim of the thesis is the search for innovative algorithms that will allow to obtain the best connectivity to offer users the resources necessary to use the 5G services in a heterogeneous scenario. Study UF that allows you to improve the search for the best candidate network and to achieve a balance that allows you to avoid congestion of the chosen networks. To achieve these two important focuses, I conducted a study on the main mathematical methods that made it possible to select the network based on QoS parameters based on the type of traffic made by users. A further goal was to improve the computational computation performance they present. Furthermore, I carried out a study in order to obtain an innovative algorithm that would allow the management of multicast. The algorithm that has been implemented responds to the needs present in the eMBMS, in realistic scenarios