Channel Access in Wireless Networks: Protocol Design of Energy-Aware Schemes for the IoT and Analysis of Existing Technologies

The design of channel access policies has been an object of study since the deployment of the first wireless networks, as the Medium Access Control (MAC) layer is responsible for coordinating transmissions to a shared channel and plays a key role in the network performance. While the original target was the system throughput, over the years the focus switched to communication latency, Quality of Service (QoS) guarantees, energy consumption, spectrum efficiency, and any combination of such goals. The basic mechanisms to use a shared channel, such as ALOHA, TDMA- and FDMA-based policies, have been introduced decades ago. Nonetheless, the continuous evolution of wireless networks and the emergence of new communication paradigms demand the development of new strategies to adapt and optimize the standard approaches so as to satisfy the requirements of applications and devices. This thesis proposes several channel access schemes for novel wireless technologies, in particular Internet of Things (IoT) networks, the Long-Term Evolution (LTE) cellular standard, and mmWave communication with the IEEE802.11ad standard. The first part of the thesis concerns energy-aware channel access policies for IoT networks, which typically include several battery-powered sensors. In scenarios with energy restrictions, traditional protocols that do not consider the energy consumption may lead to the premature death of the network and unreliable performance expectations. The proposed schemes show the importance of accurately characterizing all the sources of energy consumption (and inflow, in the case of energy harvesting), which need to be included in the protocol design. In particular, the schemes presented in this thesis exploit data processing and compression techniques to trade off QoS for lifetime. We investigate contention-free and contention-based chanel access policies for different scenarios and application requirements. While the energy-aware schemes proposed for IoT networks are based on a clean-slate approach that is agnostic of the communication technology used, the second part of the thesis is focused on the LTE and IEEE802.11ad standards. As regards LTE, the study proposed in this thesis shows how to use machine-learning techniques to infer the collision multiplicity in the channel access phase, information that can be used to understand when the network is congested and improve the contention resolution mechanism. This is especially useful for massive access scenarios; in the last years, in fact, the research community has been investigating on the use of LTE for Machine-Type Communication (MTC). As regards the standard IEEE802.11ad, instead, it provides a hybrid MAC layer with contention-based and contention-free scheduled allocations, and a dynamic channel time allocation mechanism built on top of such schedule. Although this hybrid scheme is expected to meet heterogeneous requirements, it is still not clear how to develop a schedule based on the various traffic flows and their demands. A mathematical model is necessary to understand the performance and limits of the possible types of allocations and guide the scheduling process. In this thesis, we propose a model for the contention-based access periods which is aware of the interleaving of the available channel time with contention-free allocations

Intelligence in 5G networks

Over the past decade, Artificial Intelligence (AI) has become an important part of our daily lives; however, its application to communication networks has been partial and unsystematic, with uncoordinated efforts that often conflict with each other. Providing a framework to integrate the existing studies and to actually build an intelligent network is a top research priority. In fact, one of the objectives of 5G is to manage all communications under a single overarching paradigm, and the staggering complexity of this task is beyond the scope of human-designed algorithms and control systems. This thesis presents an overview of all the necessary components to integrate intelligence in this complex environment, with a user-centric perspective: network optimization should always have the end goal of improving the experience of the user. Each step is described with the aid of one or more case studies, involving various network functions and elements. Starting from perception and prediction of the surrounding environment, the first core requirements of an intelligent system, this work gradually builds its way up to showing examples of fully autonomous network agents which learn from experience without any human intervention or pre-defined behavior, discussing the possible application of each aspect of intelligence in future networks

A Game-Theoretic and Experimental Analysis of Energy-Depleting Underwater Jamming Attacks

Security aspects in underwater wireless networks have not been widely investigated so far, despite the critical importance of the scenarios in which these networks can be employed. For example, an attack to a military underwater network for enemy targeting or identification can lead to serious consequences. Similarly, environmental monitoring applications such as tsunami prevention are also critical from a public safety point of view. In this work, we assess a scenario in which a malicious node tries to perform a jamming attack, degrading the communication quality of battery-powered underwater nodes. The legitimate transmitter may use packet level coding to increase the chances of correctly delivering packets. Because of the energy limitation of the nodes, the jammer\u2019s objective is twofold, namely: (i) disrupting the communication and (ii) reducing the lifetime of the victim by making it send more redundancy. We model the jammer and the transmitter as players in a multistage game, deriving the optimal strategies. We evaluate the performance both in a model-based scenario and using real experimental data, and perform a sensitivity analysis to evaluate the performance of the strategies if the real channel model is different from the one they use