A Study of Path Selection Algorithms for Mobile Video Streaming QoE

This thesis discusses the problem of path selection for video streaming over 4G mobile networks; its final goal is to devise path selection strategies and test them with a pre-existing network simulator. The objective of path selection algorithms is to optimize both the use of network resources and the Quality of Experience of end users, quantified by various objective metrics. We will propose several path selection algorithms for LTE video transmission, testing them with a simulationope

Quality-Aware Broadcasting Strategies for Position Estimation in VANETs

The dissemination of vehicle position data all over the network is a fundamental task in Vehicular Ad Hoc Network (VANET) operations, as applications often need to know the position of other vehicles over a large area. In such cases, inter-vehicular communications should be exploited to satisfy application requirements, although congestion control mechanisms are required to minimize the packet collision probability. In this work, we face the issue of achieving accurate vehicle position estimation and prediction in a VANET scenario. State of the art solutions to the problem try to broadcast the positioning information periodically, so that vehicles can ensure that the information their neighbors have about them is never older than the inter-transmission period. However, the rate of decay of the information is not deterministic in complex urban scenarios: the movements and maneuvers of vehicles can often be erratic and unpredictable, making old positioning information inaccurate or downright misleading. To address this problem, we propose to use the Quality of Information (QoI) as the decision factor for broadcasting. We implement a threshold-based strategy to distribute position information whenever the positioning error passes a reference value, thereby shifting the objective of the network to limiting the actual positioning error and guaranteeing quality across the VANET. The threshold-based strategy can reduce the network load by avoiding the transmission of redundant messages, as well as improving the overall positioning accuracy by more than 20% in realistic urban scenarios.Comment: 8 pages, 7 figures, 2 tables, accepted for presentation at European Wireless 201

A dynamic approach to rebalancing bike-sharing systems

Bike-sharing services are flourishing in Smart Cities worldwide. They provide a low-cost and environment-friendly transportation alternative and help reduce traffic congestion. However, these new services are still under development, and several challenges need to be solved. A major problem is the management of rebalancing trucks in order to ensure that bikes and stalls in the docking stations are always available when needed, despite the fluctuations in the service demand. In this work, we propose a dynamic rebalancing strategy that exploits historical data to predict the network conditions and promptly act in case of necessity. We use Birth-Death Processes to model the stations' occupancy and decide when to redistribute bikes, and graph theory to select the rebalancing path and the stations involved. We validate the proposed framework on the data provided by New York City's bike-sharing system. The numerical simulations show that a dynamic strategy able to adapt to the fluctuating nature of the network outperforms rebalancing schemes based on a static schedule

A QUIC Implementation for ns-3

Quick UDP Internet Connections (QUIC) is a recently proposed transport protocol, currently being standardized by the Internet Engineering Task Force (IETF). It aims at overcoming some of the shortcomings of TCP, while maintaining the logic related to flow and congestion control, retransmissions and acknowledgments. It supports multiplexing of multiple application layer streams in the same connection, a more refined selective acknowledgment scheme, and low-latency connection establishment. It also integrates cryptographic functionalities in the protocol design. Moreover, QUIC is deployed at the application layer, and encapsulates its packets in UDP datagrams. Given the widespread interest in the new QUIC features, we believe that it is important to provide to the networking community an implementation in a controllable and isolated environment, i.e., a network simulator such as ns-3, in which it is possible to test QUIC's performance and understand design choices and possible limitations. Therefore, in this paper we present a native implementation of QUIC for ns-3, describing the features we implemented, the main assumptions and differences with respect to the QUIC Internet Drafts, and a set of examples.Comment: 8 pages, 4 figures. Please cite it as A. De Biasio, F. Chiariotti, M. Polese, A. Zanella, M. Zorzi, "A QUIC Implementation for ns-3", Proceedings of the Workshop on ns-3 (WNS3 '19), Firenze, Italy, 201

Fast Context Adaptation in Cost-Aware Continual Learning

In the past few years, DRL has become a valuable solution to automatically learn efficient resource management strategies in complex networks with time-varying statistics. However, the increased complexity of 5G and Beyond networks requires correspondingly more complex learning agents and the learning process itself might end up competing with users for communication and computational resources. This creates friction: on the one hand, the learning process needs resources to quickly convergence to an effective strategy; on the other hand, the learning process needs to be efficient, i.e., take as few resources as possible from the user's data plane, so as not to throttle users' QoS. In this paper, we investigate this trade-off and propose a dynamic strategy to balance the resources assigned to the data plane and those reserved for learning. With the proposed approach, a learning agent can quickly converge to an efficient resource allocation strategy and adapt to changes in the environment as for the CL paradigm, while minimizing the impact on the users' QoS. Simulation results show that the proposed method outperforms static allocation methods with minimal learning overhead, almost reaching the performance of an ideal out-of-band CL solution.Comment: arXiv admin note: text overlap with arXiv:2211.1691

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

Hidden Markov Model-Based Encoding for Time-Correlated IoT Sources

As the use of Internet of Things (IoT) devices for monitoring purposes becomes ubiquitous, the efficiency of sensor communication is a major issue for the modern Internet. Channel coding is less efficient for extremely short packets, and traditional techniques that rely on source compression require extensive signaling or pre-existing knowledge of the source dynamics. In this work, we propose an encoding and decoding scheme that learns source dynamics online using a Hidden Markov Model (HMM), puncturing a short packet code to outperform existing compression-based approaches. Our approach shows significant performance improvements for sources that are highly correlated in time, with no additional complexity on the sender side.Comment: Preprint version of the paper published in IEEE Communications Letter

On the Role of Preemption for Timing Metrics in Coded Multipath Communication

Recent trends in communication networks have focused on Quality of Service (QoS) requirements expressed through timing metrics such as latency or Age of Information (AoI). A possible way to achieve this is coded multipath communication: redundancy is added to a block of information through a robust packet-level code, transmitting across multiple independent channels to reduce the impact of blockages or rate fluctuation. The number of these links can grow significantly over traditional two-path schemes: in these scenarios, the optimization of the timing metrics is non-trivial, and latency and AoI might require different settings. In particular, packet preemption is often the optimal solution to optimize AoI in uncoded communication, but can significantly reduce the reliability of individual blocks. In this work, we model the multipath communication as a fork-join D/M/(K,N)/L queue, where K blocks of information are encoded into N>K redundant blocks. We derive the latency and Peak AoI (PAoI) distributions for different values of the queue size L. Our results show that preemption is not always the optimal choice, as dropping a late packet on one path might affect the reliability of the whole block, and that minimizing the PAoI leads to poor latency performance.Comment: Submitted for publication to the IEEE Transactions on Communication

An Adaptive Broadcasting Strategy for Efficient Dynamic Mapping in Vehicular Networks

In this work, we face the issue of achieving an efficient dynamic mapping in vehicular networking scenarios, i.e., obtaining an accurate estimate of the positions and trajectories of connected vehicles in a certain area. State-of-the-art solutions are based on the periodic broadcasting of the position information of the network nodes, with an inter-transmission period set by a congestion control scheme. However, the movements and maneuvers of vehicles can often be erratic, making transmitted data inaccurate or downright misleading. To address this problem, we propose to adopt a dynamic transmission scheme based on the actual positioning error, sending new data when the estimate overcomes a preset error threshold. Furthermore, the proposed method adapts the error threshold to the operational context according to an innovative congestion control algorithm that limits the collision probability among broadcast packet transmissions. This threshold-based strategy can reduce the network load by avoiding the transmission of redundant messages, and is shown to improve the overall positioning accuracy by more than 20% in realistic urban scenarios