Vehicles or Pedestrians: On the gNB Placement in Ultradense Urban Areas

This paper tackles the problem of base stations placement to guarantee line of sight connectivity to vehicles in urban areas, when high frequency communications (mmWave or TeraHertz) are used. We introduce a novel methodology mixing vehicular networks simulations and show that the density of base stations per squared km is low enough to be feasibly reached. However, optimizing the placement for vehicles coverage provides an advantage but may not be enough for pedestrians coverage

On the Properties of Next Generation Wireless Backhaul

With the advent of 5G, cellular networks require a high number of base stations, possibly interconnected with wireless links, an evolution introduced in the last revision of 5G as the Integrated Access and Backhaul (IAB). Researchers are now working to optimize the complex topologies of the backhaul network, using synthetic models for the underlying visibility graph, i.e., the graph of possible connections between the base stations. The goal of this paper is to provide a novel methodology to generate visibility graphs starting from real data (and the data sets themselves together with the source code for their manipulation), in order to base the IAB design and optimization on assumptions that are as close as possible to reality. We introduce a GPU-based method to create visibility graphs from open data, we analyze the properties of the realistic visibility graphs, and we show that different geographic areas produce very different graphs. We run state-of-the-art algorithms to create wireless backhaul networks on top of visibility graphs, and we show that the results that exploit synthetic models are far from those that use our realistic graphs. Our conclusion is that the data-based approach we propose is essential to design mobile networks that work in a variety of real-world situations

Reliability analysis in a wireless ISP

Report de recerca del Departament d'Arquitectura de ComputadorsThis brief report investigates different quality parameters to assess the reliability in Wireless Internet Service Providers, WISPs. In our analysis we use a Markov chain approach. We investigate the time to failure, failure probability and reliability. We obtain a closed-form reliability formula for the failure of a system subject to the failure of k devices.Preprin

Toward Smart Community Networks

The advent of IEEE 802.11 in the late 1990s spurred the development of new network paradigms. In particular, new technology enthusiasts saw the potential of WiFi to bring broadband Internet connections to under-provisioned areas, giving rise to networks deployed and maintained by their users. This paradigm led to non-profit decentralized structures that grow by the unplanned addition of heterogeneous network devices: community networks (CNs). There have been hundreds of CN deployments worldwide; some have disappeared, while others have blossomed into complex networks with thousands of nodes. The networking research community has been aware of CNs, and many works studied CNs in their various aspects: design (routing, scalability, security), deployment, measurements, services, and so on. We argue that emerging technologies will give a new impetus to CNs by transforming them into smart CNs. This article aims to lay out the technical features of future CNs and encourage the research community to tackle the stimulating research challenges they raise

Joint Routing and Energy Optimization for Integrated Access and Backhaul with Open RAN

Energy consumption represents a major part of the operating expenses of mobile network operators. With the densification foreseen with 5G and beyond, energy optimization has become a problem of crucial importance. While energy optimization is widely studied in the literature, there are limited insights and algorithms for energy-saving techniques for Integrated Access and Backhaul (IAB), a self-backhauling architecture that ease deployment of dense cellular networks reducing the number of fiber drops. This paper proposes a novel optimization model for dynamic joint routing and energy optimization in IAB networks. We leverage the closed-loop control framework introduced by the Open Radio Access Network (O-RAN) architecture to minimize the number of active IAB nodes while maintaining a minimum capacity per User Equipment (UE). The proposed approach formulates the problem as a binary nonlinear program, which is transformed into an equivalent binary linear program and solved using the Gurobi solver. The approach is evaluated on a scenario built upon open data of two months of traffic collected by network operators in the city of Milan, Italy. Results show that the proposed optimization model reduces the RAN energy consumption by 47%, while guaranteeing a minimum capacity for each UE.Comment: 6 pages, Accepted at IEEE GLOBECOM 202

Towards scalable Community Networks topologies

Community Networks (CNs) are grassroots bottom-up initiatives that build local infrastructures, normally using Wi-Fi technology, to bring broadband networking in areas with inadequate offer of traditional infrastructures such as ADSL, FTTx or wide-band cellular (LTE, 5G). Albeit they normally operate as access networks to the Internet, CNs are ad-hoc networks that evolve based on local requirements and constraints, often including additional local services on top of Internet access. These networks grow in highly decentralized manner that radically deviates from the top-down network planning practiced in commercial mobile networks, depending, on the one hand, on the willingness of people to participate, and, on the other hand, on the feasibility of wireless links connecting the houses of potential participants with each other. In this paper, we present a novel methodology and its implementation into an automated tool, which enables the exercise of (light) centralized control to the dynamic and otherwise spontaneous CN growth process. The goal of the methodology is influencing the choices to connect a new node to the CN so that it can grow with more balance and to a larger size. Input to our methodology are open source resources about the physical terrain of the CN deployment area, such as Open Street Map and very detailed (less than 1 m resolution) LIDAR-based data about buildings layout and height, as well as technical descriptions and pricing data about off-the-shelf networking devices that are made available by manufacturers. Data related to demographics can be easily added to refine the environment description. With these data at hand, the tool can estimate the technical and economic feasibility of adding new nodes to the CN and actively assist new CN users in selecting proper equipment and CN node(s) to connect with to improve the CN scalability. We test our methodology in four different areas representing standard territorial characterization categories: urban, suburban, intermediate, and rural. In all four cases our tool shows that CNs scale to much larger size using the assisted, network-aware methodology when compared with de facto practices. Results also show that the CNs deployed with the assisted methodology are more balanced and have a lower per-node cost for the same per-node guaranteed bandwidth. Moreover, this is achieved with fewer devices per node, which means that the network is cheaper to build and easier to maintain.Peer ReviewedPostprint (author's final draft

TrueNets: a Topology Generator for Realistic Network Analysis

The availability of realistic topology generators is a key component in the study of network performance. This work describes a new approach for realistic generation of topologies, named TrueNets, that uses open data provided by public administrations and crowd-sensing efforts for populated areas, maps, altitude of land and buildings; TrueNets estimates link performance with classical propagation models and produces annotated topologies of networks that can actually exist in the selected areas, thus providing not only an abstract tool for performance evaluation, but also a design tool for planning. We use TrueNets to model distributed mesh networks and we show that the generated topologies differ substantially from state-of the-art synthetic generators

Anomaly detection for fault detection in wireless community networks using machine learning

Machine learning has received increasing attention in computer science in recent years and many types of methods have been proposed. In computer networks, little attention has been paid to the use of ML for fault detection, the main reason being the lack of datasets. This is motivated by the reluctance of network operators to share data about their infrastructure and network failures. In this paper, we attempt to fill this gap using anomaly detection techniques to discern hardware failure events in wireless community networks. For this purpose we use 4 unsupervised machine learning, ML, approaches based on different principles. We have built a dataset from a production wireless community network, gathering traffic and non-traffic features, e.g. CPU and memory. For the numerical analysis we investigated the ability of the different ML approaches to detect an unprovoked gateway failure that occurred during data collection. Our numerical results show that all the tested approaches improve to detect the gateway failure when non-traffic features are also considered. We see that, when properly tuned, all ML methods are effective to detect the failure. Nonetheless, using decision boundaries and other analysis techniques we observe significant different behavior among the ML methods.This work has received funding through the DiPET CHIST-ERA under grant agreement PCI2019-111850-2; Spanish grant PID2019-106774RB-C21; Romanian DIPET (62652/15.11.2019) project funded via PN 124/2020; and has been partially supported by the EU research project SERRANO (101017168) and hardware resources courtesy of the Romanian Ministry of Research and Innovation UEFISCDI COCO research project PN III-P4-ID-PCE-2020-0407.Peer ReviewedPostprint (published version

A Realistic Open-Data-based Cost Model for Wireless Backhaul Networks in Rural Areas

Broadband Internet provision is an increasing demand in many rural areas and wireless internet service providers have emerged as an opportunity to fill this need. However, this type of operator typically consists of a small business with little resources, and difficulty to plan and assess a reliable and economically sustainable infrastructure. In this paper, we try to bring some aid to this challenging problem by describing a reliable mesh-based backhaul design, together with a detailed CapEx/OpEx economic assessment. We apply our model using real data from ten Italian rural municipalities. Our numerical results show that having clusters of 200 subscribers, a reliable backhaul could be deployed with a monthly subscription and price per Mb/s extremely competitive compared to existing market offers

Centrality-based Route Recovery in Wireless Mesh Networks

Wireless Mesh Networks are subject to frequent node and link failures, and routing protocols currently used, such as Optimized Link State Routing (OLSR) or Babel (Babel), suffer from relatively long recovery times characterized by broken and looped routes due to long timeouts that can not be shortened to keep the overhead at an acceptable level. This paper experiments a novel timer management technique named Pop-Routing on top of OLSR. Pop-Routing exploits the notion of betweenness centrality to tune timers depending on the node position in the network, so that failures that lead to larger traffic losses can be recovered faster. Pop-Routing maintains the overhead constant, but favors the most central nodes, whose failure is devastating from the performance point of view, and penalizes peripheral ones and leaves of the topology, whose failure has a very little impact on the entire network. Pop-Routing has been implemented as a plug-in in the OLSR daemon, coupled with an external process, named Prince, that computes centrality and timer values without interfering with the routing daemon. Experiments are run on the WiSHFUL showing the benefit of Pop-tuning of OLSR Hello and Traffic Control timers