Rural areas often face significant challenges in accessing reliable broadband Internet due to high infrastructure costs and low population density. To address this issue, we propose a model for evaluating the performance and the cost of a mesh-based, last-and middle-mile replacement for broadband connection in these underserved regions. We use open data from ten underserved municipalities to assess the demand, plan the mesh network, and estimate the allocated capacity per user. We consider two designs: a low-cost network using the classical 5 unlicensed band, and a high-performance one using mmWave frequency. For both designs, we estimate the Operating Expenditure and the amortized Capital Expenditure using realistic device prices and operating cost estimations. We compare the price of the mesh-based solution with alternatives based on xDSL and satellite connectivity and show that it has competitive prices compared to existing offers, covering a larger portion of households than DSL. We open-source both the code and the elaborated data to reproduce, extend, and improve our results in different settings.

Next-generation wireless networks target high network availability, ubiquitous coverage, and extremely high data rates for mobile users. This requires exploring new frequency bands, e.g., mmWaves, moving toward ultra-dense deployments in urban locations, and providing ad hoc, resilient connectivity in rural scenarios. The design of the backhaul network plays a key role in advancing how the access part of the wireless system supports next-generation use cases. Wireless backhauling, such as the newly introduced Integrated Access and Backhaul (IAB) concept in 5G, provides a promising solution, also leveraging the mmWave technology and steerable beams to mitigate interference and scalability issues. At the same time, however, managing and optimizing a complex wireless backhaul introduces additional challenges for the operation of cellular systems. This paper presents a strategy for the optimal creation of the backhaul network considering various constraints related to network topology, robustness, and flow management. We evaluate its feasibility and efficiency using synthetic and realistic network scenarios based on 3D modeling of buildings and ray tracing. We implement and prototype our solution as a dynamic IAB control framework based on the Open Radio Access Network (RAN) architecture, and demonstrate its functionality in Colosseum, a large-scale wireless network emulator with hardware in the loop.

Softwarized and programmable Radio Access Networks (RANs) come with virtualized and disaggregated components, increasing the supply chain robustness and the flexibility and dynamism of the network deployments. This is a key tenet of Open RAN, with open interfaces across disaggregated components specified by the O-RAN ALLIANCE. It is mandatory, however, to validate that all components are compliant with the specifications and can successfully interoperate, without performance gaps with traditional, monolithic appliances. Open Testing & Integration Centers (OTICs) are entities that can verify such interoperability and adherence to the standard through rigorous testing. However, how to design, instrument, and deploy an OTIC which can offer testing for multiple tenants, heterogeneous devices, and is ready to support automated testing is still an open challenge. In this paper, we introduce a blueprint for a programmable OTIC testing infrastructure, based on the design and deployment of the Open6G OTIC at Northeastern University, Boston, and provide insights on technical challenges and solutions for O-RAN testing at scale.

Open Radio Access Networks (O-RAN) are expected to revolutionize the telecommunications industry with benefits like cost reduction, vendor diversity, and improved network performance through AI optimization. Supporting the O-RAN ALLIANCE’s mission to achieve more intelligent, open, virtualized and fully interoperable mobile networks, O-RAN Open Testing and Integration Centers (OTICs) play a key role in accelerating the adoption of O-RAN specifications based on rigorous testing and validation. One theme in the recent O-RAN Global PlugFest Spring 2024 focused on demonstrating consistent and repeatable Open Fronthaul testing in multiple labs. To respond to this topic, in this paper, we present a detailed analysis of the testing methodologies and results for O-RAN Distributed Unit (O-DU) in O-RAN across two OTICs. We identify key differences in testing setups, share challenges encountered, and propose best practices for achieving repeatable and consistent testing results. Our findings highlight the impact of different deployment technologies and testing environments on performance and conformance testing outcomes, providing valuable insights for future O-RAN implementations.

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.

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.

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.

The use of ultra high frequencies in 5G and future networks to improve transmission speeds and capacity requires that users’ equipment remain in Line of Sight with the access antennas most of the service time. This requirement implies a change in perspective to plan the coverage: Antennas cannot be placed on roofs or remote antenna sites, and a robust coverage is based on multi-antenna visibility from any point. This paper tackles the problem of public street coverage in urban areas with a data-driven methodology. Starting from 3D digital maps, we formalize the problem of antenna placement as a set coverage problem and leverage powerful heuristics to implement a general algorithm that allows the exploration of different policies, returning the detailed coverage, the antenna placement, and the cost of the coverage. Results on 15 areas in 3 Italian cities show the properties of different policies and confirm for the first time on large scale real data the feasibility of Line of Sight communications with a sustainable number of antennas per km2.

Mesh backhauls are getting attention for 5G networks, but not only. A backhaul mesh is attractive due to its multiple potential paths that grants redundancy and robustness. The real topology and its properties, however, is heavily influenced by the characteristics of the place where it is deployed, a fact that is rarely taken into account by scientific literature, mainly due to the lack of detailed topographic data. This WIP analyzes the impact of true topography on small backhaul meshes in nine different locations in Italy. Initial results stress how true data influence results and can help designing better networks and better services.

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, suffer from relatively long recovery times characterized by broken and looped routes due to long management 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, 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 OLSR Hello and Traffic Control timers.