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O-RAN is radically shifting how cellular networks are designed, deployed and optimized through network programmability, disaggregation, and virtualization. Specifically, RAN Intelligent Controllers (RICs) can orchestrate and optimize the Radio Access Network (RAN) operations, allowing fine-grained control over the network. RICs provide new approaches and solutions for classical use cases such as on-demand traffic steering, anomaly detection, and Quality of Service (QoS) management, with an optimization that can target single User Equipments (UEs), slices, cells, or entire base stations. While this comes with the potential to enable intelligent, programmable RANs, there are still significant challenges to be faced, primarily related to data collection at scale, development and testing of custom control logic for the RICs, and availability of Open RAN simulation and experimental tools for the research and development communities. To address this, we introduce ns-O-RAN, a software integration between a real-world near-real-time RIC and an ns-3 simulated RAN which provides a platform for researchers and telco operators to build, test and integrate xApps. ns-O-RAN extends a popular Open RAN experimental framework (OpenRAN Gym) with simulation capabilities that enable the generation of realistic datasets without the need for experimental infrastructure. We implement it as a new open-source ns-3 module that uses the E2 interface to connect different simulated 5G base stations with the RIC, enabling the exchange of E2 messages and RAN KPMs to be consumed by standard xApps. Furthermore, we test ns-O-RAN with the OSC and OpenRAN Gym RICs, simplifying the onboarding from a test environment to production with real telecom hardware controlled without major reconfigurations required. ns-O-RAN is open source and publicly available, together with quick-start tutorials and documentation., Comment: 10 pages, 6 figures, 1 table, 5 listings. Accepted at the 2023 Workshop on ns-3 (WNS3 2023), June 28-29, 2023, Arlington, VA, USA. ACM, New York, NY, USA

5G and beyond mobile networks will support heterogeneous use cases at an unprecedented scale, thus demanding automated control and optimization of network functionalities customized to the needs of individual users. Such fine-grained control of the Radio Access Network (RAN) is not possible with the current cellular architecture. To fill this gap, the Open RAN paradigm and its specification introduce an open architecture with abstractions that enable closed-loop control and provide data-driven, and intelligent optimization of the RAN at the user level. This is obtained through custom RAN control applications (i.e., xApps) deployed on near-real-time RAN Intelligent Controller (near-RT RIC) at the edge of the network. Despite these premises, as of today the research community lacks a sandbox to build data-driven xApps, and create large-scale datasets for effective AI training. In this paper, we address this by introducing ns-O-RAN, a software framework that integrates a real-world, production-grade near-RT RIC with a 3GPP-based simulated environment on ns-3, enabling the development of xApps and automated large-scale data collection and testing of Deep Reinforcement Learning-driven control policies for the optimization at the user-level. In addition, we propose the first user-specific O-RAN Traffic Steering (TS) intelligent handover framework. It uses Random Ensemble Mixture, combined with a state-of-the-art Convolutional Neural Network architecture, to optimally assign a serving base station to each user in the network. Our TS xApp, trained with more than 40 million data points collected by ns-O-RAN, runs on the near-RT RIC and controls its base stations. We evaluate the performance on a large-scale deployment, showing that the xApp-based handover improves throughput and spectral efficiency by an average of 50% over traditional handover heuristics, with less mobility overhead., Comment: 15 pages, 2 algorithms, 1 table, 11 figures, 42 references

Artificial intelligence (AI) techniques have emerged as a powerful approach to make wireless networks more efficient and adaptable. In this paper we present an ns-3 simulation framework, able to implement AI algorithms for the optimization of wireless networks. Our pipeline consists of: (i) a new geometry-based mobility-dependent channel model for V2X; (ii) all the layers of a 5G-NR-compliant protocol stack, based on the ns3-mmwave module; (iii) a new application to simulate V2X data transmission, and (iv) a new intelligent entity for the control of the network via AI. Thanks to its flexible and modular design, researchers can use this tool to implement, train, and evaluate their own algorithms in a realistic and controlled environment. We test the behavior of our framework in a Predictive Quality of Service (PQoS) scenario, where AI functionalities are implemented using Reinforcement Learning (RL), and demonstrate that it promotes better network optimization compared to baseline solutions that do not implement AI., Comment: 8 pages, 4 figures, submitted to WNS3 2022

In recent years, autonomous networks have been designed with Predictive Quality of Service (PQoS) in mind, as a means for applications operating in the industrial and/or automotive sectors to predict unanticipated Quality of Service (QoS) changes and react accordingly. In this context, Reinforcement Learning (RL) has come out as a promising approach to perform accurate predictions, and optimize the efficiency and adaptability of wireless networks. Along these lines, in this paper we propose the design of a new entity, implemented at the RAN-level that, with the support of an RL framework, implements PQoS functionalities. Specifically, we focus on the design of the reward function of the learning agent, able to convert QoS estimates into appropriate countermeasures if QoS requirements are not satisfied. We demonstrate via ns-3 simulations that our approach achieves the best trade-off in terms of QoS and Quality of Experience (QoE) performance of end users in a teleoperated-driving-like scenario, compared to other baseline solutions., Comment: 6 pages, 5 figures, 2 tables. The paper has been submitted to IEEE WCNC 2022. Copyright may change without notice

5th generation (5G) systems have been designed with three main objectives in mind: increasing throughput, reducing latency, and enabling reliable communications. To meet these (often conflicting) constraints, the 3GPP released a set of specifications for 5G NR, one of the main innovations being the support for communications in the millimeter wave (mmWave) bands. However, how to implement lower complexity, energy efficient, mid-market Internet of Things (IoT) applications is still an on-going investigation, currently led by the 3GPP which is extending the NR standard with NR-Light specifications to support devices with reduced capabilities (REDCAP). While REDCAP devices may also operate at mmWaves to improve the network performance, hardware/software simplifications are needed to support balanced and mixed requirements compared to 5G NR systems. In this context, the contributions of this paper are threefold. First, we present some NR-Light use cases for which the support of the mmWave bands is desirable. Second, we describe how 5G NR can be simplified to achieve NR-Light requirements and expectations. Finally, we evaluate via simulation the performance of NR-Light devices operating at mmWaves in an industrial IoT setup, in terms of cost and complexity, throughput, and latency., Comment: Changed title, revised article and submitted to a different venue

The next generations of mobile networks will be deployed as ultra-dense networks, to match the demand for increased capacity and the challenges that communications in the higher portion of the spectrum (i.e., the mmWave band) introduce. Ultra-dense networks, however, require pervasive, high-capacity backhaul solutions, and deploying fiber optic to all base stations is generally considered to be too expensive for network operators. The 3rd Generation Partnership Project (3GPP) has thus introduced Integrated Access and Backhaul (IAB), a wireless backhaul solution in which the access and backhaul links share the same hardware, protocol stack, and also spectrum. The multiplexing of different links in the same frequency bands, however, introduces interference and capacity sharing issues, thus calling for the introduction of advanced scheduling and coordination schemes. This paper proposes a semi-centralized resource allocation scheme for IAB networks, designed to be flexible, with low complexity, and compliant with the 3GPP IAB specifications. We develop a version of the Maximum Weighted Matching (MWM) problem that can be applied on a spanning tree that represents the IAB network and whose complexity is linear in the number of IAB-nodes. The proposed solution is compared with state-of-the-art distributed approaches through end-to-end, full-stack system-level simulations with a 3GPP-compliant channel model, protocol stack, and a diverse set of user applications. The results show how that our scheme can increase the throughput of cell-edge users up to 5 times, while decreasing the overall network congestion with an end-to-end delay reduction of up to 25 times., Comment: 30 pages, 10 figures. Please cite as M. Pagin, T. Zugno, M. Polese and M. Zorzi, "Resource Management for 5G NR Integrated Access and Backhaul: a Semi-centralized Approach," in IEEE Transactions on Wireless Communications, 2021

Millimeter wave (mmWave) communication represents one of the main innovations of the next generation of wireless technologies, allowing users to reach unprecedented data rates. To overcome the high path loss at mmWave frequencies, these systems make use of directional antennas able to focus the transmit power into narrow beams using BeamForming (BF) techniques, thus making the communication directional. This new paradigm opens up a set of challenges for the design of efficient wireless systems, in which antenna and BF components play an important role also at the higher layer of the protocol stack. For this reason, accurate modeling of these components in a full-stack simulation is of primary importance to understand the overall system behavior. This paper proposes a novel framework for the end-to-end simulation of 5G mmWave cellular networks, including a ray-tracing based channel model and accurate models for antenna arrays and BF schemes. We showcase this framework by evaluating the performance of different antenna and BF configurations considering both link-level and end-to-end metrics and present the obtained results.

This paper analyzes Hybrid Beamforming (HBF) and Multi-User Multiple-Input Multiple-Output (MU-MIMO) in millimeter wave (mmWave) 5th generation (5G) cellular networks considering the full protocol stack with TCP/IP traffic and MAC scheduling. Prior work on HBF and MU-MIMO has assumed full-buffer transmissions and studied link-level performance. We report non-trivial interactions between the HBF technique, the front-loaded channel estimation pilot scheme in NR, and the constraints of MU-MIMO scheduling. We also report that joint multi-user beamforming design is imperative, in the sense that the MU-MIMO system cannot be fully exploited when implemented as a mere collection of single-user analog beams working in parallel. By addressing these issues, throughput can be dramatically increased in mmWave 5G networks by means of Spatial Division Multiple Access (SDMA)., Comment: This is the author's pre-print of a paper submitted to IEEE ICC 2021. arXiv admin note: substantial text overlap with arXiv:2010.04220

This paper studies the cross-layer challenges and performance of Hybrid Beamforming (HBF) and Multi-User Multiple-Input Multiple-Output (MU-MIMO) in 5G millimeter wave (mmWave) cellular networks with full-stack TCP/IP traffic and MAC scheduling. While previous research on HBF and MU-MIMO has focused on link-level analysis of full-buffer transmissions, this work reveals the interplay between HBF techniques and the higher layers of the protocol stack. To this aim, prior work on full stack simulation of mmWave cellular network has been extended by including the modeling of MU-MIMO and HBF. Our results reveal novel relations between the networking layers and the HBF MU-MIMO performance in the physical layer. Particularly, throughput can be increased in 5G networks by means of Spatial Division Multiple Access (SDMA). However, in order to achieve such benefits it is necessary to take into account certain trade-offs and the implementation complexity of a full-stack HBF solution., Comment: 30 pages, 4 figures, 1 table. This is the author's pre-print version of a manuscript to be submitted

3GPP NR V2X represents the new 3GPP standard for next-generation vehicular systems which, among other innovations, supports vehicle-to-vehicle (V2V) operations in the millimeter wave (mmWave) spectrum to address the communication requirements of future intelligent automotive networks. While mmWaves will enable massive data rates and low latency, the propagation characteristics at very high frequencies become very challenging, thereby calling for accurate performance evaluations as a means to properly assess the performance of such systems. Along these lines, in this paper MilliCar, the new ns-3 module based on the latest NR V2X specifications, is used to provide an end-to-end performance evaluation of mmWave V2V networks. We investigate the impact of different propagation scenarios and system parameters, including the inter-vehicle distance, the adopted frame numerology, and the modulation and coding scheme, and provide guidelines towards the most promising V2V deployment configurations., Comment: 6 pages, 7 figures. Submitted to IEEE Globecom 2020

The ever increasing number of connected devices and of new and heterogeneous mobile use cases implies that 5G cellular systems will face demanding technical challenges. For example, Ultra-Reliable Low-Latency Communication (URLLC) and enhanced Mobile Broadband (eMBB) scenarios present orthogonal Quality of Service (QoS) requirements that 5G aims to satisfy with a unified Radio Access Network (RAN) design. Network slicing and mmWave communications have been identified as possible enablers for 5G. They provide, respectively, the necessary scalability and flexibility to adapt the network to each specific use case environment, and low latency and multi-gigabit-per-second wireless links, which tap into a vast, currently unused portion of the spectrum. The optimization and integration of these technologies is still an open research challenge, which requires innovations at different layers of the protocol stack. This paper proposes to combine them in a RAN slicing framework for mmWaves, based on carrier aggregation. Notably, we introduce MilliSlice, a cross-carrier scheduling policy that exploits the diversity of the carriers and maximizes their utilization, thus simultaneously guaranteeing high throughput for the eMBB slices and low latency and high reliability for the URLLC flows., Comment: 8 pages, 8 figures. Proc. of the 18th Mediterranean Communication and Computer Networking Conference (MedComNet 2020), Arona, Italy, 2020

Vehicle-to-vehicle (V2V) communications have opened the way towards cooperative automated driving as a means to guarantee improved road safety and traffic efficiency. The use of the millimeter wave (mmWave) spectrum for V2V, in particular, holds great promise since the large bandwidth available offers the possibility of realizing high-data-rate connections. However, this potential is hindered by the significant path and penetration loss experienced at these frequencies. It then becomes fundamental to practically evaluate the feasibility of installing mmWave-based technologies in the vehicular scenario, in view of the strict latency and throughput requirements of future automotive applications. To do so, in this paper we present MilliCar, the first ns-3 module for V2V mmWave networks, which features a detailed implementation of the sidelink Physical (PHY) and Medium Access Control (MAC) layers based on the latest NR V2X specifications, the 3GPP standard for next-generation vehicular systems. Our module is open-source and enables researchers to compare possible design options and their relative performance through an end-to-end full-stack approach, thereby stimulating further research on this topic., Comment: 8 pages, 5 figures. Submitted to WNS3 2020. The code related to this paper can be found at https://github.com/signetlabdei/millicar

The next generation of wireless networks will feature a more flexible radio access design, integrating multiple new technological solutions (e.g., massive Multiple-Input Multiple-Output (MIMO), millimeter waves) to satisfy different verticals and use cases. The performance evaluation of these networks will require more complex models to represent the interactions of different components of the networks accurately. For example, channel models, which are of paramount importance to precisely characterize the behavior of such systems, need to account for multi-antenna systems and new frequency bands. This paper presents the ns-3 implementation of a spatial channel model for the 0.5-100 GHz spectrum, following the 3GPP Technical Report 38.901. The code, designed to be flexible and easily extensible, is integrated in ns-3's antenna, propagation and spectrum models, and offers the support for the investigation of future wireless systems in ns-3., Comment: This paper has been accepted for presentation at the 2020 Workshop on ns-3 (WNS3 2020), June 17--18, 2020, Gaithersburg, MD, USA

IEEE 802.11bd and 3GPP NR V2X represent the new specifications for next generation vehicular networks, exploiting new communication technologies and new spectrum, such as the millimeter wave (mmWave) band, to improve throughput and reduce latency. In this paper, we specifically focus on the challenges that mmWaves introduce for Vehicle-to-Vehicle (V2V) networking, by reviewing the latest standard developments and the issues that 802.11bd and NR V2X will have to address for V2V operations at mmWaves. To the best of our knowledge, our work is the first that considers a full-stack, end-to-end approach for the design of mmWave V2V networks, discussing open issues that span from the physical to the higher layers, and reporting the results of an end-to-end performance evaluation that highlight the potential of mmWaves for V2V communications., Comment: 7 pages, 4 figures, 1 table

Integrated Access and Backhaul (IAB) is being investigated as a means to overcome deployment costs of ultra-dense 5G millimeter wave (mmWave) networks by realizing wireless backhaul links to relay the access traffic. For the development of these systems, however, it is fundamental to validate the performance of IAB in realistic scenarios through end-to-end system level simulations. In this paper, we shed light on the most recent standardization activities on IAB, and compare architectures with and without IAB in mmWave deployments. While it is well understood that IAB networks reduce deployment costs by obviating the need to provide wired backhaul to each cellular base-station, in this paper we demonstrate the cell-edge throughput advantage offered by IAB using end-to-end system level simulations. We further highlight some research challenges associated with this architecture that will require further investigations., Comment: Submitted to IEEE Communications Magazine, 7 pages, 4 figures

During incidents and disasters it is fundamental to provide to first responders high performance and reliable communications, in order to improve their coordination capabilities and their awareness of the surrounding environment, and to allow them to promptly transmit and receive alerts on possible dangerous situations or emergencies. The accurate evaluation of the performance of different Public Safety Communication (PSC) networking and communications technologies is therefore of paramount importance, and the characterization of the scenario in which these technologies need to operate is fundamental to obtain meaningful results. In this paper, we present the implementation of three reference PSC scenarios, which are open source and made publicly available to the research community, describing the incidents, the mobility and applications of first responders, and providing examples on how a mmWave-based Radio Access Network (RAN) can support high-traffic use cases. Moreover, we present the implementation of two novel mobility models for ns-3, which can be used to enable the simulation of realistic PSC scenarios in ns-3., Comment: 8 pages, 9 figures, submitted to WNS3 2019

Thanks to the wide availability of bandwidth, the millimeter wave (mmWave) frequencies will provide very high data rates to mobile users in next generation 5G cellular networks. However, mmWave links suffer from high isotropic pathloss and blockage from common materials, and are subject to an intermittent channel quality. Therefore, protocols and solutions at different layers in the cellular network and the TCP/IP protocol stack have been proposed and studied. A valuable tool for the end-to-end performance analysis of mmWave cellular networks is the ns-3 mmWave module, which already models in detail the channel, Physical (PHY) and Medium Access Control (MAC) layers, and extends the Long Term Evolution (LTE) stack for the higher layers. In this paper we present an implementation for the ns-3 mmWave module of multi connectivity techniques for 3GPP New Radio (NR) at mmWave frequencies, namely Carrier Aggregation (CA) and Dual Connectivity (DC), and discuss how they can be integrated to increase the functionalities offered by the ns-3 mmWave module., Comment: 9 pages, 7 figures, submitted to the Workshop on ns-3 (WNS3) 2018