Predictive Quality of Service (PQoS) makes it possible to anticipate QoS changes, e.g., in wireless networks, and trigger appropriate countermeasures to avoid performance degradation. A promising tool for PQoS is given by Reinforcement Learning (RL), a methodology that enables the design of decision-making strategies for stochastic optimization. In this manuscript, we present PRATA, a new simulation framework to enable PRedictive QoS based on AI for Teleoperated driving Applications. PRATA consists of a modular pipeline that includes (i) an end-to-end protocol stack to simulate the 5G Radio Access Network (RAN), (ii) a tool for generating automotive data, and (iii) an Artificial Intelligence (AI) unit to optimize PQoS decisions. To prove its utility, we use PRATA to design an RL unit, named RAN-AI, to optimize the segmentation level of teleoperated driving data in the event of resource saturation or channel degradation. Hence, we show that the RAN-AI entity efficiently balances the trade-off between QoS and Quality of Experience (QoE) that characterize teleoperated driving applications, almost doubling the system performance compared to baseline approaches. In addition, by varying the learning settings of the RAN-AI entity, we investigate the impact of the state space and the relative cost of acquiring network data that are necessary for the implementation of RL.

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 Artificial Intelligence (AI) training. In this paper, we address this by introducingns-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 at the same time the development of xApps, automated large-scale data collection and testing of Deep Reinforcement Learning (DRL)-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 (REM), a Conservative$Q$-learning (CQL) algorithm, combined with a state-of-the-art Convolutional Neural Network (CNN) 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 the ns-O-RAN base stations. We evaluate the performance on a large-scale deployment with up to 126 users with 8 base stations, showing that the xApp-based handover improves throughput and spectral efficiency by an average of 50% over traditional handover heuristics, with less mobility overhead.

Terahertz (THz) communication is considered as one of the potential candidate technologies in the sixth generation (6G) wireless systems. This paper introduces channel measure-ments in two sub-THz bands, i.e., 140- and 220-GHz bands, in an outdoor street canyon environment for both line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios with a frequency-domain vector network analyzer (VNA)-based sounder. Based on the measurement results, we computed and analyzed the statistical features of wireless propagation channels, including path loss, root mean square (RMS) delay spreads (DSs), azimuth spreads of arrival (ASA), and elevation spreads of arrival (ESA). Moreover, we observe the birth and death of clusters over a straight trajectory under the NLoS condition. A high-resolution param-eter estimation algorithm, i.e., the space-alternating generalized expectation-maximization (SAGE) algorithm, was employed to eliminate the effects of antenna patterns and the density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to clustered the multipath components (MPCs). The obtained statistical properties of measured channels and obser-vations on channel evolution can be employed in THz channel modeling and system design.

The role of fast yet reliable wireless communications in various application domains is getting ever more important. At the same time, as use cases are becoming more and more complex, application requirements are getting ever more stringent. One example is intelligent transportation, where the efficiency and reliability of wireless data delivery is essential for effective service support. As a consequence, in this context the adoption of AI techniques is widely considered crucial for enabling vehicular communications to adapt to dynamic changes of the environment. In this position paper, we discuss some representative applications of advanced AI tools in vehicular communications. In particular, we elaborate on the potential of distributed learning based on federated learning, of proactive service provisioning, and of graph neural network for enabling AI-native vehicular communications.

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, Reinforce-ment 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, integrated at the RAN level that implements PQoS functionalities with the support of an RL framework. 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 better results in terms of QoS and Quality of Experience (QoE) performance of end users in a teleoperated driving scenario.

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 the full-stack evaluation of mmWave cellular networks 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 at the physical layer. Particularly, throughput can be increased in 5G networks by means of Space 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.

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 3gpp has thus introduced 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 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 that our scheme can increase the throughput of cell-edge users up to 3 times, while decreasing the overall network congestion with an end-to-end delay reduction of up to 25 times.

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 raytracing 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.

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 we use MilliCar, the new ns-3 module based on the latest NR V2X specifications, 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.