Path planning and optimization for unmanned aerial vehicles (UAVs)-assisted next-generation wireless networks is critical for mobility management and ensuring UAV safety and ubiquitous connectivity, especially in dense urban environments with street canyons and tall buildings. Traditional statistical and model-based techniques have been successfully used for path optimization in communication networks. However, when dynamic channel propagation characteristics such as line-of-sight (LOS), interference, handover, and signal-to-interference and noise ratio (SINR) are included in path optimization, statistical and model-based path planning solutions become obsolete since they cannot adapt to the dynamic and time-varying wireless channels, especially in the mmWave bands. In this paper, we propose a novel model-free actor-critic deep reinforcement learning (AC-DRL) framework for path optimization in UAV-assisted 5G mmWave wireless networks, which combines four important aspects of UAV communication: flight time, handover, connectivity and SINR. We train an AC-RL agent that enables a UAV connected to a gNB to determine the optimal path to a desired destination in the shortest possible time with minimal gNB handover, while maintaining connectivity and the highest possible SINR. We train our model with data from a powerful ray tracing tool called Wireless InSite, which uses 3D images of the propagation environment and provides data that closely resembles the real propagation environment. The simulation results show that our system has superior performance in tracking high SINR compared to other selected RL algorithms.

With the advent of Advanced Driver Assistance Systems (ADAS) and intelligent transport system applications, recognizing driver emotions has become essential for a decision support system (DSS) with humans in the loop (HITL). Multimodal approaches using visual cues, speech, physiological signals, and driving patterns improve emotion recognition but are challenging in resource-constrained environments where only a subset of modalities is available. This work addresses these challenges by combining multi-modal benefits with single-modality inference for emotion recognition using unlabeled external road condition data. Unlike traditional methods that average teachers' contribution, the proposed cross-modal distillation (CMD) weights teachers thanks to the Shapley additive global explanation (SAGE) aid, which improves the student model's accuracy and provides an interpretation of it. Experimental evaluations of the PPBEmo dataset show that XA-CMD improves emotion recognition accuracy with other baselines and provides deeper insights into decision-making.

Modern vehicles are equipped with sophisticated systems that continuously monitor both their mechanical condition and the well-being of passengers. In the vehicular network scenario, this availability of a vast amount of data has encouraged ever more the development of ML-based systems to enable highly reliable functionalities for supporting autonomous driving. However, a major challenge is to enable vehicles to process ML-based complex models quickly and efficiently. This problem can be solved by utilizing edge-based computing solutions where computing and storage resources available in network infrastructures are located near the vehicles. By offloading some of the processing tasks to these local resources, vehicles can achieve faster response times and enhanced efficiency. In this paper, we analyze the computing and communication performance of a federated multimodal distillation approach for driver emotion detection for a vehicular communication semi-urban scenario, which will be modeled by implementing stochastic geometry models. The aim of our analysis is to evaluate the impact of a complex ML-based approach on the communication infrastructure, where locally distilled models suitable for constrained devices are federated into a global model. We also investigate the impact on the network of the load due to the learning procedure when IID data and non-IID are considered. The numerical results highlight the strengths and weaknesses of the communication infrastructure when heterogeneous wireless technologies such as 5G and WiFi are involved in handling this type of approach for vehicles with limited computational resources.

The emergence of 6G networks demands environment-aware communication paradigms to ensure reliable and efficient connectivity, and Channel Knowledge Maps (CKMs) offer a promising solution by mapping spatial locations to detailed channel characteristics for proactive network optimization. In this context, this paper proposes an explainable Machine Learning (ML)-based framework that uses geometrical features to predict receiver state probabilities in UAV-based mmWave communication networks. Geometrical characteristics extracted from the environment surrounding each receiver are used to train ML models, namely Decision Tree (DT), K-Nearest Neighbors (KNN), and Deep Neural Network (DNN) models, to predict three receiver states probabilities: Line-of-Sight (LOS), No-Line-of-Sight (NLOS), and Blocked. Experimental results show that the DNN model outperforms DT and KNN, achieving higher accuracy across all states, albeit with no inherent explainability. To address this, the SHapley Additive exPlanations (SHAP) method is applied to indicate feature contributions to each state prediction of the black-box DNN model. This improves the interpretability and reliability of the proposed environment-aware framework for$\mathbf{6 G}$UAV-based networks.

Emotion recognition has attracted a lot of interest in recent years in various application areas such as healthcare and autonomous driving. Existing approaches to emotion recognition are based on visual, speech, or psychophysiological signals. However, recent studies are looking at multimodal techniques that combine different modalities for emotion recognition. In this work, we address the problem of recognizing the user’s emotion as a driver from unlabeled videos using multimodal techniques. We propose a collaborative training method based on cross-modal distillation, i.e., “FedCMD” (Federated Cross-Modal Distillation). Federated Learning (FL) is an emerging collaborative decentralized learning technique that allows each participant to train their model locally to build a better generalized global model without sharing their data. The main advantage of FL is that only local data is used for training, thus maintaining privacy and providing a secure and efficient emotion recognition system. The local model in FL is trained for each vehicle device with unlabeled video data by using sensor data as a proxy. Specifically, for each local model, we show how driver emotional annotations can be transferred from the sensor domain to the visual domain by using cross-modal distillation. The key idea is based on the observation that a driver’s emotional state indicated by a sensor correlates with facial expressions shown in videos. The proposed “FedCMD” approach is tested on the multimodal dataset “BioVid Emo DB” and achieves state-of-the-art performance. Experimental results show that our approach is robust to non-identically distributed data, achieving 96.67% and 90.83% accuracy in classifying five different emotions with IID (independently and identically distributed) and non-IID data, respectively. Moreover, our model is much more robust to overfitting, resulting in better generalization than the other existing methods.

In Non-Terrestrial Networks (NTNs), where LEO satellites and User Equipment (UE) move relative to each other, Line-of-Sight (LOS) tracking, and adapting to channel state variations due to endpoint movements are a major challenge. Therefore, continuous LOS estimation and channel impairment compensation are crucial for a UE to access a satellite and maintain connectivity. In this paper, we propose a Actor-Critic (AC)-Reinforcement Learning (RL) framework for traffic scheduling in NTN scenarios where the channel state is non-stationary due to the variability of LOS, which depends on the current satellite elevation. We deploy the framework as an agent in a Multi-Path Routing (MPR) scheme where the UE can access more than one satellite simultaneously to improve link reliability and throughput. We study how the agent schedules traffic on multiple satellite links by adopting the AC version of RL. The agent continuously trains based on variations in satellite elevation angles, handoffs, and relative LOS probabilities. We compare the agent retraining time with the satellite visibility intervals to investigate the effectiveness of the agent's learning rate. We carry out performance analysis considering the dense urban area of Chicago, where high-rise buildings significantly affect the LOS. The simulation results show how the learning agent selects the scheduling policy when it is connected to a pair of satellites. The results also show that the retraining time of the learning agent is up to 0.1 times the satellite visibility time at certain elevations, which guarantees efficient use of satellite visibility.

The paper proposes a data-driven approach to air-to-ground channel estimation in a millimeter-wave wireless network on an unmanned aerial vehicle. Unlike traditional centralized learning methods that are specific to certain geographical areas and inappropriate for others, we propose a generalized model that uses Federated Learning (FL) for channel estimation and can predict the air-to-ground path loss between a low-altitude platform and a terrestrial terminal. To this end, our proposed FL-based Generative Adversarial Network (FL-GAN) is designed to function as a generative data model that can learn different types of data distributions and generate realistic patterns from the same distributions without requiring prior data analysis before the training phase. To evaluate the effectiveness of the proposed model, we evaluate its performance using Kullback-Leibler divergence (KL), and Wasserstein distance between the synthetic data distribution generated by the model and the actual data distribution. We also compare the proposed technique with other generative models, such as FL-Variational Autoencoder (FL-VAE) and stand-alone VAE and GAN models. The results of the study show that the synthetic data generated by FL-GAN has the highest similarity in distribution with the real data. This shows the effectiveness of the proposed approach in generating data-driven channel models that can be used in different regions.

A distributed decision support system has been developed to assist seafarers during several navigation tasks, for instance, in avoiding a collision with a detected obstacle in the sea and envisioning a future autonomous navigation system. In this paper, the decision support system is based on the results of a customized simulation model representing the ship’s behavior, including hydrodynamics, propulsion, and control effects. Sensors monitor and collect the parameters of the environment and the ship onboard. The telemetry and the calculated route are visualized on a wearable visor exploiting augmented reality. Such context information is also replicated ashore through a narrow-band satellite link using an IoT publish-subscribe communication paradigm to allow one or more remote seafarers to supervise the situation in a virtual reality environment. Overall, the potential of the proposed system is presented and discussed for application in the context of autonomous navigation.

Automatic traffic classification is increasingly important in networking due to the current trend of encrypting transport information (e.g., behind HTTP encrypted tunnels) which prevent intermediate nodes to access end-to-end transport headers. This paper proposes an architecture for supporting Quality of Service (QoS) in hybrid terrestrial and SATCOM networks based on automated traffic classification. Traffic profiles are constructed by machine-learning (ML) algorithms using the series of packet sizes and arrival times of QUIC connections. Thus, the proposed QoS method does not require explicit setup of a path (i.e. it provides soft QoS), but employs agents within the network to verify that flows conform to a given traffic profile. Results over a range of ML models encourage integrating ML technology in SATCOM equipment. The availability of higher computation power at low-cost creates the fertile ground for implementation of these techniques.

Reinforcement Learning (RL) has recently found wide applications in network traffic management and control because some of its variants do not require prior knowledge of network models. In this paper, we present a novel scheduler for real-time multimedia delivery in multipath systems based on an Actor-Critic (AC) RL algorithm. We focus on a challenging scenario of real-time video streaming from an Unmanned Aerial Vehicle (UAV) using multiple wireless paths. The scheduler acting as an RL agent learns in real-time the optimal policy for path selection, path rate allocation and redundancy estimation for flow protection. The scheduler, implemented as a module of the GStreamer framework, can be used in real or simulated settings. The simulation results show that our scheduler can target a very low loss rate at the receiver by dynamically adapting in real-time the scheduling policy to the path conditions without performing training or relying on prior knowledge of network channel models.

Providing reliable and efficient connectivity for maritime systems is a key objective to enable new services and to offer anytime-anywhere communication solutions to vessels operating in remote areas (e.g. oceans). Such a task has recently become quite compelling because of the increasing maritime traffic, which should be supported by an information distribution infrastructure in order to guarantee efficient data communication among vessels and control centres on land. To this end, satellites are the perfect candidates for achieving such an objective. This paper focuses on the case of messaging from moving vessels to fixed control centres on land by means of advanced channel random access schemes exploiting time diversity. In more detail, the paper extends an existing theoretical framework to evaluate the packet loss probability in Framed Slotted ALOHA systems by delving into the probability distribution of both colliding users within a frame and their replicas in time slots. The theoretical framework is validated through simulations campaigns, showing a good match between analytical and simulated results.

This work describes a real testbed for enabling Unmanned Aerial Vehicles (UAVs)-to-ground real-time video streaming. The aim is in providing a video feed to the pilot on the ground for Beyond Visual Line of Sight (BVLoS) operations exploiting cellular networks in urban/suburban areas. Towards this aim, multipath communications are used in a multi-operator setup to counteract the intermittent network coverage in urban and suburban areas. The main requirements are low latency and a continuous video stream of reasonable quality. We rely on both upper-layer Forward Error Correction (FEC) techniques and link diversity, so to increase the probability of fluid video playback with acceptable quality. We report per-link statistics, collected during field trials, of three different cellular operators, to analyse the impact of using a set of physical links as a single logical one on an RTP-based video streaming. In our tests, such a setup has provided a good level of performance.

This work analyses the use of the WebRTC framework on resource-constrained platforms. WebRTC is a consolidated solution for real-time video streaming, and it is an appealing solution in a wide range of application scenarios. We focus our attention on those in which power consumption, size and weight are of paramount importance because of size, weight and power requirements, such as the use case of unmanned aerial vehicles delivering real-time video streams over WebRTC to peers on the ground. The testbed described in this work shows that the power consumption can be reduced by changing WebRTC default settings while maintaining comparable video quality.

Nowadays, Machine-to-Machine and Internet of Things traffic sources puts the terrestrial networks under great pressure. While 5G is still on its way, satellites are used to deliver a fraction of such an enormous traffic rate. In this work, we investigate the use of the Constrained Application Protocol (CoAP) to reliably deliver Machine-to-Machine and Internet of Things traffic in a push fashion, which also implements a Selective Repeat Automatic Repeat reQuest and a sender-based variant of the TCP Friendly Rate Control protocol. We aim at providing an analytical model to evaluate the working point of the system in non-saturated conditions as a function of the MAC parameters in use, when such a closed-loop congestion control mechanism is in use over a random access satellite channel. The proposed analytical model is then validated against simulation results, showing a good precision.

Nowadays, Machine-to-Machine (M2M) and Internet of Things (IoT) traffic rate is increasing at a fast pace. The use of satellites is expected to play a large role in delivering such a traffic. In this work, we investigate the use of two of the most common M2M/IoT protocols stacks on a satellite Random Access (RA) channel, based on DVB-RCS2 standard. The metric under consideration is the completion time, in order to identify the protocol stack that can provide the best performance level.

Application of random access schemes in the return link of satellite systems shows appealing advantages over demand assignment multiple access (DAMA) especially in case of bursty traffic (e.g., M2M). The interactions of TCP with random access has been however only marginally explored by the scientific community, thus limiting the appeal of random access. This paper attempts to bridge this gap, by providing insights into the performance of TCP-based services running on top of DVB-S2/RCS2 satellite networks implementing Contention Resolution Diversity Slotted Aloha (CRDSA) as random access scheme. A special attention has been also dedicated to TCP modeling, since classical TCP throughput formulations fail in the proposed scenario, whereby a simple but effective enhancement is proposed in this paper.

In this work, we discuss the two-ray radio propagation model when an UAV acts as a mobile node belonging to a ground wireless sensor network. Currently, UAVs are attracting attention in the market and in the research field, thus more accurate radio propagation models are needed to properly estimate

This study aims at summarising the work done in the SatNEx project for the development and the performance evaluation of a new access scheme for multimedia data transmissions in digital video broadcasting-return channel via satellite (DVB-RCS)/satellite second generation (S2), applied to a mobile environment. The work has been stimulating because, as far as the authors know, this research topic has not yet been fully addressed in the literature. The authors started thinking about a realistic scenario of mobility, where novel services could be offered; having defined the scenario, the authors developed a satellite access scheme suitable for transmission control protocol (TCP) sessions and video streams in mobility. In this study, the authors present the reference architecture, the access scheme, the choice of the most suitable transmission parameters and the simulation results that are obtained when transmitting the two different types of data.

The goodput of long-lived TCP connections can be maximized in error-prone channels by trading bandwidth for Bit Error Rate (BER). This fact can be exploited in the bandwidth and transmission parameters' assignment, by means of Adaptive Coding and Modulation (ACM), in satellite systems; in such a case, multiple source- destination (SD) pairs actually "see" channels with diverse characteristics, owing to unequal weather conditions. However, when dealing with multiple SD assignments, both aspects of goodput-maximization and fairness must be dealt with. Improving the effectiveness of TCP may be relatively easy, but maintaining the effectiveness while maintaining the fairness is a challenge. The paper numerically analyzes the fairness behavior of two bandwidth allocation methods to TCP connections, experiencing diverse fading conditions.