Low Earth orbit (LEO) mega-constellations enable global, low-latency connectivity but challenge the security function chain (SFC) orchestration due to fast-changing visibility, resource volatility, and heterogeneous quality of service (QoS) requirements. To tackle this issue, we present in this paper SATStab, a stability-aware orchestration framework that co-designs: (i) stability-regularized objectives penalizing configuration churn across time windows; (ii) temporal decoupling with visibility-aware repair and warm starts; and (iii) hierarchical decoupling that separates fast association decisions from slower SFC placement and resource allocation. We formulate SATStab as a mixed-integer non-linear programming (MINLP) problem and evaluate it with realistic LEO dynamics. We solve it through a two-stage approach, where in the first stage, we optimize satellite-user associations while in the second stage, we optimize SFC placements. Through extensive simulations, we show that, relative to a monolithic MINLP, SATStab reduces user-satellite handovers by 54.3%, satellite-ground station (GS) re-associations by 59.3%, and SFC migrations by 32.7%, while cutting solution time by 56.8% without degrading end-to-end (E2E) delay. These results prove the efficiency of SATStab in terms of resource orchestration and resilience.

Large Language Models (LLMs) have showcased remarkable proficiency in various information-processing tasks. They excel at data extraction, literature summarization, content generation, predictive modeling, decision-making, and system control. Moreover, Vision-Language Models (VLMs) and Multimodal LLMs (MLLMs), collectively referred to in this work as Cross-modal Language Models (XLMs), integrate multiple data modalities with language understanding, thereby advancing Autonomous Driving Systems (ADS). On the implemented Artificial Intelligence (AI) side, we analyze core techniques such as prompt engineering, supervised fine-tuning, reinforcement learning from human feedback, knowledge distillation, quantization and pruning, and safety alignment/verification, together with edge-aware deployment strategies. On the application of AI side, we map XLMs capabilities to the driving stack, including perception, prediction, planning, control, and human–machine interaction/vehicle-to-everything, and summarize how XLMs improve scene understanding, intent forecasting, decision-making, and closed-loop control by coupling natural-language reasoning with multimodal sensory inputs, such as panoramic images, Light Detection and Ranging (LiDAR), and radar. In this survey, we synthesize the state of XLMs for ADS: we review the relevant literature on ADS and XLMs, including their architectures, tools, and frameworks. We then compare deployment approaches across the driving stack and summarize datasets, simulators, and benchmarks for both open- and closed-loop evaluation. Finally, we analyze key challenges, such as grounding and hallucination, long-tail robustness, real-time and resource constraints, safety alignment and verification, and data governance and privacy, and outline research directions toward safe, efficient, and trustworthy XLM-enabled ADS.

Power consumption in mobile communication networks constitutes 20-40% of the operating expenditure. The energy footprint is especially high at the radio access network (RAN), where Base stations (BSs) account for 60-80% of network power usage. In this context, we propose in this paper a novel power coordination framework that efficiently utilizes multiple power sources including conventional grid power, renewable energy, and battery storage systems. The proposed model incorporates dynamic pricing schemes and considers environmental impact while maintaining operational cost efficiency. Using Mixed Integer Linear Programming (MILP), we develop a scheduling framework that optimizes when to charge batteries and utilize renewable energy sources for either BS operation or battery charging, aiming to reduce energy consumption costs. Our simulation results demonstrate that the proposed solution achieves significant cost reduction compared to traditional greedy power consumption methods while maintaining service quality. Moreover, it outperforms purely renewable energy-based solutions which, while cost-effective, suffer from service interruptions with a Mean Time Between Failures (MTBF) of 10 minutes. Also, our proposed solution achieves superior performance than heuristic methods, by obtaining optimal results in 2 seconds compared to 5 minutes for heuristic approaches. This work contributes to the development of more sustainable and economically viable mobile network operations without penalizing service quality.

Optimizing wireless communications is crucial to meet the growing connectivity demands. High Altitude Platform Stations (HAPS) and Reconfigurable Intelligent Surfaces (RIS) offer promising solutions to achieve this goal. Indeed, HAPS, with its strong communication links to the ground, and RIS, with its passive control capability of signals, can collaborate to improve communications quality. In this paper, we examine the RIS-assisted non-orthogonal HAPS uplink, aiming to enhance the ground users' sum data rate under power and phase shifting constraints. This problem involves joint power control at the users and phase shift configuration at the RIS, which is inherently non-convex. To solve it, we propose to use several optimization methods, including random optimization, particle swarm optimization, Whale optimization algorithm, and alternating optimization (AO). Numerical results demonstrate the efficiency of the investigated methods, with a preference for AO for its trade-off between performance and complexity.

In the face of increasingly complex cyberattacks, particularly within 5G networks, accurately predicting the duration of an ongoing attack has become essential for effective attack mitigation. In this work, we tackle this issue by exploring the use of deep learning models to forecast cyber attack duration, thus enabling improved resource allocation and mitigation strategies. Using the diverse UNSW-NB15 dataset, our approach proposes data transformation and the creation of new key features, modeling the problem as a time-series forecasting one to predict the remaining attack time of ongoing attacks. Subsequently, several deep-learning models, suited for time-series data, have been designed. Based on automated hyperparameter tuning and feature engineering, we further refine the developed models. Through extensive simulations, we evaluate the performance of developed approaches in terms of mean absolute error (MAE). Obtained results indicate that the Transformer-based model outperforms other methods by achieving the lowest validation MAE with nearly 60 % reduction compared to the well-known Long Short-Term Memory (LSTM) model, thus showcasing its robustness in accurately predicting the duration of any type of attacks.

Non-Terrestrial Networks (NTNs), comprising Unmanned Aerial Vehicles (UAVs) and High Altitude Platform Stations (HAPS) equipped with Mobile Edge Computing (MEC), offer promising solutions for network traffic and tasks offloading from ground users. To enhance the reliability and energy efficiency of such systems, Reconfigurable Intelligent Surfaces (RIS) can be deployed to control wireless signal propagation. In this context, we propose in this paper a novel MEC-enabled framework with HAPS and RIS-equipped UAVs (UAV-RISs) to optimize task offloading from ground users. Our objective is to minimize the tasks' average end-to-end (E2E) delay, under constraints of UAV and HAPS power capacity and E2E service delay threshold, through the optimization of task assignment and UAV-RIS phase-shift configuration. Given the NP-hardness of the problem, we decompose it into two subproblems. The first consists of optimizing the RIS phase shifts to minimize the RIS-assisted communication delay. The second tackles the task assignment problem using a Particle Swarm Optimization (PSO)-based approach, considering the RIS phase shifting solution previously developed. Through simulations, we validate the efficacy of our approach, which outperforms other benchmarks in terms of task average E2E delay and task offloading success rate.

The Open radio access network (ORAN) supports the multiclass wireless services required in beyond 5th-generation (B5G) mobile networks. However, it also increases the threat surface, thus requiring enhanced cyberattack detection mechanisms. To do so, advanced Artificial Intelligence (AI) algorithms combined with RAN intelligent controllers (RICs) can be leveraged to detect cyberattacks, such as distributed denial-of-service (DDoS) attacks. Nevertheless, data privacy becomes a significant concern when using AI-based operations. To bypass this issue, secured Federated Learning (FL) can be leveraged. Specifically, training cyberattack detection models locally and securely communicating the models' data for aggregation would guarantee protection against eavesdropping. In addition, the usage of Peer-to-Peer (P2P) FL would allow to avoid the centralized FL's single point of failure. However, securing P2P FL with encryption/decryption or using the Secure Average Computation (SAC) would incur high communication costs that scale poorly with the number of FL clients. Hence, we propose in this paper a novel P2P FL strategy that guarantees secure FL, while significantly reducing the communication cost. Specifically, we incorporate client selection and transfer learning within the RIC-based P2P FL system to detect cyberattacks. Through experiments, we demonstrate our method's performances across different scenarios with both balanced and unbalanced dataset distributions. Finally, its superiority in terms of accuracy, robustness, and cost, compared to existing benchmarks, is illustrated.

The advent of 5G and beyond networks is envisioned to support lower latency, higher data rates, and wider connectivity than previous cellular network generations. However, given the denser deployment of base stations (BSs) to accommodate such improvements, this results inevitably in a significant and unsustainable increase in the network’s energy consumption. Sleep Control (SC), which allows switching off some BS hardware components during light-traffic time, is considered a viable solution for greener and more energy-efficient Radio Access Networks (RAN). However, the optimization of SC is a highly challenging large-scale network combinatorial problem that depends on dynamic wireless channel conditions and varying traffic demands with stringent Quality-of-Service (QoS) requirements. Driven by the benefits and efficiency of Deep Reinforcement Learning (DRL), which has been successfully applied to multiple wireless network optimization problems, this paper investigates DRL approaches addressing sleep control in 5G and beyond RAN. To this end, we propose a taxonomy to classify the related literature. Then, we provide an overview of the different components of the Markov Decision Process (MDP) modeling the sequential decision-making of sleep control and the applied DRL algorithms. Finally, we highlight the main challenges in existing works and suggest novel strategies to address them.

We propose a federated learning (FL) in stratosphere (FLSTRA) system, where a high altitude platform station (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data. FLSTRA overcomes the challenges faced by FL in terrestrial networks, such as slow convergence and high communication delay due to limited client participation and multi-hop communications. HAPS leverages its altitude and size to allow the participation of more clients with line-of-sight (LOS) links and the placement of a powerful server. However, handling many clients at once introduces computing and transmission delays. Thus, we aim to obtain a delay-accuracy trade-off for FLSTRA. Specifically, we first develop a joint client selection and resource allocation algorithm for uplink and downlink to minimize the FL delay subject to the energy and quality-of-service (QoS) constraints. Second, we propose a communication and computation resource-aware (CCRA-FL) algorithm to achieve the target FL accuracy while deriving an upper bound for its convergence rate. The formulated problem is non-convex; thus, we propose an iterative algorithm to solve it. Simulation results demonstrate the effectiveness of the proposed FLSTRA system, compared to terrestrial benchmarks, in terms of FL delay and accuracy.

Vehicle-to-Everything (V2X) communication standards, which mainly rely on the 5G New Radio (NR) technology, can be subject to attacks such as Distributed Denial of Service (DDoS), which flood the network with non-expected control information. This causes network performance degradation and leads to accidents involving vehicles and/or vulnerable road users. A potential approach to mitigate DDoS attacks is to isolate the hijacked vehicular users in sinkhole-type slices that contain a small amount of network resources. Nevertheless, DDoS attacks may be unpredictable since it can modify its communication protocol for example, which makes it difficult to determine the proper moment to release mitigated users from the sinkhole-type slices once the security breach ceases to exist. In such a context, we propose a Reinforcement Learning-based approach that evaluates multiple types of DDoS attacks on sinkhole-type slices and estimates the optimal time to keep a mitigated user in such a slice before releasing it. The proposed approach is trained and tested with a dataset collected from a SG-V2X testbed. Results show that our approach outperforms a benchmark of random actions, in terms of the mean cumulative reward and error over time.

This paper develops a deep reinforcement learning solution to simultaneously optimize the multi-UAV cell-association decisions and their moving velocity decisions on a given 3D aerial highway. The objective is to improve both the transportation and communication performances, e.g., collisions, connectivity, and HOs. We cast this problem as a Markov decision process (MDP) where the UAVs' states are defined based on their velocities and communication data rates. We have a 2D transportation-communication action space with decisions like UAV acceleration/deceleration, lane-changes, and UAV-base station (BS) assignments for a given UAV's state. To deal with the multi-dimensional action space, we propose a neural architecture having a shared decision module with multiple network branches, one for each action dimension. A linear increase of the number of network outputs with the number of degrees of freedom can be achieved by allowing a level of independence for each individual action dimension. To illustrate the approach, we develop Branching Dueling Q-Network (BDQ) and Branching Dueling Double Deep Q-Network (Dueling DDQN). Simulation results demonstrate the efficacy of the proposed approach, i.e., 18.32% improvement compared to the existing benchmarks.

5G is improving networks via numerous technologies such as network slicing. The latter introduces capabilities to cater to the needs of heterogeneous services. Although 5G ensures resource allocation to satisfy slices with different requirements, it is not straightforward when the cellular coverage is extended or strengthened using unmanned aerial vehicles (UAVs) as NodeB, a.k.a., UxNBs. Indeed, a UxNB can be used to extend the coverage on the edge of a terrestrial BS or to support the temporary dense traffic in a targeted urban area. When deployed, the UxNB must ensure that the 5G services are seamlessly supported without degradation. In this context, we study in this paper how a UxNB should behave in order to support ground users’ services within different 5G slices. Specifically, we formulate the maximization problem of the number of satisfied user requests within different 5G slices by the UxNB, by optimizing the UxNB location and the allocated resources, e.g., subchannels, for communication. Due to the problem’s NP-hardness, we propose a hybrid dueling deep Q-learning (DDQL)-heuristic solution, where a dueling deep Q-learning (DDQL) algorithm for 3D placement is combined with a heuristic resource allocation approach to satisfy the weighted number of service requests. Obtained results demonstrate the efficacy of the proposed method in achieving high satisfaction rates, which are superior to those of other benchmarks. Moreover, without any information about the users’ locations, it performs as well as the offline benchmark.

Unmanned aerial vehicle (UAV) technology has recently attracted interest due to its rapid and flexible deployment. It became a key component of several applications such as aerial delivery and precision agriculture. Moreover, with enhanced payloads, e.g., storage and computing, UAVs can support critical services including road traffic monitoring, accident prediction, and connected-automated vehicles (CAVs). Particularly, computing-enabled UAVs permit CAVs’ task offloading. However, efficient offloading that accounts for the UAVs’ inherent characteristics remains under-investigated. In this context, we propose to study a UAV-assisted vehicular network, where a UAV flies according to a pre-defined come-and-go trajectory and communicates with nearby CAVs to offload their tasks. We target maximizing the ratio of successfully offloaded tasks by jointly optimizing the initial UAV launching point and traveling direction and strategically associating CAVs to the UAV for successful task offloading. Due to the formulated problem’s complexity, we propose two approaches to solve it, namely genetic algorithm (GA) based solution and an iterative exhaustive-linear programming (IE-LP) based one. Through experiments, we demonstrate the proposed algorithms’ superior performance in terms of task offloading success ratio compared to benchmarks, and in different conditions. These results can serve as guidelines for the development of more sophisticated UAV-enabled task offloading approaches in next-generation wireless networks.

Millions of dollars are spent yearly to trim trees along rights-of-way and guarantee reliable distribution line systems. To reduce these costs, power utilities are embracing a new approach based on light detection and ranging (LiDAR) data. They aim to automatically detect the locations of critical branches/trees and assess their risks. In this paper, we propose a novel and robust power lines detection framework with several LiDAR data processing steps, which combines machine learning and geometric approaches. By combining these methods, we efficiently detect distribution lines with an Intersection-over-Union performance superior to those of deep-learning-based benchmarks, and less complex than most of them. The benefit is that by prescribing the use of geometrical/mathematical approaches for the post-processing of deep-learning/machine-learning outputs, we are able to further improve lines detection. Finally, we expect our novel framework to be generalized to detect various LiDAR objects such as poles, cars, buildings and roads.

Unmanned aerial vehicle (UAV)-enabled edge federated learning (FL) has sparked a rise in research interest as a result of the massive and heterogeneous data collected by UAVs, as well as the privacy concerns related to UAV data transmissions to edge servers. However, due to the redundancy of UAV collected data, e.g., imaging data, and non-rigorous FL participant selection, the convergence time of the FL learning process and bias of the FL model may increase. Consequently, we investigate in this paper the problem of selecting UAV participants for edge FL, aiming to improve the FL model’s accuracy, under UAV constraints of energy consumption, communication quality, and local datasets’ heterogeneity. We propose a novel UAV participant selection scheme, called data-efficient energy-aware participant selection strategy (DEEPS), which consists of selecting the best FL participant in each sub-region based on the structural similarity index measure (SSIM) average score of its local dataset and its power consumption profile. Through experiments, we demonstrate that the proposed selection scheme is superior to the benchmark random selection method, in terms of model accuracy, training time, and UAV energy consumption.

In this paper, we investigate the performance of reconfigurable intelligent surface (RIS)-assisted aerial communications, where a ground base station (GBS) communicates with distant terrestrial and/or aerial users through the assistance of a RIS-equipped unmanned aerial vehicle (RIS-UAV). The GBS uses the non-orthogonal multiple access (NOMA) scheme to transmit its signal, which is directed to the users via the RIS-UAV. First, the end-to-end channel is characterized, by considering the shadowed Rician fading, then the outage probability performance metric is derived for the underlying system model. Through numerical results, we demonstrate the impact of several system parameters on the performance of NOMA users. Specifically, we found that RIS elements need to be carefully allocated among different NOMA users, according to their channel conditions, in order to achieve the needed quality of service.

Fog computing (FC) emerged as a new paradigm enabling the deployment of new Internet of Things (IoT) applications. Fog infrastructure is composed of heterogeneous nodes characterized by a complex distribution, mobility, and sporadic resource availability. Hence, resource coordination for continuous Quality-of-Service (QoS) satisfaction becomes challenging, and accurate resource tracking is needed for flawless servicing. In this context, we investigate and propose online resource allocation solutions. The main objective is to maximize the number of satisfied users within a predefined latency requirement. Hence, we model the FC environment as a Markov Decision Process, and then, we formulate the optimization problem. Due to the problem’s NP-hardness, we leverage the reinforcement learning (RL) tool to develop resource allocation schemes. First, a centralized method where a smart fog controller possesses a global awareness of the FC environment is proposed. Next, a more practical and collaborative solution is presented, where each RL-enabled agent manages a group of fog nodes and their resources in order to satisfy computing requests. Based on real-world mobility data sets, simulation results illustrate the high efficiency of the proposed solutions with a preference for the collaborative approach. The superiority of our proposed solutions over state-of-the-art methods is also illustrated.

Reconfigurable Smart Surface (RSS) is assumed to be a key enabler for future wireless communication systems due to its ability to control the wireless propagation environment and, thus, enhance communications quality. Although optimal and continuous phase-shift configuration can be analytically obtained, practical RSS systems are prone to both channel estimation errors, discrete control, and curse of dimensionality. This leads to relaying on a finite number of phase-shift configurations that is expected to degrade the system’s performances. In this paper, we tackle the problem of quantized RSS phase-shift configuration, aiming to maximize the data rate of an orthogonal frequency division multiplexing (OFDM) point-to-point RSS-assisted communication. Due to the complexity of optimally solving the formulated problem, we propose here a sub-optimal greedy algorithm to solve it. Simulation results illustrate the performance superiority of the proposed algorithm compared to baseline approaches. Finally, the impact of several parameters, e.g., quantization resolution and RSS placement, is investigated.

Within the last few years, the multiple-input multiple-output (MIMO) technology has been developing to cope with the stringent 5G and beyond requirements, in terms of high throughput, energy efficiency, and coverage. With the rapid development and miniaturization of Internet-of-Things (IoT) devices, there is an urgent need to design compact-size, low-cost and efficient MIMO antennas for IoT. In this context, we propose here the design of a novel four-element wideband MIMO antenna, supplied using the coplanar waveguide (CPW) technology. The design covers a wide spectrum band between 1.5 and 5.5 GHz, with a total size of 80 × 80 × 1.6 mm3, which is suitable for small IoT devices. The designed MIMO antenna achieves a diversity gain above 9.9 and an envelope correlation coefficient lower than 0.016. Its mutual coupling among any pair of elements is lower than -15 dB, while the group delay demonstrates stability for the targeted band of operation. Hence, the proposed MIMO antenna has a great potential for future wireless communication systems, especially for the sub-6 GHz portable and IoT devices.

With the booming deployment of Internet of Things, health monitoring applications have gradually prospered. Within the recent COVID-19 pandemic situation, interest in permanent remote health monitoring solutions has raised, targeting to reduce contact and preserve the limited medical resources. Among the technological methods to realize efficient remote health monitoring, federated learning (FL) has drawn particular attention due to its robustness in preserving data privacy. However, FL can yield to high communication costs, due to frequent transmissions between the FL server and clients. To tackle this problem, we propose in this paper a communication-efficient federated learning (CEFL) framework that involves clients clustering and transfer learning. First, we propose to group clients through the calculation of similarity factors, based on the neural networks characteristics. Then, a representative client in each cluster is selected to be the leader of the cluster. Differently from the conventional FL, our method performs FL training only among the cluster leaders. Subsequently, transfer learning is adopted by the leader to update its cluster members with the trained FL model. Finally, each member fine-tunes the received model with its own data. To further reduce the communication costs, we opt for a partial-layer FL aggregation approach. This method suggests partially updating the neural network model rather than fully. Through experiments, we show that CEFL can save up to to 98.45% in communication costs while conceding less than 3% in accuracy loss, when compared to the conventional FL. Finally, CEFL demonstrates a high accuracy for clients with small or unbalanced datasets.

As the World is still facing the COVID-19 pandemic, several researchers and industry players have proposed technological solutions to help fight the pandemic and pave the way for post-pandemic era precautions. In this matter, the potential benefits of remote health monitoring have been brought back to the spotlight. Indeed, with current advances in wireless communications, core network virtualization, and computing architectures as enablers, consistently guaranteeing the stringent quality-of-service (QoS) requirements of remote health monitoring, e.g., ultra-low latency, may be achievable. Notably, the fog computing (FC) paradigm has been advocated as a potential solution for remote health monitoring. However, the unreliability of fog nodes in FC networks is a critical aspect often overlooked despite its significant impact on vital latency requirements. This paper proposes a reliable fog-based remote health monitoring framework operating under uncertain fog computing conditions. Specifically, we formulate the problem of assigning tasks of remote sensors attached to patients to their adequate applications deployed in fog nodes aiming to maximize the number of satisfied tasks with respect to the fog nodes’ availability and communication latency constraints. Due to the problem’s NP-hardness, we leverage a differential evolution-based algorithm enhanced by reinforcement learning to deploy applications in fog nodes. Numerical results demonstrate the superior reliability performance of our proposed solution, in terms of the average success ratio of tasks, compared to benchmarks. Specifically, our simulations show up to 60 % performance improvement compared to benchmarks in specific scenarios. Moreover, by investigating the impact of several key parameters, we identify a design trade-off between the number of fog nodes and the latter’s intrinsic failure rates.

In this paper, we propose a collaborative smart solution for online traffic offloading among device-to-device (D2D) users underlying a cellular network. Specifically, we investigate the distributed pairing problem between requesting users and caching devices in their vicinity. Given that this problem is NP-hard, we propose a novel multi-agent reinforcement learning approach based on QMIX algorithm, where each requesting user is an agent capable of deciding to which cache device to pair, while respecting the quality-of-service of cellular users. Through simulations, we show the efficiency of the proposed algorithm in achieving D2D pairing. Finally, the impact of several parameters, such as the size of the network, size of files library, and communication requirements, is investigated.

On-board battery consumption, cellular disconnectivity, and frequent handoff are key challenges for unmanned aerial vehicle (UAV) based delivery missions, a.k.a., cargo-UAV. Indeed, with the introduction of UAV technology into cargo shipping and logistics, designing energy-efficient paths becomes a serious issue for the next retail industry transformation. Typically, the latter has to guarantee uninterrupted or slightly interrupted cellular connectivity for the UAV’s command and control through a small number of handoffs. In this paper, we formulate the trajectory planning as a multi-objective problem aiming to minimize both the UAV’s energy consumption and the handoff rate, constrained by the UAV battery size and disconnectivity rate. Due to the problem’s complexity, we propose a dynamic programming based solution. Through simulations, we demonstrate the efficiency of our approach in providing opti-mized UAV trajectories. Also, the impact of several parameters, such as the cargo-UAV altitude, disconnectivity rate, and type of environment, are investigated. The obtained results allow to draw recommendations and guidelines for cargo-UAV operations.

In this article, we investigate an energy harvesting (EH) wireless sensor network for the Internet of Things (IoT) where monitoring applications require a continuous update of sensing information. The considered system consists of independent EH sensor nodes equipped with capacitors and providing, through unreliable channels, status updates to a non EH sink. The distribution of the interdelivery time, i.e., the time elapsed between two successive and successful status update deliveries, is derived in the closed-form expression considering a random EH arrival process. Moreover, the interdelivery violation probability metric, defined as the probability to exceed a predetermined interdelivery threshold, is analyzed. Our analysis reveals that the violation probability is highly dependent on the size of the capacitor. Both analytical and simulation results demonstrate the existence of an optimal capacitor size that achieves the minimum violation probability. Moreover, our findings reveal an interesting tradeoff in the system design. On one hand, a small capacitor charges quickly and thus status updates are sent more frequently but with lower transmit power and thus a high error rate. On the other hand, a large capacitor increases the transmit power and boosts the successful data transmission probability, at the expense of a higher waiting time before filling the capacitor and transmitting sensed data.

In this article, we aim to sustain unmanned aerial vehicle (UAV)-based missions for longer periods of times through different techniques. First, we consider on-the-mission UAV recharging by a low-power laser source (below 1 kW). In order to achieve the maximal energy gain from the low-power laser source, we propose an operational compromise, which consists of the UAV resting over buildings with cleared line of sight to the laser source. Second, to provide a precise energy consumption/harvesting estimation at the UAV, we investigate the latter's dynamics in a mission environment. Indeed, we study the UAV's battery dynamics by leveraging the electrical models for motors and battery. Subsequently, using these models, the path planning problem in a particular Internet-of-Things-based usecase is revisited from the battery perspective. The objective is to extend the UAV's operation time using both laser-charging and accurate battery level estimation. Through a graph theory approach, the problem is solved optimally, and compared to benchmark trajectory approaches. Numerical results demonstrate the efficiency of this novel battery perspective for all path planning approaches. In contrast, we found that the energy perspective is very conservative and does not exploit optimally the available energy resources. Nevertheless, we propose a simple adjustment method to correct the energy perspective, by carefully evaluating the energy as a function of the UAV motion regimes. Finally, the impact of several parameters, such as turbulence and distance to charging source, is studied.

In this work, we deal with the violation probability of data update interval for a wireless sensor network with energy harvesting capabilities, in the context of monitoring applications requiring a continuous update of sensing information. Specifically, we consider a wireless sensor network consisting of independent energy harvesting sensor nodes providing status updates to a non energy harvesting sink. A sensor node generates a status update message when its battery becomes fully charged. The generated message is then transmitted without further energy management, i.e., using all the available harvested energy and according to a first-come-first-served access method. In this paper, we first derive the update interval distribution for a random energy arrival process. Then, the violation probability of the update interval is derived in closed-form for the one-node wireless sensor network. It is shown that it highly depends on the size of the battery. Furthermore, the update interval of a multi-node system is characterized. Obtained analytical and numerical results show that there exists an optimal battery size that minimizes the violation probability. Moreover, the design of the system introduces an interesting trade-off. On one hand, a small battery is charged quickly and thus updates are sent more frequently but with a high error rate. On the other hand, a larger battery increases the transmit power and boosts the successful data transmission probability but increases the time required before transmitting.

Unmanned aerial vehicles (UAVs) can be users that support new applications, or be communication access points that serve terrestrial and/or aerial users. In this paper, we focus on the connectivity problem of aerial users when they are exclusively served by aerial base stations (BS), i.e., UAV-BSs. Specifically, the 3D placement problem of a directional-antenna equipped UAV-BS, aiming to maximize the number of covered aerial users under a spectrum sharing policy with terrestrial networks, is investigated. Given a known spectrum sharing policy between the aerial and terrestrial networks, we propose a 3D placement algorithm that achieves optimality. Simulation results show the performance of our approach, in terms of number of covered aerial users for different configurations and parameters, such as the spectrum sharing policy, antenna beamwidth, transmit power, and aerial users density. These results represent novel guidelines for exclusive aerial networks deployment and applications, distinctively for orthogonal and non-orthogonal spectrum sharing policies with terrestrial networks.

In this paper, we focus on energy-efficient UAV-based IoT data collection in sensor networks in which the sensed data have different time deadlines. In the investigated setting, the sensors are clustered and managed by cluster heads (CHs), and multiple UAVs are used to collect data from the CHs. The formulated problem is solved through a two-step approach. In the first step, an efficient method is proposed to determine the minimal number of CHs and their best locations. Subsequently, the minimal number of UAVs and their trajectories are obtained by solving the associated capacitated vehicle routing problem. Results show the efficiency of our proposed CHs placement method compared to baseline approaches, where bringing the CHs closer to the dockstation allows significant energy savings. Moreover, among different UAV trajectory planning algorithms, Tabu search achieves the best energy consumption. Finally, the impact of the battery capacity and time deadline are investigated in terms of consumed energy, number of visited CHs, and number of deployed UAVs.

The advent of a new breed of enhanced multimedia services has put network operators into a position where they must support innovative services while ensuring both end-to-end Quality of Service requirements and profitability. Recently, Network Function Virtualization (NFV) has been touted as a cost-effective underlying technology in 5G networks to efficiently provision novel services. These NFV-based services have been increasingly associated with multi-domain networks. However, several orchestration issues, linked to cross-domain interactions and emphasized by the heterogeneity of underlying technologies and administrative authorities, present an important challenge. In this paper, we tackle the cross-domain interaction issue by proposing an intelligent and profitable auction-based approach to allow inter-domains resource allocation.

In this paper, we propose a framework design for wireless sensor networks based on multiple unmanned aerial vehicles (UAVs). Specifically, we aim to minimize deployment and operational costs, with respect to budget and power constraints. To this end, we first optimize the number and locations of cluster heads (CHs) guaranteeing data collection from all sensors. Then, to minimize the data collection flight time, we optimize the number and trajectories of UAVs. Accordingly, we distinguish two trajectory approaches: 1) where a UAV hovers exactly above the visited CH; and 2) where a UAV hovers within a range of the CH. The results of this include guidelines for data collection design. The characteristics of sensor nodes' K-means clustering are then discussed. Next, we illustrate the performance of optimal and heuristic solutions for trajectory planning. The genetic algorithm is shown to be near- optimal with only 3:5% degradation. The impacts of the trajectory approach, environment, and UAVs' altitude are investigated. Finally, fairness of UAVs trajectories is discussed.

5G networks are required to provide ultra reliable low latency communications while dealing with the permanent growth of data traffic. In Heterogeneous Networks (Hetnets) assisted with Device-to-Device (D2D) communications, traffic can be offloaded to small base stations or to devices in order to improve the transmission delays even with small caching. In this paper, we aim at reducing the average content delivery delay by optimizing the caching placement strategy in the context of D2D-assisted Hetnets. First, we analytically derive an upper bound on the average content delivery delay. Then, we formulate the problem of minimizing this upper bound through caching placement. The optimal solution is obtained for a single file, then used to propose a low-complex heuristic solution for multiple files. Numerical results illustrate the efficiency of our solution compared to other strategies.

Fog computing has emerged as a promising technology that can bring cloud applications closer to the devices at the network edge. The fog infrastructure contains mainly distributed and heterogeneous fog devices such as in the context of the Internet of Things. Unlike traditional data centers, those devices are characterized by sporadic resources availability, mobility, and increased flexibility. However, resource allocation mechanisms proposed currently for fog computing still lack the support of dynamic behavior. In this article, we propose novel resource management algorithms capable of flexible service provisioning in a dynamic fog computing environment. Specifically, the joint problem of container placement and task provisioning is formulated with integer linear programming. Due to its NP-hardness, we propose a low-complex particle-swarm-optimization-based metaheuristic and a greedy heuristic. Our solutions aim to optimize the number of served end-users with a predefined delay-threshold while considering dynamic fog nodes behavior/mobility and resources availability of fog nodes. Using real-world mobility data sets and different resources' availability models, conducted simulations demonstrate that the PSO-based algorithm achieves near-optimal results. Whereas, the greedy algorithm realizes only 10%-30% less success ratio than the optimal solution with negligible execution time.

Device-to-device (D2D) communications combined with Heterogeneous networks (Hetnets) has attracted growing interest. Indeed, Hetnets deploy small-cells within macro-cells in order to offload traffic and improve the overall network coverage and capacity. Whereas, D2D promotes the use of communications between users for content delivery without going through the small or macro bases stations. Hence, it reduces communication delays and improves the spectral efficiency. In this context, we aim in this paper at reducing the average transmission delay, defined as the average sum delays of contents transmission to satisfy users' requests in a macro-cell, by jointly optimizing caching placement and channel resource allocation, in cache-enabled Hetnet with D2D assistance. At first, a lower-bound expression of the average transmission delay is derived. Then, the optimization problem is formulated. Afterwards, we propose a sub-optimal random search algorithm and a low-complexity greedy algorithm that solve the problem. Finally, numerical results illustrate the performances of the proposed algorithms.

Device-to-Device (D2D) communication has gained interest as a promising technology for next generation wireless networks. D2D communication promotes the use of point-to-point communications between users without going through the base stations. In this paper, we aim at maximizing the sum rate of a D2D network, under the assumption of realistic time-varying channels and D2D interference. Specifically, we formulate channels as Finite-State Markov Channels (FSMC). With realistic FSMC, the complexity of the problem is high. Consequently, we propose the use of a centralized Deep Reinforcement Learning (DRL) transmission scheme for D2D communications, where transmission decisions are taken by one agent that has a global knowledge of the D2D network. We compare the DRL-based scheme with other transmission schemes. The results show that it outperforms other approaches in terms of achieved sum rate.

Cognitive radio (CR) systems allow unlicensed secondary users to transmit on the licensed frequency bands without degrading the transmissions of licensed primary users. Combining CR with other emerging techniques such as multi‐antenna relaying may bring many benefits for the secondary transmissions. In this study, the authors propose and investigate a new relay‐based cooperation scheme for a CR network to improve the secondary access to the licensed spectrum band without causing additional interference to the simultaneous primary transmission. The proposed scheme considers one multi‐antenna relay node that can assist either the primary or the secondary transmission using beam‐forming (BF). In the proposed new scheme, the BF weights are designed in the presence of imperfect channel state information (CSI). Simulation results show that the secondary's channel capacity is significantly improved and outperforms conventional transmission schemes. The results also reveal the impact of imperfect CSI on the primary outage performance and the efficiency of the proposed solution for minimising the interference due to imperfect CSI.

In this paper, we examine the problem of secondary access blocking in cognitive radio networks when secondary transmissions cause unacceptably high interference to primary transmissions. In general, the access of secondary users (SUs) to a licensed spectrum band is only allowed when this access does not alter the performance of primary users that can be defined by the primary QoS requirement. In this paper, we propose a cooperative scheme that allows SUs to increase their access to the spectrum band and access the spectrum even when the primary QoS is not satisfied. Using relay selection and a proper power allocation method, we show that the secondary outage performance can be significantly improved, whereas the primary outage performance is either not altered or slightly improved. Moreover, closed-form expressions of the primary and secondary outage probabilities are derived, and the achieved diversity order is calculated. Finally, analytical and simulation results illustrate the primary outage performance and secondary outage performance of the proposed scheme and show its advantages compared with conventional schemes.

ABSTRACT User cooperation has evolved as a popular coding technique in wireless relay networks (WRNs). Using the neighboring nodes as relays to establish a communication between a source and a destination achieves an increase of the diversity order. The relay nodes can be seen as a distributed multi‐antenna system, which can be exploited for transmit diversity by using distributed space–time block coding (STBC). In this paper, we investigate the bit error rate (BER) of multi‐hop WRNs employing distributed STBC at the relay nodes. We develop the general model of WRNs using distributed STBC, and we derive the pairwise error probability and an approximation of the BER. We examine the impact of several parameters, such as distributed STBC at the relays, the number of relays, the distances between the nodes, and the channel state information available at the receivers, on the BER performance of the multi‐hop WRN. The obtained results provide guidelines about the expected error performance and the design of channel estimation for these networks. Copyright © 2011 John Wiley & Sons, Ltd.

Secondary access to the licensed primary spectrum band at the same time as the primary nodes is generally conditioned on the satisfaction of a Quality-of-Service (QoS) requirement at the primary transmission (such as a Signal-to-Noise-Ratio -SNR- threshold or a primary outage probability threshold). Consequently, at low primary SNR that is below a cut-off value, secondary transmissions are totally blocked. In this paper, we propose a new cooperative scheme for cognitive radio networks, where secondary access to the primary spectrum band is granted whether or not the primary transmission satisfies its QoS requirement thanks to the utilization of secondary relay nodes. Using relay selection and proper power allocation at the secondary nodes, we show that the proposed scheme allows secondary access with low secondary outage performance without degrading the primary outage performance. We also compare the proposed scheme to other ones presented in the literature and we study the impact of the number of available relay nodes and the primary outage threshold value on the primary and secondary outage probabilities.

Cognitive radio (CR) systems allow unlicensed secondary users to transmit on the licensed frequency bands without degrading the licensed primary transmissions. Combining CR with other emerging transmission techniques, such as user cooperation may have many benefits on both the primary and secondary transmissions. In this study, the authors propose and investigate an adaptive relay‐based cooperation scheme for CR networks that improves the secondary outage performance, while respecting a primary outage probability threshold. The proposed adaptive scheme considers one multi‐antenna relay node that, by selecting the antenna(s) to use, can assist either the primary, the secondary or both transmissions simultaneously. Expressions of the conditional primary outage probability for Rayleigh fading channels are derived and used to investigate the associated power allocation problem. Simulation results show that both primary and secondary outage probabilities of the proposed scheme are significantly improved and outperform non‐cooperative and cooperative schemes given in the literature.

In this paper, we investigate and evaluate the performance of incremental relaying and relay selection, when used in the context of cognitive radio networks. Assuming that a number of cognitive radio relay nodes N (N ≥ 2) are co-located with simultaneous primary and secondary transmissions, the “best” relays are chosen to assist the primary and/or the secondary transmission(s) (in case of decoding failure at the destination(s) using the direct link source-destination). The outage probability of both primary and secondary systems is investigated and the associated power allocation problem analyzed. Results show that incremental relaying allows to improve greatly the secondary outage probability with respect to a primary outage probability threshold, compared to the non-cooperative case. Moreover, they suggest that selecting at first the “best” relay to assist the primary transmission before the one that would assist the secondary transmission is more beneficial than choosing at first the “best” relay that would help the secondary transmission. Finally, by proposing an adequate transmit power allocation scheme, we bypass the secondary transmissions' blocking at low primary Signal-to-Noise-Ratio.

Combining cognitive radio technology with user cooperation could be advantageous to both primary and secondary transmissions. In this paper, we propose a first relaying scheme for cognitive radio networks (called “Adaptive relaying scheme 1”), where one relay node can assist the primary or the secondary transmission with the objective of improving the outage probability of the secondary transmission with respect to a primary outage probability threshold. Upper bound expressions of the secondary outage probability using the proposed scheme are derived over Rayleigh fading channels. Numerical and simulation results show that the secondary outage probability using the proposed scheme is lower than that of other relaying schemes. Then, we extend the proposed scheme to the case where the relay node has the ability to decode both the primary and secondary signals and also can assist simultaneously both transmissions. Simulations show the performance improvement that can be obtained due to this extension in terms of secondary outage probability.

In this paper, we investigate several relaying schemes for cooperative communications in Cognitive Radio Networks (CRNs) in order to improve the performances of secondary transmissions while respecting a certain Quality of Service (QoS) requirement at the primary transmissions. We propose relaying schemes where a number of relay nodes, randomly located, may help either the primary or the secondary transmission. By defining proper relay selection criteria and power allocation schemes, we illustrate the secondary outage probability performance while guaranteeing the primary QoS. Using simulations, we present the impact of different parameters, such as the QoS requirement, the chosen relay selection criteria, the number of available relays, the positions of the relays, etc., on the secondary transmission performance. The obtained results show the potential of the proposed relaying schemes, and provide guidelines about the expected secondary performance under the impact of several parameters.

In underlay cognitive radio networks, unlicensed secondary users are allowed to share the spectrum with licensed primary users when the interference induced on the primary transmission is limited. In this paper, we propose a new cooperative transmission scheme for cognitive radio networks where a relay node is able to help both the primary and secondary transmissions. We derive exact closed-form and upper bound expressions of the conditional primary and secondary outage probabilities over Rayleigh fading channels. Furthermore, we proposed a simple power allocation algorithm. Finally, using numerical evaluation and simulation results we show the potential of our cooperative transmission scheme in improving the secondary outage probability without harming the primary one.

The error performance, in terms of Bit Error Rate (BER), of multi-stage (multi-hop) Wireless Relay Networks (WRNs) with distributed STBC at the relay stages is presented. One relay stage is defined by a set of relays located at the same distance from the source node where the distance is measured by the number of hops. We develop the multi-stage WRN model for Amplify-and-Forward (AF) and Decode-and-Forward (DF) procedures at the relaying nodes. The system's performance with imperfect Channel State Information (iCSI) at the receivers is also examined. Simulation results show that the tolerated error on the channel estimation increases when iCSI occurs at the channels between nodes that are far from the source node rather than close to it. This result gives good guidelines about the design of CSI knowledge at the receivers in such a way to reduce delay time and increase data rate.

It has been shown that cooperative communication techniques have a great potential to increase the diversity in wireless relay networks and hence improve the Bit Error Rate (BER). When exploiting many users as relay nodes, a multi-antenna network called virtual-MIMO (Multiple Input Multiple Output) is set up. This special technique helps to solve the problem of transmission error occurrences when sending information through a low quality radio channel. Consequently, the transmission gets a better reliability and higher transmission rate. In this work, we focus on the distributed Space-Time-Block- Coding (STBC) with Amplify-and-Forward (AF) and Decode-and-Forward (DF) relays, for various network configurations and channel knowledge conditions. We investigate and evaluate the performance - in term of BER - of a cooperative communication system using multiple relays equipped with multiple antennas when DSTBC coding is employed at the relays with AF (or DF) relaying. Also, we examine the behavior of these cooperative communication techniques when the Channel State Information (CSI) available at the receivers is imperfect.