This paper explores the utilization of Reconfigurable Intelligent Surfaces (RISs) within multi-cell vehicular open radio access networks to redirect signals toward users with wireless link blockages. To address this, a stochastic optimization problem is formulated to determine the optimal transmit precoding for Road Side Units (RSUs) and adjust the phase shifts of corresponding RISs, considering random obstacles in wireless links. The objective is to enhance long-term data throughput while guaranteeing the Quality of Service (QoS) for each user. To solve the problem, a federated learning framework is introduced that employs multi-agent Deep Reinforcement Learning (DRL) and aligns with the O-RAN architecture. Specifically, deep learning agents are deployed at network edge servers, integrated within the Near-Real-Time Radio Access Network Intelligent Controller (Near-RT RIC), where they gather network information, train local models, and perform online executions. A global model is constructed in the Non-Real-Time RIC, which resides on a central server, by aggregating the local models received from edge servers. Simulation results confirm that the proposed method significantly improves average network data rates while ensuring users receive adequate link quality.

Extending wireless connectivity to remote areas is essential for delivering intelligent services in critical sectors, i.e., healthcare, agriculture, and disaster management. Unmanned aerial vehicles (UAVs) have emerged as a promising solution due to their agile mobility, low deployment cost, and line-of-sight (LoS) communication capabilities. However, efficiently managing UAV resources while integrating sensing, communication, and computing (ISCC) functionalities presents significant challenges. In this paper, we propose a multi-UAV-assisted ISCC framework that simultaneously supports wireless communication links for computational task offloading, remote computing, and active target sensing. A comprehensive system model is developed, and a joint optimization problem is formulated to minimize the weighted sum energy consumption of UAVs and remote users, subject to constraints on latency, power budget, and UAV mobility. To solve the resulting non-convex problem, we design a decomposition-based solution that integrates a convex optimization technique with the deep deterministic policy gradient (DDPG) algorithm. Simulation results demonstrate the effectiveness of the proposed framework in achieving energy-efficient operation under practical system constraints.

This paper presents an intelligent device selection framework for Federated Learning (FL) in Low Earth Orbit (LEO) satellite constellation networks. In particular, the high mobility, intermittent connectivity and long communication delays in LEO satellite networks significantly impact FL convergence and model performance. To address these challenges, we formulate a device selection optimization problem that determines the optimal subset of LEO satellites to participate in global model aggregation at each round. The objective is to minimize the average loss while satisfying latency constraints. To solve the formulated problem, we develop a deep reinforcement learning (DRL)-based solution that enables adaptive satellite selection considering the stochastic nature of satellite connectivity and channel variability. The proposed framework selects participating satellites dynamically based on their communication latency and computational capabilities to improve the overall efficiency of the FL process. Simulation results show that the proposed approach accelerates convergence compared to conventional synchronous FL while maintaining model accuracy.

Reconfigurable Intelligent Surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by dynamically controlling the propagation environment. Recently, a non-diagonal RIS architecture has been proposed, enabling more advanced signal manipulation by allowing signals impinging on one element to be reflected from another element after appropriate phase-shift adjustment. This paper analyzes the energy efficiency of non-diagonal RIS-assisted wireless communication systems in high- and low-signal-to-noise-ratio (SNR) regime. We derive closed form expressions of the spectral and energy efficiency for both the non-diagonal and its diagonal counterpart, which is used as a benchmark for comparison. Simulation results reveal that non-diagonal RIS systems are the preferred choice for communication systems that prioritize spectral efficiency. Interestingly, for energy efficiency, the selection between diagonal and non-diagonal RIS architectures depends on the received SNR conditions, with diagonal RIS systems excelling at high SNR and non-diagonal RIS systems performing better at low SNR scenarios.

Low Earth Orbit (LEO) satellite constellations consist of numerous satellites orbiting at different altitudes to serve diverse terrestrial users. Efficient resource management in such dynamic and large-scale networks presents a significant challenge. This paper studies Resource Blocks (RBs) and transmit power allocation at each LEO satellite, aiming at enhancing network performance while meeting Quality of Service (QoS) requirements. A stochastic optimization problem is formulated where the required QoS by each user is expressed as a chance constraint of the minimum data rate requirements considering network dynamics and uncertainties in channel conditions and traffic demands. The Conditional Value at Risk (CVaR) is employed to reformulate the chance constraint into a convex form and achieve a robust solution. Then, an alternating optimization approach is applied to solve the optimization problem. Through simulations using real data from the Starlink constellation, we demonstrate the efficacy of the proposed approach in improving network data rates while maintaining the required QoS levels.

Enhancing reliability in high-throughput satellites (HTS) operating in geostationary orbit (GEO) is critical, particularly under adverse channel conditions. This study investigates the potential of multi-connectivity (MC) enabled by carrier aggregation (CA) to improve the data rate, ensuring a high level of reliability of multi-band $\mathrm{K a} / \mathrm{Ku}$ GEO HTS systems. A system and channel model for the multi-band GEO satellite system is developed, and an inter-band CA algorithm is proposed. This algorithm dynamically adjusts the user link transmission scheme based on channel quality and user requirements, ranging from a single Ka-band connectivity to MC, utilizing both bands with CA via packet duplication or packet splitting. The numerical results in various weather scenarios validate the effectiveness of the algorithm, demonstrating significant improvements in system performance, reduced outage probability, and improved overall system reliability. These findings highlight the importance of MC and multi-band technologies in future $\mathrm{6 G}$ networks.

Through the transition from 5G to 6G, a significant rise in the number of Internet of Things (IoT) devices is anticipated, enabling pervasive and uninterrupted connectivity for several applications, in different verticals. Coping with the substantial influx of IoT devices and fulfilling the high capacity demands of different IoT technologies, such as NB-IoT, will necessitate the involvement of Non-Terrestrial Networks (NTNs), which will serve as crucial complements to terrestrial systems, enhancing the availability, resilience, and coverage of the network and will guarantee cost/benefit for some services and will fully satisfy some key requirements. Nevertheless, a primary obstacle to be faced when integrating IoT terrestrial communication systems in NTN, in particular with Non-Geostationary (NGSO) satellites, lies in the short visibility time of the flying platform due to its high speed. The latter introduces criticalities in various communication phases, including the Random Access (RA) procedure. In a highly congested scenario, the large Round Trip Delay and a limited visibility window, which varies for each user within the satellite’s coverage area, contribute to reducing the number of users successfully concluding the RA procedure. In this paper, to enhance the percentage of users who successfully conclude the RA, we introduce the concept of Coverage Enhancement Levels in time and a novel backoff mechanism, namely Smart Backoff, that leverages the beam coverage visibility period of individual users to adjust the random backoff interval. The numerical results obtained from our proposed scheme substantiate significant improvements compared to the standard backoff scheme. Specifically, our approach yields an increase of up to 16% per channel in the percentage of users who successfully complete the RA process.

Next-generation (NextG) cellular networks are expected to evolve towards virtualization and openness, incorporating reprogrammable components that facilitate intelligence and real-time analytics. This paper builds on these innovations to address the network slicing problem in multi-cell open radio access wireless networks, focusing on two key services: enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communications (URLLC). A stochastic resource allocation problem is formulated with the goal of balancing the average eMBB data rate and its variance, while ensuring URLLC constraints. A distributed learning framework based on the Deep Reinforcement Learning (DRL) technique is developed following the Open Radio Access Networks (O-RAN) architectures to solve the formulated optimization problem. The proposed learning approach enables training a global machine learning model at a central cloud server and sharing it with edge servers for executions. Specifically, deep learning agents are distributed at network edge servers and embedded within the Near-Real-Time Radio access network Intelligent Controller (Near-RT RIC) to collect network information and perform online executions. A global deep learning model is trained by a central training engine embedded within the Non-Real-Time RIC (Non-RT RIC) at the central server using received data from edge servers. The performed simulation results validate the efficacy of the proposed algorithm in achieving URLLC constraints while maintaining the eMBB Quality of Service (QoS).

This paper proposes a robust beamforming algorithm for massive multiple-input multiple-output (MIMO) low earth-orbit (LEO) satellite communications under uncertain channel conditions. Specifically, a Conditional Value at Risk (CVaR)-based stochastic optimization problem is formulated to optimize the hybrid digital and analog precoding aiming at maximizing the network data rate while considering the required Quality-of-Service (QoS) by each ground user. In particular, the CVaR is used as a risk measure of the downlink data rate to capture the high dynamic and random channel variations of the satellite network, achieving the required QoS under the worst-case scenario. Utilizing the decomposition and relaxation optimization techniques, an alternating optimization algorithm is developed to solve the formulated problem. Simulation results demonstrate the efficacy of the proposed approach in achieving the QoS requirements under uncertain satellite channel conditions.

This paper proposes a distributed learning framework for network slicing in multi-cell open radio access networks providing two services: Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). In particular, a resource allocation optimization problem is formulated with an objective to maximize the average eMBB data rate while considering URLLC constraints and the data rate variance among eMBB users. A multi-agent Deep Reinforcement Learning (DRL) based algorithm is developed to solve the formulated problem, where network components collaboratively train a global machine learning model and then share learning parameters for distributed executions at network edges. Specifically, DRL agents are installed at Near-Real-Time Radio access network Intelligent Controllers (Near-RT RICs) located in the network edge servers to provide online resource allocation decisions while the training process is performed offline at the Non-Real-Time RIC (Non-RT RIC) located in a regional cloud server. The achieved simulation results show that the proposed algorithm can ensure the required URLLC reliability while keeping the Quality-of-Service (QoS) requirements of the eMBB service.

Recently, unmanned aerial vehicles (UAVs)-assisted multi-access edge computing (MEC) systems emerged as a promising solution for providing computation services to mobile users outside of terrestrial infrastructure coverage. As each UAV operates independently, however, it is challenging to meet the computation demands of the mobile users due to the limited computing capacity at the UAV’s MEC server as well as the UAV’s energy constraint. Therefore, collaboration among UAVs is needed. In this article, a collaborative multi-UAV-assisted MEC system integrated with an MEC-enabled terrestrial base station (BS) is proposed. Then, the problem of minimizing the total latency experienced by the mobile users in the proposed system is studied by optimizing the offloading decision as well as the allocation of communication and computing resources while satisfying the energy constraints of both mobile users and UAVs. The proposed problem is shown to be a nonconvex, mixed-integer nonlinear programming (MINLP) problem that is intractable. Therefore, the formulated problem is decomposed into three subproblems: 1) users tasks offloading decision problem; 2) communication resource allocation problem; and 3) UAV-assisted MEC decision problem. Then, the Lagrangian relaxation and alternating direction method of multipliers (ADMMs) methods are applied to solve the decomposed problems, alternatively. Simulation results show that the proposed approach reduces the average latency by up to 40.7% and 4.3% compared to the greedy and exhaustive search methods.

Utilizing the millimeter-wave (mmWave) frequency is a promising solution to meet fast-growing traffic demand over wireless networks. However, mmWave communications are sensitive to physical obstructions on signal propagation. In this paper, the reconfigurable intelligent surfaces (RISs) are investigated to overcome the limitations of mmWave communications. Particularly, an RIS is deployed to reflect the mmWave signals towards vehicular users who experience direct link blockages that may occur due to static or dynamic obstacles. To this end, a risk-averse optimization problem is designed to optimize the Base Station (BS) precoding matrix and the RIS phase shifts under stochastic link blockages. A solution approach is developed in two phases: the BS precoding optimization and the RIS phase shift control phases. In the first phase, a Decomposition and Relaxation-based Precoding Optimization (DRPO) algorithm is developed to obtain the optimal precoding matrix. In the second phase, a learning-based method is introduced to dynamically adjust the direction of reflected signals under channel uncertainty. Extensive simulations are presented to validate the efficacy of the developed algorithms. The obtained results show that the developed algorithms can ensure reliable transmissions to users in non-LoS areas and improve network performance.

Unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) has become one promising solution for energy-constrained devices to run the applications with high computation demand and stringent delay requirement in beyond 5G era. In this work, we study a multi-UAV-assisted two-stage MEC system in which UAVs provide the computing and relaying services to the mobile devices. Due to the limited computing resources, each UAV executes only a portion of the offloaded tasks from its associated MDs in the first stage. Hence, in the second stage, each UAV relays the portions of the tasks to the terrestrial base station (TBS) which has rich computing resources enough to handle all the tasks relayed to it. In this regard, we formulate a joint task offloading, communication and computation resource allocation problem to minimize the energy consumption of MDs and UAVs by considering the limited resources of UAVs and the tolerable latency of the tasks. The formulated problem is a mixed-integer non-convex problem which is NP hard. To solve the formulated optimization problem, we apply the Block Successive Upper-bound Minimization (BSUM) method which guarantees to obtain the stationary points of the non-convex objective function. Finally, the extensive evaluation results are conducted to show the superior performance of our proposed framework.

The network capacity is fastened by utilizing unmanned aerial vehicles (UAVs) and mobile users can get feasible services independent of the infrastructure coverage. Furthermore, with the help of network virtualization technology, mobile network operators (MNOs) can lease their cellular network infrastructures and wireless network resources to the service providers (SPs) who are providing specific services to their mobile users. Wireless resource leasing among SPs, on the other hand, is problematic because each aims to maximize its own profit whilst assuring the QoS requirement of their users. Thus, in this paper, we propose a wireless resource sharing problem in the UAVs-assisted virtualized wireless networks with the goal of maximizing the total profit of SPs whilst guaranteeing the QoS requirement of each mobile user and satisfying the resource constraint of the UAVs. Then, we deploy the Lagrangian relaxation-based solution approach in order to address our proposed problem. Finally, we provide detailed numerical results to show the effectiveness of our proposed algorithm.

Ultra-reliable low-latency communication (uRLLC) and enhanced mobile broadband (eMBB) are two influential services of the emerging 5G cellular network. Latency and reliability are major concerns for uRLLC applications, whereas eMBB services claim for the maximum data rates. Owing to the trade-off among latency, reliability and spectral efficiency, sharing of radio resources between eMBB and uRLLC services, heads to a challenging scheduling dilemma. In this paper, we study the co-scheduling problem of eMBB and uRLLC traffic based upon the puncturing technique. Precisely, we formulate an optimization problem aiming to maximize the minimum expected achieved rate (MEAR) of eMBB user equipment (UE) while fulfilling the provisions of the uRLLC traffic. We decompose the original problem into two sub-problems, namely scheduling problem of eMBB UEs and uRLLC UEs while prevailing objective unchanged. Radio resources are scheduled among the eMBB UEs on a time slot basis, whereas it is handled for uRLLC UEs on a mini-slot basis. Moreover, for resolving the scheduling issue of eMBB UEs, we use penalty successive upper bound minimization (PSUM) based algorithm, whereas the optimal transportation model (TM) is adopted for solving the same problem of uRLLC UEs. Furthermore, a heuristic algorithm is also provided to solve the first sub-problem with lower complexity. Finally, the significance of the proposed approach over other baseline approaches is established through numerical analysis in terms of the MEAR and fairness scores of the eMBB UEs.

In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). While eMBB services focus on high data rates, URLLC is very strict in terms of latency and reliability. In view of this, the resource slicing problem is formulated as an optimization problem that aims at maximizing the eMBB data rate subject to a URLLC reliability constraint, while considering the variance of the eMBB data rate to reduce the impact of immediately scheduled URLLC traffic on the eMBB reliability. To solve the formulated problem, an optimization-aided Deep Reinforcement Learning (DRL) based framework is proposed, including: 1) eMBB resource allocation phase, and 2) URLLC scheduling phase. In the first phase, the optimization problem is decomposed into three subproblems and then each subproblem is transformed into a convex form to obtain an approximate resource allocation solution. In the second phase, a DRL-based algorithm is proposed to intelligently distribute the incoming URLLC traffic among eMBB users. Simulation results show that our proposed approach can satisfy the stringent URLLC reliability while keeping the eMBB reliability higher than 90%.

Long-Term Evolution in the unlicensed spectrum (LTE-U) is considered as an indispensable technique to mitigate the spectrum scarcity in wireless networks. Typical LTE transmissions are contention-free and centrally controlled by the base station (BS); however, the wireless networks that work in unlicensed bands use contention-based protocols for channel access, which raises the need to derive an efficient and fair coexistence mechanism among different radio access networks. In this work, we propose a novel neural networks (NNs) based mechanism for the coexistence of an LTE-U base station (BS) in the unlicensed spectrum alongside with a WiFi access point (WAP). Specifically, we model the coexistence problem as a Hopfield Neural Network (HNN) based optimization problem that aims a fair coexistence considering both the LTE-U data rate and the QoS requirements of the WiFi network. Using the energy function of HNN, precise investigation of its minimization property can directly provide the solution of the optimization problem. Numerical results show that the proposed mechanism allows the LTE-U BS to work efficiently in the unlicensed spectrum while protecting the WiFi network.

With the help of multi-access edge computing (MEC) system, traditional mobile network operators (MNOs) can provide various services to their mobile users with the minimum delay by installing micro-datacenters at the base stations (BSs)(i.e., at the edge of the radio access network). However, the capital and operational expenditures become significant challenge for the MNOs. Fortunately, multiple MNOs can coexist on the same infrastructure and share the network resources with the help of upcoming technologies such as network virtualization (i.e., network slicing) and software defined networking (SDN). In this work, we introduce a virtualized MEC system in which an infrastructure provider (InP) deploys a BS integrated with a micro-datacenter and owns the wireless network resource i.e., bandwidth. Then, InP creates the virtual network by slicing its network resources including bandwidth and the computation resource of the MEC server, and shares these resource slices to multiple virtual network operators (MVNOs) where MVNOs provide specific services to their mobile users. To solve our proposed problem, we first decompose the original problem into two subproblems. Then, we apply the Karush-Kuhn-Tucker (KKT) conditions to solve each subproblem. Moreover, simulation results prove that our proposed algorithm for joint communication and computation resources sharing outperforms the existing schemes.