The proliferation of data-intensive, low-latency applications has driven the adoption of multi-access edge computing (MEC) to meet the demand for high-performance computing at the network edge. However, ensuring reliable communication under non-line-of-sight (NLoS) conditions remains a significant challenge. While reconfigurable intelligent surfaces (RISs) and the more recent simultaneously transmitting and reflecting RISs (STAR-RISs) offer promising solutions, their passive nature and susceptibility to multiplicative fading limit performance gains. To address these challenges, we propose a novel active STAR-RIS-assisted MEC system that enhances signal strength and adaptability by enabling amplification and joint control over signal transmission and reflection. Our objective is to minimize the energy consumption of user devices, considering both local task computation and uplink task offloading, while maintaining task queue stability. We formulate a joint energy minimization problem with system constraints and long-term queue stability requirements. This problem is decomposed into subproblems: (i) sequential fractional programming is applied to optimize user transmit power, (ii) convex optimization is used to determine partial task offloading ratios, and (iii) a modified Lyapunov optimization combined with double deep Q-networks (DDQN) is proposed to iteratively solve the active STAR-RIS parameters (amplitude and phase shift), amplification control, and task admission at the user side. Numerical results indicate that our proposed system outperforms the conventional passive STAR-RIS-assisted system by 18.64% and the conventional passive RIS-assisted system by 30.43%, respectively.

Integrated terrestrial-nonterrestrial networks have recently gained much attention because they can bridge the gap between the conventional terrestrial infrastructure and nonterrestrial networks. In addition to seamless connectivity, such networks can offer edge computing services to the users with real-time data processing demand. In this article, an integrated terrestrial-nonterrestrial network with multiaccess edge computing (ITNT-MEC) system is considered in which the aerial users (AUEs) share the resources of terrestrial base stations (TBSs) with their existing terrestrial users (TUEs) and that of low-Earth orbit (LEO) satellites with their neighboring satellites. The goal is to minimize the total energy consumption of AUEs, TUEs, and LEO satellites by jointly optimizing the AUE-TBS/LEO satellite association, AUEs’ trajectories, task allocation, as well as network resource allocation. Due to the dynamic nature of network environment and nonconvex characteristics, it is significantly challenging to solve the formulated optimization problem. Therefore, a block coordinate descent (BCD)-based algorithm that integrates deep reinforcement learning (DRL) methods, such as double deep Q-learning network (DDQN), deep deterministic policy gradient (DDPG), and convex optimization methods, is proposed. Simulation results show that the total energy consumption in the proposed approach is reduced by 14%, 26.9%, 34%, 35.8%, 45.5%, and 55.4%, respectively, when compared to the baselines, such as DDPG-based task offloading (DDPG-TO), DDQN-based task offloading (DDQN-TO), DQN-based task offloading (DQN-TO), equal resource allocation (ERA), random association (RA), and fixed trajectory (FT).

Urban air Mobility (UAM) has been conceived as a new form of transportation. UAM ultimately aims to operate unmanned, so it needs to select its trajectory and periodically send its status to the base station (BS). As an status indicator, the age of information (AoI) signifies the freshness of the information, and it is crucial for applications like real-time control systems. In this article, we address two main challenges: optimizing the UAM’s trajectory and updating the AoI between the UAM and the BS. We formulate an algorithm to maximize the energy efficiency of each UAM’s trajectory and jointly minimize the AoI cycle. As a complicated and non-convex problem, we approach proximal policy optimization (PPO) as our solution in this paper. Experiment results show that our proposed method outperformed the direct trajectory baseline in similar energy efficiency but achieved 46% increased efficiency in average AoI.

Vehicle-to-everything (V2X) communications is pivotal for modern transportation systems, but the challenges arise in scenarios with buildings, leading to signal obstruction and limited coverage. To alleviate these challenges, reconfigurable intelligent surface (RIS) is regarded as an effective solution for communication performance by tuning passive signal reflection. RIS has acquired prominence in 6G networks due to its improved spectral efficiency, simple deployment, and cost-effectiveness. Nevertheless, conventional RIS solutions have coverage limitations. Researchers are exploring on the promising concept of simultaneously transmitting and reflecting RIS (STAR-RIS), which provides 360° coverage while utilizing the advantages of RIS technology. In this article, an STAR-RIS-assisted V2X communication system is investigated. An optimization problem is formulated to maximize the achievable data rate for vehicle-to-infrastructure (V2I) users while satisfying the latency and reliability requirements of vehicle-to-vehicle (V2V) pairs by jointly optimizing the spectrum allocation, amplitude and phase shift values of STAR-RIS elements, digital beamforming vectors for V2I links, and transmit power for V2V pairs. Since it is challenging to solve in polynomial time, we decompose our problem into two subproblems. For the first subproblem, we model the control variables as a Markov Decision Process and propose a combined double deep$Q$-network (DDQN) with an attention mechanism so that the model can potentially focus on relevant inputs. For the latter, a standard optimization-based approach is implemented to provide a real-time solution, reducing computational costs. Numerical results demonstrate that our solution approach outperforms the vanilla DDQN approach by 5.2%, and our proposed system outperforms the conventional RIS by 39%.

The significant growth in data consumption among mobile users necessitates the development of new architecture to meet the increasing demand. On the other hand, reconfigurable intelligent surface (RIS) has grown in popularity in 6G due to its improved spectral efficiency, simplicity of deployment, and low cost. However, with the constrained limitation of the coverage by conventional RIS, the research direction has turned towards simultaneously transmitting and reflecting RIS (STAR-RIS) to provide 360° coverage alongside the benefits of RIS. In this paper, a STAR-RIS-assisted downlink communication system for both indoor and outdoor users is investigated. Then, the optimization problem to maximize the spectral efficiency while jointly controlling the beamforming power for each user and phase shift values of the STAR-RIS is formulated. Since the formulated problem is NP-hard and challenging to solve in polynomial time, a policy gradient method for reinforcement learning named proximal policy optimization (PPO) is implemented to solve the problem. To demonstrate the effectiveness of our proposed algorithm, extensive simulation results are executed. Numerical results prove that our proposed algorithm outperforms several benchmark schemes in the literature.

Unmanned aerial vehicles (UAVs) nowadays are developing rapidly for various applications such as UAV taxis and delivery drones. However, the limited battery energy restricts the flight distance of the UAVs. Thus, urban prosumers equipped with drone recharge stations are introduced to provide charging services for the UAVs. In this article, first, a day-ahead energy scheduling problem for UAV charging-enabled urban prosumers is studied, where the objective is to maximize the overall energy satisfaction of the prosumers with ensuring the Quality of Service (QoS) of the charged UAVs. Specifically, to deal with the considered problem, we decompose it into two stages: 1) the day-ahead energy requirement data prediction stage and 2) energy scheduling stage per prosumer. Thus, second, a joint method based on hierarchical federated learning (HFL) on long short-term memory (LSTM) architecture (HFL-LSTM) and stochastic game-based multi-agent double deep$Q$-learning (MADDQN) with community agent-independent approach is proposed. In particular, the HFL-LSTM approach is leveraged to forecast each prosumer’s energy requirement data without centralized collecting local prosumers’ data such that to protect data privacy. Then, the stochastic game is adopted to analyze the formulated problem, aiming to find the Nash equilibrium (NE) strategy. Afterward, MADDQN with a community agent-independent method is utilized to achieve the best energy scheduling strategy per prosumer. Finally, the experimental results demonstrate the superiority of the proposed joint method that can achieve the lowest mean squared error with the value of 0.0152 and the highest energy satisfaction$(36388)$achieved by the NE policy compared with the benchmarks.

Reconfigurable Intelligent Surfaces (RISs) have become an emerging paradigm to improve the average sum-rate, enhance energy efficiency and extend coverage areas in wireless communications. In this paper, a multiple RISs-enabled energy-efficient downlink communication system is investigated. Then, to maximize energy efficiency for the proposed system, the joint optimization problem of user-RIS association, reflective elements ON/OFF states, phase shift, and transmit power is formulated. However, as the formulated problem is mixed-integer, non-convex, and NP-hard, it is challenging to solve in polynomial time. To overcome the challenge, by using the Block Coordinate Descent (BCD) method, the formulated problem is decomposed into two sub-problems: 1) joint user-RIS association, reflective elements ON/OFF states, and phase shift problem, and 2) power control problem. Then, the deep reinforcement learning (DRL) algorithm and convex optimization technique are deployed in order to solve the decomposed sub-problems alternatively to find close optimal solutions. Finally, comprehensive simulation results are established to demonstrate the effectiveness of our proposed algorithms.

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.

Task offloading scheme provides opportunistic energy saving for computation-intensive on-vehicle applications. The evolution of the Vehicular Edge Computing (VEC) paradigm has contributed a vast potential that can enhance the performance of such vehicles with energy-hungry and delay-sensitive services. However, determining how much workload to compute locally or offload to the VEC server is still quite challenging. Moreover, when all the vehicles try to offload their computation tasks to the same VEC server, it leads to deterioration in the performance gain due to overburden. Recently, unmanned aerial vehicle (UAV) as the edge server has gained huge attraction due to its well maneuverability and cost efficiency. In this paper, we study the energy-efficient offloading as well as association of the vehicles between the road side unit (RSU) and UAV. First, we formulate the joint offloading and association problem. Next, we decompose the formulated mixed integer linear (MIL) problem into two subproblems and then solve them by using standard convex optimization. Finally, we compare our proposed algorithm with benchmark schemes and the numerical results demonstrate that our algorithm outperforms the benchmark solutions.