Multi-access edge computing (MEC)-enabled integrated space-air-ground networks have drawn much attention recently, as they can provide communication and computing services to wireless devices in areas that lack terrestrial base stations (TBSs). They could make it possible for battery-powered Internet of Things (IoT) devices to offload their computation tasks to MEC-enabled unmanned aerial vehicles (UAVs) assisted aerial networks and low earth orbit (LEO) satellites and thus reduce their energy consumption and allow them to complete the execution of tasks on time. However, due to the limited computation capacity of the MEC servers at UAVs and satellites, an efficient offloading decision and computation resource allocation scheme is essential. Therefore, this paper investigates the problem of minimizing the latency experienced by the wireless devices in the MEC-enabled integrated space-air-ground network by optimizing the offloading decision while assuring the energy constraints of both devices and UAVs. The problem is proved to be a non-convex problem, and the block successive upper-bound minimization (BSUM) method is proposed as a solution. Finally, extensive simulation results are presented to exhibit the effectiveness of the BSUM algorithm in solving the proposed problem.

The use of Terahertz (THz) technology in sixth-generation (6G) networks will bring high-speed and capacity data services. But limitations like molecular absorption, rain attenuation, and limited coverage range cause communication losses. To overcome these losses and improve coverage in rural areas, a high number of base stations are required. Hence, an aerial communication platform, which uses line-of-sight (LoS) communication to avoid losses, is needed. To address this, we study the deployment and optimization of multi-access edge computing (MEC)-powered unmanned aerial vehicle (UAV) for sub-THz communication in remote areas. To this end, we solve an optimization problem to minimize energy consumption and delay for MEC-UAV and mobile users. The formulated problem is a mixed-integer non-linear programming (MINLP) problem. As the problem is an MINLP, we decompose the main problem into two subproblems. Due to its convex nature, we solve the first subproblem with a standard optimization solver, i.e., CVXPY. To solve the second subproblem, we design a resources-based multi-agent proximal policy optimization (RMAPPO) deep reinforcement learning (DRL) algorithm with an attention mechanism. The considered attention mechanism is utilized for encoding a diverse number of observations. This is designed by the network coordinator to provide a differentiated fit reward to each agent in the network. The simulation results show that the proposed algorithm outperforms the benchmark and yields a network utility that is 2.22%, 15.55%, and 17.77% more than the benchmarks.

The proliferation of intelligent transportation systems (ITS) has led to increasing demand for diverse network applications. However, conventional terrestrial access networks (TANs) are inadequate in accommodating various applications for remote ITS nodes, i.e., airplanes and ships. In contrast, satellite access networks (SANs) offer supplementary support for TANs, in terms of coverage flexibility and availability. In this study, we propose a novel approach to ITS data offloading and computation services based on SANs. We use low-Earth orbit (LEO) and cube satellites (CubeSats) as independent mobile edge computing (MEC) servers that schedule the processing of data generated by ITS nodes. To optimize offloading task selection, computing, and bandwidth resource allocation for different satellite servers, we formulate a joint delay and rental price minimization problem that is mixed-integer non-linear programming (MINLP) and NP-hard. We propose a cooperative multi-agent proximal policy optimization (Co-MAPPO) deep reinforcement learning (DRL) approach with an attention mechanism to deal with intelligent offloading decisions. We also decompose the remaining subproblem into three independent subproblems for resource allocation and use convex optimization techniques to obtain their optimal closed-form analytical solutions. We conduct extensive simulations and compare our proposed approach to baselines, resulting in performance improvements of 9.9%, 5.2%, and 4.2%, respectively.

The proliferation of novel infotainment services, such as virtual reality (VR)-based services, has fundamentally changed the existing mobile networks. These bandwidth-hungry services expanded at a tremendously rapid pace, thus, generating a burden of data traffic in the mobile networks. To cope with this issue, one can use multiaccess edge computing (MEC) to bring the resource to the edge. By doing so, we can release the burden of the core network by taking the communication, computation, and caching resources nearby the end users (UEs). Nevertheless, due to the vast adoption of VR-enabled devices, MEC resources might be insufficient in peak times or dense settings. To overcome these challenges, we propose a system model where the service provider (SP) might rent unmanned area vehicles (UAVs) from UAV SPs (USPs) to serve as micro-base stations (UBSs) that expand the service area and improve the spectrum efficiency. In which, UAV can precached certain sets of VR-based contents and serve UEs via air-to-ground (A2G) communication. Furthermore, future intelligent devices are capable of 5G and B5G communication interfaces, and thus, they can communicate with UAVs via A2G links. By doing so, we can significantly reduce a considerable amount of data traffic in mobile networks. In order to successfully enable such kinds of services, an attractive incentive mechanism is required. Therefore, we propose a contract theory-based incentive mechanism for UAV-assisted MEC in VR-based infotainment services, in which the MEC offers an amount reward to a UAV for serving as a UBS in a specific location for certain time slots. We then derive an optimal contract-based scheme with individual rationality and incentive compatibility conditions. The numerical findings show that our proposed approach outperforms the linear pricing (LP) technique and is close to the optimal solution in terms of social welfare. Additionally, our proposed scheme significantly enhanced the fairness of utility for UAVs in asymmetric information problems.

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.

Accurate channel estimation and allocation are vital in the provision of reconfigurable intelligent surfaces (RIS)-aided wireless network services to mobile users. Typically, channel estimation is carried out using a pilot signal. However, RIS elements cannot transmit or receive pilot signals because they are passive elements. Therefore, to maximize the gain of using the RIS, it is essential to accurately estimate a cascaded channel using a pilot signal between a base station (BS) and a terminal through an RIS. Moreover, although using a large number of pilot signals can guarantee accurate channel estimation performance, this can also drastically lower the wireless communication system's efficiency. Thus, in this letter, a new paradigm for learning-based pilot allocation and channel estimation in RIS systems is proposed. A masked autoencoder (MAE) is trained to achieve high channel estimation accuracy with a limited number of pilots. Then, a deep reinforcement learning(DRL) agent learns pilot allocation policies through MAE. Simulation results show that the MAE channel estimator has almost the same channel estimation performance even though it uses up to 33% fewer pilots than the autoencoder (AE)-based channel estimator. Furthermore, the proposed DRL-based pilot optimization method achieves higher channel estimation performance with 20% fewer pilots than the general autoencoder and other learning algorithms without the proposed RL-based pilot optimization algorithm.

Nowadays, unmanned aerial vehicles (UAVs)-assisted mobile-edge computing (MEC) systems have been exploited as a promising solution for providing computation services to mobile users outside of terrestrial networks. However, it remains challenging for standalone UAVs to meet the computation requirement of numerous users due to their limited computation capacity and battery lives. Therefore, we propose a collaborative scheme among UAVs to share the workload between them. Furthermore, this work is the first to consider the task topology of offloading in MEC-enabled UAVs networks while restricting their power consumption. We study the task topology, in which a task consists of a set of subtasks, and each subtask has dependencies upon other subtasks. In the real world, subtasks with dependencies must wait for their preceding subtasks to complete before being executed, and this affects the offloading strategy. Next, we formulate an optimization problem to minimize the average latency of users by jointly controlling the offloading decision for dependent tasks and allocating the communication resources of UAVs. The formulated problem is NP-hard and cannot be solved in polynomial time. Therefore, we divide the problem into two subproblems: 1) offloading decision problem and 2) communication resource allocation problem. Then, a metaheuristic method is proposed to find the suboptimal solution to the former problem, while the latter problem is solved by using convex optimization. Finally, we conduct simulation experiments to prove that our proposed offloading technique outperforms several benchmark schemes in minimizing the average latency of users for dependency tasks and achieving higher uplink transmission rates.

Unmanned aerial vehicles (UAVs) have been conceived as an available solution to substitute terrestrial base stations (TBSs) to provide downloading services for user equipment (i.e. mobile devices) that have difficulty communicating directly with TBSs. However, the mobility of user equipment (UE) and the random nature of the number of UE will cause several challenges including 1) the hardness of determining optimal user association and UAV resource (i.e., bandwidth and transmit power) allocation decision, 2) the burden of network function maintenance owing to the necessity of shutting down the entire system. Therefore, in this article, joint user association and resource allocation are designed for a software-defined network (SDN)-adopted leaderless softwarized UAV network, where each UAV is regarded as a flying SDN controller to enhance the control ability of the considered network. The purpose is to maximize energy efficiency (EE) with satisfying the quality of service (QoS). To this end, a joint method based on hierarchical agglomerative clustering (HAGC) and multi-agent deep deterministic policy gradient (MADDPG) is proposed. Specifically, the HAGC approach is utilized to determine the optimal MDs association with UAVs. Afterward, MADDPG approach is leveraged to obtain the best policy for resource allocation, aiming to achieve the maximum EE. Finally, the effectiveness of the proposed method is confirmed by the evaluation results.

We propose a reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV)-enabled joint communication and sensing (JSAC) system in which multiple JSAC nodes equipped with both sensing and communication functions are deployed to perform sensing and communication simultaneously, and a communication-enabled RIS is installed to provide strong wireless links for JSAC nodes to communicate with the UAV (i.e., aerial base station). We then formulate the problem of maximizing the sum communication rate of JSAC nodes while ensuring their minimum sensing performance requirements, i.e., minimum estimation rate requirements, by jointly optimizing sub-band assignment, adaptive bandwidth allocation, and the RIS phase shift. The resulting problem is a mixed-integer nonlinear programming (MILP) problem with a non-convex structure, thus, to solve the problem efficiently, we decompose the problem into two coupled sub-problems, a sub-band assignment and adaptive bandwidth allocation problem, and a RIS phase shift problem. We propose a low-complexity iterative algorithm for solving the sub-problems in an alternating manner. Extensive simulations show that the proposed algorithm can increase the communication rate by up to 27.51 %,48.747%, and 62.0942% compared to RSA, RPS, and RSRPS schemes.

Integrated space-air-ground (ISAG) networks have emerged as a promising technology for next-generation commu-nication networks, which demand higher throughput and wider coverage. However, several challenges must be addressed, such as wireless resource and interference management, transmit power control, and optimal deployment of unmanned aerial vehicles (UAV s) to achieve higher throughput. To this end, in this paper, we propose the joint transmit power control and the deployment UAV problem in the ISAG network to maximize the sum-rate of devices while guaranteeing the minimum rate requirement of each device and the wireless backhaul link constraints between UAV s and the satellite. However, solving the resulting problem is challenging since it has a non-convex structure. As a solution, we propose to decompose the problem into two subproblems. We then transform the decomposed subproblems into convex forms and solve them using successive convex approximation (SCA). Finally, we conduct extensive simulations to show the effectiveness of the proposed method, and the numerical results show that the proposed method achieves a performance gain: up to 8.35%, and 4.08% in comparison to FPA and C-UAVs schemes.