The rapid advancement of 5G and 6G technologies has introduced various innovative applications, such as autonomous driving and augmented reality, which significantly increase network traffic and computational demands, particularly in densely populated areas. This study focuses on the utilization of unmanned aerial vehicles (UAVs) for Mobile Edge Computing (MEC) to address these challenges. Specifically, we explore the joint optimization of base station (BS) selection, computing resource allocation and UAV trajectory to minimize system delays and energy consumption in scenarios where computational requirements are related to user movement. A deep reinforcement learning-based approach, UM-DDPG, is proposed to optimize task offloading strategies and UAV trajectories. With our simulation platform, the results show that our method has been effective in reducing the system cost, including both delay and energy consumption, compared to traditional methods.

As 6G technology continues to evolve, future communication systems demand extremely low latency, improved energy efficiency, and support for massive device connectivity. To address these demands, the integration of Space-Air-Ground Integrated Networks (SAGIN) with Mobile Edge Computing (MEC) has emerged as a compelling strategy. This integration leverages edge nodes deployed on low Earth orbit (LEOs) satellites, unmanned aerial vehicles (UAVs), and terrestrial small base stations (SBSs) to deliver distributed computational resources. Nevertheless, due to the inherent heterogeneity and limited capabilities of these edge devices, reliably offloading computing tasks from ground user equipments (UEs) to appropriate edge nodes—whether satellite-based, aerial, terrestrial, or via local execution—remains a significant challenge. In this paper, we design a metadata-driven intelligent offload prediction and global resource optimization framework for centipede games, the metadata only contains the key information of the computing task, but not the actual task data itself. Then we envision a 6G smart city network architecture with complex computing scenarios, formulate the problem of minimizing the global average delay and energy consumption as a Markov Decision Process (MDP) by combining with Bellman’s optimization equations, and propose an asynchronous metadata driven centipede game method (AMDCG) based on deep reinforcement learning (DRL). Simulation results show that the AMDCG method significantly reduces the system offloading overhead in terms of latency and energy consumption compared to other benchmark algorithms.

With the proliferation of sixth-generation (6G) communication networks, mobile edge computing (MEC) has become essential for addressing diverse user demands. The increasing heterogeneity of mobile devices, along with the rise of delay-sensitive and environmentally friendly tasks, exacerbates latency and energy consumption challenges. This paper proposes GERL-OA, a task offloading and resource allocation algorithm based on graph attention auto-encoder (GATE) assisted deep reinforcement learning (DRL). The MEC environment is modeled as an undirected graph, with spatial features extracted via graph neural network (GNN). GATE is utilized for unsupervised feature extraction, and a DRL framework is applied to optimize offloading decisions and resource allocation, minimizing the weighted sum of delay and energy consumption. Simulation results validate the superior convergence speed and global optimization capability of GERL-OA. Furthermore, extensive experimental results demonstrate the algorithm’s strong adaptability to different task types, confirming its effectiveness in balancing the requirements of delay-sensitive and environmentally friendly applications.

With the rapid development of the Internet of Things (IoT), 6G space-air-ground integrated networks (SAGIN) have emerged to provide key solutions for wide-area coverage, data management and resource allocation. This paper presents an efficient data offloading and resource allocation strategy for 6G SAGIN assisted by Unmanned Aerial Vehicle (UAV) and Low Earth Orbit (LEO) satellite. In this paper, our goal is to minimize the total system cost, which is the weighted sum of latency and energy consumption, under UAV storage capacity constraint. Due to the complexity of optimization problem, we decompose the original problem into two sub-problems: the computation resource allocation sub-problem and the data offloading and transmission power allocation sub-problem. To address the first sub-problem, we demonstrate its convex optimization nature and propose the Newton Interior-Point Method (IPM)-based Computation resource Allocation (NICA) algorithm. For the second sub-problem, we introduce the Genetic Algorithm-based UAV Data Offloading and transmission Power Allocation (GADOPA) algorithm. Different offloading methods are provided for different types of data (i.e., computational and collected data) to meet their differentiated needs. Through simulation experiments, we demonstrate the effectiveness of our proposed methods.

As the requirements of wireless communication networks change, the space-air-ground integrated network (SAGIN) architecture will be the primary goal of future communication networks. However, due to the pressure of user devices (UDs) to process compute-intensive tasks and the limited power of aircraft, this remains a challenge of reducing energy consumption in SAGIN system. Multi-access edge computing (MEC) provides a promising solution by offloading tasks to edge nodes with more computing resources. In this paper, we study a multi-node cooperative task offloading problem, where user devices generate computing tasks that can be processed locally, offloaded to multiple unmanned aerial vehicles (UAVs) or a satellite. The aim is to minimize the total energy consumption of the system while maintaining the task latency constraints by allocating the optimal mmWave band bandwidth, and jointly optimizing UAV trajectories, offloading decisions making, and computing resource allocation. Since the proposed problem optimization is NP-hard, the proposed Deep Reinforcement learning based Cooperative Task Offloading (DRCTO) algorithm, which leverage the proximal policy optimization method, can accelerate the learning process of searching for the optimal solution. Numerical results indicate that the proposed DRCTO algorithm performs better than other comparable algorithms and significantly reduces the total system energy consumption.

In the context of integrated aerial-terrestrial networks, the use of unmanned aerial vehicles (UAVs) as aerial base stations (ABS) to provide additional edge computing and communication capabilities to ground users (GDs) is envisioned as a promising solution. Meanwhile, with the rapid development of 6G networks, there has been an explosive growth in GD demand for videos. Edge caching has been proposed to reduce redundant video transmission in the network and minimize GD waiting latency. In this paper, we investigate a video adaptive caching solution in UAV-assisted aerial-ground networks. Our objective is to minimize GD waiting latency by jointly optimizing caching decisions for both UAVs and base stations (BSs), as well as the trajectories of the UAVs. Due to the complexity of the proposed problem, which is difficult to solve by traditional optimisation methods, and the highly dynamic nature of video requests, we propose a reinforcement learning algorithm based on proximal policy optimisation and a long short-term memory neural network (PPO-L) to solve the problem. We use a real-world YouTube video request dataset as our simulation experiment dataset. Through a large number of simulation experiments and performance comparisons with other algorithms, we demonstrate the superiority of the proposed algorithm. The simulation results demonstrate that the proposed algorithm significantly improves performance in system experiments compared to other algorithms.

With the continuous development of integrated satellite ground networks, edge servers are laid out on low orbit earth (LEO) satellites to provide seamless services for some remote areas. In order to further improve service quality for mobile devices, the collaborative work between edge and cloud has received widespread attention. In this article, we consider the scenario of insufficient base station (BS) coverage in remote areas and investigate a hybrid model of computing offloading and resource allocation for edge cloud collaborative computing, in which edge servers with limited resources collaborate with the cloud by purchasing cloud computing resources. In this way, cloud server (CS) and edge servers separately price their computing and storage resources to stimulate each other to participate in cooperation and maximize their respective utility. We jointly optimize the size of offloading tasks, offloading strategies for devices and resource pricing issues for edge servers and CS based on comprehensive consideration of price cost, energy cost and latency limitation. A multi-level Stackelberg game is formulated among the CS (leader), BSs (followers) and satellites (followers) in level I-II, the BSs (leaders), satellites (leaders) and IoT devices (followers) in level II-III. Furthermore, we prove the existence and uniqueness of Stackelberg equilibrium (SE) in Stackelberg game. The SERI algorithm is proposed and simulations is conducted to show that SERI algorithm has good convergence performance and better entity utility.

As an edge cache device, the unmanned aerial vehicle (UAV) auxiliary edge network provides a wider coverage for mobile edge computing (MEC), and its flexibility and low delay bring great convenience to user equipments (UEs). To fully utilize the characteristics of UAVs, we need to consider the deployment trajectory and associated UEs of UAV. For computational intensive tasks, proper caching decision and offloading decision also help reduce delay. In this paper, we jointly optimize UE-UAV association, UAV deployment, caching decision, and offloading decision to minimize service delay. Considering the service rate of UAVs, we use the M/M/1 queues to model the calculation process. The problem of minimizing delay is formulated as a multi-objective optimization problem. We decompose the objective into three sub problems and use corresponding algorithms to solve them, namely the successive convex approximation (SCA) method, binary particle swarm optimization (BPSO) algorithm, and convex function difference (DC) method. The simulation results show that our algorithm is better than the other three baseline algorithms in minimizing service delay, and verify the delay effect of different edge storage capabilities in our optimization.

Unmanned aerial vehicles (UAVs) play a crucial role in mobile edge computing (MEC). On one hand, UAVs can serve as relay nodes, being flexibly deployed in various complex areas to provide network coverage services for users in remote regions. On the other hand, UAVs can carry edge servers, enabling them to approach terminal devices more conveniently and enhance the efficiency of task computation. However, in the face of a large number of computing tasks, the load balancing, network performance and computational resource management of the network are facing serious challenges due to the differences in task density caused by the irregular movement of ground users (GUs), as well as the limitations of the UAV’s computational capability and coverage. In order to address the above challenges, in this paper, We propose a multi-UAV collaborative MEC system. Our goal is to offload tasks as efficiently as possible with full and reasonable utilisation of computational resources, and to evenly distribute computational resources to meet the needs of varying levels of task intensity in a region through rational deployment of UAV locations and collaboration among UAVs. Specifically, in our proposed system framework, GUs can offload tasks to their associated UAVs and further decide whether or not to offload tasks to collaborative UAV or remote Base station (BS) based on the load of computational resources. We study the problem of location deployment of multiple UAVs for better offloading and resource allocation to tasks. Then, we model the task offloading and resource allocation process as a Markov decision process (MDP) focusing on minimising the average user cost of the system and propose a deep reinforcement learning (DRL)-based multi-uav collaborative computation offloading and resource allocation (DMCOA) algorithm. It has been shown through extensive simulation experiments that the algorithm can effectively reduce the average user cost of the MEC system.

The paradigm of unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) has emerged as an effective scheme for handling intensive tasks in heterogeneous networks. In this work, we consider a user-equipment-rich city network scenario. Due to the limited user equipments (UEs) resources and base station (BS) coverage, we utilise multiple-UAV-assisted UEs and partial offloading to handle the tasks. Meanwhile, considering the impact of task diversity on the quality of service (QoS) of the system, we design an integrated scheme that combines improved clustering techniques and deep reinforcement learning (DRL) for dynamic resource scheduling. Firstly, a random forest-based clustering algorithm (RFCA) is used to cluster UEs according to the service requirements (SR) of tasks, as a way to reduce the complexity of task processing and user association. Then a DRL-based computational offloading and bandwidth allocation algorithm (DCOBA) is used to improve the QoS by jointly optimising UAV-user associations, offloading ratios, and bandwidth allocation to reduce the system latency and energy consumption. Finally, experimental simulation data shows that our scheme can better optimise the Qos compared to traditional schemes.

In order to cope with the increasing data traffic, we try to enable Intelligent Reflecting Surface (IRS) interference elimination in Device-to-Device (D2D) communication networks to improve the Signal Interference Noise Ratio (SINR). We build the system model and divide the original problem into two subproblems: IRS reflection parameter adjustment and IRS allocation. We use the the Cross Entropy Global Optimization Method (CEGOM) to solve the first subproblem. For the second subproblem, in order to ensure the fairness of user rates and avoid user starvation, we propose a heuristic algorithm based on the Max-Min Fairness Method (MMFM) to solve the problem. Simulation results demonstrate the superiority of the proposed algorithms, which improves Jain's fairness index by 70%, 72% and 13% and reduces the blocking probability by 92%, 90% and 82%, respectively, when compared to the random, shortest distance, and traditional MMFM strategies.

With the emergence of the Internet of Things (IoT), mobile edge computing (MEC) effectively reduces task delay of multiple applications. The limitations of computing and storage capabilities, as well as the complex dynamic network environments, make efficient edge service computing stressful. To address this challenge, digital twin (DT) technology is a promising solution that bridges the virtual and physical worlds by creating digital representations of physical objects. DT technology can model the behaviors of physical entities through virtual mirroring and assist physical networks in making optimal network strategies. In this paper, we combine DT technology with MEC networks to develop a utility-based task offloading and resource allocation scheme, aiming to maximize the quality of experience (QoE) of user equipments (UEs) and the utility of base stations (BSs). Specifically, we construct a hierarchical Stackelberg game model, study the interaction between BSs and UEs, and prove that the UE layer is an exact potential game with Nash equilibrium (NE). To achieve Stackelberg equilibrium (SE), we propose a game-based hierarchical interaction algorithm (GHIA) and analyze it. The experimental results show that GHIA has good convergence, and the utility performance of participants is better comparing to other algorithms.

With the explosive growth of compute-intensive and time-sensitive applications, edge service caching has been recognized as an effective solution. In this paper, we study a two-tier unmanned Aerial vehicle(UAV) system supporting service caching, where the lower-tier UAV acts as an airborne base station and the upper-tier UAV acts as a small cloud server. In order to improve quality of service for users and increase UAV endurance time, we propose a two time-scale decision framework to jointly optimize system latency and energy consumption. The framework consists of a short time scale and a long time scale, where the lower-tier UAV cache placement and ground user task offloading decisions are updated in the short time scale (i.e., each system time slot t). In the long time scale$\mathbf{T}$, the cache placement and flight trajectory decisions of the upper-tier UAV are updated. We then propose a two-tier decision-making algorithm based on deep deterministic policy gradient (DDPG) to solve the problem, and use the long and short-term memory neural networks(LSTM) algorithm to predict the service content information requested by the ground user. Simulation experimental results show that the proposed algorithm can significantly reduce the system latency while maintaining low energy consumption compared to other schemes.

With the successful commercialization of 5G technology and the accelerated research process of 6G technology, smart cities are entering the 3.0 era. In 6G smart cities, Multi-access Edge Computing (MEC) can provide computing support for a large number of computation-intensive applications. However, the randomness of the wireless network environment and the mobility of nodes make designing the best offloading schemes is challenging. In this article, we investigate the dynamic offloading optimization problem of base station (BS) selection and computational resource allocation for mobile users (MUs). We first envision a MEC-enabled 6G Smart City Network architecture, then formulate the minimizing average system user cost problem as a Markov Decision Process (MDP), and propose a deep reinforcement learning-based offloading optimization and resource allocation algorithm (DOORA). Numerical results illustrate that DOORA scheme significantly outperforms the benchmarks and can remarkably improves the quality of experience (QoE) of MUs.

With the gradual development of unmanned aerial vehicles (UAV) related disciplines such as mechanics, electronics and wireless communication. UAV technology is an important direction for the future development of sixth generation mobile communications (6G) technology. When the BS computing capacity is insufficient or unavailable, UAV-aided 6G mobile edge computing (MEC) is considered a practical solution to maintain the balance between the number of computing tasks and the number of base stations (BS). Firstly, we consider the transmission power setting and design a prioritization mechanism to determine the link selection. Secondly, we construct energy consumption models for user devices (UDs), BS and UAVs separately. Finally, we propose UAV-aided MEC Genetic Algorithm (UMGA) based on genetic algorithm (GA) to minimize the overall energy consumption of the UAV-aided MEC system. Simulation results show that the system energy consumption of our proposed scheme is lower than the system energy consumption of other baseline schemes. Compared with the three baseline algorithms, the performance of our algorithm is improved by 660%, 300% and 41.2%, respectively.

Nowadays, the surge of user data traffic has brought great challenges to the computing and energy capacity of mobile terminals (MTs). Mobile edge computing (MEC) technology is reckoned to be an efficient method to alleviate this problem. It can transfer tasks to MEC server and improve quality of service (QoS). In case of network failure, unmanned aerial vehicle (UAV) is deployed as a data transmission hub connecting MEC server to restore the network. In this article, we consider a UAV transmission hub (UTH) to communicate with the macro base station (MBS). MTs can offload tasks to MBS for processing through UTH, and the MEC server in MBS allocates computing resources to MTs. We raise a computing offloading and resource allocation decision scheme based on deep deterministic policy gradient (DDPG). The scheme considers the continuous generation of dynamic tasks, and the optimization objectives is to minimize the long-term average system cost. The simulation experiment datas verify the performance of DDPG-Based offloading and resource allocation decision scheme. It can validly optimize the average system cost in a random dynamic environment.

The emerging Vehicle to Vehicle (V2V) technology supports the direct communication about road condition information between vehicles, which improves the communication efficiency and driving safety. Network slicing technology can provide network isolation for different users, which can reduce channel interference and save the cost of network resources. However, due to different resource demands and task requirements of each user, how to reasonably allocate resources and reduce the usage cost of resources is still a challenge. In this paper, we study the cost of V2V users to complete the transmission task in the scenario of shared channel with eMBB mobile cellular network users in Radio Access Network (RAN). In addition, we consider the bandwidth resource occupancy rate and the required resource size, and develop a dynamic resource pricing model to ensure the quality of user experience. Under the condition of ensuring the basic rate of cellular users, we divide the cost minimization problem of V2V users transmission tasks into two subproblems: control of transmission power and allocation of slicing bandwidth resources. Then, we propose an Evolutionary Strategy-based Bandwidth Allocation (ESBA) algorithm to complete the transmission tasks within the tolerable delay, which can avoid local minima. Simulation results show that ESBA algorithm has good convergence, and reduce the cost of V2V users transmission tasks effectively.

With the rapid development of 5G technologies, Internet-of-Vehicles (IoV) has become a promising and important research hotspot. The high-speed mobility of vehicles brings great challenges for services with low delay and high stability requirements. To address these challenges, this paper takes the relative movement between vehicles into account and analyzes the mobility in detail based on probability distribution. We propose a proactive caching and multi-link cooperative communication scheme to cope with mobility. According to the driving and content request information of vehicle users, the requested content is cached in the road side units (RSUs) and neighboring vehicles in advance. Furthermore, the optimal bandwidth is allocated for each communication link in order to improve the stability of vehicle communication and data transmission efficiency. We propose a Deep Reinforcement Learning-based Proactive Caching and Bandwidth Allocation Algorithm (DPCBA) by considering the high-dimensional continuity of the state and action space. The extensive simulation results demonstrate that our DPCBA scheme can effectively improve the Quality-of-Experience (QoE) of vehicle users in various situations, and outperforms traditional benchmark algorithms.

The decision of request mapping-which server to handle user request and response routing-which transit route to carry response back to user has great impact on the performance and cost of Content Delivery Networks (CDNs). Request mapping and response routing are traditionally treated independently. The information invisibility and inconsistent objectives may lead to worse performance and high cost. However, the rapid globalization of Internet eXchange Points (IXPs) has facilitated the cooperation between CDN and ISP. In this paper, we consider request mapping and response routing jointly. We formulate the joint problem to navigate the performance and cost tradeoff. To solve the large-scale optimization, we develop a distributed tide algorithm based on Gauss-Seidel. The joint problem can be decomposed to sub-problems which allows for a parallel implementation. Experiment result shows that the relative error between our distributed tide algorithm that iterates within 50 rounds and theoretical optimum is about 0.7 percent. Furthermore, the parallel runtime demonstrates the efficiency of our algorithm.

The Internet of Things (IoT) increases the number of connected devices and supports the ever-growing complexity of applications. Owing to the constrained physical size, the IoT devices can significantly enhance the computational capacity by offloading computation-intensive tasks to the resource-rich edge servers deployed at the base station (BS) via wireless networks. However, how to achieve optimal resource scheduling remains a challenge due to stochastic task arrivals, time-varying wireless channels, and imperfect estimation of channel state information (CSI). In this article, by virtue of the Lyapunov optimization technique, we propose the toward optimal resource scheduling algorithm under imperfect CSI (TORS) to optimize resource scheduling in an IoT environment. A convex transmit power and subchannel allocation problem in TORS is formulated. This problem is then solved via the Lagrangian dual decomposition method. We derive analytical bounds for the time-averaged system throughput and queue backlog. We show that TORS can arbitrarily approach the optimal system throughput by simply tuning an introduced control parameter $\beta $ without prior knowledge of stochastic task arrivals and the CSI of wireless channels. Extensive simulation results confirm the theoretical analysis on the performance of TORS.