关键词： Autonomous aerial vehicles   Task analysis   Trajectory   Heuristic algorithms   Delays   Reinforcement learning   Resource management   Unmanned aerial vehicle   mobile edge computing   computation offloading   trajectory control   reinforcement learning  

摘要： Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged to support computation-intensive tasks in 6G systems. Since the battery capacity of a UAV is limited, to serve as many users as possible, a joint design on UAV trajectory and offloading strategy with consideration for service fairness is essential to provide energy-efficient computation offloading to the users in UAV-MEC networks. Unfortunately, such a joint decision-making problem is not straightforward due to various task types required from users and various functionalities of different UAVs enabled by different application programs. Considering the above issues, we take energy efficiency and service fairness as the objective, and propose a Multi-Agent Energy-Efficient joint Trajectory and Computation Offloading (MA-ETCO) scheme. To adapt to dynamic demands of users, we develop an optimization-embedding multi-agent deep reinforcement learning (OMADRL) algorithm. Each UAV autonomously learns the trajectory control decision based on MADRL to adapt to dynamic demands. Then, it will obtain the optimal computation offloading decision by solving a mixed-integer nonlinear programming problem. The computation offloading result, in turn, will be used as an indicator to guide UAVs' trajectory design. Compared to relying solely on deep reinforcement learning, such an optimization-embedding way reduces action space dimension and improves convergence efficiency.

关键词： Internet of vehicle   Mobile edge computing   Budget   Offloading strategy   Latency  

摘要： Advancing in communication systems requires nearby devices to act as networks when devices are not in use. Such technology is mobile edge computing, which provides enormous communication services in the network. In this research, we explore a multiuser smart Internet of Vehicles (IoV) network with mobile edge computing (MEC) assistance, where the first edge server can assist in completing the intense computing jobs from the vehicular users. Many currently available works for MEC networks primarily concentrate on minimising system latency to ensure the quality of service (QoS) for users by designing some offloading strategies. Still, they need to account for the retail prices from the server and, as a result, the budgetary constraints of the users. To solve this problem, we present a Dueling Double Deep Q Network (D3QN) with an Optimal Stopping Theory (OST) strategy that helps to solve the multi-task joint edge problems and minimises the offloading problems in MEC-based IoV networks. The multi-task-offloading model aims to increase the likelihood of offloading to the ideal servers by utilising the OST characteristics. Lastly, simulators show how the proposed methods perform better than the traditional ones. The findings demonstrate that the suggested offloading techniques may be successfully applied in mobile nodes and significantly cut the anticipated time required to process the workloads.

关键词： Intelligent reconfigurable surface   mobile edge computing   energy consumption minimization  

摘要： Intelligent Reflecting Surface (IRS) is a promising approach to effectively improve the propagation environment, which includes a controller and numerous reflecting elements. In this paper, we consider an IRS-assisted mobile edge computing (MEC) system, which has a base station (BS), multiple single-antenna user terminals (UTs), and an IRS. Aiming at minimizing the system energy consumption, the transmission power of UTs, the BS receiving beamforming vector, the BS computing resources allocation, and the IRS effective phase shifts are jointly optimized. As these four variables are coupled together and the problem is non-convex, block coordinate descent method is adopted to decompose the optimization problem into four subproblems. In order to address the transmission power subproblem, quadratic transform based fractional programming, Lagrange dual transformation, and difference of convex function algorithm are used. Quadratic transformation and Lagrange dual transformation are also used to optimize the phase shift matrix and the receiving beamforming vector, while the quadratic transform in the multidimensional and complex case is used additionally in the IRS phase-shift subproblem to tackle the fractional term. Meanwhile, the computation resource allocation is derived in a closed-form expression. Simulation results confirm that for the IRS-assisted MEC system, the proposed optimization method is effective.

关键词： Intelligent reflecting surface   mobile edge computing   multi-cell networks  

摘要： The explosive development of the Internet of Things (IoT) has led to increased interest in mobile edge computing (MEC), which provides computational resources at network edges to accommodate computation-intensive and latency-sensitive applications. Intelligent reflecting surfaces (IRSs) have gained attention as a solution to overcome blockage problems during the offloading uplink transmission in MEC systems. This paper explores IRS-aided multi-cell networks that enable servers to serve neighboring cells and cooperate to handle resource exhaustion. We aim to minimize the joint energy and latency cost by jointly optimizing the computation tasks, edge computing resources, user beamforming, and IRS phase shifts. The problem is decomposed into two subproblems-the MEC subproblem and the IRS communication subproblem-using the block coordinate descent (BCD) technique. The MEC subproblem is reformulated as a nonconvex quadratic constrained problem (QCP), while the IRS communication subproblem is transformed into a weight-sum-rate problem with auxiliary variables. We propose an efficient algorithm to alternately optimize the MEC resources and IRS communication variables. Numerical results show that our algorithm outperforms benchmarks and that multi-cell MEC systems achieve additional performance gains when supported by IRS.

关键词： Computation offloading in MEC   Machine learning   Multi-access edge computing   Mobile edge computing   Computation for 5G/6G communication  

摘要： The advancement of technology towards the realization of the evolving mobile computing paradigm brings a rapid paradigm shift in its usage, especially in the Internet, computation, and communications, that has a profound impact on businesses, services, and users. With the rise in resource -intensive or edge -based mobile applications such as autonomous driving, Amazon Go, virtual and augmented reality, and healthcare -related applications, countless challenges in computation and communication parameters like latency, bandwidth, and energy consumption are evolving. As a result, the multi-access edge computing (MEC) paradigm receives enormous attention where some portions of the user applications are offloaded to powerful machines for their efficient execution to optimize different evaluation metrics or to achieve performance goals. While a few survey works are available in this direction, none of them focuses explicitly on the emerging machine learning (ML) based computation offloading techniques and various associated sub -problems together. This paper aims to provide a detailed but precise overview of the research on using ML techniques for MEC environments. In this survey, we focus on how authors and researchers utilize the ML models in computation offloading problems on MEC architecture. We extend our study by considering several edge architectures, offloading parameters, ML approaches, and problem formulation strategies concerning computation offloading. Additionally, this paper discusses the potential challenges in the direction of computation offloading on MEC architecture.

关键词： Mobile edge computing   Service offloading   Deep reinforcement learning  

摘要： Computing offloading is a potential avenue to reduce transmission delay by moving computing tasks from cloud to edge nodes. Due to the limited computing capacity of edge nodes, performing effective computing offloading strategy is challenging. To simplify the problem, it is often assumed that the timeline is discrete and offloading decisions are only made at the end of each time slot. However, the assumption results in decision waiting time and increases service delay. In this paper, we first formulate a time-continuous computing offloading problem without the assumption of discrete timeline. In order to reduce service delay while guaranteeing user fairness, the optimization objective is constructed as the alpha-fair utility function of average task delay rather than just average task delay. Then, we propose a novel algorithm by adjusting the reward in standard reinforcement learning to solve this problem, and also prove the convergence of our algorithm theoretically. To the best of our knowledge, this paper is the first attempt to study the time-continuous computing offloading problem with fairness. Evaluations show that the proposed algorithm has better performance in terms of service delay and user fairness compared to five baselines.

关键词： Optimization   Uplink   Servers   Massive MIMO   Task analysis   Resource management   Computer architecture   Cell-free massive MIMO   computation offloading   mobile edge computing   sequential optimization  

摘要： The coupling of cell-free massive MIMO (CF-mMIMO) with Mobile Edge Computing (MEC) is investigated in this paper. A MEC-enabled CF-mMIMO architecture implementing a distributed user-centric approach both from the radio and the computational resource allocation perspective is proposed. A multi-objective optimization problem (MOOP) for the joint allocation of radio and remote computational resources is formulated, aimed at striking an optimal balance between total uplink power minimization and sum spectral efficiency maximization, under resource budget and latency constraints. In order to solve such a challenging non-convex problem, we convert the MOOP to an equivalent single-objective optimization problem (SOOP) through the weighted sum method and propose an iterative algorithm based on alternating optimization and sequential convex programming, along with an alternative heuristic resource allocation for distributed networks. Finally, we provide a detailed performance comparison between the proposed MEC-enabled CF-mMIMO architecture with its co-located counterpart, and its small-cell implementation. Numerical results reveal the effectiveness of the proposed resource allocation scheme, under different access point selection strategies, and the natural suitability of CF-mMIMO in supporting computation-offloading applications with benefits over users' transmit power and energy consumption, the effective latency experienced, and the computation offloading efficiency.

关键词： Task analysis   Privacy   Servers   Delays   Wireless communication   Costs   Perturbation methods   Differential privacy   location privacy   mobile edge computing   task offloading  

摘要： In mobile edge computing (MEC), users can offload tasks to nearby MEC servers to reduce computation cost. Considering that the size of offloaded tasks could disclose user location information, several location privacy-preserving task offloading mechanisms have been proposed under the single-server scenario. However, to the best of our knowledge, none of them could provide a strict privacy protection guarantee or be applicable to the multi-server scenario where the user's location can be inferred more accurately if servers collude with each other. In this paper, we propose a novel location privacy-aware task offloading framework (LPA-Offload) for both single-server and multi-server scenarios, which provides strict and provable location privacy protection while achieving efficient task offloading. Specifically, we propose a location perturbation mechanism that allows each user to perturb its real location within a rational perturbation region and provides a differential privacy guarantee. To make a satisfactory offloading strategy, we propose a perturbation region determination mechanism and an offloading strategy generation mechanism that adaptively select a proper perturbation region according to the customized privacy factor, and then generate an optimal offloading strategy based on the perturbed location within the decided region. The determination of the perturbation region could achieve personalized privacy requirements while reducing computation cost. LPA-Offload is proved to satisfy (epsilon,delta)-differential privacy, and the experiments demonstrate the effectiveness of our framework.

关键词： Mobile edge computing   Multi-task multi-server   Non-orthogonal multiple access   Processing capability   Resource allocation   Task offloading  

摘要： With the rapid development of technology, the Internet of vehicles (IoV) has become increasingly important. However, as the number of vehicles on highways increases, ensuring reliable communication between them has become a significant challenge. To address this issue, this paper proposes a novel approach that combines Non-Orthogonal Multiple Access (NOMA) with a time-optimized multitask offloading model based on Optimal Stopping Theory (OST) principles. NOMA-OST is a promising technology that can address the high volume of multiple access and the need for reliable communication in IoV. A NOMA-OST-based IoV system is proposed to meet the Vehicle-to-Vehicle (V2V) communication requirements. This approach optimizes joint task offloading and resource allocation for multiple users, tasks, and servers. NOMA enables efficient resource sharing by accommodating multiple devices, whereas OST ensures timely and intelligent task offloading decisions, resulting in improved reliability and efficiency in V2V communication within IoV, making it a highly innovative and technically robust solution. It suggests a low-complexity sub-optimal matching approach for sub-channel allocation to increase the effectiveness of offloading. Simulation results show that NOMA with OST significantly improves the system's energy efficiency (EE) and reduces computation time. The approach also enhances the effectiveness of task offloading and resource allocation, leading to better overall system performance. The performance of NOMA with OST under V2V communication requirements in IoV is significantly improved compared to traditional orthogonal multiaccess methods. Overall, NOMA with OST is a promising technology that can address the high reliability of V2V communication requirements in IoV. It can improve system performance, and energy efficiency and reduce computation time, making it a valuable technology for IoV applications.

关键词： unmanned aerial vehicle   mobile edge computing   UAV-aided edge computing   task offloading   offloading decision   trajectory planning  

摘要： Recently, the integration of unmanned aerial vehicles (UAVs) with edge computing has emerged as a promising paradigm for providing computational support for Internet of Things (IoT) applications in remote, disaster-stricken, and maritime areas. In UAV-aided edge computing, the offloading decision plays a central role in optimizing the overall system performance. However, the trajectory directly affects the offloading decision. In general, IoT devices use ground offload computation-intensive tasks on UAV-aided edge servers. The UAVs plan their trajectories based on the task generation rate. Therefore, researchers are attempting to optimize the offloading decision along with the trajectory, and numerous studies are ongoing to determine the impact of the trajectory on offloading decisions. In this survey, we review existing trajectory-aware offloading decision techniques by focusing on design concepts, operational features, and outstanding characteristics. Moreover, they are compared in terms of design principles and operational characteristics. Open issues and research challenges are discussed, along with future directions.