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： Intelligent reflecting surface (IRS)   mobile edge computing   quality of service   unmanned aerial vehicles (UAV)   wireless inland waterway communications  

摘要： Due to its exceptional ability to create favorable line-of-sight (LoS) propagation environments, the intelligent reflecting surface (IRS) is widely recognized as a technological enabler for wireless inland waterway communications. In this article, an IRS-assisted hybrid unmanned aerial vehicles (UAV)-terrestrial network architecture with unmanned surface vehicles (USVs) is proposed and a USVs energy minimization problem is formulated by jointly considering offloading decisions, computation capability, beamforming vector design and IRS phase shift-vector. To address the formulated problem, we decouple the original problem into two subproblems, in which the first subproblem focuses on joint offloading decisions and computation capability and the second subproblem concerns joint IRS phase shift-vector and the beamforming vector design. The enhanced differential evolution algorithm (EDE) is proposed to solve the former subproblem, and the minimum-variance-distortionless-response (MVDR) and enhanced min-maximization (EMM) algorithms are proposed to obtain optimized beamforming vector and IRS phase shift-vector in the second subproblem, respectively. Simulation results show how the proposed solution realizes a good tradeoff between network energy consumption and capacity in comparison to three alternative algorithms. The results also show that the proposed algorithm can improve the network performance in terms of the number of successfully offloaded tasks.

关键词： Internet of Things   mobile edge computing   unmanned aerial vehicle   age of information   clustering   trajectory  

摘要： With the development of the Internet of Things (IoT) technology, massive amounts of sensor data in applications such as fire monitoring need to be transmitted to edge servers for timely processing. However, there is an energy-hole phenomenon in transmitting data only through terrestrial multi-hop networks. In this study, we focus on the data collection task in an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) network, where a UAV is deployed as the mobile data collector for the ground sensor nodes (SNs) to ensure high information freshness. Meanwhile, the UAV is equipped with an edge server for data caching. We first establish a rigorous mathematical model in which the age of information (AoI) is used as a measure of information freshness, related to both the data collection time and the UAV's flight time. Then a mixed-integer non-convex optimization problem is formulated to minimize the peak AoI of the collected data. To solve the problem efficiently, we propose an iterative two-step algorithm named the AoI-minimized association and trajectory planning (AoI-MATP) algorithm. In each iteration, the optimal SN-collection point (CP) associations and CP locations for the parameter epsilon are first obtained by the affinity propagation clustering algorithm. The optimal UAV trajectory is found using an improved elite genetic algorithm. Simulation results show that based on the optimized epsilon, the AoI-MATP algorithm can achieve a balance between data collection time and flight time, reducing the peak AoI of the collected data.

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： 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： 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： IoV   Task offloading   Mobile edge computing   Fog computing   Fuzzy neural network  

摘要： The Internet of Vehicles (IoV) technology is progressively maturing because of the growth of private cars and the establishment of intelligent transportation systems. The development of smart cars has, therefore, been followed by a parallel rise in the volume of media and video games in the automobile and a massive increase in the need for processing resources. Smart cars cannot process the enormous quantity of requests created by vehicles because they have limited computing power and must maintain many outstanding jobs in their queues. The distribution of edge servers near the customer side of the highway may also accomplish real-time resource requests, and edge servers can assist with the shortage of computational power. Nevertheless, the substantial amount of energy created while processing is also an issue we must address. A joint task offloading strategy based on mobile edge computing and fog computing (EFTO) was presented in this paper to address this problem. Practically, the position of the processing activity is first discovered by obtaining the computing task's route, which reveals all the task's routing details from the starting point to the desired place. Next, to minimize the time and time expended during offloading and processing, a multi-objective optimization problem is implemented using the task offloading technique F-TORA based on the Takagi-Sugeno fuzzy neural network (T-S FNN). Finally, comparative trials showing a decrease in time consumed and an optimization of energy use compared to alternative offloading techniques prove the effectiveness of EFTO.

关键词： Digital twin (DT)   mobile edge computing   vehicular ad-hoc networks (VANETs)   blockchain   synchronized privacy-preserving authentication (SPPA)  

摘要： Digital Twin (DT) technology, by performing simulation, analysis, and prediction over the data mapped to digital space, can create a digital replica of the physical object. It can be combined with edge computing or cloud computing to provide broad vehicle-to-everything applications and improve the service quality of vehicular ad-hoc networks (VANETs). In this article, DT technology and mobile edge computing are integrated into VANETs to introduce a framework of mobile digital twin edge network-driven VANETs (MDTEN-Driven VANETs). Moreover, facing the security and privacy challenges in the framework, we propose a synchronized privacy-preserving authentication (SPPA) scheme. In SPPA, we first design a synchronized anonymous certificateless aggregate signature (SA-CLAS) to achieve the authentication with time state synchronization and the privacy preservation of the real identity. Furthermore, to deal with malicious vehicles, we adopt blockchain technology and devise a smart contract algorithm to manage the public information of vehicles. The security analysis demonstrates that SA-CLAS is existentially unforgeable under adaptive chosen message attacks, and SPPA can satisfy the necessary security requirements. The performance evaluation shows the efficiency and practicality of SA-CLAS and SPPA. Besides, the designed smart contract is implemented in an Ethereum test network, which presents an acceptable blockchain consumption.

关键词： Autonomous aerial vehicles   Task analysis   NOMA   Relays   Trajectory   Computational modeling   Signal to noise ratio   Unmanned aerial vehicle   mobile edge computing   trajectory design   reconfigurable intelligence surface  

摘要： In this letter, we investigate the mobile edge computing framework with unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) collaboration, in which a dedicated non-orthogonal multiple access (NOMA) based protocol is introduced for task offloading. In the model, the UAV acts as a relay node and computation server to support multiple ground users for offloading computation tasks to remote access point with the assistance of the RIS. To explore the impact of the joint UAV and RIS design with NOMA on computation performance, the sum computation bits maximization problem is formulated by optimizing computation and offloading bits, RIS phase shift design, UAV trajectory and bandwidth allocation. In order to obtain the solution, the original problem is decomposed into three more tractable subproblems, which can be solved by applying semidefinite relaxation method and successive convex approximation technique. Finally, the numerical solution demonstrates that our proposed algorithm achieves improvements on computation capacity compared with the benchmark and outperforms considerably the scheme without collaboration of the UAV and RIS.

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.