关键词： Task analysis   Delays   Resource management   Optimization   Servers   Training   Interference   Ultra-dense network   double deep Q-network   mobile edge computing   resource allocation   task offloading  

摘要： Driven by the vision of 6G, the need for diverse computation-intensive and delay-sensitive tasks continues to rise. The integration of mobile edge computing with the ultra-dense network is not only capable of handling traffic from a large number of smart devices but also delivers substantial processing capabilities to the users. This combined network is expected as an effective solution for meeting the latency-critical requirement and will enhance the quality of user experience. Nevertheless, when a massive number of devices offload tasks to edge servers, the problem of channel interference, network load and energy shortage of user devices (UDs) would increase. Therefore, we investigate the joint uplink and downlink resource allocation and task offloading optimization problem in terms of minimizing the overall task delay while sustaining the UD battery life. Thus, to achieve long-term gains while making quick decisions, we propose an improved double deep Q-network scheme named Prioritized double deep Q-network. In this, the prioritized experience replay has been improved by considering the experience freshness factor along with temporal difference error to achieve fast and efficient learning. Extensive numerical results prove the efficacy of the proposed scheme by analyzing delay and energy consumption. Especially, our scheme can considerably decrease the delay by 11.86%, 26.22%, 48.56%, and 61.04% compared to the OELO scheme, DQN scheme, LOS, and EOS, respectively, when the number of UDs varied from 30 to 180.

关键词： Task analysis   Autonomous aerial vehicles   Optimization   Energy consumption   Collaboration   Planning   Iterative methods   Age of information   mobile edge computing   path planning   proximal policy optimization   reinforcement learning   task offloading   unmanned aerial vehicle  

摘要： This paper studies the age of information (AoI) and energy tradeoff (AET) problem in an aerial-ground collaborative mobile edge computing system, where a high-altitude platform and an unmanned aerial vehicle (UAV) work together to offer computing services for ground devices (GDs). The AET problem is formulated as a multi-objective optimization problem (MOP) that aims at simultaneously minimizing the total AoI of GDs and total energy consumption of the UAV by optimizing its flight paths and task offloading ratios. Addressing the AET problem poses a significant challenge due to the inherent conflict between the two objectives. The existing methods cannot well address the MOP because they adopt the linear combination to transform an MOP into a single-objective optimization problem using fixed weights (i.e., preferences), ignoring the conflict between objectives. Moreover, user preferences may change over time in dynamic MEC systems. To overcome these challenges, we first build a multi-objective Markov decision process model with a vectorial reward for the AET problem. There are one-to-one relationships between each component of the reward and one of the two objectives. Then, we propose a multi-objective learning algorithm based on proximal policy optimization (PPO), which primarily comprises a training phase and an evolutionary phase. The former adopts multi-objective PPO to iteratively optimize multiple learning individuals, aiming to obtain a nondominated policy set. The latter employs a genetic operator to further improve the quality of each policy in the set. Specifically, the crossover and mutation operators operate at the parameter level of policy networks, avoiding stagnation and premature convergence. The experiment results validate that the proposed approach obtains a set of excellent nondominated policies and a favorable balance between objectives. Moreover, the proposed approach achieves improvements of at least 39.8%, 2.1%, and 15.3% regarding AoI, energy c

关键词： Energy consumption   Genetic algorithm   Nonlinear function approximation   Mobile edge computing   Service placement  

摘要： In mobile edge computing (MEC), quality of service (QoS) is closely related to optimizing service placement strategies, which is crucial to providing efficient services that meet user needs. However, due to the mobility of users and the energy consumption limit of edge servers, the existing policies make it difficult to ensure the QoS level of users. In this paper, a novel genetic algorithm based on a simulated annealing algorithm is proposed to balance the QoS of users and the energy consumption of edge servers. Finally, the effectiveness of the algorithm is verified by experiments. The results show that the QoS value obtained by the proposed algorithm is closer to the maximum value, which has significant advantages in improving QoS value and resource utilization. In addition, in software development related to mobile edge computing, our algorithm helps improve the program's running speed.

关键词： Mobile edge computing   Edge federation   Tripartite game   Resource pricing approach  

摘要： Mobile Edge Computing (MEC) deploys edge servers close to end-users to address their computing requirements, with an emphasis on minimizing latency and enhancing security. However, the significant deployment costs associated with edge servers and the potential for resource wastage due to redundant deployment by various Edge Infrastructure Providers (EIPs) highlight the importance of adopting edge federation architecture. This architectural approach aims to deliver superior computing services tailored for MEC scenarios. Nevertheless, effective resource allocation and pricing strategies are crucial to ensure optimal resource sharing within the edge federation. This paper presents a game-theoretic model involving three primary stakeholders: terminal users, resource demanders within the federation, and resource providers within the federation. The game among these stakeholders is structured into two levels, with negotiation rounds playing a pivotal role. At the first level, the game outlines the initial resource demands of terminal users and the initial pricing by EIPs, while also considering the influence of historical pricing trends on resource demands. The second level focuses on defining the psychological expectations of resource demanders within the federation and the initial pricing by resource providers. To solve this game model, this paper introduces the Tripartite Game Pricing Approach (TGPA). Simulation results corroborate that TGPA outperforms existing pricing approaches, yielding higher total revenue for the edge federation and ensuring a certain utilization of computational resources within acceptable time constraints.

关键词： Digital twins   Heuristic algorithms   Approximation algorithms   Network slicing   Delays   Data models   Synchronization   Digital twin   mobile edge computing   network slicing request   age of information   approximation algorithm   online algorithm   and resource allocation  

摘要： Digital twins are poised to enter our lives with Industry 4.0. The Digital Twin Network (DTN) paradigm is projected to deliver upon the promise of efficient collaboration among digital twins to enable complicated and systematic services across many domains, through depicting an overall picture of a group of physical objects. To achieve timely data processing of digital twins, Mobile Edge Computing (MEC) shifts the computational power towards the network edge, and network slicing is well-suited to bundle heterogeneous physical resources to build logical networks based on edge servers for accommodating DTNs. In light of this, in this paper we investigate DTN slicing-enabled service provisioning in MEC, where each DTN slice consists of one master digital twin and a set of worker digital twins, and each worker digital twin is synchronized through collecting data from a respective object periodically. The master digital twin aggregates the processed data from worker digital twins to model the DTN continuously for user query services, whilst meeting delay requirements of users. We capture the utility gain of a DTN slicing request based on the DTN model quality at its master digital twin that is impacted by the Age of Information (AoI), and we focus on two novel optimization problems: the utility maximization problem for a single DTN slicing request, and the dynamic utility maximization problem for multiple DTN slicing requests. We propose an approximation algorithm for the former, and an online algorithm with a provable competitive ratio for the latter. We also evaluate the performance of the proposed algorithms through simulations. Experimental results demonstrate that the proposed algorithms are promising, outperforming their counterparts by at least 10.2%.

关键词： Terahertz communications   Task analysis   Servers   Wireless communication   Bandwidth   Pedestrians   Resource management   Offline reinforcement learning   terahertz communication   augmented reality   mobile edge computing   mobility awareness  

摘要： Augmented reality (AR) holds great promise within the Internet of vehicles (IoV), offering real-time, context-aware information to enhance user experiences. Since wireless AR services have stringent requirements for latency and energy efficiency, terahertz (THz) transmission and mobile edge computing (MEC) can be leveraged to address these issues. Besides, reinforcement learning (RL) is widely employed to design resource allocation and offloading decision schemes, further boosting the benefits of THz and MEC communication. However, conventional RL-based schemes necessitate online trial-and-error interactions and policy updates, which may result in unsafe actions and pose risks, e.g., vehicle collisions. Inspired by this, we propose a novel static data-driven AR tasks offloading framework for the IoV with THz communication. The proposed framework utilizes an offline RL algorithm to train the offloading decision agents without real-world interactions. Specifically, we formulate an optimization problem of maximizing user experience by jointly optimizing bandwidth and computation resource allocation. To reduce the complexity of solving the original problem, we decompose it into two subproblems: 1) THz bandwidth allocation and 2) MEC offloading decisions. Considering the unique characteristics of THz communication that users' available bandwidth varies with their distances to the base station (BS), we design a heuristic THz bandwidth allocation algorithm to achieve fair allocation. To address the typical out-of-distribution (OOD) action issue encountered in offline RL, we exploit the conservative Q-learning (CQL) to make the optimal MEC offloading decisions enhancing user experience. Simulation results show that the proposed scheme outperforms state-of-the-art algorithms in terms of algorithm stability and user feedback.

关键词： Mobile edge computing   task offloading   multiple features matching   two-stage federated learning   Mobile edge computing   task offloading   multiple features matching   two-stage federated learning  

摘要： Mobile edge computing provides relatively rich computation resources for Internet-of-Things (IoT) task offloading at the edge of networks. As time goes on, user tasks present diverse requirements in function, type, dependency, urgency, etc., which makes edge servers take on dynamically diversified service features to adapt to the requirements of user tasks. Moreover, cache has been studied a lot in recent years for reducing the execution cost of related or dependent tasks. However, jointly considering which result data required to be cached and where to cache is still an intractable problem in task offloading due to dynamically diversified and sensitive features of task and edge servers for prediction. To provide more comprehensive consideration, we propose a multiple features matching scheme, coupled with federated learning-assisted collaborative caching, to enhance the efficiency of task offloading. Specifically, we first build a common features of historical tasks based FI-tree to help search for an edge server that best matches the requested task features. This helps to obtain optimal task allocation and improve offloading performance. Further, the results of tasks related to or dependent on cached results can be obtained directly through the collaborative edge cache prediction model trained by two-stage federated learning. In this way, the amount of data executed for offloaded tasks is reduced, thereby speeding up the return of final results as well as reducing the delay and energy of task execution. Meanwhile, it avoids massive transmission of task results correlated data and also protects the privacy of these data when training the prediction model. Experimental results show that our proposed method outperforms the benchmark approaches through reducing the time delay and energy consumption by at least 15.6% and 18.2%.

关键词： Mobile edge computing   Truthfulness   Energy consumption   Algorithm design   Polynomial time approximation scheme  

摘要： In the context of mobile edge computing (MEC), the delay-sensitive tasks can achieve real-time data processing and analysis by offloading to the MEC servers. The objective is maximizing social welfare in an auction- based model. However, the distances between mobile devices and access points lead to differences in energy consumption. Unfortunately, existing works have not considered both maximizing social welfare and minimizing energy consumption. Motivated by this, we address the problem of joint resource allocation and task offloading in MEC, with heterogeneous MEC servers providing multiple types of resources for mobile devices (MDs) to perform tasks remotely. We split the problem into two sub-problems: winner determination and offloading decision. The first sub-problem determines winners granted the ability to offload tasks to maximize social welfare. The second sub-problem determines how to offload tasks among the MEC servers to minimize energy consumption. In the winner determination problem, we propose a truthful algorithm that drives the system into equilibrium. We then show the approximate ratios for single and multiple MEC servers. In the offloading decision problem, we propose an approximation algorithm. We then show it is a polynomial- time approximation scheme for a single MEC server. Experiment results show that our proposed mechanism finds high-quality solutions in changing mobile environments.

关键词： Collaboration   Training   Computational modeling   Computer architecture   Federated learning   Servers   Distributed databases   mobile edge computing   option-critic   multi-agent reinforcement learning  

摘要： Decentralized federated learning (DFL) has emerged as a conducive paradigm, facilitating a distributed privacy-preserving data collaboration mode in mobile edge computing (MEC) systems to bolster the expansion of artificial intelligence applications. Nevertheless, the dynamic wireless environment and the heterogeneity among collaborating nodes, characterized by skewed datasets and uneven capabilities, present substantial challenges for efficient DFL model training in MEC systems. Consequently, the design of an efficient collaboration strategy becomes essential to facilitate practical distributed knowledge sharing and cost reduction for MEC. In this paper, we propose an adaptive decentralized federated learning framework that enables heterogeneous nodes to learn tailored collaboration strategies, thereby maximizing the efficiency of the DFL training process in collaborative MEC systems. Specifically, we present an effective option critic-based collaboration strategy learning (OCSL) mechanism by decomposing the collaboration strategy model into two sub-strategies: local training strategy and resource scheduling strategy. In addressing inherent issues such as large-scale action space and overestimation in collaboration strategy learning, we introduce the option framework and a dual critic network-based approximation method within the OCSL design. We theoretically prove that the learned collaboration strategy achieves the Nash equilibrium. Extensive numerical results demonstrate the effectiveness of the proposed method in comparison with existing baselines.

关键词： Hardware impairments   Rate splitting multiple access   Mobile edge computing   Successful computation probability  

摘要： In this paper, we investigate a cognitive radio (CR) inspired rate splitting multiple access (RSMA) aided mobile edge computing (MEC) network, where hardware impairments (HIs) in all transceivers are considered. Given that allowing the secondary user (SU) to share the resource with the primary user (PU) for data offloading does not degrade the PU's offloading performance, we derive closed-form expressions for the rate-splitting parameter and power allocation coefficient at the SU to maximize its offloading rate. Using the derived parameters, we then derive the successful computation probability (SCP), which is defined as the probability that both the PU and SU can successfully compute their task bits within a given latency budget, into the closed form. To further enhance the performance of the CR inspired RSMA-MEC network with HIs, a SCP maximization-based problem is formulated to jointly optimize the task offloading ratio and task offloading time of both the SU and PU. Leveraging the convex theory, we obtain the optimal solutions in the closed form. Simulation results confirm the following two insights. First, the presence of HIs leads to a decreasing on the SCP and the SCP approaches to a constant which is less than 1 with the increase of the transmit power. Second, with the optimal task offloading ratio and time, the CR inspired RSMA-MEC achieves the highest SCP compared to the existing schemes.