Communication-Computation-Aware User Association in MEC HetNets: A Meta-Analysis

The stochastic geometry modeling and analysis of mobile edge computing (MEC) networks contribute to the effective network configuration. Most prior works investigate the MEC network performance by spatial averaging the topological randomness, which can hide the differences among users and result in inaccurate analytical results. To address this limitation, an analytical framework is developed to study the meta distribution of the task offloading performance. We consider the Poisson MEC network where the users with dynamic task requests offload the tasks to the MEC server. Given the model, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$b$</tex-math></inline-formula> -th moment and the meta distribution of the success task offloading probability are first analyzed. Further, the exact yet convenient approximation of the MEC computing load is derived, based on which the edge computing delay is obtained. Our results demonstrate that the proposed meta-analytical framework allows tractable yet accurate analytical results, and provides additional fine-grained information about MEC-network planning.

This chapter studies mobile edge computing (MEC) networks assisted by unmanned aerial vehicles (UAVs). According to the application scenarios, we consider three roles for UAVs in MEC networks: exploiting MEC computing capabilities, serving as a computing server, and serving as a relay for computation offloading. Furthermore, the details for resource allocation and optimization are presented in the three scenarios of UAV-assisted MEC networks. In addition, we focus on the situation in which a UAV not only functions as an MEC server to inspect turbines on a wind farm, but also performs task computation. To facilitate wide applications of UAV-assisted MEC in practice, this chapter highlights the main implementation issues of UAV-assisted MEC, including optimal UAV deployment, wind models, and joint trajectory–computation performance optimization.

This paper considers the computation offloading and resource allocation for system sum cost minimization in a mobile edge computing (MEC)network. The computation offloading decision of the user equipments (UEs)can be made through wireless link to access the MEC server. Our intent is to minimize the weighted sum cost of the system, which takes into account the energy consumption and time delay, for both task processing and data transmissions, subject to the force delay requirement of the devices. We first formulate the problem as a mixed-integer and non-convex optimization scheme. Enlightened by the superior performance of reinforcement learning (RL)on solving resource control problems, we put forward a novel RL-based scheme for computation offloading and resource allocation in MEC network. Specifically, the MEC node is considered as an “agent”, which first makes decision on whether to offloading the computing task to the MEC server, and then utilizes convex optimization algorithms to settle the radio spectrum and computation resource scheduling problem in each decision phase. The simulation results present that the RL-based method can achieve better computation offloading and resource allocation performance compared to those of local computing and remote computing modes. It also achieves very similar or slightly better performance compared to the genetic algorithm based method.

Resource allocation in RISs-assisted UAV-enabled MEC network with computation capacity improvement

Collaborative computation offloading and wireless charging scheduling in multi-UAV-assisted MEC networks: A TD3-based approach

Mobile edge computing (MEC) networks have been recently adopted to accommodate the fast-growing number of mobile devices performing complicated tasks with limited hardware capability. Recently, edge nodes with communication, computation, and caching capacities are starting to be deployed in MEC networks. Due to the physical separation of these resources, efficient coordination and scheduling are important for efficient resource utilization and optimal network performance. In this paper, we study mobility load balancing for communication, computation, and caching-enabled heterogeneous MEC networks. Specifically, we propose to tackle this problem via a multi-agent deep reinforcement learning-based framework. Users served by overloaded edge nodes are handed over to less loaded ones, to minimize the load in the most loaded base station in the network. In this framework, the handover decision for each user is made based on the user’s own observation which comprises the user’s task at hand and the load status of the MEC network. Simulation results show that our proposed multi-agent deep reinforcement learning-based approach can reduce the time-average maximum load by up to 30% and the end-to-end delay by 50% compared to baseline algorithms.

To realize the vision of the Internet of Things (IoT), mobile-edge computing (MEC) has recently emerged as a promising paradigm to meet the computation demand from mobile users (MUs). In this article, we study the network performance in large-scale stochastic MEC wireless networks, where the tasks can be computed locally by the local computation capabilities (LCCs) or be offloaded to MEC servers for edge computing. To this end, a MEC network is modeled featuring random node distribution, dynamic task requests, orthogonal frequency-division multiple access, task retransmission, and parallel computing in MEC servers. Given the model, a 2-D discrete-time Markov chain is first adopted to characterize the task execution process, including local computing and task offloading. Based on the coupling between communication and computing, the average outage probability of the task transmission and the average MEC computation load are derived by integrating the stochastic geometry and queuing theory. Furthermore, by jointly analyzing the local computation latency, transmission latency, and edge computation latency, we derive the average end-to-end latency of the task execution. Our results show that the LCCs in MUs can improve the network performance, including communication and computation performance, in stochastic MEC networks. In addition, useful guidelines for MEC network provisioning and planning are provided to avoid either the local computing or the task offloading being the latency performance bottleneck.

Energy-efficient communication is one of the most pertaining issues in realizing mobile edge computing (MEC) networks. How to effectively use non-orthogonal multiple access (NOMA) to realize energy efficient MEC networks has been of high interest and calls for in-depth study. In particular, there has been yet no work to study the energy efficient communication scheme from system perspective in this area. In this work, we focus on the design of a system-centric computation energy efficiency (EE) scheme in a NOMA-based MEC network. The system computation EE is maximized by jointly optimizing the computing frequencies of the MEC server and the local users, the offloading time, the transmit powers of local users, as well as the execution time for the local users and the MEC server. Employing the fractional programming theory and introducing auxiliary variables, we develop an efficient Dinkelbach-based iterative scheme to tackle the formulated non-linear fractional programming problem. For obtaining instrumental insights, we further derive closed-form expressions for the partial solutions based on Karush-Kuhn-Tucker conditions. Simulation results are provided to verify the quick convergence of the proposed iterative algorithm, to obtain insights, as well as to demonstrate the superiority of the proposed resource allocation scheme in terms of the system computation EE by comparing it with benchmark schemes.

To address the severe challenge from the rapid growth of number of downloads of contents via mobile network, it is imperative to develop efficient content caching scheme in mobile edge computing (MEC) networks, which can alleviate the heavy burden on backhaul links and reduce the delay for content delivery. However, the caching performance is highly related to the MEC server placement. In this paper, we jointly optimize the server placement problem and content caching problem in MEC networks. We propose a heuristic algorithm based on Kuhn-Munkres (KM) algorithm and greedy algorithm to minimize the average delay and average bandwidth resource usage. The simulation shows that our proposed algorithm can reduce delay and bandwidth, compared with other schemes.

In this paper, we investigate a new mobile blockchain-enabled edge computing (MBEC) network, where mobile users can join the empowered process of public blockchains and meanwhile offload computation-intensive mining tasks to the mobile edge computing (MEC) server. However, the trustiness of the MEC server and the fairness of computation resources allocated by the MEC server for each user become key challenges. To tackle these challenges, we consider an untrusted MEC server and propose a nonce hash computing ordering (HCO) mechanism in MBEC networks. Then we formulate nonce hash computing demands of an individual user as a non-cooperative game that maximizes the personal revenue. Moreover, we also analyze the existence of Nash equilibrium of the non-cooperative game and design an alternating optimization algorithm to achieve the optimal nonce selection strategies for all users. With the proposed HCO mechanism, the MEC server can provide much fairer computation resources for all users, and we can achieve the optimal nonce strategies of hash computing demands by using the proposed alternating optimization algorithm. Numerical results demonstrate that the proposed HCO mechanism can provide fairer computation resource allocation than the traditional weighted round-robin mechanism, and further verify the effectiveness of this alternating optimization algorithm.

Using Unmanned aerial vehicles (UAVs) to assist mobile edge computing (MEC) network is a promising solution to provide flexible and low-latency computing service for resource limited user equipments (UEs). However, the computing resources that one UAV can provide are often limited and cannot provide computing services for massive mobile users, thus limiting the application of this technology. So, a novel multi-UAV-assisted MEC network is proposed in this paper. The optimization problem is formulated with the aim to make full use of network's computing resources to reduce the number of failed tasks and energy consumption. To solve this challenging problem, a novel solution is proposed. First, we use a swarm intelligence algorithm to investigate the deployment of UAVs. Second, we offer an efficient matching algorithm to explore the tasks assignments under the given deployment of UAVs. The simulation results show that the proposed solution can effectively reduce the number of failed tasks and energy consumption.

Mobile edge computing (MEC) has emerged as a key enabler for low-latency and computation-intensive applications. However, conventional terrestrial MEC networks face challenges in coverage, scalability, and dynamic environments. To address these limitations, this paper explores unmanned aerial vehicle (UAV)-assisted MEC, leveraging the mobility and flexibility of UAVs to enhance edge computing performance. However, challenges such as dynamic mobile user association, multi-UAV collaborative trajectory design, and limited communication and computation resources remain significant hurdles. We then formulate a joint optimization problem for user association, trajectory design, and resource allocation for the multi-UAV collaborative MEC network, aiming to minimize system cost considering computation task demands and UAV resource constraints. The problem, modeled as a Mixed Integer NonLinear Program (MINLP), is reformulated as a decentralized partially observable Markov decision process (Dec-POMDP) to capture complex system dynamics. Afterwards, we propose a multi-agent generative-enhanced deep reinforcement learning method that integrates generative diffusion model (GDM) with multi-agent deep reinforcement learning (MADRL) to effectively handle high-dimensional action spaces and enable collaborative multi-UAV decision-making. Extensive simulations using a real-world mobility dataset demonstrate that the proposed method consistently outperforms three baselines in terms of time-average system cost, task completion delay, task satisfactory ratio, and energy consumption ratio when varying the number of UAVs.

In a mobile edge computing (MEC) network, mobile devices could selectively offload tasks to the edge server(s) to save time and energy. However, we should consider many dynamic factors in task offloading optimization, which increases the complexity of this problem. Instead of executing the traditional optimization algorithm repeatedly, a well-trained empirical model such as an artificial neural network could be more efficient in decision making. In this research, considering the potential uneven spatial distribution of mobile devices in an MEC network with multiple wireless edge gateways, we allow an edge gateway to offload tasks to a nearby edge gateway further. We propose a deep reinforcement learning-based joint optimization approach for both device-level and edge-level task offloading. Experimental results show that the proposed approach achieves a near-optimal task delay performance and a better trade-off between the task delay and the energy consumption on tasks.

Deploying federated learning (FL) in mobile edge computing (MEC) networks has become a prevalent approach to distributed learning. However, the inherent heterogeneity in computing, transmission and data on edge devices (EDs) brings challenges in improving training efficiency and speed. Existing approaches primarily focus on increasing batch sizes or employing adaptive batching to expedite convergence, but often overlook the generalization ability of the model. In this paper, we propose an energy-efficient adaptive batching approach for FL in MEC networks, aiming at minimizing the energy consumption by balancing training efficiency and speed. Initially, we exploit the relationship between batch size and loss improvement while determining the optimal learning rate corresponding to the batch size and understanding the correlation among loss improvement, learning rate, and gradient noise scale (GNS). Then we dynamically adjust the batch size based on the GNS and propose a low-complexity approach for measuring GNS. Finally, we fine-tune the batch size by assessing gradient similarity on each ED to ensure an optimal level of gradient noise during training, thereby enhancing the model's generalization. Experiment results demonstrate the effectiveness of our approach with an approximate 50% and 20% reduction in energy consumption and time consumption compared with existing approaches.

This letter investigates the scenario of resisting intelligent reactive jammer in mobile edge computing (MEC) networks. To solve the multi-dimensional offloading allocation problem while avoiding malicious jamming attacks, a hierarchical reinforcement learning (HRL) based hybrid hidden strategy is proposed to allocate multi-dimensional resources and reduce the probability of being perceived in the offloading process. Specifically, the HRL framework is divided into two layers for asynchronous update: the upper layer with the discrete policy network taking charge of outputting the channel access strategies to resist the dynamic jamming attack in the frequency domain, and the lower layer with the continuous policy network network mainly offering hidden offloading strategies to avoid the detection of intelligent reactive jammers. By integrating the double deep Q-Network and twin delayed deep deterministic network, and exploiting their advantages in solving the hybrid strategy problems, the mobile device (MD) is able to execute effective hidden offloading strategies to reduce the computing cost as well as to significantly avoid intelligent reactive jamming in dynamic and unknown environments.