Multi-Access Edge Computing Empowered Heterogeneous Networks: A Novel Architecture and Potential Works

Abstract

One of the most promising approaches to address the mismatch between computation- intensive applications and computation-limited end devices is multi-access edge computing (MEC). To overcome the rapid increase in traffic volume and offload the traffic from macrocells, a massive number of small cells have been deployed, so-called heterogeneous networks (HetNets). Strongly motivated by the close integration of MEC and HetNets, in this paper, we propose an envisioned architecture of MEC-empowered HetNets, where both wireless and wired backhaul solutions are supported, flying base stations (BSs) can be equipped with MEC servers, and mobile users (MUs) need both communication and computation resources for their computationally heavy tasks. Subsequently, we provide the research progress summary of task offloading and resource allocation in the proposed MEC-empowered unmanned aerial vehicle (UAV)-assisted heterogeneous networks. We complete this article by spotlighting key challenges and open future directives for researches.

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

“…First, the use case "consumer-oriented services" aims to bring direct benefits to users through the capability of running computation-heavy and latency-sensitive applications at the network edge. By means of computation offloading, users can exploit substantial computing resources on the edge server [53]. Applications and services under the first category can include graphical rendering applications (e.g., 3D gaming, AR/VR, assisted reality, and cognitive assistance), intermediate data-processing (e.g., data analytics and video analysis), and low-latency applications (e.g., remote surgery on tactile Internet, AR/VR, video games, and interactive applications), and location-based service recommendation.…”

Efficient Computation Offloading in Multi-Tier Multi-Access Edge Computing Systems: A Particle Swarm Optimization Approach

“…As noted, the proposed algorithm JROPSO uses PSO to jointly find optimal solutions to problems concerning computation-offloading decisions in Equation (21) and computational resource allocation in Equation (20). The JROPSO algorithm is illustrated in Algorithm 2.…”

“…Set the iteration index as t = 1. Compute the resource allocation according to Equation (20). Calculate the fitness value of each particle according to Equation (27).…”

“…repeat for each the number of particles K m do Update the velocity V k and position X k according to Equations (22) and (26), respectively. end Update the resource allocation according to Equation (20). Calculate the fitness of each particle solution according to Equation (27).…”

“…Therefore, integrating MEC into 5G networks in order to increase network performance, to reduce MD-related power consumption, and to utilize macro and small cells represents a research interest in academia and industry. Recent studies have focused on computation offloading in 5G networks [5], with other research focusing on single-tier MEC servers used in heterogeneous scenarios [9][10][11][12][13][14][15][16]; however, achieving effective offloading decisions related to computation-completion time and energy consumption in a multi-tier MEC server remains an optimization problem that attracts considerable attention [17][18][19][20]. Moreover, there have been several MEC-related studies associated with its use in vehicular networks [21][22][23][24] and video processing [25,26].…”