Revenue Model with Multi-Access Edge Computing for Cellular Network Architecture

Due to the drastic growth in the number of devices connected to mobile networks, the demand for further expansion of the capacity of wireless communication is increasing. It is necessary to decrease the traffic on the backhaul and reduce the end-to-end latency. To address it, in the last few years, the authors have proposed the concept of mmWave overlay heterogeneous cellular networks, where mmWave small cell base stations are introduced into the conventional macro cells. The conventional cloud applications are deployed on the Multi-access Edge Computing (MEC) according to the application requirements and data traffic situations, and the users are controlled with out-of-band C-plane to connect to the surrounding MEC applications. For the installation of MEC in the heterogeneous cellular networks, to the best of authors knowledge, there is no research conducted on whether a business model can be established when additional fees are paid for MEC. In this paper, the authors attempt to introduce a new concept of ecosystem for MEC based on the combination of ultra-broadband mmWave communications. Moreover, the authors propose a business model for MEC, and report the numerical analysis results from simulations.

[1]  Sergio Barbarossa,et al.  5G-MiEdge: Design, standardization and deployment of 5G phase II technologies: MEC and mmWaves joint development for Tokyo 2020 Olympic games , 2017, 2017 IEEE Conference on Standards for Communications and Networking (CSCN).

[2]  Sergio Barbarossa,et al.  The Edge Cloud: A Holistic View of Communication, Computation, and Caching , 2018, 1802.00700.

[3]  Khaled Ben Letaief,et al.  Delay-optimal computation task scheduling for mobile-edge computing systems , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).

[4]  Liang Tong,et al.  A hierarchical edge cloud architecture for mobile computing , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[5]  Biplab Sikdar,et al.  An integrated model for the latency and steady-state throughput of TCP connections , 2001, Perform. Evaluation.

[6]  阪口 啓,et al.  D1.1 ~ Use Cases and Scenario Definition ~ , 2017 .

[7]  Kei Sakaguchi,et al.  Performance Evaluation of 5G mmWave Edge Cloud with Prefetching Algorithm - Invited Paper , 2018, 2018 IEEE 87th Vehicular Technology Conference (VTC Spring).

[8]  Kei Sakaguchi,et al.  User Satisfaction Constraint Adaptive Sleeping in 5G mmWave Heterogeneous Cellular Network , 2018, IEICE Trans. Commun..

[9]  Tarik Taleb,et al.  An analytical model for Follow Me Cloud , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[10]  K. B. Letaief,et al.  A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.

[11]  H. Vincent Poor,et al.  Latency and Reliability-Aware Task Offloading and Resource Allocation for Mobile Edge Computing , 2017, 2017 IEEE Globecom Workshops (GC Wkshps).

[12]  Ahmed Helmy,et al.  Weighted waypoint mobility model and its impact on ad hoc networks , 2005, MOCO.

[13]  Yoshihisa Kishiyama,et al.  A novel architecture for LTE-B :C-plane/U-plane split and Phantom Cell concept , 2012, 2012 IEEE Globecom Workshops.

[14]  Kei Sakaguchi,et al.  Millimeter-wave Evolution for 5G Cellular Networks , 2014, IEICE Trans. Commun..

[15]  Robert W. Heath,et al.  Where, When, and How mmWave is Used in 5G and Beyond , 2017, IEICE Trans. Electron..