TY - JOUR
T1 - Control-Data Separation with Decentralized Edge Control in Fog-Assisted Uplink Communications
AU - Kang, Jinkyu
AU - Simeone, Osvaldo
AU - Kang, Joonhyuk
AU - Shamai Shitz, Shlomo
N1 - Funding Information:
Manuscript received May 16, 2017; revised October 29, 2017; accepted February 24, 2018. Date of publication March 14, 2018; date of current version June 8, 2018. This work was supported by the National Research Foundation of Korea Grant funded by the Korea Government (MSIT) (No. 2017R1A2B2012698). The work of O. Simeone was supported in part by the U.S. NSF under Grant CCF-1525629 and in part by the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme under Grant 725731. The work of S. Shamai was supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 694630. This work was presented in part at the IEEE Wireless Communications and Networking Conference, San Francisco, CA, USA, March 2017 [1]. The associate editor coordinating the review of this paper and approving it for publication was A. Abrardo. (Corresponding author: Joonhyuk Kang.) J. Kang is with the School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138 USA (e-mail: jkkang@g.harvard.edu).
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2018/6
Y1 - 2018/6
N2 - Fog-aided network architectures for 5G systems encompass wireless edge nodes, referred to as remote radio systems (RRSs), as well as remote cloud center (RCC) processors, which are connected to the RRSs via a fronthaul access network. RRSs and RCC are operated via network functions virtualization, enabling a flexible split of network functionalities that adapts to network parameters such as fronthaul latency and capacity. This paper focuses on uplink communications and investigates the cloud-edge allocation of two important network functions, namely, the control functionality of rate selection and the data-plane function of decoding. Three functional splits are considered: 1) distributed radio access network, in which both functions are implemented in a decentralized way at the RRSs; 2) cloud RAN, in which instead both functions are carried out centrally at the RCC; and 3) a new functional split, referred to as fog RAN (F-RAN), with separate decentralized edge control and centralized cloud data processing. The model under study consists of a time-varying uplink channel with fixed scheduling and cell association in which the RCC has global but delayed channel state information due to fronthaul latency, while the RRSs have local but more timely CSI. Using the adaptive sum-rate as the performance criterion, it is concluded that the F-RAN architecture can provide significant gains in the presence of user mobility.
AB - Fog-aided network architectures for 5G systems encompass wireless edge nodes, referred to as remote radio systems (RRSs), as well as remote cloud center (RCC) processors, which are connected to the RRSs via a fronthaul access network. RRSs and RCC are operated via network functions virtualization, enabling a flexible split of network functionalities that adapts to network parameters such as fronthaul latency and capacity. This paper focuses on uplink communications and investigates the cloud-edge allocation of two important network functions, namely, the control functionality of rate selection and the data-plane function of decoding. Three functional splits are considered: 1) distributed radio access network, in which both functions are implemented in a decentralized way at the RRSs; 2) cloud RAN, in which instead both functions are carried out centrally at the RCC; and 3) a new functional split, referred to as fog RAN (F-RAN), with separate decentralized edge control and centralized cloud data processing. The model under study consists of a time-varying uplink channel with fixed scheduling and cell association in which the RCC has global but delayed channel state information due to fronthaul latency, while the RRSs have local but more timely CSI. Using the adaptive sum-rate as the performance criterion, it is concluded that the F-RAN architecture can provide significant gains in the presence of user mobility.
KW - 5G
KW - Cloud-radio access network (C-RAN)
KW - control data separation
KW - fog-radio access network (F-RAN)
KW - fronthaul
KW - network functions virtualization (NFV)
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U2 - 10.1109/TWC.2018.2813363
DO - 10.1109/TWC.2018.2813363
M3 - Article
AN - SCOPUS:85043779177
SN - 1536-1276
VL - 17
SP - 3686
EP - 3696
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 6
ER -