Centralized Radio Access Networks (C-RAN) exploiting millimeter wave (mm-wave) technology in remote radio heads (RRHs) are regarded as a promising approach to satisfy the challenging service requirements of fifth generation (5G) mobile communication. However, ultra-dense deployment of mm-wave RRHs will generate enormous amount of traffic that will require effective design and operation of C-RAN backhaul. In this paper, we focus on developing an optimal mm-wave RRHs placement strategy that exploits resource and traffic assignment in RRHs to achieve reliable and energy efficient backhaul transmissions. Specifically, in this paper, mm-wave is considered both to provide end users access and to interconnect RRHs in same frequency band, hence achieving energy saving thanks to hardware and frequency reuse. In this scenario, leveraging the traffic predictions obtained by a deep neural network, we present a real-time traffic assignment scheme where traffic from affected RRHs can be rerouted to other RRHs to protect against backhaul failures and traffic migrates to as few RRHs as possible to switch off some backhaul links for energy efficiency. Due to the inherent short-range transmission of mm-wave, different RRH deployment locations significantly affect interconnections in RRHs. Therefore, we model the mm-wave RRH placement problem into an optimization framework that jointly maximizes backhaul survivability and energy efficiency, whilst subjects to constraints as network coverage and capacity. To guarantee scalability of the proposed scheme as network scale increases, a heuristic algorithm is also proposed. Numerical evaluations show that, with appropriate RRH placement strategies, significant survivability and energy efficiency improvements can be achieved.

Joint Optimization of Survivability and Energy Efficiency in 5G C-RAN with mm-Wave Based RRH

Tornatore M.
2020-01-01

Abstract

Centralized Radio Access Networks (C-RAN) exploiting millimeter wave (mm-wave) technology in remote radio heads (RRHs) are regarded as a promising approach to satisfy the challenging service requirements of fifth generation (5G) mobile communication. However, ultra-dense deployment of mm-wave RRHs will generate enormous amount of traffic that will require effective design and operation of C-RAN backhaul. In this paper, we focus on developing an optimal mm-wave RRHs placement strategy that exploits resource and traffic assignment in RRHs to achieve reliable and energy efficient backhaul transmissions. Specifically, in this paper, mm-wave is considered both to provide end users access and to interconnect RRHs in same frequency band, hence achieving energy saving thanks to hardware and frequency reuse. In this scenario, leveraging the traffic predictions obtained by a deep neural network, we present a real-time traffic assignment scheme where traffic from affected RRHs can be rerouted to other RRHs to protect against backhaul failures and traffic migrates to as few RRHs as possible to switch off some backhaul links for energy efficiency. Due to the inherent short-range transmission of mm-wave, different RRH deployment locations significantly affect interconnections in RRHs. Therefore, we model the mm-wave RRH placement problem into an optimization framework that jointly maximizes backhaul survivability and energy efficiency, whilst subjects to constraints as network coverage and capacity. To guarantee scalability of the proposed scheme as network scale increases, a heuristic algorithm is also proposed. Numerical evaluations show that, with appropriate RRH placement strategies, significant survivability and energy efficiency improvements can be achieved.
2020
Backhaul link
Centralized radio access network
Energy efficiency
Millimeter wave
Survivability
File in questo prodotto:
File Dimensione Formato  
Tian_Access_20.pdf

accesso aperto

Descrizione: Tian_Access_20
: Publisher’s version
Dimensione 2.33 MB
Formato Adobe PDF
2.33 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1165576
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 4
social impact