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

Bo Tian,Qi Zhang,Massimo Tornatore,Yiqiang Li

doi:10.1109/access.2020.2997396

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.

Highlights

The continuous growth in mobile broadband service and the emergence of new machine-centric applications are creating unprecedented network requirements that exceed the capabilities of current mobile network architecture [1], [2]
In this scenario, leveraging traffic prediction obtained by a deep neural network, we propose a real-time traffic assignment scheme that is capable of i) rerouting traffic from an affected remote radio heads (RRHs) to other RRHs to protect against backhaul failures, and ii) migrating traffic to as few RRHs as possible so that switching off some backhaul links for energy efficiency
We show how to optimally place mm-wave RRHs to jointly maximize survivability and energy efficiency

Summary

INTRODUCTION

The continuous growth in mobile broadband service and the emergence of new machine-centric applications are creating unprecedented network requirements that exceed the capabilities of current mobile network architecture [1], [2]. We first give a description of the considered 5G C-RAN network architecture with mm-wave RRHs. we give an overview of the traffic prediction based real-time traffic assignment in RRHs for survivable and energy efficient backhaul transmission. A. PROBLEM STATEMENT The mm-wave RRHs placement problem in our work can be stated as follows: Given the network topology, coverage radius of mm-wave RRHs, the set of traffic pattern and maximum number of RRHs that can be supported per backhaul link, the coverage requirement ratio; Decide the optimal mm-wave RRHs placement scheme; to maximize the number of survivable RRHs and the number of switched-off backhaul links whilst meeting the coverage requirement, such that achieving maximum backhaul survivability and energy efficiency. E) The number of RRHs constraints: Equation (16) guarantee the number of mm-wave RRHs to be placed at candidate locations

HEURISTIC ALGORITHM

Findings

CONCLUSION