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

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. INDEX TERMS Centralized radio access network, backhaul link, millimeter wave, survivability, energy efficiency.

Parallel Deployment and Performance Analysis of a Multi-Hop Routing Protocol for 5G Backhaul Networks Using Cloud and HPC Platforms

“…The optimization design is restricted by a maximum number of relays per hops to three. The work in [28] proposed a RRH placement strategy to assign traffic and resources to a few low-traffic RRHs for maximizing backhaul survivability and energy efficiency. This solution depends on deep neural networks for learning and predicting the data traffic.…”

Design of a RoF fronthaul link based on optical generation of mm-waves for 5G C-RANs

“…On the other hand, the complexity of cell-site is another weakness factor that digitized RoF suffers from which become very complicated and more costly especially in 5G and beyond systems because many RRHs are needed to be supported. The use of millimeter-wave frequency bands leads to the smaller cell-sites deployment which require more RRHs in result [13]. Consequently, in order to avoid these issues of digitized RoF, the deployment of analog RoF which provides the most spectrally efficient solution and most power efficient RRH is an urgent necessity [14].…”

Dynamic wireless resource allocation in ultra-dense network based on cooperative computing

“…When only considering single-resource optimization, the genetic algorithm solves the function division problem in the network and reduces operating costs through proper planning [7]. At the same time, spectrum and energy are usually formulated as multiresource optimization problems [8][9][10][11][12][13] . In contrast to single-resource optimization, multi-resource optimization produces a set of optimal solutions, primarily referred to as a Pareto optimal solution rather than a single optimal solution.…”