An energy consumption comparison of different mobile backhaul and fronthaul optical access architectures

Carapellese, Nicola; Pizzinat, A.; Tornatore, Massimo; Chanclou, P.; Gosselin, Stéphane

doi:10.1109/ecoc.2014.6964023

Cited by 13 publications

(8 citation statements)

References 1 publication

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“…Modern cellular architectures support a more flexible deployment of radio resources that may be distributed using a cloud radio access network technique, where a BS is split into two parts [42], one part where the RRHs are at remote cell sites, and in the other part, one centralized BBU is located up to tens of kilometers away (see Fig. 1).…”

Section: A Backhaul and Fronthaulmentioning

confidence: 99%

Overview of Millimeter Wave Communications for Fifth-Generation (5G) Wireless Networks—With a Focus on Propagation Models

Rappaport¹,

Xing²,

MacCartney³

et al. 2017

IEEE Trans. Antennas Propagat.

1,260

746

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This paper provides an overview of the features of fifth generation (5G) wireless communication systems now being developed for use in the millimeter wave (mmWave) frequency bands. Early results and key concepts of 5G networks are presented, and the channel modeling efforts of many international groups for both licensed and unlicensed applications are described here. Propagation parameters and channel models for understanding mmWave propagation, such as line-of-sight (LOS) probabilities, large-scale path loss, and building penetration loss, as modeled by various standardization bodies, are compared over the 0.5-100 GHz range.

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Section: A Backhaul and Fronthaulmentioning

confidence: 99%

Overview of Millimeter Wave Communications for Fifth-Generation (5G) Wireless Networks—With a Focus on Propagation Models

Rappaport¹,

Xing²,

MacCartney³

et al. 2017

IEEE Trans. Antennas Propagat.

1,260

746

View full text Add to dashboard Cite

show abstract

“…f) Summary and Lessons Learnt: In a typical CRAN deployment where the RF I/Q is transported from RRH to BBU, the fronthaul traffic is independent of the user data which results in a constant bit rate over fronthaul links at all times to support the normal operations of BBU and RRH. Hence, there can be significant power consumption overhead for the CRAN deployment [307], [308]. Therefore, the new designs of fronthaul solutions should consider the overall energy consumption in addition to the end-to-end latency [309].…”

Section: Sub-block4mentioning

confidence: 99%

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Nasrallah

Thyagaturu

Alharbi

et al. 2019

IEEE Commun. Surv. Tutorials

476

219

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Many network applications, e.g., industrial control, demand Ultra-Low Latency (ULL). However, traditional packet networks can only reduce the end-to-end latencies to the order of tens of milliseconds. The IEEE 802.1 Time Sensitive Networking (TSN) standard and related research studies have sought to provide link layer support for ULL networking, while the emerging IETF Deterministic Networking (DetNet) standards seek to provide the complementary network layer ULL support. This article provides an up-to-date comprehensive survey of the IEEE TSN and IETF DetNet standards and the related research studies. The survey of these standards and research studies is organized according to the main categories of flow concept, flow synchronization, flow management, flow control, and flow integrity. ULL networking mechanisms play a critical role in the emerging fifth generation (5G) network access chain from wireless devices via access, backhaul, and core networks. We survey the studies that specifically target the support of ULL in 5G networks, with the main categories of fronthaul, backhaul, and network management. Throughout, we identify the pitfalls and limitations of the existing standards and research studies. This survey can thus serve as a basis for the development of standards enhancements and future ULL research studies that address the identified pitfalls and limitations.

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“…[44]. Thus, decreasing the consumed energy by base stations significantly impacts the total energy consumption of the RAN part, which will also enable to decrease the energy consumption of all ICT sectors and in particular the cellular networks [45]. In fact, centralizing network functions causes the energy consumption to decrease and virtualizing the network functions results in reducing further the energy consumption [46].…”

Section: E a Comparative Studymentioning

confidence: 99%

Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges

Brik

Boutiba

Ksentini

2022

IEEE Open J. Commun. Soc.

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Open Radio Access Network (O-RAN) alliance was recently launched to devise a new RAN architecture featuring open, softwaredriven, virtual, and intelligent radio access architecture. O-RAN architecture is based on (1) disaggregated RAN functions that run as Virtual Network Function (VNF) and Physical Network Function (PNF); (2) the notion of RAN controller that runs centrally RAN applications such as mobility management, users' scheduling, radio resources allocation, etc. The RAN controller is in charge of enforcing the application decisions by using open interfaces with the RAN functions. One important feature introduced by O-RAN is the heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent RAN applications that are able to fulfill the Quality of Service (QoS) requirements of the envisioned 5G and beyond network services. In this work, we first give an overview of the evolution of RAN architectures toward 5G and beyond, namely C-RAN, vRAN, and O-RAN. We also compare them based on various perspectives, such as edge support, virtualization, control and management, energy consumption, and AI support. Then, we review existing DL-based solutions addressing the RAN part. We also show how they can be integrated/mapped to the O-RAN architecture since these works were not initially adapted to the O-RAN architecture. In addition, we present two case studies for DL techniques deployment in O-RAN. Furthermore, we describe how the main steps of deployed DL models in O-RAN can be automated, to ensure stable performance of these models, introducing ML system operations (MLOps) concept in O-RAN. Finally, we identify key technical challenges, open issues, and future research directions related to the Artificial Intelligence (AI)-enabled O-RAN architecture.

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An energy consumption comparison of different mobile backhaul and fronthaul optical access architectures

Cited by 13 publications

References 1 publication

Overview of Millimeter Wave Communications for Fifth-Generation (5G) Wireless Networks—With a Focus on Propagation Models

Overview of Millimeter Wave Communications for Fifth-Generation (5G) Wireless Networks—With a Focus on Propagation Models

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges

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