Cloud-RAN functional split for an efficient fronthaul network

Rodriguez, Veronica Quintuna; Guillemin, Fabrice; Ferrieux, Alexandre; Thomas, Laurent

doi:10.1109/iwcmc48107.2020.9148093

Cited by 29 publications

(12 citation statements)

References 7 publications

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“…Helm charts uses the SLA parameters extracted from the service order (number of users, coverage, etc), as well as, the information of the hosting K8s nodes previously selected. Launching the deployment of RAN units (7): While using created helm charts, the BCR-s triggers the deployment of RU, DU, CU units from the K8s Master node. Creation of RAN containers (8): The K8s master creates the containers of each RAN unit, RU, DU, CU according to the helm chart information.…”

Section: Resource Discovery (3)mentioning

confidence: 99%

“…The 3GPP [5] particularly aims to split the RAN functions into three units Central Unit (CU), Distributed Unit (DU) and Remote Unit (RU), this latter placed near to antennas. [6], [7]. When considering split Cloud-RAN architectures [5] the time budget needs to be shared between the runtime of RAN functions and the transmission time between the distant RAN units.…”

Section: Introductionmentioning

confidence: 99%

“…As a proof of concept, we implement the proposed ZTC mechanism and the NMS including a graphic interface to create, delete and monitor deployments and resources. We validate the ZTC model while using open source Cloud-RAN units [7], [10] developed on the basis of Open Air Interface (OAI) [11] code. We finally evaluate the performance of the proposed model in terms of agility and resource consumption in a fully cloudified infrastructure.…”

Section: Introductionmentioning

confidence: 99%

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Towards 6G zero touch networks: The case of automated Cloud-RAN deployments

Bini¹,

Corbel²,

Rodriguez³

et al. 2022

Preprint

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The arrival of 6G technologies shall massively increase the proliferation of on-demand and ephemeral networks. Creating and removing customized networks on the fly notably requires fully automation to guarantee commissioning-time acceleration. In this paper, we address the deployment automation of an end-to-end mobile network, with special focus on RAN units (referred to as Cloud-RAN). The Cloud-RAN automation is especially challenging due to the strong latency constraints expected in 6G as well as the required management of physical antennas. To automatically instantiate a Cloud-RAN chain, we introduce a Zero Touch Commissioning (ZTC) model which performs resource discovery while looking for both antennas and computing capacity as near as possible to the targeted coverage zone. We validate the ZTC model by a testbed which deploys, configures and starts the network service without human intervention while using Kubernetes-based infrastructures as well as open-source RAN and core elements implementing the mobile network functions.

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Section: Resource Discovery (3)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards 6G zero touch networks: The case of automated Cloud-RAN deployments

Bini¹,

Corbel²,

Rodriguez³

et al. 2022

Preprint

Self Cite

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“…Similarly, the authors of [27] propose an algorithm to allocate Customer Virtual Networks (CVN) considering functional split. Another proposal can be found in [28] where Rodriguez et al suggest a split selection algorithm to ensure efficient utilization of the fronthaul network, while allowing a cooperation among BSs. In this same line, the authors of [29] propose adjusting the split selection to enable URLLC services, exploiting Coordinated Multi-Point techniques.…”

Section: Related Workmentioning

confidence: 99%

Flexible Functional Split and Fronthaul Delay: A Queuing-Based Model

et al. 2021

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We study the delay over virtual RAN (vRAN) topologies, entailing base stations that are divided into centralized and distributed units, as well as the packet-switched fronthaul network that connects them. We consider the use of flexible functional split, where the functions that are executed at each of these two entities can be dynamically shifted. We propose a queuing-based model, which is able to precisely mimic the behavior of such nodes, and we validate it by means of extensive simulations. We also exploit Jackson Networks theory to establish the end-to-end delay over the fronthaul network, allowing us to assess the impact of having different networking policies and conditions (for instance, background traffic or heterogeneous technologies). Thanks to the simulator we can also broaden the analysis, by studying the delay variability. In addition, we conduct an in-depth analysis of the performance exhibited by a realistic network setup, whose particular characteristics might hinder the services performance, due to the longer dwell times at each split configuration. The results evince the validity of the proposed model, even under realistic conditions. We show that it might not be enough to guarantee an average stable operation of the centralized/distributed units, but the traffic load should remain below the slowest service rate, to avoid reaching unacceptable delays. An increase of > 100× is observed in the delay, using the realistic network setup, when these conditions do not hold.• We first extend the DU/CU model, by considering not only different service rates per split configuration, but also dwell times. In addition, this enhanced model also permits having different stand-by times after each split, and avoids the possibility of going to the same split after leaving it. • We exploit Jackson Theory and the CU/DU model to yield the overall end-to-end delay, considering the

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“…Hence, the pragmatic capacity is representative of the achievable rate under the assumption of separate detection and decoding, i.e., without "turbo" reprocessing of the decoder output (see, e.g., [13], [14] and references therein). In practice, iterative "turbo" detection is very hard to implement since often the detector is implemented in hardware (e.g., in an integrated circuit) and the decoder is implemented in software, and maybe even in a different location (as for example in the so-called 7.2 split between hardware and software, enabling cloud-based processing of the signals from remote radio heads [15]). For this reason, we believe that the pragmatic capacity for separated detection and decoding is a very meaningful performance metric to compare different modulation formats and the associated pilot schemes and soft-output detectors.…”

Section: Introductionmentioning

confidence: 99%

OTFS vs. OFDM in the Presence of Sparsity: A Fair Comparison

Gaudio

Colavolpe

2022

IEEE Trans. Wireless Commun.

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Many recent works in the literature declare that Orthogonal Time-Frequency-Space (OTFS) modulation is a promising candidate technology for high mobility communication scenarios. However, a truly fair comparison with its direct concurrent and widely used Orthogonal Frequency-Division Multiplexing (OFDM) modulation has not yet been provided. In this paper, we present such a fair comparison between the two digital modulation formats in terms of achievable communication rate. In this context, we explicitly address the problem of channel estimation by considering, for each modulation, a pilot scheme and the associated channel estimation algorithm specifically adapted to sparse channels in the Doppler-delay domain, targeting the optimization of the pilot overhead to maximize the overall achievable rate. In our achievable rate analysis we consider also the presence of a guard interval or cyclic prefix. The results are supported by numerical simulations, for different time-frequency selective channels including multiple scattering components and under non-perfect channel state information resulting from the considered pilot schemes. This work does not claim to establish in a fully definitive way which is the best modulation format, since such choice depends on many other features which are outside the scope of this work (e.g., legacy, intellectual property, ease and know-how for implementation, and many other criteria). Nevertheless, we provide the foundations to properly compare multi-carrier communication systems in terms of their information theoretic achievable rate potential, within meaningful and sensible assumptions on the channel models and on the receiver complexity (both in terms of channel estimation and in terms of soft-output symbol detection).

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Cloud-RAN functional split for an efficient fronthaul network

Cited by 29 publications

References 7 publications

Towards 6G zero touch networks: The case of automated Cloud-RAN deployments

Towards 6G zero touch networks: The case of automated Cloud-RAN deployments

Flexible Functional Split and Fronthaul Delay: A Queuing-Based Model

OTFS vs. OFDM in the Presence of Sparsity: A Fair Comparison

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