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.

To meet ever more stringent requirements in terms of latency, 5G/6G networks are evolving from centralized to distributed architectures, for which the cloud-native paradigm with services decomposed into microservices is utmost relevant. This in turn raises the issue related to the distribution of network functions. In this paper, we introduce a novel microservice placement strategy considering the internal service composition, notably the communication between microservices. We formulate the placement as an optimization problem with the aim of minimizing end-to-end service latency. We solve the optimization problem with a combination of greedy and genetic algorithms.

The evolution of telecommunication network towards cloud-native environments enables flexible centralization of the base band processing of radio signals. There is however a trade-off between the centralization benefits and the fronthaul cost for carrying the radio data between distributed antennas and data processing centers, which host the virtual RAN functions. In this paper, we present a specific split solution for an efficient fronthaul, which enables reducing the consumed bandwidth while being compliant with advanced cooperative radio technologies (interference reduction and data rate improvements). The proposed split has been implemented on the basis of Open Air Interface code and shows important gains in the required fronthaul bandwidth as well as significant latency reduction in the processing of radio frames.

We develop and demonstrate a network slice management architecture on top of the so-called Open Network Automation Platform (ONAP). A slice manager is introduced to allow a client to describe the desired slice along with the associated Service Level Agreement (SLA), and to interface with ONAP in order to deploy and maintain the negotiated slice. This approach is illustrated by considering a private mobile network based on open source software components orchestrated by ONAP.

We consider in this paper the implementation of a cloud radio access network (C-RAN) on a centralized multicore system supporting the base band processing of several distributed antennas. We present a parallel processing model based on both functional and data decomposition of virtualized base band unit (BBU) functions in order to reduce their runtime. We study two scheduling strategies of parallel runnable BBU jobs, where computing resources can be allocated either per user equipments or else per code blocks. By using data obtained when running an open source RAN code (namely, OAI), we introduce a batch queuing model (the M[X ]/M/C multi-service system) to assess the needed processing capacity in a data center while meeting tight latency requirements in the down-link and up-link directions. The proposed model is validated by simulation when processing a hundred LTE-cells in a multi-core system. Results provide valuable guidelines for sizing and deploying Cloud-RAN systems.

In the framework of Network Function Virtualization (NFV), we address in this work the design and sizing of Cloud-RAN architectures. We concretely investigate the execution time of software-based Base Band Units (BBUs) on multi-core systems. Since Cloud-RAN requires a real-time behavior, we use parallel programming techniques in order to minimize the runtime of BBU functions. For an efficient utilization of computing resources, we investigate the relevance of resource pooling where a global scheduling algorithm allocates processing units to runnable BBU-jobs. We specifically examine the gain that can be obtained when applying data parallelism on the channel decoding BBU-function which is the most expensive one in terms of processing time. Performance results show a significant reduction in the runtime of PHY functions which enables the deployment of a fully centralized Cloud-RAN architecture.