Network slicing is a key enabler in the Next Generation 5G Radio Access Network (RAN) to build the RAN-as-a-Service concept. Cloud-RAN, Network Function Virtualization, Software Defined Network and RAN functional splits are the main pillars expected to be integrated to provide the required flexibility. One of the major concerns is to efficiently allocate RAN resources for slices, while supporting multiple use-cases with heterogeneous Quality-of-Service (QoS) requirements. Current related work is adopting radio resource allocation scheme by considering a cell-centric deployment approach for slice embedding. However, to achieve greater flexibility and fine-grained tunable resource utilization, we believe that the deployment scheme should be integrated in the slice design. In this paper, we go a step further and propose a RAN slicing approach with customized deployment scheme on user basis. As the corresponding optimization problem is NP-Hard, we propose a low-cost and efficient heuristic algorithm for RAN Slice allocation based on the Particle Swarm Optimization approach. Our proposal jointly harnesses radio, processing and link resources at user level tailored to the QoS requirements, while customizing efficiently the underlying physical RAN resource usage.

Network slicing is proposed as a new paradigm to serve the plethora of 5G services on a shared infrastructure. Within this context, a Radio Access Network (RAN) slice is considered as the proportion of physical spectrum resources to be served to third parties. Interestingly, 3GPP standardized options of RAN processing dis-aggregation into network functions while enabling their placement whether in distributed or centralized locations. The adoption of an end-to-end RAN slicing raises new challenges related to the allocation efficiency of joint radio, link and computational resources. To deal with the stringent latency requirements of 5G services, we propose, in this paper, a Deep Learning based approach for User-centric end-to-end RAN Slice Allocation scheme. It can decide in real-time, to jointly allocate the amount of radio resources and functional split for each end- user. Our proposal satisfies end-user's requirements in terms of throughput and latency, while minimizing the infrastructure deployment cost.

Next Generation 5G Radio Access Network (NGRAN) is envisioned to integrate the slicing approach to build a flexible network supporting diverse use-cases with customized architectures, features and services. RAN processing functional splits have been standardized to add new deployment design capabilities and enhance cost efficiency. A further challenge consists in how to meet the multitude use-case's requirements while considering different design models in the physical infrastructure. Current related works are tackling the slice embedding problem from a cell-centric perspective. However, to achieve greater flexibility and better resource utilization, a user-centric approach should be more exploited. In this paper, we propose a SLICE-HPSO scheme that jointly harnesses radio, processing and link resources at the user level to build multiple user slices on top of the physical infrastructure. Our proposal is tailored to different user quality-of-service requirements and to the diverse functional splits resource requests. SLICE-HPSO is in compliance with the 3GPP and optimizes further the heterogeneous resource usage while meeting the scalability requirement.

5G RAN aims to evolve new technologies spanning the Cloud infrastructure, virtualization techniques and Software Defined Network capabilities. Advanced solutions are introduced to split the functions of the Radio Access Network (RAN) between centralized and distributed locations. Such paradigms improve RAN flexibility and reduce the infrastructure deployment cost without impacting the user quality of service. We propose a novel functional split orchestration scheme that aims at minimizing the RAN deployment cost, while considering the requirements of its processing network functions and the capabilities of the Cloud infrastructure. With a fine grained approach on user basis, we show that the proposed solution optimizes both processing and bandwidth resource usage, while minimizing the overall energy consumption compared to i) cell-centric, ii) distributed and iii) centralized Cloud-RAN approaches. Moreover, we evaluate the effectiveness of our proposal in a 5G experimental prototype, based on Open Air Interface (OAI). We show that our solution achieves good performance in terms of total deployment cost and resolution time.

5G radio access networks are expected to leverage the Cloud environment for building a cost effective network infrastructure. Advanced mechanisms with functional split are introduced to split the RAN functionalities into centralized and distributed locations. This novelty has brought more flexibility to RAN deployment but is still conditioned to the radio resource availability. In this paper, we propose a new orchestration framework for joint radio and functional split scheme on user basis. Specifically, we address the orchestration of heterogeneous resources in a multi-sited Cloud-RAN infrastructure. The key idea behind our proposal is to optimize jointly the functional split and End-to-End resource allocation in order to achieve an enhanced throughput satisfaction and a low deployment cost. Results show that our approach E2E-US optimizes the heterogeneous resource usage. Indeed, the appropriate user radio load and functional split are dynamically and jointly selected, which outperforms cell centric functional split approaches.

The massive adoption of Cloud technology in mobile access networks has driven the operators and vendors to work together in order to make Radio Access Network (RAN) ecosystem more agile. In this context, the virtualization of network functions is the cornerstone of a successful Network Function Virtualization (NFV) environment. However, the stringent requirements of RAN functions make their deployment in a Cloud infrastructure more complex and prone to performance issues. In this respect, this paper puts forward a novel approach that adopts an agile orchestration of fine-grained RAN network functions in order to achieve higher flexibility and improve performances. Specifically, we address the orchestration of baseband processing network functions in a multi-sited Cloud infrastructure. To do so, we propose a user-centric solution, denoted UCS-CRAN, that optimizes the split of the baseband units, while considering both the requirements of its processing network functions and the capabilities of Cloud infrastructure. Based on extensive simulations, the results show that our proposal optimizes both processing and bandwidth usage while minimizing the energy consumption compared to cell-centric, distributed and centralized Cloud-RAN approaches.