RAN Slicing Research Articles

RAN Slicing with Inter-Cell Interference Control and Link Adaptation for Reliable Wireless Communications

RAN Slicing with Inter-Cell Interference Control and Link Adaptation for Reliable Wireless Communications

RAN Slicing Toward Coexistence of Time-Sensitive Networking and Wireless Networking

RAN Slicing Toward Coexistence of Time-Sensitive Networking and Wireless Networking

Optimizing Resource Allocation and VNF Embedding in RAN Slicing

Optimizing Resource Allocation and VNF Embedding in RAN Slicing

Deep Reinforcement Learning for Scalable Dynamic Bandwidth Allocation in RAN Slicing With Highly Mobile Users

Deep Reinforcement Learning for Scalable Dynamic Bandwidth Allocation in RAN Slicing With Highly Mobile Users

RAN Slicing with Joint Resource Allocation for a Multi-Tenant Multi-Service System

RAN Slicing with Joint Resource Allocation for a Multi-Tenant Multi-Service System

Intelligent Traffic Steering in Beyond 5G Open RAN Based on LSTM Traffic Prediction

Open radio access network (ORAN) Alliance offers a disaggregated RAN functionality built using open interface specifications between blocks. To efficiently support various competing services, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">namely</i> enhanced mobile broadband (eMBB) and ultra-reliable and low-latency (uRLLC), the ORAN Alliance has introduced a standard approach toward more virtualized, open, and intelligent networks. To realize the benefits of ORAN in optimizing resource utilization, this paper studies an intelligent traffic steering (TS) scheme within the proposed disaggregated ORAN architecture. For this purpose, we propose a joint intelligent traffic prediction, flow-split distribution, dynamic user association, and radio resource management (JIFDR) framework in the presence of unknown dynamic traffic demands. To adapt to dynamic environments on different time scales, we decompose the formulated optimization problem into two long-term and short-term subproblems, where the optimality of the latter is strongly dependent on the optimal dynamic traffic demand. We then apply a long-short-term memory (LSTM) model to effectively solve the long-term subproblem, aiming to predict dynamic traffic demands, RAN slicing, and flow-split decisions. The resulting non-convex short-term subproblem is converted to a more computationally tractable form by exploiting successive convex approximations. Finally, simulation results are provided to demonstrate the effectiveness of the proposed algorithms compared to several well-known benchmark schemes.

RAN Slicing in Multi-MVNO Environment Under Dynamic Channel Conditions

With the increasing diversity in the requirement of wireless services with guaranteed quality of service(QoS), radio access network(RAN) slicing becomes an important aspect in implementation of next generation wireless systems(5G). RAN slicing involves division of network resources into many logical segments where each segment has specific QoS and can serve users of mobile virtual network operator(MVNO) with these requirements. This allows the Network Operator(NO) to provide service to multiple MVNOs each with different service requirements. Efficient allocation of the available resources to slices becomes vital in determining number of users and therefore, number of MVNOs that a NO can support. In this work, we study the problem of Modulation and Coding Scheme(MCS) aware RAN slicing(MaRS) in the context of a wireless system having MVNOs which have users with minimum data rate requirement. Channel Quality Indicator(CQI) report sent from each user in the network determines the MCS selected, which in turn determines the achievable data rate. But the channel conditions might not remain the same for the entire duration of user being served. For this reason, we consider the channel conditions to be dynamic where the choice of MCS level varies at each time instant. We model the MaRS problem as a Non-Linear Programming problem and show that it is NP-Hard. Next, we propose a solution based on greedy algorithm paradigm. We then develop an upper performance bound for this problem and finally evaluate the performance of proposed solution by comparing against the upper bound under various channel and network configurations.

Statistical Federated Learning for Beyond 5G SLA-Constrained RAN Slicing

A key enabler for both scalability and sustainability in beyond 5G (B5G) network slicing consists on minimizing the exchange of raw monitoring data across different domains. This is achieved by bringing the analysis functions closer to the data collection points. To this end, we introduce in this paper <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">statistical federated learning</i> (SFL) provisioning models that can learn over a live network non independent identically distributed (non-IID) datasets in an offline fashion while respecting slice-level service level agreement (SLA) long-term statistical constraints. Specifically, we consider three resource SLA metrics, namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cumulative distribution function</i> (CDF), <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th percentile</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maximum/minimum bounds</i> . These metrics are dataset-dependent and non-convex non-differentiable and, to sidestep the inaccuracy of settling only for surrogates, we propose a novel formulation that jointly considers the statistical objective and constraints as well as their smooth approximation using the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">proxy-Lagrangian</i> framework, which we solve via a non-zero sum two-player game strategy. Numerical results on various slice-level resources show that SFL enables SLA enforcement while significantly reducing the overhead compared to both state-of-the-art FedAvg and centralized constrained deep learning schemes. Finally, we provide an analysis for the lower bound of the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reliable convergence probability</i> in the SFL setup.

무인기 RAN 슬라이싱 지원을 위한 슬라이스 간 동적 자원 할당 알고리즘

5G 생태계에서 무인기는 다양한 서비스들의 quality of service (QoS)를 만족하기 위해 radio access network (RAN) 슬라이싱을 적용할 수 있다. 하지만 무인기는 탑재 가능한 기능이 제한적이기 때문에 공유 무선자원을 각 슬라이스들이 적절히 분배하는 방안이 필요하다. 또한 5G에서는 QoS에 따라 physical resource block (PRB)가 변화하기 때문에 이러한 특성도 자원 할당에 반영해야 한다. 따라서 본 논문에서는 각 서비스들의 QoS를 고려하여, 무인기의 자원 사용량을 최대한으로 하는 동적 슬라이스 간 자원 할당 알고리즘을 제안한다. 본 알고리즘은 동적 네트워크 환경에서 주기적으로 서비스 분포를 고려한 다음, QoS와 자원 효율성을 고려해 가장 성능이 좋은 PRB를 선택하여 자원 할당을 수행하는 과정을 거친다.

Corrections to “Joint RAN Slicing and Computation Offloading for Autonomous Vehicular Networks: A Learning-Assisted Hierarchical Approach” [2021 doi: 10.1109/OJVT.2021.3089083

Corrections to “Joint RAN Slicing and Computation Offloading for Autonomous Vehicular Networks: A Learning-Assisted Hierarchical Approach” [2021 doi: 10.1109/OJVT.2021.3089083

Dynamic Resource Allocation With RAN Slicing and Scheduling for uRLLC and eMBB Hybrid Services

To cope with the limited radio and power resources, designing energy- and cost-efficient resource allocation strategy with RAN slicing and scheduling is important in ensuring the extreme QoS of differentiated Internet of things (IoT) services. In this regard, we focus on guaranteeing the latency and reliability of sporadic uRLLC uplink traffic while improving the quality of continuous eMBB services (e.g., quality of the video) together in this paper. Firstly, a dynamic optimization model considering power consumption and service quality is used to construct the cost function in both time domain and frequency bandwidth for heterogeneous services, subject to the latency constraint. Secondly, given its complexity, a novel two-timescale algorithm with employing Lyapunov optimization is designed, including two sub-algorithms: long-timescale bandwidth allocation and short-timescale service control. In further, the theoretical optimality is analyzed according to the relationships between control parameters and service performances. The utility of our approach and its hard latency guarantee are also illustrated through simulation results under tolerable power consumption.

Resource Allocation for Network Slicing in 5G Telecommunication Networks: A Survey of Principles and Models

With the rapid and sustained growth of network demands, 5G telecommunication networks are expected to provide flexible, scalable, and resilient communication and network services, not only for traditional network operators, but also for vertical industries, OTT, and third parties to satisfy their different requirements. Network slicing is a promising technology to establish customized end-to-end logic networks comprising dedicated and shared resources. By leveraging SDN and NFV, network slices associated with resources can be tailored to satisfy diverse QoS and SLA. Resource allocation of network slicing plays a pivotal role in load balancing, resource utilization, and networking performance. In this article, we focus on the principles and models of resource allocation algorithms in 5G network slicing. We first introduce the basic ideas of the SDN and NFV with their roles in network slicing. The MO architecture of network slicing is also studied, which provides a fundamental framework of resource allocation algorithms. Then, resource types with corresponding isolation levels in RAN slicing and CN slicing are analyzed, respectively. Furthermore, we categorize the mathematical models of resource allocation algorithms based on their objectives and elaborate them with typical examples. Finally, open research issues are identified with potential solutions.

On the automation of RAN slicing provisioning: solution framework and applicability examples

Network slicing is a fundamental feature of 5G systems that allows the partitioning of a single network into a number of segregated logical networks, each optimized for a particular type of service, or dedicated to a particular customer or application. While support for network slicing (e.g. identifiers, functions, signalling) is already defined in the latest 3GPP Release 15 specifications, solutions for efficient automated management of network slicing (e.g. automatic provisioning of slices) are still at a much more incipient stage, especially for what concerns the next-generation Radio Access Network (NG-RAN). In this context, and consistently with the new service-based management architecture defined by 3GPP for 5G systems, this paper presents a functional framework for the management of network slicing in a NG-RAN infrastructure, delineating the interfaces and information models necessary to support the dynamic and automatic deployment of RAN slices. A discussion on the complexity of such automation follows together with an illustrative description of the applicability of the overall framework and information models in the context of a neutral host provider scenario that offers RAN slices to third party service providers.

RAN Slicing to Realize Resource Isolation Utilizing Ordinary Radio Resource Management for Network Slicing

RAN Slicing to Realize Resource Isolation Utilizing Ordinary Radio Resource Management for Network Slicing

Toward Enforcing Network Slicing on RAN: Flexibility and Resources Abstraction

Knowing the variety of services and applications to be supported in the upcoming 5G systems, the current one size fits all network architecture is no more efficient. Indeed, each 5G service may have different needs in terms of latency, bandwidth, and reliability, which cannot be sustained by the same physical network infrastructure. In this context, network virtualization represents a viable way to provide a network slice tailored to each service. Several 5G initiatives (from industry and academia) have been pushing for solutions to enable network slicing in mobile networks, mainly based on SDN, NFV, and cloud computing as key enablers. The proposed architectures focus principally on the process of instantiating and deploying network slices, while ignoring how they are enforced in the mobile network. While several techniques of slicing the network infrastructure exist, slicing the RAN is still challenging. In this article, we propose a new framework to enforce network slices, featuring radio resources abstraction. The proposed framework is complementary to the ongoing solutions of network slicing, and fully compliant with the 3GPP vision. Indeed, our contributions are twofold: a fully programmable network slicing architecture based on the 3GPP DCN and a flexible RAN (i.e., programmable RAN) to enforce network slicing; a two-level MAC scheduler to abstract and share the physical resources among slices. Finally, a proof of concept on RAN slicing has been developed on top of OAI to derive key performance results, focusing on the flexibility and dynamicity of the proposed architecture to share the RAN resources among slices.