Software-defined Radio Access Network Research Articles

Delphi: Computing the Maximum Achievable Throughput in SD-RAN Environments

Software-Defined Radio Access Networks (SD-RANs) foster the concepts of programmability and flexibility, which are vital for next generation cellular networks. However, SD-RANs render network management and orchestration very challenging. Indeed, related works indicate that when thousands of connected devices are spread across the underlying network, SD-RAN approaches with a single controller become deficient and exhibit undesired behavior. Despite this, state-of-the-art research papers lack concrete solutions and evaluations with respect to throughput predictability, where the latter is jeopardized by irregularities in the SD-RAN control plane, specifically in realistic testbeds. In order to overcome the aforementioned issues, in this work, we present : a novel platform that provides both analytical and experimental methods to achieve our goal, which is computing the maximum achievable throughput in SD-RAN environments. Analyzing the results provided by, we can capture the impact of the SD-RAN control plane on throughput. Moreover, we can design important guidelines as to which policy to choose given objectives such as throughput maximization or robustness. Providing a platform for SD-RAN evaluations based on open-source components, enables new avenues for research in the mobile network community. Focusing on FlexRAN SD-RAN controller for our initial results, overall, our findings show that when the number of Base Stations (BSs) and User Equipment (UEs) in the network increases beyond 5000, due to non-timely received control packets for the maximum Channel Quality Indicator (maxCQI) policy the overall throughput decreases by more than 20%.

Yazılım Tanımlı Radyo Erişim Ağı için Mobil Kullanıcı Türüne Duyarlı Yük Dengeleme Algoritması

Under extreme increase on video contents in eMBB applications; the 5G requirements cannot been handled by the conventional self-organizing in 4G infrastructure. While executing load balancing in 5G RAN, mobile user type for eMBB applications should be considered. Nowadays, eMBB has been carried by QUIC and HTTP2.0 protocol for Android and iOS users, respectively. In mobile user aware load balancing, Deep Packet Inspection (DPI) up to application layer for packet routing is required. This can be only handled by Software-Defined Network (SDN) without any hardware expenditure in physical infrastructure. Therefore, this paper proposed Software-Defined Radio Access Network (SD-RAN) with two novel functions: Waiting Time Function (WTF) and Load Balancing Function (LBF). In WTF; the queuing inspired approach is proposed for the low complex implementation of the mobile user aware load balancing in 5G-RAN. Waiting Time parameters for iOS and Android users are modeled by M/G/1 and G/G/1 markov queues. It is also executed by M/M/c/K markov model SD-RAN topology. In LBF; a novel Mixed Integer Linear Problem is defined for waiting time optimization. To overcome NP hardness, a local search for the eMBB load threshold analysis is performed and determined as 0.79 and 0.94 for UMas and UMis. A low complex load balancing algorithm is proposed in the light of these thresholds. According to performance results; SD-RAN outperforms nearly 40 % QoS then the conventional SON according to received packet count. It can serve 40\% more user than the conventional one without any extra expenditure on physical infrastructure. As a result, it can handle eMBB flows with acceptable waiting time under 2 milliseconds level.

MARC: On Modeling and Analysis of Software-Defined Radio Access Network Controllers

Network programmability also sneaked into the mobile world leading to the emergence of Software-Defined Radio Access Network (SD-RAN) architectures. Interestingly, while only a small number of prototype architectures exist for SD-RAN, their performance evaluations are unfortunately also limited. Recent evaluations are carried out for small network dimensions of up to 50 devices, while emerging 5G/6G networks envision numbers of devices beyond 5000. Although 5G/6G applications are more stringent with respect to latency guarantees, performance evaluations of such low scale remain questionable. To fill this void, this paper presents <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MARC</monospace> : a novel benchmarking tool for SD-RAN architectures and their controllers. We use <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MARC</monospace> to measure, analyze and identify performance implications for two state-of-the-art open source SD-RAN solutions: <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FlexRAN</monospace> and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5G-EmPOWER</monospace> . We perceive results for monitoring application scenarios considering fully centralized control. For this setting, our findings show that the proposed architectures with a single SD-RAN controller are not scalable and can even lead to unpredictable network operations. Using our tool and based on our insights, we provide and implement design guidelines for the internal working behavior of the existing controllers.

Hyperbolic K-means for traffic-aware clustering in cloud and virtualized RANs

As the internet and connected objects gain more and more in popularity, serving the ever-increasing data traffic becomes a challenge for the mobile operators. The traditional cellular radio access network (RAN), where each base station is co-located with its own processing unit and is responsible for a specific geographic area, has evolved first with the so-called Cloud RAN (C-RAN), and is currently undergoing further architectural evolution under the virtualized RAN (vRAN), Software-Defined RAN (SD-RAN), and Open RAN (O-RAN) architectures. In all these versions, the data processing units can be dynamically centralized into a pool and shared between several base stations, enlarging the geographical view for scheduling and resource allocation algorithms. For instance, resource utilization is improved by avoiding resource idling during off-peak hours. C-RAN and vRAN gains depend strongly on the clustering scheme of radio units (RRHs and RUs). In this paper, we propose a novel radio clustering algorithm that takes into account both the traffic demand and the position of stations, by using the hyperbolic distance in 3-dimensions. We introduce a modified K-means clustering algorithm, called Hyperbolic K-means, and show that this generates geographically compact RU clusters with traffic charge equally shared among them. Application of our algorithm on real-world mobile data traffic, collected from the cities of Nantes and Lille in France, shows an increase in resource utilization by 25%, and a reduction in deployment cost by 15%, compared to the standard RAN. Furthermore, the performance of our Hyperbolic K-means algorithm is compared extensively against alternative C-RAN clustering proposals from the literature and is shown to outperform them, in resource utilization as well as in cost reduction.

Task Allocation Framework For Software-Defined Fog v-RAN

The fifth-generation wireless technology (5G) has been developed with an aim to provide ubiquitous and scalable connectivity for Internet-of-Things (IoT) nodes. Likewise, the cloud radio access network (C-RAN) architecture can be exploited to enable efficient network access to IoT nodes. Nevertheless, the 5G C-RAN architecture is based on large data centers geographically located far apart, which introduces an inevitable overhead. Therefore, to supply real-time data services near by the data terminals, fog computing emerges as a promising solution. However, constrained physical fog resources and delay-sensitive services hinder the application of new virtualization technologies in the baseband unit (BBU) task allocation management of the fog network. To tackle these challenges, a task allocation framework for hierarchical software-defined fog virtual radio access networks (v-RANs) is proposed in this article. Precisely, we apply an enhanced ant colony optimization (ACO) in combination with a max-min algorithm to efficiently determine the optimal path for BBU task allocation management, while minimizing the transmission time for parallel task execution scheduling. Experimental results demonstrate that the queue delay in our approach is 98.38% and 98.82% lower than the round-robin (RR) algorithm and least connection technique (LCT), respectively.

Support for 5G Mission-Critical Applications in Software-Defined IEEE 802.11 Networks.

With the emergence of 5G networks and the stringent Quality of Service (QoS) requirements of Mission-Critical Applications (MCAs), co-existing networks are expected to deliver higher-speed connections, enhanced reliability, and lower latency. IEEE 802.11 networks, which co-exist with 5G, continue to be the access choice for indoor networks. However, traditional IEEE 802.11 networks lack sufficient reliability and they have non-deterministic latency. To dynamically control resources in IEEE 802.11 networks, in this paper we propose a delay-aware approach for Medium Access Control (MAC) management via airtime-based network slicing and traffic shaping, as well as user association while using Multi-Criteria Decision Analysis (MCDA). To fulfill the QoS requirements, we use Software-Defined Networking (SDN) for airtime-based network slicing and seamless handovers at the Software-Defined Radio Access Network (SD-RAN), while traffic shaping is done at the Stations (STAs). In addition to throughput, channel utilization, and signal strength, our approach monitors the queueing delay at the Access Points (APs) and uses it for centralized network management. We evaluate our approach in a testbed composed of APs controlled by SD-RAN and SDN controllers, with STAs under different workload combinations. Our results show that, in addition to load balancing flows across APs, our approach avoids the ping-pong effect while enhancing the QoS delivery at runtime. Under varying traffic demands, our approach maintains the queueing delay requirements of 5 ms for most of the experiment run, hence drawing closer to MCA requirements.

Controlling Next-Generation Software-Defined RANs

Compared to previous mobile generations, the 5G system architecture offers more flexibility to fulfill a richer set of diverse requirements. Three system aspects play a major role here: support of network slicing, a split between the user plane and the control plane, and a service oriented control architecture. Emerging techniques of network softwarization such as software-defined networking are leveraged to provide this notion of flexibility manifested in a software-defined core network. However, in addition to the 5G core (5GC), analogous concepts could be applied to the next generation radio access network to sustain the required system performance. Driven by the increased attention that is currently paid to a software-defined radio access network, in this article, we propose the radio access network control function (RANCF) as an additional 5G control function. We define the main functionalities of RANCF, and propose interfaces and protocols for the interaction with 5GC functions. Exploiting network slicing as an example, we demonstrate how RANCF facilitates the slice selection and slice management procedure.

A Robust and Resilient Load Balancing Framework for SoftRAN-Based HetNets With Hybrid Energy Supplies

Heterogeneous networks (HetNets) have been widely accepted as a promising architecture to fulfill the ever-increasing demand for capacity expansion. However, the energy consumed by the dense underlay of the large number of micro base stations that is required to achieve capacity expansion, exacerbates the energy inefficiency of cellular networks. Hybrid energy sources, i.e., the grid and green energy sources, can be used to meet the HetNets excessive demand for energy. In such networks, traffic load balancing becomes crucial to balance the trade-off between green energy utilization and quality of service (QoS) provisioning. Leveraging software-defined radio access networks (SoftRAN) and considering inaccuracy of vital network measurements, we develop an autonomous, robust and resilient load balancing framework. The framework consists of two major modules. First, the $H_{\infty }$ regulator module, which guides the temporal utilization of green energy and distribution of network loads among base stations (BSs) in order to achieve long-term average QoS provisioning. Second, a user association module that optimizes user association and its corresponding traffic loads to minimize the network traffic latency while respecting loads proposed by the $H_{\infty }$ regulator. Extensive performance evaluations demonstrate the efficacy of the proposed framework in autonomously balancing the trade-off between green energy consumption and traffic latency. Furthermore, performance evaluations confirm the robustness of the proposed framework to estimation inaccuracy and its resilience to sudden changes in network parameters.

A Hierarchical Soft RAN Slicing Framework for Differentiated Service Provisioning

Network slicing is a key technology to allow resource sharing among heterogeneous operators/ services, which achieves QoS isolation for service provisioning in future communication networks. In this article, a comprehensive hierarchical soft-slicing framework is proposed to enable software-defined radio access networks supporting differentiated services with diverse QoS requirements. The proposed framework consists of network-level slicing and gNodeB-level slicing. In the network level, radio RBs are pre-allocated to each gNodeB in a large time scale, while in the gNodeB level, the pre-allocated RBs are dynamically scheduled to the services in response to the small time scale (mini-slot-level) RB request variations. The proposed framework allows the accommodation of time-varying traffic loads of differentiated services over multiple gNodeBs, while enabling dynamic inter-gNodeB RB sharing to increase the resource multiplexing gain. A case study is presented to demonstrate the effectiveness of the proposed framework, followed by a discussion on open research issues.

Design and Experimental Validation of a Software-Defined Radio Access Network Testbed with Slicing Support

Network slicing is a fundamental feature of 5G systems to partition 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. The realization of network slicing is particularly challenging in the Radio Access Network (RAN) part, where multiple slices can be multiplexed over the same radio channel and Radio Resource Management (RRM) functions shall be used to split the cell radio resources and achieve the expected behaviour per slice. In this context, this paper describes the key design and implementation aspects of a Software-Defined RAN (SD-RAN) experimental testbed with slicing support. The testbed has been designed consistently with the slicing capabilities and related management framework established by 3GPP in Release 15. The testbed is used to demonstrate the provisioning of RAN slices (e.g., preparation, commissioning, and activation phases) and the operation of the implemented RRM functionality for slice-aware admission control and scheduling.

LMR and LTE for Public Safety in 700 MHz Spectrum

This paper presents a concise overview of current public safety communication networks known as LMR (Land Mobile Radio) and emerging LTE- (Long-Term Evolution-) based broadband public safety networks to be deployed in the 700 MHz band. A broadband nationwide network for public safety based on LTE is inevitable where shared or dedicated types of LTE-based public safety networks are possible. Current LTE services do not meet mission-critical requirements and several enhancements have been defined by 3GPP to address this in Releases 12 and 13. First responders are familiar with LMR and consider it to be a reliable technology with massive deployment everywhere. Therefore, it is expected that LMR will continue to exist alongside any new LTE-based broadband public safety network. Recent LTE releases (particularly Release 15) addressed the LMR-LTE interoperability issue and described comprehensive interworking facilities. New and upcoming features and services of LTE in Releases 14 and 15, such as mission-critical data, mission-critical video, and aerial user equipments, are also directly applicable to public safety. The paper endeavours to provide a quick yet meaningful review of all these issues. It also offers a look ahead at the new and rapidly advancing virtualization technologies, such as software-defined radio access network, and radio access network slicing, as enablers for future public safety networks.

Data Plane Framework for Software-Defined Radio Access Network Based on ETSI-Standard Mobile Device Architecture

This paper addresses how to achieve efficient programmability and software portability in the data plane of a software-defined radio access network (SDRAN). We assume a cloud RAN environment that builds on multi-vendor hardware components. Recent literature on SDRAN data plane indicates that software portability remains an issue in terms of efficient execution of software, even if the software is abstracted from the underlying hardware. In addition, software interfaces typically vary across different hardware components in the SDRAN data plane, leading to platform-dependent software management. Generalizing the European Telecommunications Standards Institute approach for a mobile device architecture, this paper presents a novel SDRAN data plane framework, providing efficient hardware platform-independent programmability and software portability. First, to resolve the software portability issue, the proposed data plane framework employs a specific (radio) virtual machine as well as a radio library; the heterogeneous hardware platforms are abstracted, enabling the joint optimization of the radio application code and hardware platform. Second, to achieve platform-independent software management, the proposed data plane framework adopts a double-layered structure enabling users to exploit high-level software management for the SDRAN data plane. Third, the feasibility of the proposed data plane framework is verified through a proof-of-concept (PoC) system with the proposed double-layered structure. Based on this PoC system, we show that users can efficiently perform software management. According to the numerical results obtained from the PoC system, the proposed double-layered structure introduces negligible additional footprint in terms of computational resources, memory requirements, and latency.

HSDRAN: Hierarchical Software-Defined Radio Access Network for Distributed Optimization

The drastic growth of mobile traffic greatly challenges the capacity of mobile infrastructures. Dense deployment of low-power small cells helps alleviate the congestion in the radio access network, yet it also introduces large complexity for network management. Software-defined radio access network has been proposed to tackle the added complexity. However, existing software-defined solutions rely on a fully centralized control plane to make decisions for the whole network, which greatly limits the scalability and responsiveness of the control plane. In this paper, we propose a hierarchical software-defined radio access network architecture. The proposed architecture leverages the hierarchical structure of radio access networks, deploying additional local controllers near the network edge. Utilizing the intrinsic locality in radio access networks, it offloads control tasks from the central controller to local controllers with limited overhead introduced. Under the architecture, a distributed optimization framework is proposed, and a typical optimization problem is studied to illustrate the effectiveness of the proposed architecture and framework. Both analysis and experiments validate that the proposed architecture and framework can improve the network objective during the optimization, meanwhile balancing load and improving scalability and responsiveness.

Orchestration and Control in Software-Defined 5G Networks: Research Challenges

The fifth generation (5G) of cellular networks promises to be a major step in the evolution of wireless technology. 5G is planned to be used in a very broad set of application scenarios. These scenarios have strict heterogeneous requirements that will be accomplished by enhancements on the radio access network and a collection of innovative wireless technologies. Softwarization technologies, such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), will play a key role in integrating these different technologies. Network slicing emerges as a cost-efficient solution for the implementation of the diverse 5G requirements and verticals. The 5G radio access and core networks will be based on a SDN/NFV infrastructure, which will be able to orchestrate the resources and control the network in order to efficiently and flexibly and with scalability provide network services. In this paper, we present the up-to-date status of the software-defined 5G radio access and core networks and a broad range of future research challenges on the orchestration and control aspects.

On Fair Rule Caching in Software Defined Radio Access Networks

In software defined radio access networks, base stations (BSs) operate according to commands received from a centralized controller. To avoid asking the controller frequently, BSs store these commands as rules in flow tables with limited size. This limitation necessitates fetching rules of some users from the controller causing longer processing delays. In this letter, we formulate the fair allocation of flow table spaces to the users within a cell as a mixed integer linear program which is an NP-hard problem. Nonetheless, we propose an optimal low-complexity solution. Finally, we show superior performance of this strategy versus other possible schemes.

Admission Control of Wireless Virtual Networks in H&lt;roman&gt;et&lt;/roman&gt;H&lt;roman&gt;et&lt;/roman&gt;N &lt;roman&gt;ets&lt;/roman&gt;

In this paper, we propose an efficient analytical method for admission control of wireless virtual networks (VNs), with heterogeneous traffic profiles, and various quality-of-experience (QoE) requirements, in the future software-defined radio access networks. We present a novel methodology for the admission control process that includes feedback information to the VN customers to improve their traffic profile accuracy, and consequently, their QoE. We formulate the virtual network admission control problem as a convex optimization problem which allows general multiple association between user equipment and base stations. Consequently, we propose an algorithm for solving this problem. The proposed method is applicable on heterogeneous networks with heterogeneous traffic distributions. We also propose a number of extensions to the problem including mechanisms that consider minimization of the network and service cost, and optimization according to the backhaul limitations. The simulation results show that our proposed methods are accurate and result in significant improvement in network resource utilization and customer satisfaction.

Multi-Objective Resource Allocation in Density-Aware Design of C-RAN in 5G

In this paper, a multi-objective resource allocation algorithm in a novel density-aware design of virtualized software-defined cloud radio access network (C-RAN) is proposed. We consider two design modes based on the average density of users: 1) high-density mode when a large number of low-cost remote radio heads (RRHs) without baseband processing capability are controlled by one single base station and 2) low-density mode when a small number of RRHs with baseband processing capability are deployed. In high-density mode, the challenge of front-haul capacity limitation is tackled via separating control plane and data plane in a heterogeneous structure. Besides, the fully centralized processing and management, and energy-efficient use of infrastructure in low traffic time by turning off RRHs are achieved. In the low-density mode, the transmission delay due to the large distance between the sparse RRHs and cloud unit, is more critical. This practical issue is handled by sharing the baseband processing and resource management among these units in a hierarchical structure. This resulting heterogeneous /hierarchical virtualized software-defined cloud-RAN (HVSD-CRAN) offers various tradeoffs in resource management objectives such as throughput and delay versus power and cost. Consequently, we resort to multi-objective optimization theory to propose a resource allocation framework in HVSD-CRAN.

Latency Control in Software-Defined Mobile-Edge Vehicular Networking

A cloud radio access network deployed on top of edge networks as a software-defined radio access network architecture has been regarded as a promising paradigm for the next generation mobile networks, but it has not received considerable attention in the vehicular industry due to distinct design needs. For mobile networks, infrastructures provide powerful computation capability for universal radio resource optimization, and therefore an inherent cloud-down design is favorable. On the contrary, to deploy vehicular technologies, sophisticated processing capability has been a baseline feature for the next generation driving machines, which may not solely rely on wireless infrastructures to guarantee driving safety, so an design is preferred. Consequently, this article surveys the recent works on software-defined vehicular networks and proposes a series of edge-up software-defined networking designs, particularly emphasizing the most crucial function of latency control to support possible wireless applications for the next generation driving machines. The proposed designs thus create a paradigm shift to enable software-defined mobileedge vehicular networking.

A service industry perspective on software defined radio access networks

Despite the rapid growth of service science, relatively little attention has been paid to the service architecture requirements in software defined radio access networks (SDRAN). In this concept paper, we propose to repurpose cloud computing network services to address issues specific to SDRAN. In particular, a multi-level backhaul slicing approach derived from cloud computing networks is discussed as a way to mitigate interference limited networks with a frequency reuse factor of one. Experimental demonstration of the control plane implementation in a virtual cloud network is presented, and implications on service provider development and training are also discussed.

A service industry perspective on software defined radio access networks

Despite the rapid growth of service science, relatively little attention has been paid to the service architecture requirements in software defined radio access networks (SDRAN). In this concept paper, we propose to repurpose cloud computing network services to address issues specific to SDRAN. In particular, a multi-level backhaul slicing approach derived from cloud computing networks is discussed as a way to mitigate interference limited networks with a frequency reuse factor of one. Experimental demonstration of the control plane implementation in a virtual cloud network is presented, and implications on service provider development and training are also discussed.