Virtualized Radio Access Network Research Articles

AIRIC: Orchestration of Virtualized Radio Access Networks With Noisy Neighbours

AIRIC: Orchestration of Virtualized Radio Access Networks With Noisy Neighbours

Low-latency remote-offloading system for accelerator

Specific workloads are increasingly offloaded to accelerators such as a graphic processing unit (GPU) and field-programmable gate array (FPGA) for real-time processing and computing efficiency. Because accelerators are expensive and consume much power, it is desirable to increase the efficiency of accelerator utilization by sharing accelerators among multiple servers over a network. However, task offloading over a network has the problem of latency due to network processing overhead in remote offloading. This paper proposes a low-latency system for accelerator offloading over a network. To reduce the overhead of remote offloading, we propose a system composed of (1) fast recombination processing of chunked data with a simple protocol to reduce the number of memory copies, (2) polling-based packet receiving check to reduce overhead due to interrupts in interaction with a network interface card, and (3) a run-to-completion model in network processing and accelerator offloading to reduce overhead with context switching. We show that the system can improve performance by 66.40% compared with a simple implementation using kernel protocol stack and confirmed the performance improvement with a virtual radio access network use case as a low-latency application. Furthermore, we show that this performance can also be achieved in practical usage in data center networks.

Auto-scaling edge cloud for network slicing

This paper presents a study on resource control for autoscaling virtual radio access networks (RAN slices) in next-generation wireless networks. The dynamic instantiation and termination of on-demand RAN slices require efficient autoscaling of computational resources at the edge. Autoscaling involves vertical scaling (VS) and horizontal scaling (HS) to adapt resource allocation based on demand variations. However, the strict processing time requirements for RAN slices pose challenges when instantiating new containers. To address this issue, we propose removing resource limits from slice configuration and leveraging the decision-making capabilities of a centralized slicing controller. We introduce a resource control agent (RC) that determines resource limits as the number of computing resources packed into containers, aiming to minimize deployment costs while maintaining processing time below a threshold. The RAN slicing workload is modeled using the Low-Density Parity Check (LDPC) decoding algorithm, known for its stochastic demands. We formulate the problem as a variant of the stochastic bin packing problem (SBPP) to satisfy the random variations in radio workload. By employing chance-constrained programming, we approach the SBPP resource control (S-RC) problem. Our numerical evaluation demonstrates that S-RC maintains the processing time requirement with a higher probability compared to configuring RAN slices with predefined limits, although it introduces a 45% overall average cost overhead.

Towards an Efficient SIMD Virtual Radio Access Network (vRAN) and Edge Cloud System

Nowadays, virtual Radio Access Network (vRAN) plays a vital role in today's mobile edge system for its better support for latency-sensitive applications. However, our characterization of vRAN on modern processors depicts a frustrating picture of Single-Instruction Multi-Data (SIMD) acceleration. Specifically, the existing data arrangement processes cannot efficiently utilize the ports in modern processors, which leads to high backend bound and fails to saturate the memory bandwidth between registers and the L1 cache. To tackle the issue, we thoroughly examine the state-of-the-art CPU architecture and observe the idle ports which could be utilized by the process. Motivated by this observation, we propose an “Arithmetic Ports Consciousness Mechanism” (APCM) utilizing these idle ports to eliminate the backend bound and saturate the memory bandwidth. The APCM decreases the data arrangement's backend bound from 45 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> to 3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and promotes its memory bandwidth utilization by 4X-16X. Moreover, we illustrate that the APCM can be utilized to promote the performance of typical mobile edge applications such as network routing, image processing, and AI applications. The CPU time of the data arrangement process time of the selected typical mobile edge applications can be reduced by 55 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> - 95 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> when utilizing the proposed mechanism.

NeuroRAN Rethinking Virtualization for AI‐native Radio Access Networks in 6G

ABSTRACTNetwork softwarization has revolutionized the architecture of cellular wireless networks. State‐of‐the‐art container based virtual radio access networks (vRAN) provide enormous flexibility and reduced life‐cycle management costs, but they also come with prohibitive energy consumption. We argue that for future AI‐native wireless networks to be flexible and energy efficient, there is a need for a new abstraction in network softwarization that caters for neural network type of workloads and allows a large degree of service composability. In this paper we present the NeuroRAN architecture, which leverages stateful function as a user facing execution model, and is complemented with virtualized resources and decentralized resource management. We show that neural network based implementations of common transceiver functional blocks fit the proposed architecture, and we discuss key research challenges related to compilation and code generation, resource management, reliability and security.

Multi-Agent Team Learning in Virtualized Open Radio Access Networks (O-RAN).

Starting from the concept of the Cloud Radio Access Network (C-RAN), continuing with the virtual Radio Access Network (vRAN) and most recently with the Open RAN (O-RAN) initiative, Radio Access Network (RAN) architectures have significantly evolved in the past decade. In the last few years, the wireless industry has witnessed a strong trend towards disaggregated, virtualized and open RANs, with numerous tests and deployments worldwide. One unique aspect that motivates this paper is the availability of new opportunities that arise from using machine learning, more specifically multi-agent team learning (MATL), to optimize the RAN in a closed-loop where the complexity of disaggregation and virtualization makes well-known Self-Organized Networking (SON) solutions inadequate. In our view, Multi-Agent Systems (MASs) with MATL can play an essential role in the orchestration of O-RAN controllers, i.e., near-real-time and non-real-time RAN Intelligent Controllers (RIC). In this article, we first provide an overview of the landscape in RAN disaggregation, virtualization and O-RAN, then we present the state-of-the-art research in multi-agent systems and team learning as well as their application to O-RAN. We present a case study for team learning where agents are two distinct xApps: power allocation and radio resource allocation. We demonstrate how team learning can enhance network performance when team learning is used instead of individual learning agents. Finally, we identify challenges and open issues to provide a roadmap for researchers in the area of MATL based O-RAN optimization.

A Sustainable Business Model for a Neutral Host Supporting 5G and beyond (5GB) Ultra-Dense Networks: Challenges, Directions, and Architecture.

With the deployment of the fifth generation (5G) mobile network systems and the envisioned heterogeneous ultra-dense networks (UDNs), both small cell (SmC) and distributed antenna system (DAS) technologies are required by mobile network operators (MNOs) and venue owners to support multiple spectrum bands, multiple radio access technologies (RATs), multiple optical central offices (COs), and multiple MNOs. As a result, the neutral host business model representing a third party responsible for managing the network enterprise on behalf of multiple MNOs has emerged as a potential solution, mainly influenced by the desire to provide a high user experience without significantly increasing the total cost of ownership (TCO). However, designing a sustainable business model for a neutral host is a nontrivial task, especially when considered in the context of 5G and beyond (5GB) UDNs. In this paper, under an integrated optical wireless network infrastructure, we review how SmC and DAS technologies are evolving towards the adoption of the neutral host business model and identify key challenges and requirements for 5GB support. Thus, we explore recent candidate advancements in heterogeneous network integration technologies for the realization of an efficient 5GB neutral host business model design capable of accommodating both SmC and DAS. Furthermore, we propose a novel design architecture that relies on virtual radio access network (vRAN) to enable real-time dynamic resource allocation and radio over Ethernet (RoE) for flexible and reconfigurable fronthaul. The results from our simulations using MATLAB over two real-life deployment scenarios validate the feasibility of utilizing switched RoE considering end-to-end delay requirements of 5GB under different switching schemes, as long as the queuing delay is kept to a minimum. Finally, the results show that incorporating RoE and vRAN technologies into the neutral host design results in substantial TCO reduction by about 81% in an indoor scenario and 73% in an outdoor scenario.

The Lively Versatility of the 5G Ecosystem [Mobile Radio

Solution testing, platform trialing, the showcasing of demos, and proofs of concept (PoCs) all are mainstays of emerging technologies, and 5G is no exception, even while its commercial rollout is already in full swing all around the world. Thanks to the versatility of the 5G ecosystem, trialing efforts are focusing on multiple areas ranging from enhanced, interactive mobile phone calls to revisited concepts in virtual radio access network (RAN) and edge computing architectures. Offering a prime stage is the 2022 Mobile World Congress (MWC), in Barcelona, returning to its consolidated large-scale, in-person format after two years of the pandemic.

FPGA-accelerated SmartNIC for supporting 5G virtualized Radio Access Network

Disaggregated, virtualized, and open next-generation eNodeB (gNB) could bring several benefits to the Next Generation Radio Access Network (NG-RAN) by enabling more market competition and customer choice, lower equipment costs, and improved network performance. This can be achieved through gNB-central unit (CU)-control plane (CP), gNB-CU-user plane (UP) and gNB-distributed unit (DU) separation, CU and DU function virtualization, and zero touch RAN management and control. However, to achieve the performance required by specific foreseen 5G usage scenarios (e.g., Ultra Reliable Low Latency Communications — URLLC), offloading selected disaggregated gNB functions into an accelerated hardware becomes a necessity.To this aim, this study proposes the implementation of 5G DU Low-PHY layer functions into an FPGA-based SmartNIC exploiting the Open Computing Language (OpenCL) framework to facilitate the integration of accelerated 5G functions within the mobile protocol stack. The proposed implementation is compared against (i) a CPU-based OpenAirInterface implementation, and (ii) a GPU-based implementation of IFFT exploiting clfft and cufft libraries. Experimental results show that the different optimization techniques implemented in the proposed solution reduce the Low-PHY processing time and the use of FPGA resources. Moreover, the GPU-based implementation of the cufft and the proposed FPGA-based implementation have a lower processing time and power consumption compared to a CPU-based implementation for up to two cores. Finally, the implementation in a SmartNIC reduces the delay added by the host-to-device communication through the Peripheral Component Interconnect Express (PCIe) interface, considering both functional split options 2 and 7-1.

A Context-Aware Radio Resource Management in Heterogeneous Virtual RANs

New-generation wireless networks are designed to support a wide range of services with diverse key performance indicators (KPIs) requirements. A fundamental component of such networks, and a pivotal factor to the fulfillment of the target KPIs, is the virtual radio access network (vRAN), which allows high flexibility on the control of the radio link. However, to fully exploit the potentiality of vRANs, an efficient mapping of the rapidly varying context to radio control decisions is not only essential, but also challenging owing to the interdependence of user traffic demand, channel conditions, and resource allocation. Here, we propose CAREM, a reinforcement learning framework for dynamic radio resource allocation in heterogeneous vRANs, which selects the best available link and transmission parameters for packet transfer, so as to meet the KPI requirements. To show its effectiveness, we develop a testbed for proof-of-concept. Experimental results demonstrate that CAREM enables an efficient radio resource allocation under different settings and traffic demand. Also, compared to the closest existing scheme based on neural network and the standard LTE, CAREM exhibits an improvement of one order of magnitude in packet loss and latency, while it provides a 65% latency improvement relatively to the contextual bandit approach.

Service Based Virtual RAN Architecture for Next Generation Cellular Systems

Service based architecture (SBA) is a paradigm shift from Service-Oriented Architecture (SOA) to microservices, combining their principles. Network virtualization enables the application of SBA in cellular systems. To better guide the software design of this virtualized cellular system with SBA, this paper presents a software perspective and a positional approach to using fundamental development principles for adapting SBA in virtualized Radio Access Networks (vRANs). First, we present the motivation for using an SBA in cellular radio systems. Then, we explore the critical requirements, key principles, and components for the software to provide radio services in SBA. We also explore the potential of applying SBA-based Radio Access Network (RAN) by comparing the functional split requirements of 5G RAN with existing open-source software and accelerated hardware implementations of service bus, and discuss the limitations of SBA. Finally, we present some discussions, future directions, and a roadmap of applying such a high-level design perspective of SBA to next-generation RAN infrastructure.

Enabling and Scaling of URLLC Verticals on 5G vRAN Running on COTS Hardware

This article discusses an Ultra-Reliable Low-La-tency Communication (URLLC) implementation on a 5G New Radio (NR) virtual radio access Network (vRAN). The vRAN application runs on a commercial off-the-shelf (COTS) test platform, on which 3GPP 5G NR RAN Layer-1 (L1) and Layer-2 (L2) containerized network functions (CNFs) are implemented. To demonstrate capability and scalability, the CNFs are dimensioned to support up to three 100 MHz cells. URLLC and enhanced mobile broadband (eMBB) slices are implemented to partition L1 processing for mixed use cases. This article illustrates an advantage of virtualization in managing L1 processing demand by L1 slice-scaling. It also describes a methodology for optimal cell capacity dimensioning based on connected URLLC users' service level agreements (SLAs) in terms of latency, reliability, and throughput. Using analysis of the test results, the article discusses maintaining a critical time budget of downlink (DL) and uplink (UL) L1 processing pipelines and points out techniques to increase L1 processing capacity to achieve URLLC SLA while serving eMBB users. The article concludes that a 5G NR vRAN application running on COTS hardware enables public/private URLLC verticals including mobile edge computing, cloud gaming, AR/VR, industrial automation, and others.

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.

From Cloud RAN to Open RAN

This paper presents the generic definitions, basic functionalities and current research trends in Cloud Radio Access Networks and its derivatives, Virtual Radio Access Networks and Open Radio Access Networks. Moreover, the paper provides practical results, insights, and lessons learned regarding the limitations and unforeseen issues of Radio Access Networks virtualization. The paper also discusses the potential developments and possibilities for commercial roll out of novel Radio Access Networks approaches.

Optimum Functional Splits for Optimizing Energy Consumption in V-RAN

A virtualized radio access network (V-RAN) is considered one of the key research points in the development of 5G and the interception of machine learning algorithms in the Telecom industry. Recent technological advancements in Network Function Virtualization (NFV) and Software Defined Radio (SDR) are the main blocks towards V-RAN that have enabled the virtualization of dual-site processing instead of all BBU processing as in the traditional RAN. As a result, several types of research discussed the trade-off between power and bandwidth consumption in V-RAN. Processing at remote locations instead of BBU reduces mid-haul bandwidth at the expense of power consumption and vice versa. As a result, the integration of NFV and SDR in V-RAN facilitates dynamic power consumption and processing whenever relaxation is needed. This paper studies several functional splits proposed by ETSI in the NFV of the dual-site network. In addition, network performance is analyzed in terms of data rate, power consumption, and energy efficiency (EE) optimization. Furthermore, the combined optimization of power consumption and mid-haul bandwidth are investigated, and optimal operating parameters are recommended for similar network operators. Thus, regulators/operators can adjust their networks with these parameters to achieve the best performance. Additionally, the UEs switching scheme is introduced to sleep some RRHs in low-density traffic to lessen power consumption.

Autonomous Resource Provisioning and Resource Customization for Mixed Traffics in Virtualized Radio Access Network

Network slicing has been introduced in fifth-generation (5G) systems to satisfy requirements of diverse applications from various service providers operating on a common shared infrastructure. However, heterogeneous characteristics of slices have not been widely explored. In this paper, we investigate dynamic network slicing strategies with mixed traffics in virtualized radio access network (RAN). Considering versatile users’ quality of service (QoS) requirements on transmission delay and rate, coarse resource provisioning scheme and deep reinforcement learning-based autonomous slicing refinement algorithm are proposed. Then, a shape-based heuristic algorithm for user resource customization is devised to improve resource utilization and QoS satisfaction. In principle, the DQN algorithm allocates only the necessary resource to slices to satisfy users’ QoS requirements. For fairness in comparison, we reserve all the unused resources back to the slices. In case there is a sudden change in user population in one slice, the algorithm provides isolation. To validate the advantage, system-level simulations are conducted. The results show that the proposed algorithm balances the satisfaction up to about 100% and resource utilization up to 80% against state-of-the-art solutions. The proposed algorithm also improves the performance of slices in mixed traffics against state-of-the-art benchmarks, which fail to balance satisfaction and resource utilization in some slices.

Decentralized Precoding for Cache-Enabled Ultra-Dense Radio Access Networks

This letter studies the fully decentralized optimization of the precoding matrices for the downlink of multiple-input-multiple-output cache-enabled ultra-dense radio access network. To avoid signalling exchange between coordinated edge radio access nodes (RANs), we first construct a virtual downlink cache-enabled ultra-dense radio access network. Following, a new concept of signal-to-interference-leakage-plus-noise ratio is defined. Based on this concept, a virtual weighted sum rate maximization problem is formulated to optimize the precoders. We then propose a computationally efficient decentralized optimization algorithm that achieves stationary point to the problem. Numerical results show that the proposed algorithm can achieve more than 96.15% of the performance of the centralized optimization without any signalling exchange between coordinated edge RANs for multiple-input single-output cases.

Open RAN Deployment Using Advanced Radio Link Manager Framework to Support Mission Critical Services in 5G

Next generation networks or 5G will be “network of networks” that can support ultra-reliable and low latency communication, high data rate, huge connectivity and high security. Network transformation stirring towards virtualized Radio Access Network (v-RAN) and intelligent resource management are foreseen as key solutions to realise such varied 5G requirements. Effective Radio Resource Management (RRM) is crucial for Mission Critical (MC) services to underpin communication between smartphone, massive machines and tiny sensor devices. The paper explores pioneering research related to architecture and intelligent RRM that helps Service Providers (SPs) to design reference framework of an advanced Radio Link Manager (RLM) enabled by Machine Learning (ML). One example optimization for commercial network/Long Term Evolution (LTE) and some preliminary results are analysed to understand the reference framework. The paper addresses the general reference architecture framework of advanced Radio Link Manager to support Mission Critical services in 5G. The paper also discusses about the ongoing standardisation activities and open source initiatives in 5G RAN.

Dynamic Resource Provisioning and Resource Customization for Mixed Traffics in Virtualized Radio Access Network

The fifth generation (5G) wireless technology is envisioned to support different applications or services on a common substrate infrastructure. To achieve this objective, network virtualization is needed to partition the physical network into various network slices with each slice providing a unique service in the network. In networks with mixed traffics, service operators are required to provide services in isolation since each service has its own defined objective. In such mixed traffics, achieving an efficient and effective resource allocation is a challenging issue. In this paper, we propose a dynamic resource provisioning scheme involving mixed traffics with multiple base stations in a virtualized radio access network. Since users in each slice have varying requirements on satisfaction regarding the delay and data rate, we propose a customized shape-based heuristic algorithm for users to improve resource utilization and QoS fulfilment. The comprehensive system-level simulation is conducted based on OFDMA air-interface design. Simulation results show that the proposed algorithm outperforms baseline algorithms in terms of QoS satisfaction and resource utilization.

Dynamic Reservation and Deep Reinforcement Learning Based Autonomous Resource Slicing for Virtualized Radio Access Networks

The elastic reconstruction of 5G network services is expected to provide the capability of network slice orchestration to access the network on demand, guarantee service experience on demand, and construct services on demand as well as to construct basic network services with lower costs. It is challenging to have different applications served independently with a proper resource allocation mechanism according to their own requirements. In this paper, we propose a dynamic resource reservation and deep reinforcement learning-based autonomous virtual resource slicing framework for the next generation radio access network. The infrastructure provider periodically reserves the unused resource to the virtual networks based on their ratio of minimum resource requirements. Then, the virtual networks autonomously control their resource amount using deep reinforcement learning based on the average quality of service utility and resource utilization of users. With the defined framework in this paper, virtual operators can customize their own utility function and objective function based on their own requirements. The simulation results show the performances on convergence rate, resource utilization, and satisfaction of the virtual networks.