Radio Access Network Architecture Research Articles

A Task Offloading Method Based on User Satisfaction in C-RAN With Mobile Edge Computing

With the continuous development of the communication service industry, users pay more attention to the quality of network service. Previous studies on offloading problems, especially in the Cloud Radio Access Network (C-RAN) architecture with Mobile Edge Computing (MEC), are primarily focused on the economic perspective, with little consideration given to user-oriented satisfaction problems. To fill this gap, this paper proposes a mathematical model for maximizing user satisfaction in the C-RAN architecture with multi-layer MEC. The problem is divided into two stages for solution. The first stage addresses the optimal connection problem between users and Remote Radio Heads (RRHs). The second stage then schedules user tasks reasonably based on the solution obtained in the first stage. The two-stage problems are all proved to be NP-Hard. Two efficient approximation algorithms, namely User-to-RRH Association Algorithm (URAA) and Maximum Satisfaction Algorithm (MSA), are proposed to solve the problems in different stages. This paper proves and analyzes the theoretical performance of the two algorithms. Finally, the performance of the proposed algorithms is verified by simulation experiments. The experimental results demonstrate that the two proposed algorithms can achieve reasonable solutions to the problems, and the user satisfaction level can be maintained at a high level.

An efficient energy saving scheme using reinforcement learning for 5G and beyond in H-CRAN

Maximizing the energy saving is one of the most important metrics in 5G and Beyond (B5G) cellular mobile networks. In order to satisfy the diverse requirements of 5G/B5G in dynamic environments, Reinforcement Learning (RL) is proven as a viable approach for solving resource management problems, especially for 5G energy resources. In this paper, we propose to apply the Q-Learning (QL) Reinforcement technique in the Heterogeneous Cloud 5G Radio Access Network (H-CRAN) architecture in order to optimize the energy efficiency in 5G/B5G networks. We compare its results with the Genetic Algorithm variant using Transformation (TGA) and Particle Swarm Optimization (PSO) under high and low traffic demands. The experimental results reveal the efficiency of RL compared to TGA and PSO techniques in terms of energy efficiency and system capacity.

QoS-driven resource allocation in fog radio access network: A VR service perspective.

While immersive media services represented by virtual reality (VR) are booming, They are facing fundamental challenges, i.e., soaring multimedia applications, large operation costs and scarce spectrum resources. It is difficult to simultaneously address these service challenges in a conventional radio access network (RAN) system. These problems motivated us to explore a quality-of-service (QoS)-driven resource allocation framework from VR service perspective based on the fog radio access network (F-RAN) architecture. We elaborated details of deployment on the caching allocation, dynamic base station (BS) clustering, statistical beamforming and cost strategy under the QoS constraints in the F-RAN architecture. The key solutions aimed to break through the bottleneck of the network design and to deep integrate the network-computing resources from different perspectives of cloud, network, edge, terminal and use of collaboration and integration. Accordingly, we provided a tailored algorithm to solve the corresponding formulation problem. This is the first design of VR services based on caching and statistical beamforming under the F-RAN. A case study provided to demonstrate the advantage of our proposed framework compared with existing schemes. Finally, we concluded the article and discussed possible open research problems.

ORAN-B5G: A Next Generation Open Radio Access Network Architecture With Machine Learning for Beyond 5G in Industrial 5.0

ORAN-B5G: A Next Generation Open Radio Access Network Architecture With Machine Learning for Beyond 5G in Industrial 5.0

Reconfigurable optical crosshaul architecture for 6G radio access networks

The radio access network (RAN) architecture is undergoing a significant evolution to support the next-generation mobile networks and their emerging applications. To realize scalable and sustainable deployment and operations, RAN needs to consider the requirements of 6G and beyond wireless technologies such as ultra densification of cells, higher data rates, ubiquitous coverage, and new radio spectrum in the millimeter-wave band. This calls for a careful redesign of every aspect of RAN, including its crosshaul. The crosshaul is an important network segment in future RAN, capable of transporting diverse traffic types with varying stringent requirements within RAN. The crosshaul towards 6G is envisioned to be highly intelligent, reconfigurable, and adaptable to dynamic service requirements and network conditions. To this end, we propose a software defined network (SDN)-enabled reconfigurable optical crosshaul architecture (ROCA) that supports heterogeneous crosshaul transport technologies and dynamic functional splittings. ROCA enables efficient and intelligent control of the crosshaul data plane. The proposed architecture with a set of the next-generation RAN (NG-RAN) transport interfaces is evaluated using network models built on the ns-3 network simulator. Simulation results demonstrate the strengths and weaknesses of different crosshaul interfaces in agreement with the understanding of respective NG-RAN interfaces from the literature, which validates the modeling accuracy. We then demonstrate the reconfigurability of the architecture using a dynamic scenario with different reconfiguration strategies for meeting the user and network demands. The results indicate that ROCA serves as a scalable and flexible foundation for supporting high-capacity delay-stringent RAN that can be used in 6G and beyond wireless technologies.

Recent Advances in Machine Learning for Network Automation in the O-RAN.

The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.

Minimizing energy consumption of IoT devices for O-RAN based IoT systems

The implementation of Open Radio Access Network (O-RAN) architecture in Internet of Things (IoT) systems has garnered significant attention as a means to fulfill the stringent requirements of ultra-low latency and ultra-low energy consumption in future IoT systems. Although the traditional edge network architecture has been extensively employed, it continues to pose challenges in terms of synchronizing the integration of global and local information for the design of an optimal offloading strategy in edge servers, thus hindering the reduction of latency and energy consumption in IoT devices. In an effort to decrease the latency and energy consumption of IoT devices (IoTDs), we propose a computation offloading problem and employs the Successive Convex Approximation (SCA) algorithm to convert the non-convex problem into a convex problem. The proposed strategy aims to minimize the energy consumption of IoTDs while ensuring Quality of Service (QoS) requirements. The results of the experiment demonstrate that the average energy consumption of terminal devices can be reduced by 20%. Additionally, this strategy is found to be more effective in reducing IoTDs' latency and energy consumption as compared to the traditional edge network architecture.

An Enhanced Deep Reinforcement Learning-based Slice Acceptance Control System (EDRL-SACS) for Cloud–Radio Access Network

In 5G networks, Network Slicing (NS) allows service providers (SPs) to create customized standalone networks on shared platforms provided by infrastructure providers (InPs). However, the challenge arises when prioritizing slice requests sent by SPs to InPs due to limited and shared resources. The Cloud Radio Access Network (C-RAN) architecture is a novel approach that supports various services in 5G networks, but efficient resource utilization and increased revenue across the three layers of C-RAN are necessary. To address this issue, we propose an Enhanced Deep Reinforcement Learning-based Slice Acceptance Control System (EDRL-SACS) technique to maximize network utility by accepting suitable requests. EDRL-SACS considers time-varying resource situations, various service requirements, and the frequency of requests from SPs to endorse model-free solutions for Slice Acceptance Control (SAC). By adding effective parameters to the deep reinforcement learning algorithm, the proposed methodology improves its efficiency and performance resulting in better resource utilization, coverage, revenue, and rejection of users’ request terms. The system’s evaluation shows that EDRL-SACS performs well and effectively maximizes the utility of the network by providing slices to SPs in response to their slice requests.

Design and deployment of optical x-haul for 5G, 6G, and beyond: progress and challenges [Invited

Sixth generation (6G) wireless technology, which is the next giant leap in the evolution of wireless communication, has been gaining great interest from academia, industry, and the general public due to its capabilities and the applications that are expected to be supported. Optical x-haul is becoming increasingly important as a key element of the 6G ecosystem due to the increasing demand for high data rates and low latency with the emergence of new 6G applications and use cases. The ability of optical x-haul to support high data rates and low latency along with its scalability and high-reliability performance makes it an essential transport technology for future wireless networks of 6G and beyond. However, optical and wireless technologies operate in different domains and with different standards. Therefore, integration of optical x-haul and 6G needs careful consideration with emphasis on bringing network intelligence softwarization. This is particularly important in supporting the quality of service requirements of emerging applications in the most cost-effective and sustainable manner. To this end, this paper reviews the research that has been carried out in the investigation of the optical x-haul network in a hybrid fiber–wireless system to cater to the next generation of wireless networks. Especially, we review the research carried out in open radio access network architecture, coordination functionalities in radio-over-fiber networks, optimization frameworks that can be used to jointly optimize wireless and optical network deployments, and reconfigurable optical x-haul. We conclude the paper by providing insights into research challenges that we need to overcome in integrating optical x-haul in the 6G ecosystem to support emerging applications.

Fully-Decoupled Radio Access Networks: A Resilient Uplink Base Stations Cooperative Reception Framework

To cope with the even more urgent spectrum and energy efficiency challenge for trillion-level terminal access and data uploading in the next generation mobile communication network (6G), in this paper, we investigate the uplink transmission in an original fully-decoupled radio access networks (FD-RAN) architecture. Specifically, we propose a resilient uplink base station cooperative reception framework in FD-RAN, which is a large-scale fading based two-tier signal combination approach for the uplink transmission, including the localized signal combination at the base station and centralized signal combination at the edge cloud, respectively. Then, we formulate a weighted sum-rate maximization problem for the uplink transmission optimization, and decompose it into two subproblems. A spectrum-efficiency maximized virtual service cluster selection (SEMVS) algorithm is designed by leveraging the channel statistical information for solving subproblem one, and a fractional programming based power control (FPPC) algorithm is introduced for the power optimization of subproblem two. Compared to the typical RAN architectures with corresponding access and power control methods, simulation results demonstrate the significant performance improvements of uplink FD-RAN with the proposed solution.

Energy Outage Analysis of Cloud-Aerial RAN

This paper proposes a cloud-aerial radio access network (CA-RAN) architecture utilizing unmanned aerial vehicles (UAVs) to rapidly deploy mobile networks for disaster recovery and emergency situations. The architecture aims to provide reliable and energy-efficient data coverage through the use of millimeter-wave bands for increased data rates and both RF and solar energy harvesting techniques for sustainable energy provision. To improve the system’s reliability, an opportunistic selective scheme is implemented. We evaluate the performance of the CA-RAN based system using the energy-outage probability metric, which, to the best of our knowledge, has not been studied in UAV-based networks. Finally, numerical results are presented to illustrate the impact of different parameters on the system’s performance.

When RAN Intelligent Controller in O-RAN Meets Multi-UAV Enable Wireless Network

Unmanned aerial vehicles (UAVs) are projected to be utilized in a variety of unexpected applications, including agriculture, firefighting, emergency response, intelligent transportation, and so on. Wireless communication is one of the primary facilitators in bringing UAVs into a new phase in such applications. To realize the vision in fifth-generation (5G) networks, we propose a 5G-integration of the flexible multi-UAV system and the Open Radio Access Network (O-RAN) architecture, named U-ORAN. Although different studies have been proposed to optimize the UAV trajectory and resource allocation in the radio access network (RAN), our work is the first study to investigate the benefits of adopting UAVs in the O-RAN architecture. In U-ORAN, we consider a flying base station system and propose a joint optimization problem of multi-UAV trajectory and offloading tasks (UTOT) in which UTOT can optimize the routes from users to the core network as well as resource allocation to process offloading tasks. We decompose UTOT into two sub-problems and provide learning solutions based on the multi-agent reinforcement learning and online learning methodologies, both of which are well supported by the O-RAN architecture. Our intensive numerical simulations show that the proposed approaches outperform in a variety of settings and validation scenarios.

Fully-Decoupled Radio Access Networks: A Flexible Downlink Multi-Connectivity and Dynamic Resource Cooperation Framework

To enable flexible base stations (BS) association and dynamic resource management for personalized user equipment (UE) download service provision in the next-generation mobile communication network (6G), in this paper, we investigate the downlink (DL) transmission scenario in an origin fully-decoupled radio access network (FD-RAN) architecture. Considering the unique fully-decoupled UL/DL access feature, we propose an efficient two-stage DL channel estimation method in the FD-RAN. We formulate a novel multi-connectivity and dynamic resource cooperation problem with joint multiple-BS and multiple-UE association and coordinated beamforming, aiming at maximizing the weighted sum achievable rate in DL FD-RAN. By leveraging the many-to-many swap-matching theory and fractional relaxation approach, we solve the dynamic UE scheduling problem with multiple-BS and multiple-UE association and the coordinated beamforming problem, respectively. Extensive simulation results based on standard 3GPP 36.873 urban micro channel demonstrate that the proposed framework can improve the average spectral efficiency by 34.9% as compared to the traditional maximum ratio transmission beamforming method.

Federated Learning Over Fully-Decoupled RAN Architecture for Two-Tier Computing Acceleration

Two-tier computing paradigm that takes full advantage of both the end-user and the cloud computation capabilities has emerged as a promising way to deal with computationally-intensive tasks in the next generation wireless networks. For promoting the integration of the two-tier computing, federated learning (FL) provides an effective framework to enable the collaboration between the end-user and the cloud. However, the key performance metric, i.e., FL training latency, will be severely affected by the worst wireless link quality in both uplink and downlink. In this paper, aiming at accelerating the FL enabled end-cloud two-tier computing over the wireless networks, we introduce the uplink and downlink fully-decoupled radio access network (FD-RAN) architecture to enhance the minimum wireless link rate via multiple base stations (BSs) access collaboration and power management solution. First, the Lagrange dual decomposition and the binary variable relaxation methods are leveraged to obtain an optimal multiple BS access scheme for the enhancement of minimum uplink and downlink SINR. Subsequently, we exploit the successive convex approximation (SCA) algorithm to deal with the uplink power control and downlink power allocation with a proved data rate lower bound. Furthermore, considering the dynamic channel realizations, a stochastic optimization technique with a convex surrogate function is utilized to find the best end-cloud two-tier computing scheme for FL applications. Simulation results have demonstrated the effectiveness of our proposed joint multiple access collaboration and power management solution over FD-RAN for achieving a faster FL enabled two-tier computing task.

Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning

The evolution of the future beyond-5G/6G networks toward a service-aware network is based on network slicing technology. With network slicing, communication service providers seek to meet all the requirements imposed by the verticals, including ultra-reliable low-latency communication (URLLC) services. In addition, the open radio access network (O-RAN) architecture paves the way for flexible sharing of network resources by introducing more programmability into the RAN. RAN slicing is an essential part of end-to-end network slicing since it ensures efficient sharing of communication and computation resources. However, due to the stringent requirements of URLLC services and the dynamics of the RAN environment, RAN slicing is challenging. In this article, we propose a two-level RAN slicing approach based on the O-RAN architecture to allocate the communication and computation RAN resources among URLLC end devices. For each RAN slicing level, we model the resource slicing problem as a single-agent Markov decision process and design a deep reinforcement learning algorithm to solve it. Simulation results demonstrate the efficiency of the proposed approach in meeting the desired quality of service requirements.

Guest editorial IJSCN special issue on 3GPP NTN standards for future satellite communications

Guest editorial IJSCN special issue on 3GPP NTN standards for future satellite communications

A Bidirectional WDM-PON Free Space Optical (FSO) System for Fronthaul 5 G C-RAN Networks

High-speed cellular technologies require low-latency and high-capacity optical networks. The Centralized Radio Access Network (C-RAN) architecture offers a cost-effective solution for mobile network deployment. To maximize flexibility and minimize deployment costs of fronthaul networks, we propose a hybrid bidirectional fronthaul C-RAN topology based on Wavelength Division Multiplexing (WDM) passive optical networks (PONs) and free space optical communication (FSO). The wavelength reuse scheme utilized here relies on reflective semiconductor optical amplifiers (RSOAs) to reduce cost and increase capacity. The system was demonstrated for 20 Gbps 16 <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> quadrature amplitude modulation (16-QAM) intensity-modulated orthogonal frequency-division multiplexing (OFDM) downstream signals and 5 Gbps On-off keying (OOK) upstream signals, respectively. A Gamma-Gamma channel model is used to demonstrate optical signal transmission over an FSO link. The bit error rate (BER) results indicate that the hybrid WDM-PON-FSO based fronthaul architecture could achieve 320 Gbps over 20 km of single-mode fiber (SMF) and 700 m free space transmission.

D-RAN: A DRL-Based Demand-Driven Elastic User-Centric RAN Optimization for 6G &amp; Beyond

With highly heterogeneous application requirements, 6G and beyond cellular networks are expected to be demand-driven, elastic, user-centric, and capable of supporting multiple services. A redesign of the one-size-fits-all cellular architecture is needed to support heterogeneous application needs. While several recent works have proposed user-centric cloud radio access network (UCRAN) architectures, these works do not consider the heterogeneity of application requirements or the mobility of users. Even though significant gains in performance have been reported, the inherent rigidity of these methods limits their ability to meet the quality of service (QoS) expected from future cellular networks. This paper addresses this need by proposing an intelligent, demand-driven, elastic UCRAN architecture capable of providing services to a diverse set of use cases including augmented/virtual reality, high-speed rails, industrial robots, E-health, and more applications. The proposed framework leverages deep reinforcement learning to adjust the size of a user-centered virtual cell based on each application’s heterogeneous requirements. Furthermore, the proposed architecture is adaptable to varying user demands and mobility while performing multi-objective optimization of key network performance indicators (KPIs). Finally, numerical results are presented to validate the convergence, adaptability, and performance of the proposed approach against meta-heuristics and brute-force methods.

Resource Allocation in Cell-Free Massive MIMO Networks With Hybrid Energy Supplies

Cell-free massive multiple-input multiple-output (CF-mMIMO) networks were proposed as a novel architecture for radio access networks, which consists of a large number of access points (APs) over a vast area that simultaneously offer services to user equipment (UEs) in a single frequency band. However, powering a large number of APs along with impossibility or high cost of accessing the electrical grid in some scenarios remains a key challenge in unleashing the full potential of CF-mMIMO networks. One remedy is to resort to various forms of renewable energy sources and come up with AP designs equipped with energy harvesting capabilities. Regarding the unpredictable nature of renewable energy sources, energy-efficient resource allocation techniques need to be developed so that APs remain operational at all times. In this paper, we envision a CF-mMIMO system in which different APs get their power from an electrical grid, a solar energy source, or a hybrid design. We aim at designing an AP-to-UE power allocation scheme with the objective of minimizing overall energy consumption from the electrical grid while satisfying the spectral efficiency (SE) constraints. Moreover, we come up with a green energy budgeting mechanism to prevent energy deficiency when solar energy is scarce. We first propose an idealistic offline formulation of the problem, and transform it into a second-order cone programming (SOCP) problem. Then, we come up with a suite of lightweight model-free myopic heuristic algorithms, and compare their performance against offline benchmark.

Energy-Efficient Sleep Mode Schemes for Cell-Less RAN in 5G and Beyond 5G Networks

In 5G and beyond 5G networks, the new cell-less radio access network architecture is adopted to overcome the extreme network capacity challenges generated by massive wireless devices used for diverse scenarios and various applications. At the same time, the evolution of mobile communications faces the important challenge of increased network power consumption. To fulfill user demands for various user densities and meanwhile reduce the power consumption, we present a novel energy-efficiency enhancement scheme, i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(3\times E)$ </tex-math></inline-formula> to increase the transmission rate per energy unit, with stable performance within the cell-less radio access network (RAN) architecture. Our proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(3\times E)$ </tex-math></inline-formula> scheme activates two-step sleep modes (i.e., certain phase and conditional phase) through the intelligent interference management for temporarily switching access points (APs) to sleep, optimizing the network energy efficiency (EE) in highly loaded scenarios, as well as in scenarios with lower load. An intelligent control over underutilized/unused APs is considered, taking their interference contribution into account as the primary main criteria in addition to load-based conditional criteria. Therefore, our proposed scheme assures a stable performance enhancement and maintains an efficient power saving when the number of UEs increases, improving existing works not addressing this performance stability in peak-traffic hours. Simulation results show that the network EE is improved up to 30% compared to the reference algorithm and up to 60% with respect to the baseline algorithm in which all APs are active all the time.