5G Radio Access Network Research Articles

Dedicated Path Protection With Wavelength Aggregation in 5G Packet-Optical Xhaul Access Networks

Packet-optical xHaul transport networks are a promising solution for assuring low-cost connectivity between a large number of antennas, located at remote sites, and a central site (hub), in which traffic is gathered and baseband processing is performed, in dense 5G radio access networks (RANs). The demand for fiber connections in such networks can be reduced by means of optical add-drop multiplexers (OADMs), which allow to aggregate traffic from a number of remote sites onto a single optical transmission path. An important issue here is the protection of transmission paths since the failure of a single link may result in the loss of connectivity of a number of antennas. In this work, we focus on the problem of planning a survivable 5G packet-optical xHaul access network, in which the remote sites are connected with the hub by means of disjoint primary-backup paths. The network scenario is based on a passive wavelength division multiplexing (WDM) system in which the maximum transmission distance depends on the number of OADMs installed on the transmission path. In the solution proposed, network survivability is achieved using a dedicated path protection with wavelength aggregation (DPPWA) mechanism. The network planning problem is modeled and solved as an integer linear programming (ILP) problem. Numerical results indicate, among others, that cost savings of between 20%–40% can be achieved in lower traffic load scenarios due to the traffic aggregation by means of OADMs, and that the savings increase with the cost of dark fiber lease.

Cloud based IoT Electronic Healthcare Data Warehouse Integration in emerging 5G Health Grid Ecosystem

The integration of heterogeneous enterprise cloud data warehouses enables the formation of national health grid ecosystem that links public and private health databases to enable supervision of healthcare utilities, costs, quality of care and observance of national healthcare administrative protocols. The development of cloud data warehouse requires information communication technology to condense the costs of patient’s healthcare by supporting care, away from hospitals, where increasingly detailed data can move from patients to centralized cloud server data warehouse, allowing efficiency in diagnosis and treatment options. The global democratization of 5G telecommunication broadband creates entirely new use case scenarios for consumers addressing healthcare sector and industries, intending to improve citizen’s life expectancy. The improved data rate transmission revolutionizes healthcare sector and offers prospects that enable the telecommunication merchants to penetrate the supply chains and initiate partnerships that profit the entire healthcare business ecosystem. This paper analyses the 5G network implementation business potentials, focusing on the 5G business opportunities that originate from industrial healthcare automation, allowing patient data aggregation required for citizens’ healthcare management. The research entrenches the 5G radio frequency access network and distributed cloud IoT server systems in constructing a healthcare grid data warehouse to enable self-optimized service-level agreements. Moreover, this paper observes that the healthcare sector prioritized telecommunication operators when partnership issues were debated above every other service sector addressing healthcare development. The cloud IoT platform offers solution to synchronize-able global device connectivity management for e-healthcare and demonstrates how these improvements enable development of new systems for public healthcare policy formulation and delivery in Nigeria.

HACM: High Availability Control Method in Container-Based Microservice Applications Over Multiple Clusters

Emerging cloud-native technologies, such as container runtime and container orchestrator, offer unprecedented agility in developing and running applications, especially when combined with microservice-style architecture. Several commercial Samsung Network products such as Samsung Element Management System (S-EMS), 5G Radio Access Network (RAN) & Core network elements are being redesigned to fit the microservice paradigm. The cloud environment allows enterprises to scale their applications on-demand with minimum cost; however, it is often difficult to use containers without sacrificing the many benefits offered by container technology. S-EMS manages 5G RAN & Core network elements (NEs) deployed nationwide, and systematically stores a huge volume of stateful data per second. Containers are characterized to have an ephemeral state, hence ‘stateful-ness’ aspect of S-EMS makes management more complex. The existing system in a container-based application does not support geo-redundancy where services/data are stateful/state dependent. In this paper, different challenges around geo-redundancy between different independent Kubernetes set up with active and standby modes between the sites where state-dependent data is stored in each site are described. To overcome these challenges, we propose the High Availability Control Method (HACM) - which enables the Kubernetes cluster to be active and standby, where state-dependent data and context-based operations are intrinsically supported by the underlying S-EMS container application. Our approach has been designed to maintain the geo-redundancy philosophy of cloud-native by associating the status of each site using high availability (HA), switching over services based on the health of applications, deciding state when there are conflicts in site state, and the option to auto fallback based on user preference and services are transferred between sites without user intervention with optimized storage that ensures consistency, persistence, reliability, and availability. Through evaluation, we show that HACM with S-EMS can facilitate geo-redundancy HA, while not posing a significant burden on the Cloud.

Cost-Aware DU Placement and Flow Routing in 5G Packet xHaul Networks

Packet-switched xHaul networks based on Ethernet technology are considered a promising solution for assuring convergent, cost-effective transport of diverse radio data traffic flows in dense 5G radio access networks (RANs). A challenging optimization problem in such networks is the placement of distributed processing units (DUs), which realize a subset of virtualized baseband processing functions on general-purpose processors at selected processing pool (PP) facilities. The DU placement involves the problem of routing of related fronthaul and midhaul data flows between network nodes. In this work, we focus on developing optimization methods for joint placement of DUs and routing of flows with the goal to minimize the overall cost of PPs activation and processing in the network, which we refer to as the PPC-DUP-FR problem. We account for limited processing and transmission resources as well as for stringent latency requirements of data flows in 5G RAN. The latency constraint makes the problem particularly difficult in a packet-switched xHaul network since it involves the non-linear and dynamic estimation of the latencies caused by buffering of packets in the switches. The latency model that we apply in this work is based on worst-case calculations with improved latency estimations that skip from processing the co-routed, but non-affecting flows. We use a mixed-integer programming (MIP) approach to formulate and solve the PPC-DUP-FR optimization problem. Moreover, we develop a heuristic method that provides optimized solutions to larger PPC-DUP-FR problem instances, which are too complex for the MIP method. Numerical experiments performed in different network scenarios indicate on the effectiveness of the heuristic in solving the PPC-DUP-FR problem. In particular, the heuristic achieves up to 63% better results than MIP (at the MIP optimality gap equal to 76%) in a medium-size mesh network, in which the MIP problem is unsolvable for higher traffic demands within reasonable runtime limits. In larger networks, MIP is able to provide some results only for the PPC-DUP-FR problem instances with very low traffic demands, whereas the solutions generated by the heuristic are at least 83% better than the ones achieved with MIP. Also, the analysis performed shows a significant impact of the PP cost factors considered and of the level of cost differentiation of PP nodes on the overall PP cost in the network. Finally, simulation results of a case-study packet xHaul network confirm the correctness of the latency model used.

Transforming the 5G RAN With Innovation: The Confluence of Cloud Native and Intelligence

Intelligence and cloudification are widely recognized as key driving forces in the evolution of 5G radio access network (RAN). This paper presents a promising architecture framework for the evolution of 5G radio access network, enabled by a deep integration with cloudification and artificial intelligence/machine learning (AI/ML) technologies. To accommodate the diversified scenarios and services and handle the complexity of the 5G network in a flexible and efficient manner, the architecture framework highlights three concepts: convergence of RAN and cloud, RAN empowered by hierarchical AI capabilities, and mutual awareness between RAN and services. The key design aspects and technologies that realize those concepts are discussed systematically. Two typical use cases including the RAN slice resource allocation optimization and RAN-aware video service assurance, are demonstrated along with the simulation or lab test results to validate the potential of the architecture framework.

Dynamically Resource Allocation in Beyond 5G (B5G) Network RAN Slicing Using Deep Deterministic Policy Gradient

Network slicing makes it possible for future applications with a variety of adaptability requirements and performance requirements by spliting the physical network into several logical networks. Radio access network (RAN) slicing’s main goal is to assign physical resource blocks (RBs) to mMTC, eMBB, and uRLLC services while ensuring the Quality of service (QoS). Consequently, it is challenging to determine the optimal strategies for 5G radio access network (5G-RAN) slicing because of dynamically changes in slice needs and environmental data, and conventional approaches have difficulty addressing resource allocation issues. In this paper, we present an energy-efficient deep deterministic policy gradient resource allocation (EE-DDPG-RA) method for RAN slicing in 5G networks to choose the resource allocation policy that increases long-term throughput while satisfying the requirements of B5G systems for quality of service. This method’s main goal is to remove unnecessary actions in order to lower the amount of available action space. The numerical outcomes demonstrate that the proposed approach outperforms boundaries by enhancing deep-rooted throughput and effectively managing resources.

Baseband-Function Placement With Multi-Task Traffic Prediction for 5G Radio Access Networks

The 5G Radio Access Network (RAN) virtualization aims to improve network quality and lower the operator’s costs. One of its main features is the functional split, i.e., dividing the instantiation of RAN baseband functions into different units over metro-network nodes. However, its optimal placement is non-trivial: it depends on the application requirements and on the expected traffic volume, whose daily variation highly impacts the total power consumption. Current optimization solutions fail to provide a placement solution capable of handling traffic fluctuations. In fact, the standard machine learning algorithms used in the literature for planning the network resources in advance result in an allocation that is inadequate to carry the actual traffic at all the time-slots. Hence, we must reserve an artificial buffer capacity in the nodes to ensure feasibility. Instead, our proposed method exploits a fine-grained two-step multi-task algorithm that predicts the mean and quantile traffic, making the artificial capacity no longer necessary. The subsequent placement uses mixed-integer linear programming and a heuristic. The former considers the expected traffic in the objective function (to estimate costs) and the quantile in the constraints (to enforce capacity limits). The heuristic combines the mean and quantile results to minimize the power and comply with the requirements. While using sufficiently large artificial buffers guarantees robustness with a mild power increase compared to the oracle, the fine-grained multi-task model improves the results, reducing the power consumption compared to the mean and meets all constraints. The heuristic enables significant computational time reduction.

An architecture and performance evaluation framework for artificial intelligence solutions in beyond 5G radio access networks

The evolution of mobile communications towards beyond 5th-generation (B5G) networks is envisaged to incorporate high levels of network automation. Network automation requires the development of a network architecture that accommodates multiple solutions based on artificial intelligence (AI) and machine learning (ML). Consequently, integrating AI into the 5th-generation (5G) systems such that we could leverage the advantages of ML techniques to optimize and improve the networks is one challenging topic for B5G networks. Based on a review of 5G system architecture, the state-of-the-art candidate AI/ML techniques, and the progress of the state of the art, and the on AI/ML for 5G in standards we define an AI architecture and performance evaluation framework for the deployment of the AI/ML solution in B5G networks. The suggested framework proposes three AI architectures alternatives, a centralized, a completely decentralized and an hybrid AI architecture. More specifically, the framework identifies the logical AI functions, determines their mapping to the B5G radio access network architecture and analyses the associated deployment cost factors in terms of compute, communicate and store costs. The framework is evaluated based on a use case scenario for heterogeneous networks where it is shown that the deployment cost profiling is different for the different AI architecture alternatives, and that this cost should be considered for the deployment and selection of the AI/ML solution.

A Survey- Resources Management in 5G Mobile Networks

 The network has various issues as a consequence of increased network traffic as a result of employing packet switching instead of circuit switching, which has a constant data rate, a large number of connected devices, and advancement of application services (download, upload, streaming, you tube, etc.). Efficiency resource management entails not only defining the present network design and technology, but also preparing for 5G. There is a link between the mobile business and the internet; how can LTE reach everything, and how many bytes/months of internet because there are so many applications, such as "immersive multimedia experience," that require high data rates and low latency. Many technologies, such as multiple-input multiple-output (MIMO), software-defined networking (SDN), edge computing, network functions virtualization (NFV), millimeter-wave, and others, have been proposed to address some of 5G's issues. This research, based on the magazine "The international journal of intelligent engineering and systems," focuses on resource allocation difficulties in 5G core networks and radio access networks. It is this that has prompted us to look at the most efficient way to manage the resources of fifth-generation mobile networks.

CoMP-Aware BBU Placements for 5G Radio Access Networks over Optical Aggregation Networks

The huge traffic demand envisioned in 5G requires radical changes in mobile network architecture. A Centralized Radio Access Network (C-RAN) was introduced as a novel mobile network architecture, designed to effectively support the challenging requirements of future 5G networks. Coordinated Multi-point (CoMP) is one of the technologies aiming to increase user traffic by transforming inter-cell interference into useful signals to maximize cell-edge users throughput. Intra-CoMP means that cooperating RRHs are assigned to the same Base Band Unit (BBU). On the other hand, in inter-CoMP, the cooperating RRHs are assigned to different BBUs, which introduce overhead signalling over an X2 interface. This paper proposes a model for BBU placement in C-RAN deployment over a 5G optical aggregation network. The model aims to minimize the number of users undergoing inter-CoMP, therefore reducing X2 signalling overhead. First, we solve the BBU placement problem using Integer Linear Programming (ILP), which minimizes the number of BBUs and the number of used links. Second, given the output of the ILP model (i.e., BBU locations and routes), we propose a heuristic algorithm to reconfigure the BBUs (i.e., the assignment of the RRHs to their corresponding BBUs), which aims at minimizing the number of users undergoing inter-CoMP. The proposed heuristic algorithm considers minimizing end-to-end delay, the number of used wavelengths, and maximizing multiplexing gain. The results show that up to 97% of inter-CoMP users migrated to intra-CoMP users. This results in a decrease in the X2 traffic, which is mainly used for the coordination between the BBUs.

Performance Analysis of a Vertical FSO Link with Energy Harvesting Strategy.

In this paper we investigate the application of free space optical (FSO) communications, energy harvesting, and unmanned aerial vehicles (UAVs) as key technology enablers of a cost-efficient backhaul/fronthaul framework for 5G and beyond (5G+) networks. This novel approach is motivated by several facts. First, the UAVs, acting as relay nodes, represent an easy-to-deploy and adaptive network that can provide line-of-sight between the base stations and the gateways connected to the core network. Second, FSO communications offer high data rates between the UAVs and the network nodes, while avoiding any potential interference with the 5G radio access networks. Third, energy harvesting in the optical domain has the potential to extend the UAVs’ battery life. Nevertheless, the presence of atmospheric turbulence, atmospheric attenuation, and pointing errors in the FSO links severely degrades their performance. For this reason an accurate yet tractable modelling framework is required to fully understand whether an UAV-FSO backhaul/fronthaul network with energy harvesting can be applied. To this end, we consider a composite channel attenuation model that includes the effect of turbulence fading, pointing errors, and atmospheric attenuation. Using this model, we derive analytical closed-form expressions of the average harvested energy as a function of the FSO link parameters. These expressions can be used to improve energy harvesting efficiency in FSO link design. We have applied our proposed expressions to evaluate the energy harvested in vertical FSO links for a variety of real scenarios under a modified on-off keying (OOK) scheme optimized for energy harvesting. From the simulations carried out in this paper, we demonstrate that significant values of harvested energy can be obtained. Such performance enhancement can complement the existing deployment charging stations.

Optimization over time of reliable 5G-RAN with network function migrations

Resource optimization in 5G Radio Access Networks (5G-RAN) has to face the dynamics over time in networks with increasing numbers of nodes and virtual network functions. In this context, multiple objectives need to be jointly optimized, and key application requirements such as latency must be enforced. In addition, virtual network functions realizing baseband processing are subject to failures of the cloud infrastructure, requiring an additional level of reliability. Overall, this is a complex problem to solve, requiring fast algorithms to cope with dynamic networks while avoiding resource overprovisioning. This paper considers the problem of optimal virtual function placement in 5G-RAN with reliability against a single DU Hotel failure and proposes a solution that takes service dynamics into account. Firstly, the joint optimization of the total number of DU Hotels, of the RU–DU latency and of the backup DU sharing in a static traffic scenario is considered, and the DUOpt algorithm, based on Lexicographic Optimization, is proposed for solving efficiently this multi-objective problem. DUOpt splits the multi-objective problem into smaller Integer Linear Programming (ILP) subproblems that are sequentially solved, adopting for each one the most effective methodology to reduce the total execution time. The proposed DUOpt algorithm is extensively benchmarked to show its effectiveness in optimization of medium to large size networks: in particular, it is shown to greatly outperform an aggregate multi-objective approach, being able to compute optimal or close to optimal solutions for networks of several tens of nodes in computing times of a few seconds. Then, the problem is extended to a dynamic traffic scenario in which optimization is performed over time. In this context, in addition to the aforementioned objectives, the total number of network function migrations induced by multiple reoptimizations must be kept to the minimum. For solving efficiently this problem the DUMig algorithm is proposed, which extends and improves DUOpt. Reoptimization over a time horizon of one day in an illustrative dynamic traffic scenario is performed to evaluate the proposed DUMig algorithm against DUOpt, the latter being oblivious of the traffic dynamics. DUMig shows remarkable savings in the total number of migrations (above 86.1% for primary virtual functions and 83% for backup virtual functions) compared to DUOpt, while preserving near-optimal resource assignment.

Series Editorial: Mobile Communications and Networks

As 5G is being widely commercially deployed around the world, it is of great interest to continue investigating how it can be further improved in the near term or evolved aiming at a longer term. This issue of Mobile Communications and Networks explores the above directions covering several aspects. These include the exploration of joint slicing and functional split in the 5G radio access network (RAN), addressing coverage issues in 5G focusing on the uplink data channel, tackling the issue of temporal blockages and outdated channel feedback in an Internet of Vehicles setting, and investigation of the inter-play between rate-splitting multiple access (RSMA) and intelligent reflecting surface (IRS).

5G radio access networks: A survey

The fifth generation (5G) technology improves the user experience and creates new possibilities for a variety of applications, including transportation, device-to-device communication, agriculture, and manufacturing. These new use-cases significantly increase the number of users, volume of traffic, throughput, and latency. Hence, there is a need to modify the radio access network (RAN) for the 5G system. This study investigates the various RAN architectures such as cloud-RAN (CRAN), heterogeneous cloud-RAN (HCRAN), and fog-RAN (FRAN). The architectures are examined in a variety of contexts, including system efficiency, spectrum and energy efficiency, fronthaul capacity, latency, resource sharing and allocation, and so on. Also, current issues with these architectures are highlighted, as well as some existing remedies.

System intelligence for UAV-based mission critical services with challenging 5G/B5G connectivity

Unmanned Aerial Vehicles (UAVs) and communication systems are fundamental elements in mission critical services, such as search and rescue. In this article, we introduce an architecture for managing and orchestrating 5G and beyond networks that operate over a heterogeneous infrastructure with UAVs' aid. UAVs are used for collecting and processing data, as well as improving communications. The proposed System Intelligence (SI) architecture was designed to comply with recent standardization works, especially the ETSI Experiential Networked Intelligence specifications. Another contribution of this article is an evaluation using a testbed based on a virtualized non-standalone 5G core and a 4G Radio Access Network (RAN) implemented with open-source software. The experimental results indicate, for instance, that SI can substantially improve the latency of UAV-based services by splitting deep neural networks between UAVs and edge or cloud equipment. Other experiments explore the slicing of RAN resources and efficient placement of virtual network functions to assess the benefits of incorporating intelligence in UAV-based mission critical services.

Industrial IoT in 5G-and-Beyond Networks: Vision, Architecture, and Design Trends

Cellular networks are envisioned to be a cornerstone in future industrial Internet of Things (IIoT) wireless connectivity in terms of fulfilling the industrial-grade coverage, capacity, robustness, and timeliness requirements. This vision has led to the design of vertical-centric service-based architecture of 5G radio access and core networks. The design incorporates the capabilities to include 5G-AI-Edge ecosystem for computing, intelligence, and flexible deployment and integration options (e.g., centralized and distributed, physical, and virtual) while eliminating the privacy/security concerns of mission-critical systems. In this article, driven by the industrial interest in enabling large-scale wireless IIoT deployments for operational agility, flexible, and cost-efficient production, we present the state-of-the-art 5G architecture, transformative technologies, and recent design trends, which we also selectively supplemented with new results. We also identify several research challenges in these promising design trends that beyond-5G systems must overcome to support rapidly unfolding transition in creating value-centric industrial wireless networks.

RANSlicing: Towards Multi-Tenancy in 5G Radio Access Networks

A significant purpose of 5G networks is allowing sharing resources among different network tenants such as service providers and Mobile Virtual network Operators. Numerous domains are taken in account regarding resource sharing containing different infrastructure (storage, compute and networking), Radio Access Network (RAN) and Radio Frequency (RF) spectrum. RAN and spectrum, transport. Spectrum sharing and RAN are anticipated as the fundamental part in multi-tenant 5G network. Nevertheless, there is a shortage of evaluation platforms to determine the number of benefits that can be acquired from multilevel spectrum sharing rather than single-level spectrum sharing. The work presented in this paper intend to address this issue by presenting a modified SimuLTE model is used for evaluating active RAN based on multi-tenant 5G networks. The result shows an understanding into the actual advantages of RAN slicing for multi-tenants in 5G networks.

RANSlicing: Towards Multi-Tenancy in 5G Radio Access Networks

A significant purpose of 5G networks is allowing sharing resources among different network tenants such as service providers and Mobile Virtual network Operators. Numerous domains are taken in account regarding resource sharing containing different infrastructure (storage, compute and networking), Radio Access Network (RAN) and Radio Frequency (RF) spectrum. RAN and spectrum, transport. Spectrum sharing and RAN are anticipated as the fundamental part in multi-tenant 5G network. Nevertheless, there is a shortage of evaluation platforms to determine the number of benefits that can be acquired from multilevel spectrum sharing rather than single-level spectrum sharing. The work presented in this paper intend to address this issue by presenting a modified SimuLTE model is used for evaluating active RAN based on multi-tenant 5G networks. The result shows an understanding into the actual advantages of RAN slicing for multi-tenants in 5G networks.

5G network deployment and the associated energy consumption in the UK: A complex systems’ exploration

Investing in the communication infrastructure transition requires significant scientific consideration of challenges, prioritisation, risks and uncertainties. To address these challenges, a bottom-up approach was used to demonstrate the future of wireless network transmission and deployment. This study developed an agent-based model to explore the future deployment of non-standalone 5G networks, synthesizing multi-dimensional data visualization. In particular, this research took the UK as an example to investigate the spatiotemporal dynamic characteristics of 5G evolution, and further analysed the energy consumption and carbon footprint of 5G networks, as well as the consequent change in the operating expenses pattern. The simulation results show that 700 MHz and 26 GHz will play an important role in 5G deployment in the UK, which allow base stations to meet short-term and long-term data traffic demands respectively. Furthermore, due to the geopolitical restrictions and embargos, telecommunications may face additional costs of £0.63bn to £1.19bn when deploying 5G radio access networks. Network densification may cause some environmental and economic problems. Take a medium demand scenario as an example, it is found that the electricity consumed by the 5G radio access network will account for more than 2.1% of the total electricity generation, and indirectly lead to 990,404 tonnes carbon emissions in 2030.

Improved Next-Generation Radio Access Networks Using a Centralized Opto-Electronic Oscillator

Next-generation 5G and 6G radio access networks (RANs) require millimetre-wave (mm-W) oscillators to generate extremely low phase-noise signals for the frequency up-conversion and down-conversion in radio units (RUs). The opto-electronic oscillator (OEO) is an outstanding candidate for generating a high-purity mm-W signal in a centralized radio access network (C-RAN) where the distributed base-stations are dislocated from the digital units (DUs) and there are only RUs on the remote side. In this paper we propose placing an OEO in the central-office, while distributing its signal from there to multiple RU base-stations through the mobile front-haul network using a radio-over-fibre (RoF) transmission approach. This new approach was used in experiments that proved the smaller degradation of the phase noise compared to a degradation of 6 dB for the well-known frequency-doubling electrical oscillator in the RU. In addition, we present the signal-strength degradation due to fibre dispersion in mm-W signal distribution, as well as the challenges in long-term stability and multimode operation. We propose solutions to overcome these drawbacks and make our new approach useful for a centralized carrier signal distribution in next-generation RANs.