Radio Access Network Research Articles

Optimizing Open Radio Access Network Systems with LLAMA V2 for Enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and Massive Machine-Type Communications: A Framework for Efficient Network Slicing and Real-Time Resource Allocation

Optimizing Open Radio Access Network Systems with LLAMA V2 for Enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and Massive Machine-Type Communications: A Framework for Efficient Network Slicing and Real-Time Resource Allocation

Reliability Assessment of Power Distribution Cyber–Physical Systems Considering the Impact of Wireless Access Networks

With the rapid increase in the number of various terminal devices in distribution systems, the important impact of communication networks on power supply reliability in cyber–physical distribution systems (CPDSs) is becoming increasingly prominent. The traditional wired communication method makes it difficult to meet the demand in some scenarios due to high cost and construction difficulty. For this reason, this study evaluated the reliability of power distribution cyber–physical systems, considering fifth-generation (5G) mobile communication technology wireless access conditions. First, a reliability model of a communication channel in a 5G communication network was established, and the indirect impact of information system failure on the power distribution system was analyzed. Secondly, a reliability assessment method based on the improved least path method was devised by combining switch action failure, self-healing processing, and load transfer. The simulation results show that 5G communication technology has significant advantages in improving system reliability, and the proposed method is closer to the development direction of future power distribution systems, which provides an effective reference basis for the optimized operation of power distribution cyber–physical systems.

Analysis of NR and LTE Target Signal Structure Based on Neural Network Approach and Deep Learning Methods

Purpose: the rapid development and consolidation of broadband wireless access networks of the fourth 4G LTE and fifth 5G NR generations determine the need to find ways to reuse frequencies. A well-known solution to this problem is the concept of cognitive radio. The use of artificial intelligence in radio networks in general and the use of a neural network approach with deep learning for recognizing signals of LTE and NR standards in particular have serious potential for the practical implementation of frequency resource reuse according to the concept of cognitive development. The aim of the work: is to analyze models, methods and algorithms that allow recognizing signals of the NR and LTE standards based on recording samples of radio signals obtained using pre-recorded NR and LTE signals in MatLab software. In this paper, the procedures for scanning and recognizing sections of the spectrum are considered. The operations of training a semantic segmentation network for the identification of these signals in a broadband spectrogram and the use of a trained network for subsequent spectrum sensing and recognition of signals of the NR and LTE standards are investigated. Methods: the method used to test the concept of cognitive radio is spectrum sensing, which results in information about the occupancy of frequency bands in a given location by primary users, and the identification of spectrum sections available for use by secondary users without interfering with the work of primary users in real time. The novelty of the work is the use of a neural network approach in the analysis of the target NR and LTE signal. Results: the model, trained on the basis of a neural network approach and deep learning methods, is able to distinguish between signals of the NR and LTE standards. Theoretical / Practical relevance: the software implementation of spectral sensing is implemented using an extension package in the MatLab environment and allows for experimental testing of a cognitive radio receiver when performing procedures for analyzing the structure of the target signal of the NR and LTE standards based on a neural network approach and deep learning methods.

Residual multiscale attention based modulated convolutional neural network for radio link failure prediction in 5G

In the realm of the 5 G environment, Radio Access Networks (RANs) are integral components, comprising radio base stations communicating through wireless radio links. However, this communication is susceptible to environmental variations, particularly weather conditions, leading to potential radio link failures that disrupt services. Addressing this, proactive failure prediction and resource allocation adjustments become crucial. Existing approaches neglect the relationship between weather changes and radio communication, lacking a holistic view despite their effectiveness in predicting radio link failures for one day. Therefore, the Dynamic Arithmetic Residual Multiscale attention-based Modulated Convolutional Neural Network (DARMMCNN) is proposed. This innovative model considers radio link data and weather changes as key metrics for predicting link failures. Notably, the proposed approach extends the prediction span to 5 days, surpassing the limitations of existing one-day prediction methods. In this, input data is collected from the Radio Link Failure (RLF) prediction dataset. Then, the distance correlation and noise elimination are used to improve the quality and relevance of the data. Following that, the sooty tern optimization algorithm is used for feature selection, which contributes to link failures. Next, a multiscale residual attention modulated convolutional neural network is applied for RLF prediction, and a dynamic arithmetic optimization algorithm is accomplished to tune the weight parameter of the network. The proposed work obtains 79.03 %, 65.93 %, and 67.51 % of precision, recall, and F1-score, which are better than existing techniques. The analysis shows that the proposed scheme is appropriate for RLF prediction.

Performance of Cellular-Connected UAV in Cell-Free Radio Access Network With Network-Assisted Full-Duplex

Performance of Cellular-Connected UAV in Cell-Free Radio Access Network With Network-Assisted Full-Duplex

Optimization of IoT perceived content caching in F-RANs: Minimum retrieval delay and resource extension with performance sensitivity

In the Internet of Things (IoT) perceived applications of monitoring the states of the environment, a feasible technology is to use fog radio access networks (F-RANs) to alleviate the problems of long response time and cloud server bottlenecks in cloud computing. In response to the above problems, this work investigates the problem of minimizing the retrieval delay of IoT contents in F-RANs under the constraints of system resources. The problem is formulated as an integer linear programming (ILP) model. Then, a polynomial-time method with linear programming (LP) relaxation and rounding is proposed to approximate the optimal solution of the problem. Through proof, the method can obtain a feasible solution with a bounded approximation ratio in polynomial time. The conducted simulations validate that the obtained feasible solution is very close to the optimal one. On the other hand, when the system resources are not enough to meet the continuous growth of content retrieval and need to be expanded, this work further establishes an association relation between cached contents and system resources. Based on the above relation, the second method of expanding system resources with performance sensitivity is proposed to provide the service provider with an effective and economical expansion of system resources. It utilizes a predefined system parameter in balancing the trade-off between the approximation ratio to the optimal solution of the problem and the extended system resources. The solution obtained by the second method is also proved to have a bounded approximation ratio.

Distributed Learning Framework for eMBB-URLLC Multiplexing in Open Radio Access Networks

Distributed Learning Framework for eMBB-URLLC Multiplexing in Open Radio Access Networks

Cost optimization in edge computing: a survey

The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions.

Intelligent Resource Monitoring and Control Method in Vehicular Ad-hoc Networks for Electric Vehicle Enabled Microgrids

Software-Defined Networks (SDN) and Cloud Radio Access Networks (CRANs) are added to vehicle ad hoc networks (VANETs). The goals include making data transfer and resource sharing more efficient, achieving the shortest possible wait and response times, and ensuring the network is reliable even when conditions change. The usual ways of handling and grouping loads in Electric Vehicles (EVs), which are made for stable environments, need to be changed to work with VANETs, which are constantly evolving. To regularly meet these high service levels, focus on more adaptable and durable solutions. This study shows a software-defined EV fog computing design that improves VANETs' resource sharing. The suggested design uses smart controls placed strategically in the network to make the flow of data and use of resources as efficient as possible. The system uses parallel processing to split up computing tasks among EV stations. This makes the network more mobile and lessens the chance of jams. Simulations and real-world tests of the model show that it makes the network much more efficient. The study found that compared to traditional methods, the average response time went up by 29%, network delay went down by 23%, and it took 27% less time to get to the best assets spread.

User-dedicated optical path switching with optical-wireless cooperative control for low-latency and seamless handover

We propose a user-dedicated optical path switching technique for low-latency and seamless handover in the radio access network (RAN) with the all-photonic network (APN). The APN is a network that connects end points directly with optical paths of dedicated wavelengths assigned to each user and provides large-capacity and low-latency connections. As mobile systems beyond 5th generation (5G) and 6th generation (6G) will require extremely low latency, applying the APN to the RAN is promising to achieve this. However, the RAN with the APN has a challenge in optical path switching during the handover procedure of mobile systems because optical switches that compose the APN do not have an Internet protocol (IP) layer function that enables routing control for handover in the conventional router network. Our proposed optical path switching technique to solve this challenge utilizes optical-wireless cooperative control and switches an optical path dedicated to user equipment (UE) executing handover in conjunction with its handover procedure. The key point of the proposed technique is that a next generation node B (gNB) transmits an optimal-timing optical path switching trigger to the optical switch. We experimentally demonstrated the generation of a dedicated optical path for UE in the RAN with the APN as a premise to enable the proposed optical path switching technique. To evaluate the feasibility and effectiveness of the proposed optical path switching technique, we experimentally compared the optical switch and router in terms of latency performance, packet loss, and packet misdirection in the path switching during handover. The results indicated successful optical path switching during handover without packet loss and packet misdirection while reducing the latency per single network equipment by 99%, from 4 µs for the router to 45 ns for the optical switch.

Transfer learning-accelerated network slice management for next generation services

The current trend in user services places an ever-growing demand for higher data rates, near-real-time latencies, and near-perfect quality of service. To meet such demands, fundamental changes were made to the traditional radio access network (RAN), introducing Open RAN (O-RAN). This new paradigm is based on a virtualized and intelligent RAN architecture. However, with the increased complexity of 5G applications, traditional application-specific placement techniques have reached a bottleneck. Our paper presents a Transfer Learning (TL) augmented Reinforcement Learning (RL) based networking slicing (NS) solution targeting more effective placement and improving downtime for prolonged slice deployments. To achieve this, we propose an approach based on creating a robust and dynamic repository of specialized RL agents and network slices geared towards popular user service types such as eMBB, URLLC, and mMTC. The proposed solution consists of a heuristic-controlled two-module-based ML Engine and a repository. The objective function is formulated to minimize the downtime incurred by the VNFs hosted on the commercial-off-the-shelf (COTS) servers. The performance of the proposed system is evaluated compared to traditional approaches using industry-standard 5G traffic datasets. The evaluation results show that the proposed solution consistently achieves lower downtime than the traditional algorithms.

Model and Methods of Traffic Routing in a Communication Network Using UAVs

Relevance. The development of 5G networks and subsequent generations is accompanied by the development of new services, in particular, virtual, augmented reality services, as well as telepresence, as well as radio access networks. In particular, there is an increase in operating frequencies, which poses additional challenges for organizing a network that can meet the requirements for the quality of traffic service from new services and ensure the availability of communication to users. These problems can be solved by various methods of placing access points, including using UAVs. This approach ensures the efficiency of construction and flexibility of the access network structure, but also requires the use of methods for placing access points in relation to users and other elements of the communication network. Problem statement: development of methods for placing routers in a UAV swarm and selecting traffic routes when organizing an access network, in order to improve the efficiency of the communication network. Purpose of the work: improving the efficiency of building an access network using UAVs through the development of clustering methods and distributing routers in a UAV swarm. Methods. The studies were carried out using the provisions of information theory, mathematical optimization methods, graph theory methods and clustering methods. The numerical results were obtained using the numerical simulation method in Python. Result. The developed model and methods allow for the distribution of network routers (access points) located on UAVs taking into account the quality of service and ensuring the construction of a connected mesh network and its connection with the mobile network, which can be used in both modern and future communication networks. Novelty: a modeling and methodological apparatus has been developed that allows for increasing the efficiency of building wireless access networks using UAVs, in particular, allowing for selecting the placement positions of routers in a UAV swarm and the logical structure of the network. The developed modeling and methodological apparatus solves the problem of traffic routing taking into account the quality of its service. Practical significance: the proposed model and methods can be used to organize service in 5G networks and subsequent generations. In particular, they allow for ensuring the availability of communication and the efficiency of network organization in cases of insufficient coverage, as well as in cases of failure of individual network elements. The ability to unload traffic to a local network allows for improving the quality of traffic service in the operator's network.

Combined Sweeping and Jumping Method to Enhance Node Insertion Algorithm for Wi-Fi Sensor Networks

Two dominant driving forces for evolving communication technologies in the current society have been the proliferation of wireless access networks to the Internet and the broadbandization of access and infrastructure networks. Through these evolutions of communication technologies, high-resolution contents are instantly delivered to wireless devices such as mobile phones, wireless tablets, and headsets. Recently, wireless sensor networks, where up to 1000 low-power sensors are wirelessly connected to each other, have been created and connected to the Internet, which presents a new challenge of efficiently coordinating the transmissions of many wireless sensors with minimal transmission overheads. Developing an efficient Medium Access Control (MAC) protocol governing the transmissions of wireless sensor networks is crucial for the success of wireless sensor networks for the realization of the Internet of Things (IoT). In 2023, the node insertion algorithm was proposed to efficiently derive the minimal number of serially connected multipolling sequences of many wireless sensors, by which Access Points (APs) can poll wireless sensors with minimal polling overheads. In this paper, the combined sweeping and jumping method is presented to dramatically enhance the searching performance of the node insertion algorithm. To validate the performance of the combined sweeping and jumping method, simulation results are presented for wireless sensor networks where wireless sensors with varying transmission ranges exist.

HSADR: A New Highly Secure Aggregation and Dropout-Resilient Federated Learning Scheme for Radio Access Networks With Edge Computing Systems

HSADR: A New Highly Secure Aggregation and Dropout-Resilient Federated Learning Scheme for Radio Access Networks With Edge Computing Systems

Guest Editorial Special Issue on Green Open Radio Access Networks: Architecture, Challenges, Opportunities, and Use Cases

Guest Editorial Special Issue on Green Open Radio Access Networks: Architecture, Challenges, Opportunities, and Use Cases

Post-Quantum Blockchain-Based Decentralized Identity Management for Resource Sharing in Open Radio Access Networks

Post-Quantum Blockchain-Based Decentralized Identity Management for Resource Sharing in Open Radio Access Networks

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

Performance Analysis of Multi-UAV Aided Cell-Free Radio Access Network With Network-Assisted Full-Duplex for URLLC

Performance Analysis of Multi-UAV Aided Cell-Free Radio Access Network With Network-Assisted Full-Duplex for URLLC

Energy-Efficient and Accelerated Resource Allocation in O-RAN Slicing Using Deep Reinforcement Learning and Transfer Learning

Abstract Next Generation Wireless Networks (NGWNs) have two main components: Network Slicing and Open Radio Access Networks (O-RAN). NS is needed to handle various Quality of Services (QoS). O-RAN adopts an open environment for network vendors and Mobile Network Operators (MNOs). In recent years, Deep Reinforcement Learning (DRL) approaches have been proposed to solve some key issues in NGWNs. The primary obstacles preventing the DRL deployment are being slowly converged and unstable. Additionally, these algorithms have enormous carbon emissions that negatively impact climate change. This paper tackles the dynamic allocation problem of O-RAN radio resources for better QoS, faster convergence, stability, lower energy and power consumption, and reduced carbon emissions. Firstly, we develop an agent with a newly designed latency-based reward function and a top-k filtration mechanism for actions. Then, we propose a policy Transfer Learning approach to accelerate agent convergence. We compared our model to another two models.

Integrating terrestrial and non-terrestrial networks via IAB technology: System-level design and evaluation

As the telecommunications industry embarks on the transition to Sixth-Generation (6G) networks, this paper examines the integration of Non-Terrestrial Networks (NTN), and in particular satellite backhauling, in the context of Fifth-Generation (5G) systems. The Integrated Access and Backhaul (IAB) technology, conceived as a wireless terrestrial backhauling system in the Next Generation Radio Access Network (NG-RAN), has been identified as a possible enabler for the integration of satellite nodes. Despite the work already done in this direction, the combination of IAB architectures with satellite nodes operating in both the access and backhaul side requires further evaluations on feasibility and limitations for networks integrating Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites. To this end, this work contributes providing insights on background technologies, as well as a detailed analysis of the issues and challenges arising from such integration and a definition of use cases to support narrow-band and broadband services. Furthermore, the design and implementation of a simulation tool is proposed for a performance evaluation in terms of registration time, link capacity, single-hop and end-to-end delay. Results show that the integration turns out to be feasible, even if with strong constraints coming from the satellite system rather than the IAB usage itself. Indeed, the earth-satellite link in LEO systems has a significant impact on the packet delivery time due to the discontinuous coverage. In case of GEO satellite instead, a non-terrestrial backhaul link could limit the performance of the whole system, especially at lower elevation angles.