Radio Access Network Research Articles

Energy-Efficient Dynamic Enhanced Inter-Cell Interference Coordination Scheme Based on Deep Reinforcement Learning in H-CRAN

The proliferation of 5G networks has revolutionized wireless communication by delivering enhanced speeds, ultra-low latency, and widespread connectivity. However, in heterogeneous cloud radio access networks (H-CRAN), efficiently managing inter-cell interference while ensuring energy conservation remains a critical challenge. This paper presents a novel energy-efficient, dynamic enhanced inter-cell interference coordination (eICIC) scheme based on deep reinforcement learning (DRL). Unlike conventional approaches that focus primarily on optimizing parameters such as almost blank subframe (ABS) ratios and bias offsets (BOs), our work introduces the transmission power during ABS subframes (TPA) and the channel quality indicator (CQI) threshold of victim user equipments (CTV) into the optimization process. Additionally, this approach uniquely integrates energy consumption into the scheme, addressing both performance and sustainability concerns. By modeling key factors such as signal-to-interference-plus-noise ratio (SINR) and service rates, we introduce the concept of energy-utility efficiency to balance energy savings with quality of service (QoS). Simulation results demonstrate that the proposed scheme achieves up to 70% energy savings while enhancing QoS satisfaction, showcasing its potential to significantly improve the efficiency and sustainability of future 5G H-CRAN deployments.

Meta surface Assisted Open radio access networks

The paradigm of the open radio access networks (O-RAN) seeks to carry intelligence and openness (multi-vendors) to the conventional proprietary and closed radio access networks (RAN) schemes and deliver performance enhancement, cost-efficiency, and flexibility, in both the network's operation and deployment. On the other hand, Reconfigurable Intelligent Surfaces (RIS) are suggested for future networks due to their effectiveness in terms of cost and energy consumption. However, due to the fast time-varying feature of the dense wireless systems, it becomes difficult to allow optimal user association and beamforming in terms of signalling overheads and processing in RIS assisted RANs with the limited capacity of the fronthaul. Therefore, the objective of this study is to attain a trade-off between the costs (signalling overhead/complexity) and the throughput performance. In other words, the study sets the challenges of the RIS aided O-RAN technology regarding the joint selection of user’s equipment (UEs)/open radio unit (O-RU)-RIS pairs and the designing of beamforming at the O-RU/RIS and open distributed unit (O-DU). From this point, to addressing the designing challenges, this work suggests a simple and potential beamforming strategy in RIS aided O-RAN architecture taking into account the specification of the interfaces between different O-RAN units to split opportunities between the radio and distributing units. In specific, a channel-gain-based selection of UEs/O-RU-RIS pairs joint with duality theory (DT) and transform of quadratic function (TQF) algorithm (namely DT_TQF) is proposed. Firstly, the non-convex optimization problem is relaxed via duality and transform of quadratic functions, and then an iterative approach is carried out for the active and passive beamformers via a simple alternating optimization approach. This approach can achieve flexibility in the environment of a high-traffic transmission while lowering the interference between radio units and the signalling burden required for beamforming tasks. Numerical simulation results justify the effectiveness of the algorithm for different systems' parameter settings and validate the important of installing RIS. For example, for a certain environment, the performance gain is about 52.9 % in comparison to the classic null-steering/random phase shifter scheme.

Adversarial machine learning threat analysis and remediation in Open Radio Access Network (O-RAN)

Adversarial machine learning threat analysis and remediation in Open Radio Access Network (O-RAN)

Research on future 6G green wireless networks

The 6G technology is expected to revolutionize wireless networks by enabling intelligent connectivity of all devices. The concept of a 6G green network aims for ubiquity, intelligence, simplicity, environmental friendliness, and carbon reduction. This paper delves into the essential energy-saving technologies for 6G radio access networks within an energy-efficient framework and proposes a multi-tiered cloud-enabled endogenous intelligent architecture for 6G wireless networks. It provides a design case for an intelligent endogenous wireless network architecture in the context of 6G. Building upon this intelligent network architecture, the paper introduces novel ideas for protocol stack design and multi-rate signaling transmission semantic transmission model. Furthermore, it analyzes the relationship between AI model deployment and energy efficiency while presenting an example of hierarchical deployment of federated learning models. The paper suggests deploying a hierarchical federated learning model within the network architecture to enhance energy efficiency.

Load adaptive multi-threshold scheduling to concurrently improvise transmission time and wait time performance of C-RAN

Low-latency requirements of 5G networks are one of the fundamental needs to support delay-sensitive applications. Uplink scheduling mechanism at wired midhaul of Centralized Radio Access Network (C-RAN) architecture significantly affects the transmission speed and delay performance of the network. Inadequate performance of conventional scheduling algorithms necessitates improvements to attain these requirements. In this paper, load adaptive scheduling with single threshold (LAS-ST) and load adaptive scheduling with multi-threshold (LAS-MT) algorithms on C-RAN architecture are proposed to reduce transmission time and wait time parameters concurrently. The proposed algorithms have been compared with two conventional scheduling algorithms, multi-scheduling domain (MSD) & single scheduling domain (SSD) and applied at fronthaul and midhaul of C-RAN for its comprehensive analysis. MSD and SSD are widely used in Ethernet Passive Optical Network (EPON) architectures. The results confirm that the proposed multi-threshold scheduling algorithm performs best in meeting the objectives of the studycompared to the existing scheduling algorithms in terms of transmission time and wait time by adapting to conditions. Further, resource utilization and resource availability are observed for several transmission cycles (TCs). In addition, data load variation and variation in number of available resources is analysed and its effect on transmission time and wait time is studied.

Machine Learning-Driven Dynamic Traffic Steering in 6G: A Novel Path Selection Scheme

Machine learning is taking on a significant role in materializing a new vision of 6G. 6G aspires to provide more use cases, handle high-complexity tasks, and improvise the current 5G and beyond 5G infrastructure. Artificial Intelligence (AI) and machine learning (ML) are the optimal candidates to support and deliver these aspirations. Traffic steering functions encompass many opportunities to help enable new use cases and improve overall performance. The emergence and advancement of the non-terrestrial network is another driving factor for creating an intelligence selection scheme to have a dynamic traffic steering function. With service-based architecture, 5G and 6G are data-driven architectures that use massive transactional data to emerge a new approach to handling highly complex processes. A highly complex process, a massive volume of data, and a short timeframe require a scheme using machine learning techniques to resolve the challenges. In this paper, the study creates a scheme to use the massive historical data and provide a decision scheme that enables dynamic traffic steering functions addressing the future emergence of the heterogeneous transport network and aligns with the Open Radio Access Network (O-RAN). The proposed scheme in this paper gives an inference to be programmed in the telecommunication nodes. It provides a novel scheme to enable dynamic traffic steering functions for the 6G transport network. The study shows an appropriate data size to create a high-performance multi-output classification model that produces more than 90% accuracy for traffic steering functions.

Priority/Demand-Based Resource Management with Intelligent O-RAN for Energy-Aware Industrial Internet of Things

The last decade has witnessed the explosive growth of the internet of things (IoT), demonstrating the utilization of ubiquitous sensing and computation services. Hence, the industrial IoT (IIoT) is integrated into IoT devices. IIoT is concerned with the limitation of computation and battery life. Therefore, mobile edge computing (MEC) is a paradigm that enables the proliferation of resource computing and reduces network communication latency to realize the IIoT perspective. Furthermore, an open radio access network (O-RAN) is a new architecture that adopts a MEC server to offer a provisioning framework to address energy efficiency and reduce the congestion window of IIoT. However, dynamic resource computation and continuity of task generation by IIoT lead to challenges in management and orchestration (MANO) and energy efficiency. In this article, we aim to investigate the dynamic and priority of resource management on demand. Additionally, to minimize the long-term average delay and computation resource-intensive tasks, the Markov decision problem (MDP) is conducted to solve this problem. Hence, deep reinforcement learning (DRL) is conducted to address the optimal handling policy for MEC-enabled O-RAN architectures. In this study, MDP-assisted deep q-network-based priority/demanding resource management, namely DQG-PD, has been investigated in optimizing resource management. The DQG-PD algorithm aims to solve resource management and energy efficiency in IIoT devices, which demonstrates that exploiting the deep Q-network (DQN) jointly optimizes computation and resource utilization of energy for each service request. Hence, DQN is divided into online and target networks to better adapt to a dynamic IIoT environment. Finally, our experiment shows that our work can outperform reference schemes in terms of resources, cost, energy, reliability, and average service completion ratio.

Retraction Note: Algorithms for high mobility environment in 5G radio access networks with millimeter wave communications

Retraction Note: Algorithms for high mobility environment in 5G radio access networks with millimeter wave communications

Dynamic multimedia transmission scheduling by unmanned aerial vehicle–mounted base stations for a vehicular ad hoc network

On highways, vehicle speed and topology changes are likely to cause transmission delays. In this study, a multimedia transmission system for highways is proposed; this system combines a next-generation cloud radio access network, which has a centralised architecture, with unmanned aerial vehicle (UAV)-mounted base stations (UAV-BSs), cellular network base stations, and roadside units. The deployment characteristics of the UAV-BSs are examined to improve the service of vehicular ad hoc networks and the quality of experience of users. A UAV location assignment algorithm and a transmission resource allocation algorithm for multiple bit rates, communication channels, and relay stations are proposed to reduce multimedia playback lag. We implemented a simulation programme to evaluate how the proposed algorithms outperformed other relevant methods. The simulation results indicate that the proposed algorithms achieve lower lag time than do existing demand- and transmission-based methods. The findings of this study have implications for the practical application of multimedia transmission decision support on highways.

Slice aware baseband function splitting and placement in disaggregated 5G Radio Access Network

Slice aware baseband function splitting and placement in disaggregated 5G Radio Access Network

Three-Dimensional Drone Cell Placement: Drone Placement for Optimal Coverage

Using drone cells to optimize Radio Access Networks is an exemplary way to enhance the capabilities of terrestrial Radio Access Networks. Drones fitted with communication and relay modules can act as drone cells to provide an unobtrusive network connection. The multi-drone-cell placement problem is solved using adapted Dispersive Flies Optimization alongside other meta-heuristic algorithms such as Particle Swarm Optimization and differential evolution. A home-brewed simulator has been used to test the effectiveness of the different implemented algorithms. Specific environment respective parameter tuning has been explored to better highlight the possible advantages of one algorithm over the other in any particular environment. Algorithmic diversity has been explored, leading to several modifications and improvements in the implemented models. The results show that by using tuned parameters, there is a performance uplift in coverage probability when compared to the default meta-heuristic parameters while still remaining within the constraints implied by the problem’s requirements and resource limitation. This paper concludes by offering a study and comparison between multiple meta-heuristic approaches, investigating the impact of parameter tuning as well as analyzing the impact of intermittent restarts for the algorithms’ persistent diversity.

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.

This study presents an advanced framework integrating LLAMA_V2, a large language model, into Open Radio Access Network (O-RAN) systems. The focus is on efficient network slicing for various services. Sensors in IoT devices generate continuous data streams, enabling resource allocation through O-RAN's dynamic slicing and LLAMA_V2's optimization. LLAMA_V2 was selected for its superior ability to capture complex network dynamics, surpassing traditional AI/ML models. The proposed method combines sophisticated mathematical models with optimization and interfacing techniques to address challenges in resource allocation and slicing. LLAMA_V2 enhances decision making by offering explanations for policy decisions within the O-RAN framework and forecasting future network conditions using a lightweight LSTM model. It outperforms baseline models in key metrics such as latency reduction, throughput improvement, and packet loss mitigation, making it a significant solution for 5G network applications in advanced industries.

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.

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.

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

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.