Search Results (74)

The evolution of 3GPP non-terrestrial networks (NTNs) is enabling new avenues for broadband connectivity via satellite, especially within the scope of 5G. The parallel rise in satellite mega-constellations has further fueled efforts toward ubiquitous global Internet access. This convergence has fostered collaboration between mobile network operators and satellite providers, allowing the former to leverage mature space infrastructure and the latter to integrate with terrestrial mobile standards. However, integrating these technologies presents significant architectural challenges. This study investigates 5G NTN architectures using satellite mega-constellations, focusing on transparent architectures where Starlink is employed to relay the backhaul, midhaul, and new radio (NR) links. The performance of these architectures is assessed through a testbed utilizing OpenAirInterface (OAI) and Open5GS, which collects key user-experience metrics such as round-trip time (RTT) and jitter when pinging the User Plane Function (UPF) in the 5G core (5GC). Results show that backhaul and midhaul relays maintain delays of 50–60 ms, while NR relays incur delays exceeding one second due to traffic overload introduced by the RFSimulator tool, which is indispensable to transmit the NR signal over Starlink. These findings suggest that while transparent architectures provide valuable insights and utility, regenerative architectures are essential for addressing current time issues and fully realizing the capabilities of space-based broadband services. Full article

The 5G network’s commercialization has revealed challenges in providing customized and personalized deployment and services for diverse vertical industrial use cases, leading to high cost, low resource efficiency and management efficiency, and long time to market. Although the 5G core network (CN) has adopted a service-based architecture (SBA) to enhance agility and elasticity, the radio access network (RAN) keeps the traditional integrated and rigid architecture and suffers the difficulties of customizing and personalizing the functions and capabilities. Open RAN attempted to introduce cloudification, openness, and intelligence to RAN but faced limitations due to 5G RAN specifications. To address this, this paper analyzes the experience and insights from 5G SBA and conducts a systematic study on the service-based RAN, including service definition, interface protocol stacks, impact analysis on the air interface, radio capability exposure, and joint optimization with CN. Performance verification shows significant improvements of service-based user plane design in resource utilization and scalability. Full article

This paper presents a novel, four-port, rectangular microstrip, inset-feed multiple-input and multiple-output (MIMO) antenna array, enhanced with metamaterials for improved gain and isolation, specifically designed for multi-operator 5G open radio access network (ORAN)-based indoor software-defined radio (SDR) applications. ORAN is an open-source interoperable framework for radio access networks (RANs), while SDR refers to a radio communication system where functions are implemented via software on a programmable platform. A 3 × 3 metamaterial (MTM) superstrate is placed above the MIMO antenna array to improve gain and reduce the mutual coupling of MIMO. The proposed MIMO antenna operates over a 300 MHz bandwidth (3.5–3.8 GHz), enabling shared infrastructure for multiple operators. The antenna’s dimensions are 75 × 75 × 18.2 mm³. The antenna possesses a reduced mutual coupling less than −30 dB and a 3.5 dB enhancement in gain with the help of a novel 3 × 3 MTM superstrate 15 mm above the radiating MIMO elements. A performance evaluation based on simulated results and lab measurements demonstrates the promising value of key MIMO metrics such as a low envelope correlation coefficient (ECC) < 0.002, diversity gain (DG) ~10 dB, total active reflection coefficient (TARC) < −10 dB, and channel capacity loss (CCL) < 0.2 bits/sec/Hz. Real-world testing of the proposed antenna for ORAN-based sub-6 GHz indoor wireless systems demonstrates a downlink throughput of approximately 200 Mbps, uplink throughput of 80 Mbps, and transmission delays below 80 ms. Additionally, a walk test in an indoor environment with a corresponding floor plan and reference signal received power (RSRP) measurements indicates that most of the coverage area achieves RSRP values exceeding −75 dBm, confirming its suitability for indoor applications. Full article

The Open Radio Access Network (O-RAN) paradigm promises unprecedented flexibility and cost efficiency for 6G networks but introduces critical security risks due to its disaggregated, AI-driven architecture. This paper proposes a secure optimization framework integrating zero-trust principles and privacy-preserving Federated Learning (FL) to address vulnerabilities in O-RAN’s RAN Intelligent Controllers (RICs) and xApps/rApps. We first establish a novel threat model targeting O-RAN’s optimization processes, highlighting risks such as adversarial Machine Learning (ML) attacks on resource allocation models and compromised third-party applications. To mitigate these, we design a Zero-Trust Architecture (ZTA) enforcing continuous authentication and micro-segmentation for RIC components, coupled with an FL framework that enables collaborative ML training across operators without exposing raw network data. A differential privacy mechanism is applied to global model updates to prevent inference attacks. We validate our framework using the DAWN Dataset (5G/6G traffic traces with slicing configurations) and the OpenRAN Gym Dataset (O-RAN-compliant resource utilization metrics) to simulate energy efficiency optimization under adversarial conditions. A dynamic DU sleep scheduling case study demonstrates 32% energy savings with <5% latency degradation, even when data poisoning attacks compromise 15% of the FL participants. Comparative analysis shows that our ZTA reduces unauthorized RIC access attempts by 89% compared to conventional O-RAN security baselines. This work bridges the gap between performance optimization and trustworthiness in next-generation O-RAN, offering actionable insights for 6G standardization. Full article

Wireless Local Area Networks (WLANs), particularly Wi-Fi, serve as the backbone of modern connectivity, supporting billions of devices globally and forming a critical component in Internet of Things (IoT) ecosystems. However, the increasing ubiquity of WLANs also presents an expanding attack surface for adversaries—especially Advanced Persistent Threats (APTs), which operate with high levels of sophistication, resources, and long-term strategic objectives. This paper provides a holistic security analysis of WLANs under the lens of APT threat models, categorizing APT actors by capability tiers and examining their ability to compromise WLANs through logical attack surfaces. The study identifies and explores three primary attack surfaces: Radio Access Control interfaces, compromised insider nodes, and ISP gateway-level exposures. A series of empirical experiments—ranging from traffic analysis of ISP-controlled routers to offline password attack modeling—evaluate the current resilience of WLANs and highlight specific vulnerabilities such as credential reuse, firmware-based leakage, and protocol downgrade attacks. Furthermore, the paper demonstrates how APT resources significantly accelerate attacks through formal models of computational scaling. It also incorporates threat modeling frameworks, including STRIDE and MITRE ATT&CK, to contextualize risks and map adversary tactics. Based on these insights, this paper offers practical recommendations for enhancing WLAN resilience through improved authentication mechanisms, network segmentation, AI-based anomaly detection, and open firmware adoption. The findings underscore that while current WLAN implementations offer basic protections, they remain highly susceptible to well-resourced adversaries, necessitating a shift toward more robust, context-aware security architectures. Full article

With the advances of Radio Access Networks, the Open RAN introduced the concept of virtualization and openness to the mobile network elements. These characteristics allow multi-vendor implementations in commercial out-of-shelf hardware with open radio interfaces beyond flexibility and scalability, permitting bringing the data processing to the network edge and easy network element escalation. In Brazil, Radio Access Networks comprise distributed and centralized architectural topology types, which do not meet the requirements of the 5G New Radio wireless mobile network. To reach the 5G needs, an upgrade in the existing network is necessary, revealing some challenges over the existing scenario. This study shows the state-of-art, political, and economic factors that challenge the implementation of Open RAN in Brazil, analyzing the actual regulatory and political facts that can make the technology affordable and possible to introduce quickly to the market. Full article

Conceptually, this paper aims to help reduce the communication blind spots originating from the design of millimeter-wave (mmW) beamforming by deploying radio units of an open radio access network (O-RAN) with free-space optics (FSOs) as the backhaul and the fiber-optic link as the fronthaul. At frequencies exceeding 24 GHz, the transmission reach of 5G/6G beamforming is limited to a few hundred meters, and the periphery area of the sector operational range of beamforming introduces a communication blind spot. Using FSOs as the backhaul and a fiber-optic link as the fronthaul, O-RAN empowers the radio unit to extend over greater distances to supplement the communication range that mmW beamforming cannot adequately cover. Notably, O-RAN is a prime example of next-generation wireless networks renowned for their adaptability and open architecture to enhance the cost-effectiveness of this integration. A 200 meter-long FSO link for backhaul and a fiber-optic link of up to 10 km for fronthaul were erected, thereby enabling the reach of communication services from urban centers to suburban and remote rural areas. Furthermore, in the context of beamforming, reinforcement learning (RL) was employed to optimize the error vector magnitude (EVM) by dynamically adjusting the beamforming phase based on the communication user’s location. In summary, the integration of RL-based mmW beamforming with the proposed O-RAN communication setup is operational. It lends scalability and cost-effectiveness to current and future communication infrastructures in urban, peri-urban, and rural areas. Full article

Open Radio Access Networks (ORANs) are transforming the traditional telecommunications landscape by offering more flexible, vendor-independent solutions. Unlike previous systems, which relied on rigid, vertical configurations, ORAN introduces network programmability that is AI-driven and horizontally scalable. This shift is facilitated by modern container orchestrators, such as Kubernetes and Red Hat OpenShift, which simplify the development and deployment of components such as gNB, CU/DU, and RAN Intelligent Controllers (RICs). While these advancements help reduce costs by enabling shared infrastructure, they also create new challenges in meeting ORAN’s stringent latency requirements, especially when managing large-scale xApp deployments. Near-RTRICs are responsible for controlling xApps that must adhere to tight latency constraints, often less than one second. Current orchestration methods fail to meet these demands, as they lack the required scalability and long latencies. Additionally, non-API-based E2AP (over SCTP) further complicates the scaling process. To address these challenges, we introduce ORAN-HAutoscaling, a framework designed to enable horizontal scaling through Kubernetes. This framework ensures that latency constraints are met while supporting large-scale xApp deployments with optimal resource utilization. ORAN-HAutoscaling dynamically allocates and distributes xApps into scalable pods, ensuring that central processing unit (CPU) utilization remains efficient and latency is minimized, thus improving overall performance. Full article

The evolution of cellular networks from fifth-generation (5G) architectures to beyond 5G (B5G) and sixth-generation (6G) systems necessitates innovative solutions to overcome the limitations of traditional Radio Access Network (RAN) infrastructures. Existing monolithic and proprietary RAN components restrict adaptability, interoperability, and optimal resource utilization, posing challenges in meeting the stringent requirements of next-generation applications. The Open Radio Access Network (O-RAN) and Multi-Access Edge Computing (MEC) have emerged as transformative paradigms, enabling disaggregation, virtualization, and real-time adaptability—which are key to achieving ultra-low latency, enhanced bandwidth efficiency, and intelligent resource management in future cellular systems. This paper presents a Federated Deep Reinforcement Learning (FDRL) framework for dynamic radio and edge computing resource allocation and slicing management in O-RAN environments. An Integer Linear Programming (ILP) model has also been developed, resulting in the proposed FDRL solution drastically reducing the system response time. On the other hand, unlike centralized Reinforcement Learning (RL) approaches, the proposed FDRL solution leverages Federated Learning (FL) to optimize performance while preserving data privacy and reducing communication overhead. Comparative evaluations against centralized models demonstrate that the federated approach improves learning efficiency and reduces bandwidth consumption. The system has been rigorously tested across multiple scenarios, including multi-client O-RAN environments and loss-of-synchronization conditions, confirming its resilience in distributed deployments. Additionally, a case study simulating realistic traffic profiles validates the proposed framework’s ability to dynamically manage radio and computational resources, ensuring efficient and adaptive O-RAN slicing for diverse and high-mobility scenarios. Full article

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. Full article

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. Full article

In the current context of global communications, HF (High Frequency) NVIS (Near Vertical Incidence Skywave) data networks can be of strategic importance, providing short- and medium-range communication capabilities independent of terrestrial configuration and existing conventional communications infrastructure. They are essential in critical conditions, such as natural disasters or conflicts, when terrestrial networks are unavailable. This paper investigates the development of such systems for HF NVIS data communications by introducing a customized Orthogonal Frequency Division Multiplexing (OFDM) protocol with parameters adapted to HF ionospheric propagation, implemented on Software-Defined Radio (SDR) systems, which provide extensive configurability and high adaptability to varying HF channel conditions. This work presents an innovative approach to the application of OFDM narrow-channel aggregation in the HF spectrum, a technique that significantly enhances system performance. The aggregation enables a more efficient utilization of the available spectrum and an increase in the data transmission rate, which represents a substantial advancement in NVIS communications. The implementation was realized using an SDR system, which allows flexible integration of the new OFDM protocol and dynamic adaptation of resources. The work also includes the development of a messaging application capable of using this enhanced HF communication system, taking advantage of the new features of channel aggregation and SDR flexibility. This application demonstrates the applicability of the protocol in real-world scenarios and provides a robust platform for data transmission under conditions of limited access to other means of communication. Thus, this study contributes to the technological advancement of NVIS communications and opens new research and deployment directions in HF communications. Full article

The use of Reinforcement Learning (RL) techniques has been widely addressed in the literature to cope with capacity sharing in 5G Radio Access Network (RAN) slicing. These algorithms consider a training process to learn an optimal capacity sharing decision-making policy, which is later applied to the RAN environment during the inference stage. When relevant changes occur in the RAN, such as the deployment of new cells in the network, RL-based capacity sharing solutions require a re-training process to update the optimal decision-making policy, which may require long training times. To accelerate this process, this paper proposes a novel Transfer Learning (TL) approach for RL-based capacity sharing solutions in multi-cell scenarios that is implementable following the Open-RAN (O-RAN) architecture and exploits the availability of computing resources at the edge for conducting the training/inference processes. The proposed approach allows transferring the weights of the previously learned policy to learn the new policy to be used after the addition of new cells. The performance assessment of the TL solution highlights its capability to reduce the training process duration of the policies when adding new cells. Considering that the roll-out of 5G networks will continue for several years, TL can contribute to enhancing the practicality and feasibility of applying RL-based solutions for capacity sharing. Full article

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. Full article

As wireless networks advance toward the Sixth Generation (6G), which will support highly heterogeneous scenarios and massive data traffic, conventional computing methods may struggle to meet the immense processing demands in a resource-efficient manner. This paper explores the potential of quantum computing (QC) to address these challenges, specifically by enhancing the efficiency of Maximum-Likelihood detection in Multiple-Input Multiple-Output (MIMO) Non-Orthogonal Multiple Access (NOMA) communication systems, an essential technology anticipated for 6G. The study proposes the use of the Quantum Approximate Optimization Algorithm (QAOA), a variational quantum algorithm known for providing quantum advantages in certain combinatorial optimization problems. While current quantum systems are not yet capable of managing millions of physical qubits or performing high-fidelity, long gate sequences, the results indicate that QAOA is a promising QC approach for radio signal processing tasks. This research provides valuable insights into the potential transformative impact of QC on future wireless networks. This sets the stage for discussions on practical implementation challenges, such as constrained problem sizes and sensitivity to noise, and opens pathways for future research aimed at fully harnessing the potential of QC for 6G and beyond. Full article