Search Results (869)

The proliferation of Unmanned Aerial Vehicles (UAVs) in various applications has created a pressing need for robust and efficient communication systems. Fifth-generation (5G) networks can support UAV connectivity through high bandwidth and low-latency communication; however, rapid three-dimensional UAV mobility creates handover-management challenges that can increase signalling overhead, service interruption, and Quality of Service (QoS) degradation. This paper presents an integrated framework that combines LSTM-based multi-UAV trajectory prediction with proactive handover optimization using an Advantage Actor–Critic (A2C) Deep Reinforcement Learning (DRL) agent. The LSTM predictor is evaluated on a real-world UAV trajectory dataset and reports a root mean square error (RMSE) of 4.37 m over a 5 s prediction horizon after conversion to a local East–North–Up coordinate frame. A lightweight simulation-level coordination mechanism is included to reduce simultaneous target-cell contention among multiple UAVs; it is not claimed as a new standardized 3GPP signalling procedure. Handover performance is evaluated by replaying 180 held-out flight trajectories in a controlled 5G simulation across ten independent random seeds. Under these stated assumptions, the proposed framework achieves a handover success rate of $94.2\pm 0.8$%, an average SINR of $15.8\pm 0.2$ dB, a handover delay of $45.2\pm 1.1$ ms, and a handover frequency of $0.85\pm 0.05$ HOs/min, outperforming the tuned 3GPP A3, reactive SINR, and CASH baselines in the reported simulation results (Wilcoxon signed-rank test, $p<0.01$, Bonferroni-corrected). The experimental setup is described in detail to support methodological transparency and facilitate future replication, but the handover results should be interpreted as simulation-based evidence rather than live-network validation. Full article

Ultra-reliable low-latency communication (URLLC) is a fundamental technology that plays a crucial role in enabling fifth-generation new radio (5G-NR) communication. URLLC aims to provide a highly reliable connection with strict block error probability requirements and extremely low latency for mission-critical and remote operations. Meanwhile, the advent of sixth-generation (6G) communication, marked by its novel, immersive, and high-stakes control applications, imposes notably more stringent demands on reliability and latency, alongside the added requirements of high data rates, scalability, precision, security, and real-time operation. This scenario introduces unparalleled challenges for both system architecture and the solutions it entails. Several previously proposed solutions, such as retransmission schemes, error correction techniques, and grant-free access, have been insufficient for emerging requirements, as most of these solutions primarily facilitate either low latency or high reliability, but not both. Latency and reliability are conflicting objectives of URLLC. Therefore, an in-depth understanding of the associated issues and careful mitigation of these challenges are essential. This article provides an extensive review of 5G URLLC, emphasizing its technical evolution from 3GPP Release 15 through 19, while also detailing its inherent shortcomings and the potential solutions required for 6G and beyond. We investigate the prerequisites and enabling technologies necessary for URLLC services, exploring related issues across various network components, including frame structure, propagation, processing, retransmission, scheduling, fading, and interference. An important discussion is provided on the fundamental trade-off between latency and reliability, particularly due to retransmission mechanisms. Furthermore, we examine the practical limitations of 5G URLLC when coexisting with other 5G application use cases, such as enhanced mobile broadband (eMBB) and massive machine-type communication (mMTC). Finally, we discuss the future trajectory of URLLC in 6G, identifying key research challenges and opportunities to meet the escalating demands of future mission-critical applications. Full article

Fifth-generation (5G) mobile networks are widely positioned as key enablers of industrial digital transformation. However, despite extensive coverage expansion, the deployment landscape remains dominated by Non-Standalone (NSA) architectures integrated with legacy 4G cores, limiting the practical availability of advanced capabilities such as Ultra-Reliable Low-Latency Communication (URLLC), Massive Machine-Type Communication (mMTC), and network slicing. This has contributed to a disparity between projected 5G functionality and realised industrial utility. This paper investigates the economic and structural factors constraining advanced 5G adoption and examines their implications for emerging sixth-generation (6G) frameworks. We conceptualise the current stagnation as arising from concurrent supply-side and demand-side constraints: elevated Radio Access Network (RAN) capital expenditure relative to previous generations, and limited demonstrable return on investment (ROI) for advanced service capabilities. To evaluate these dynamics empirically, a regional stakeholder study was conducted across industrial and public sector organisations in Ayrshire, Scotland. Data were collected through structured surveys and workshop-based questionnaires involving 34 participants, with proportional sectoral analysis performed to assess representativeness. The results indicate that high initial deployment costs and ROI uncertainty are the primary adoption barriers, with 45.83% of respondents reporting no immediate operational requirement for advanced 5G features. The findings identify an implementation gap in which economic viability, rather than technical feasibility, limits progression beyond basic 5G deployment. The paper argues that unless cost-efficiency and sector-specific value articulation are addressed, similar adoption constraints may extend into 6G development. These results provide empirically grounded insights to inform more economically aligned next-generation network planning. Full article

The uplink/downlink (UL/DL) decoupled access, which allows users to associate with different base stations (BSs), including small BSs (SBSs) and macro BSs (MBSs), has emerged as a network architecture in heterogeneous cellular vehicle-to-everything (C-V2X) communications. It can be tailored to mitigate the signal interference and attenuation impairments that cell-edge vehicles face, while vehicles closer to a BS can opt for coupled access. Therefore, a UL/DL intelligent decoupled access network that integrates decoupled and coupled access approaches is urgently needed for C-V2X communications. In this paper, we present a novel framework for UL/DL intelligent decoupled access in C-V2X networks in the context of fifth-generation mobile communications (5G) and beyond 5G (B5G). We propose a joint optimization approach for radio resource allocation, power control, and user association to enhance the network throughput of UL and DL while meeting the service quality requirements of vehicle users. Specifically, we formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and transform it into a standard convex optimization problem by introducing various auxiliary variables. An efficient iterative algorithm based on successive convex optimization techniques is introduced to obtain a sub-optimal solution. The proposed framework uniquely integrates decoupled and coupled access modes within a unified optimization formulation, enabling dynamic mode selection based on network load. Extensive simulation results demonstrate a significant performance improvement of the proposed UL/DL intelligent decoupled access in C-V2X networks compared with benchmark schemes. Full article

Background: The incidence of thyroid cancer has increased globally. In recent years, robotic surgical systems have been applied in thyroid surgery, and the rapid development of fifth-generation (5G) communication technology has laid a solid foundation for the smooth implementation of remote surgery. Objective: The aim was to explore the feasibility and safety of telerobotic radical thyroidectomy using 5G communication technology to treat thyroid cancer. Methods: From August 2024 to October 2024, telerobotic radical thyroidectomy was performed on seven female patients using a 5G wireless network and a dedicated line network (or ordinary wired broadband) spanning 22–2200 km. The patients’ clinical and information transmission data were analyzed. Results: All patients (papillary thyroid carcinoma, female, with an average age of 44.0 ± 4.6 years) underwent uneventful surgical procedures without any transfer to open surgery or complications. The average surgical duration was 91.3 ± 11.8 min, the average blood loss was 11.4 ± 4.8 mL, and the average postoperative hospital stay was 3.6 ± 0.8 days. All subjects were successfully discharged within 5 days after surgery. The average total latency time of the intraoperative network was 137.5 (range, 121–159) ms, and there were no adverse events, such as network disconnection, frame loss, or network attacks. The operator worked smoothly without any obvious delay or lag, and the recorded audio and video are clear. Conclusions: Telerobotic radical thyroidectomy for thyroid cancer over a 5G network demonstrates promising feasibility and safety. With stable network transmission and a clear surgical field, the precise operations required in thyroid surgery can be performed reliably. These findings suggest that this technology can facilitate high-quality surgical care in remote areas, contributing to a more balanced distribution of medical resources. Full article

Herein, we present the analysis, design, optimization, and fabrication of a broadband, stepped-impedance Wilkinson power divider. The proposed structure employs stepped-impedance transmission lines and open-circuited stubs, achieving a simple and compact implementation while maintaining a wideband frequency response. Initially, transmission-line-based circuit analysis was performed to extract the design equations, followed by simulation and optimization to enhance impedance matching and output-port isolation over a broad bandwidth. Finally, the proposed divider was fabricated using microstrip-line technology, and experimental measurements were conducted using the Agilent E5071C vector network analyzer. The simulation and measurement results showed efficient wideband operation over the 1–4 GHz frequency range. Specifically, the measured return loss at the input port was <−10 dB; the corresponding return loss at the output ports was <−15 dB. The measured insertion loss was −3.73 ± 0.42 dB. The isolation between the output ports was <−10 dB, reaching approximately −30 dB at 2.1 GHz and −25 dB at the center operating frequency (f₀ = 2.5 GHz). The amplitude and phase imbalances were 0 ± 0.2 dB and 0^o ± 0.8^o, respectively. Furthermore, the overall size of the proposed wideband Wilkinson power divider was 0.35λg × 0.21λg. Compared to previous designs, the divider proposed in this study exhibits an improved and more symmetric frequency response, as well as a substantially reduced size, making it suitable for several modern wireless technologies such as Wi-Fi, Bluetooth, GPS, DCS, WCDMA, and sub-6 GHz 5G communication systems. Full article

The use of millimeter-wave spectrum in fifth-generation (5G) systems is increasing the need for accurate prediction of received power and coverage in real deployment scenarios. In this context, ray tracing (RT) is a promising approach for site-specific analysis, although its reliability depends on how accurately different tools reproduce measurements in complex urban environments. This work presents a comparative assessment at 27 GHz of three RT tools: in-house Exact tool based on Vertical Plane Launching (VPL), Matlab 5G and open-source Sionna RT based on Shooting and Bouncing Rays (SBR). The comparison relies on a large outdoor walk-test campaign, including about 14,725 measurement points collected in a real urban area around a 27 GHz mMIMO base station, using real operator-provided antenna radiation patterns. Measured and simulated power levels are compared using statistical metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and a planning-oriented coverage-rate metric. The results show a reasonable agreement between simulations and measurements, with RMSE and MAE values around 10–12 dB, highlighting tool-specific behaviors related to boundary effects, interaction modeling, and high-power overestimation. This work confirms that RT is a flexible support for 5G preliminary network design, reducing the need for extensive drive tests. Full article

With the deployment and application of the Fifth-Generation (5G) mobile communication technologies and the ongoing research and development of the Sixth-Generation (6G) mobile communication technologies, the space–air–ground–sea integrated network has become the core development vision for future communications. As aerial nodes, Unmanned Aerial Vehicles (UAVs) can be applied in a wide range of scenarios, including emergency rescue, surveying and mapping, environmental monitoring, and communication coverage enhancement. In terms of communication coverage enhancement, the space–air–ground integrated network, with UAVs as a key component, can provide seamless communication coverage for the full-domain three-dimensional space such as remote areas, deserts, and oceans. Benefiting from advantages such as low cost and high flexibility, UAVs have become a critical research focus, and the one-hop Base Station (BS)–relay UAV–slave UAV architecture for communication coverage enhancement has emerged as an important development direction. However, the high mobility and wide coverage characteristics of UAVs also pose significant synchronization challenges. Aiming at the uplink synchronization problem on the sidelink between slave UAVs and the relay UAV, a two-step random-access scheme based on Asynchronous Non-Orthogonal Multiple Access (A-NOMA) is designed to mitigate the Doppler Frequency Offset (DFO), improve access efficiency, reduce resource consumption, and accommodate the asynchrony among different users. This scheme leverages the existing preamble sequences of the Physical Random Access Channel (PRACH) and realizes DFO estimation in combination with the pairing index. On this basis, a Successive Interference Cancellation (SIC) algorithm based on DFO and phase compensation is designed to complete the demodulation of user data. For the downlink synchronization problem on the sidelink between slave UAVs and the relay UAV, the frequency offset estimation performance is improved by redesigning the resource allocation scheme of the Sidelink Synchronization Signal Block (S-SSB). Meanwhile, considering the energy constraint of UAVs, a downsampling-based detection scheme is designed to reduce UAV power consumption, and a full-link algorithm is developed to support the practical implementation of the proposed scheme. Full article

The sixth-generation (6G) mobile communication systems are anticipated to be operational by 2030, prompting extensive research efforts by governments and private entities. Designed to meet societal, economic, and technological demands unaddressed by fifth-generation (5G) networks, 6G integrates scalability, security, and reliability with ubiquity and resource-intensive artificial intelligence. Envisaged as multi-band, decentralized, autonomous, flexible, and user-centric, 6G networks incorporate innovative technologies, including cell-free (CF), three-dimensional heterogeneous networks (3D HetNet), reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), as well as artificial intelligence/machine learning (ML). In 6G 3D HetNets, the densification of access points (APs) continues, accommodating increased connections and traffic volumes, alongside the use of higher frequency bands. Although 6G networks are not fully standardized, they target demanding Quality of Service (QoS) standards, such as a peak data rate of 1.0 Tbps and latency of 0.1 ms. This paper conducts a comprehensive literature review on radio resource management (RRM) in 6G cell-free and 3D HetNet systems, emphasizing challenges such as interference mitigation. It presents a taxonomy of RRM approaches, systematically studying, categorizing, and qualitatively analyzing recent techniques, outlining the current state, and indicating future trends, technologies, and challenges shaping 6G systems. Full article

The expansion of fifth-generation (5G) wireless networks has increased environmental exposure to mid-band and millimeter-wave radiofrequency electromagnetic fields (RF-EMF), but their molecular effects on male reproductive tissues remain insufficiently understood. This study evaluated whether repeated exposure to 3.5 GHz and 24 GHz RF-EMF alters testicular stress-associated molecular responses by integrating electromagnetic dosimetry with an in vivo rat model. Whole-body specific absorption rate (SAR) and 10 g peak SAR were estimated using a rat voxel model and scaled to the 20 cm antenna-to-cage geometry used during exposure. Thirty-six adult male Sprague Dawley rats were allocated to sham, 3.5 GHz, or 24 GHz groups and exposed for 1 h/day or 7 h/day over 60 days. Testes were examined histologically and assessed for HSP27, HSP70, and HSP90 protein expression. SAR values were low overall, although absorption was higher at 3.5 GHz than at 24 GHz. Histological evaluation showed preserved seminiferous tubule architecture without consistent structural injury. In contrast, molecular analysis demonstrated frequency- and duration-dependent modulation of heat shock proteins, including early HSP70 downregulation at both frequencies, followed by HSP90 upregulation at 3.5 GHz and HSP27 upregulation at 24 GHz. These findings indicate that low-level 5G-relevant RF-EMF exposure can modify molecular stress responses in testicular tissue even in the absence of overt histological damage. Full article

This research utilizes machine learning (ML)-based malicious node detection techniques to effectively incorporate security and trustworthiness into fifth-generation (5G) and Vehicular Ad hoc Network (VANET) systems, in contrast to traditional methods that do not employ modern techniques. VANET may be vulnerable due to vehicle mobility, network openness, and the conventional network architecture. Therefore, security and trust management using modern methodologies, such as ML approaches, is essential for 5G-enabled VANET integration, which has become a paramount concern. And due to limitations imposed by traditional security methods, which are unable to identify malicious nodes in VANET completely, processing delays are longer. Therefore, this research utilizes the VANET malicious-node dataset designed for real-time malicious node/attack detection in VANET. The proposed ML methodology uses a Random Forest (RF) and an optimized ensemble ML classifier, such as XGBoost and LightGBM, which require a security and trustworthiness solution provided by the RF Trust Extended Authentication (TEA). We simulate vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) mobility, communication behaviors, and trust metrics to assess the accuracy of malicious-vehicular-node features for the identification and detection of attacks, including False Injection, Sybil, blackhole, and Denial-of-Service (DoS). The proposed ML methodology also identifies these attack patterns, providing a realistic dataset for Intelligent Transportation System (ITS) research. In contrast, traditional VANET methods do not. We compared the performance of the proposed ML method with other literature-standard ML and RF methods using metrics such as accuracy, confusion matrices, and precision, Recall, and F1-score to measure effectiveness. In our proposed machine learning (ML) method, we achieve 99% accuracy in classifying MVN and predicting both attack, including False Injection, Sybil, blackhole, and Denial-of-Service (DoS), and benign classes, with precision, recall, and F1-score of 100% each, and establish a trustworthiness score of 100%, Whilst the standard models, such as other VANET methods achieved an accuracy of only 95%, with precision, recall, and F1-score of 98%, without a confusion matrix to confirm the model’s performance. Full article

Spectrum scarcity has become a critical issue due to the rapid deployment of fifth-generation (5G) networks and the explosive growth of future wireless data traffic. Spectrally Efficient Frequency Division Multiplexing (SEFDM) is a promising technique to enhance spectral efficiency by compressing subcarrier spacing and allowing spectral overlap; however, it suffers from severe inter-carrier interference (ICI) caused by the loss of orthogonality. In particular, under Rayleigh fading channels, the combined effects of ICI and multipath fading lead to significant degradation in bit error rate (BER) performance. Conventional SEFDM systems employing a cyclic prefix (CP) encounter an unavoidable error floor due to residual interference stemming from non-orthogonality. On the other hand, while zero-padding (ZP)-based SEFDM offers superior multipath tolerance, further enhancement in communication quality is still desired. This paper proposes a novel receiver architecture utilizing sector antennas to spatially separate multipath components based on the angle of arrival (AoA). Furthermore, we investigate and compare sector selection algorithms specifically tailored for SEFDM systems. Simulation results demonstrate that the proposed method, employing a sector selection scheme based on the maximum channel response power, effectively suppresses inter-symbol interference (ISI) and improves BER performance for both CP-SEFDM and ZP-SEFDM. Furthermore, our quantitative evaluations confirm that the proposed architecture successfully achieves the theoretical maximum spectral efficiency even in higher-order modulation schemes (16QAM), while maintaining a low computational complexity compared to conventional spatial diversity techniques. Full article

The rapid advancement of digital and mobile technologies has reshaped the educational landscape, fostering the adoption of interactive and learner-centered methodologies. Among these, immersive technologies such as Augmented Reality (AR), when coupled with next-generation wireless communication systems, hold the potential to revolutionize knowledge acquisition and student engagement. In this paper, we present the design and development of an AR-based educational tool specifically oriented to teaching concepts of fifth-generation (5G) mobile networks. The tool provides a real-time interactive visualization of 3D network components on mobile devices, enabling learners to explore 5G NSA/SA architectures in an accessible manner with real-world environments through mobile devices and their integrated cameras. The application was developed using Blender for 3D modeling and Unity as the rendering engine, incorporating the Vuforia SDK for marker-based AR tracking, and it was deployed on the Android operating system. Unlike traditional static approaches, the proposed solution enables learners to explore complex network architectures and key functionalities of 5G in an interactive and accessible manner. To assess its perceived effectiveness, quantitative surveys were conducted with both university and high school students, focusing on usability, engagement, and perceived learning outcomes. Results indicate that the tool is user-friendly, enhances motivation, and supports conceptual understanding as perceived by participants of 5G technologies. These findings highlight the potential of AR, supported by advanced wireless networks, as a pedagogical strategy to improve STEM education and foster technological literacy in the era of digital transformation. Full article

Fifth-generation (5G) mobile networks must simultaneously satisfy stringent latency targets, high user density, and energy-aware operation across heterogeneous services. Cloud Radio Access Networks (C-RAN) provide architectural flexibility through centralized baseband processing, but they also introduce new control challenges related to fronthaul constraints, dynamic traffic variations, and joint radio–compute coordination with Mobile Edge Computing (MEC). This paper proposes a unified AI-driven optimization framework for adaptive 5G C-RAN management, where the controller dynamically tunes key system decisions—including functional split selection, TDD downlink ratio, user–RU association, fronthaul load management, and MEC offloading proportion. To enable fair benchmarking under identical simulation settings, a static baseline policy is compared against five adaptive control strategies: Deep Q-Network (DQN), Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Multi-Objective Reinforcement Learning (MORL), and a Deterministic Service-Level Agreement (SLA)-aware controller Penalty-Constrained Hierarchical Action Controller (PCHAC). Performance evaluation across techno-economic and service KPIs shows that intelligent control significantly improves operational profit, tail-latency behavior, and energy efficiency while enhancing SLA compliance compared with non-adaptive operation. The results highlight the practicality of multi-objective and constraint-aware learning for next-generation C-RAN orchestration under scaling traffic demand. Full article