Search Results (385)

(1) Background: The disaggregated and software-defined nature of fifth-generation (5G) core networks and the Open Radio Access Network (O-RAN) architecture increase the attack surface and produce large volumes of heterogeneous evidence that must be analyzed in real time to support incident reconstruction. Open-source 5G stacks (including Open5GS and srsRAN) have become reference platforms in the literature, yet recent research, such as the RANsacked study that reported 119 vulnerabilities and 97 unique CVEs across multiple LTE/5G implementations, have highlighted the pressing need for AI-based detection and forensic capabilities specific to these stacks. (2) Methods: We introduce an experimental framework consisting of a reproducible srsRAN+Open5GS testbed and an AI-driven forensic and detection pipeline. The pipeline receives control-plane (NAS, NGAP, F1AP) and Service-Based Interface (SBI) traffic, extracts protocol- and statistically grounded features and classifies traffic into seven attack types using a hybrid CNN–LSTM model. Integrity-protected and timeline-correlated forensic artifacts (PCAP, logs, memory dumps) assist in reconstructing an incident. (3) Results: The proposed hybrid model achieves a macro F1-score of 0.972 and an AUC-ROC of 0.995 (5-fold CV) and degrades gracefully under load. (4) Conclusions: We show that AI-based detection can be coupled with a scientifically sound evidence chain in open-source 5G stacks deployed as disaggregated mobile networks. Full article

The goal of the study presented in this article is to investigate all current issues related to the proper deployment of load forecasting (LF) techniques in smart grids (SGs). The latter concept has recently emerged as a potential solution to the global energy problem as well as to the ever-increasing and diverse consumer demands. To this end, more flexible dispersed production units are involved, mainly based on renewable energy sources (RESs). Another key novelty of SGs is their ability to gather information directly from consumers and production units in real time, thus facilitating optimum network planning and recovery as well as minimization of outage probability. Hence, it is important to use appropriate advanced infrastructure, which, in combination with modern telecommunication networks, will enable the full exploitation of SGs. In this context, to make the electricity system more efficient, avoid voltage and frequency imbalance issues and implement optimal production and consumption planning, LF is a vital process and plays a key role in the management of future electricity systems. Therefore, recent state-of-the art approaches in LF methods are also presented and discussed. In the same context, current limitations and proposals for future work are identified as well. Full article

In wireless networks with severe blockage, path loss critically limits communication coverage. Reconfigurable Intelligent Surfaces (RIS) offer a promising remedy. However, the fine-grained control of massive reflecting elements incurs prohibitive computational overhead, which hinders real-time deployment. To address these challenges, this paper proposes a low-complexity scheme integrating RIS block selection with adaptive beamforming. The large-scale RIS is partitioned into multiple sub-arrays to enable block-wise phase control. By activating only those blocks with dominant channel gains, the system maximizes reflection gain while minimizing control overhead. To avoid the exponential complexity of exhaustive search, we develop a deep neural network (DNN)-based prediction architecture. By learning the mapping from channel states to optimal configurations, the DNN enables instantaneous selection of near-optimal RIS block combinations. Simulation results show that the proposed data-driven scheme achieves near-optimal bit error rate (BER) performance compared to exhaustive search. Notably, it avoids the exponential complexity growth typically associated with an increasing number of reflecting elements. The proposed mechanism extends reliable coverage range and improves link stability, offering an efficient solution for future wireless networks. Full article

5G electromagnetic field (EMF) measurement is more challenging than measurement in previous cellular generations because 5G New Radio uses time-division duplexing, flexible bandwidths, beam sweeping, massive MIMO, and user-specific traffic beams. As a result, the measured synchronisation signal block (SSB) or PBCH-DMRS level may not directly represent the maximum exposure produced by data transmission. This motivates a combined tutorial and structured survey of existing 5G EMF measurement studies and procedures. This paper reviews the literature on 5G EMF measurement by classifying existing methods into frequency-selective measurement, code-selective measurement, actual exposure assessment, maximum-exposure extrapolation, and network-counter-based assessment. Representative field studies, public measurement reports, and network-data-based studies are compared according to their measurement scenarios, exposure objectives, and limitations. The paper further discusses key uncertainty sources, including beam/gain offset, TDD duty cycle, bandwidth extrapolation, traffic variation, spatial sampling, and equipment-related uncertainty. Finally, open challenges related to FR2 millimetre-wave measurements and reconfigurable propagation environments are discussed. By combining tutorial background with a structured survey, this paper clarifies 5G EMF measurement procedures, maximum-exposure extrapolation, uncertainty sources, and FR2 millimetre-wave measurement challenges. Full article

In most production systems, the objective is to minimize input consumption while maximizing the generation of desirable outputs. However, many production processes also generate undesirable outputs as by-products of desirable outputs. In many real-world systems, undesirable outputs are inherently linked to the production of desirable outputs. Several studies in the Data Envelopment Analysis (DEA) literature have addressed performance evaluation of decision-making units (DMUs) in the presence of undesirable outputs. However, most existing models assume that desirable and undesirable outputs are independent, which may not reflect real production environments. The objective of this study is to model the dependency between desirable and undesirable outputs and to develop a novel DEA framework based on an axiomatic approach. Specifically, the classical axiom of output disposability is decomposed into two separate axioms: disposability of desirable outputs and disposability of undesirable outputs. Based on these axioms, a new production possibility set (PPS) is constructed. The proposed DEA model explicitly incorporates the dependency between desirable and undesirable outputs. A case study involving sensor monitoring systems is presented to demonstrate the applicability of the proposed approach. Full article

Vehicular Ad Hoc Networks (VANETs) are mobile networks that offer new services and communication between moving vehicles, roadside infrastructure, and a trusted authority. With the development of autonomous and connected vehicles, the issue of authentication in VANETs has become increasingly prominent due to the lack of mutual trust among network entities. However, standard authentication models for VANETs must account for total computational and communication overhead, regardless of the timing of authentication message generation. To address this limitation, this work proposes an advanced authentication paradigm for VANETs called the online/offline VANET framework, and formalizes this novel framework to realize lightweight authentication by shifting heavy computational overhead to the offline phase. The proposed model is divided into an offline phase and an online phase. In the offline phase of the free time before the message becomes available, it allows more powerful trusted authority to pre-compute, and in the online phase, resource-constrained devices only execute a small set of residual operations. Based on this model and a new identity-based signature, we give an efficient instantiation and use a mobile platform to evaluate it. The experimental results demonstrate that our construction achieves low online computational and communication overhead. Full article

This paper presents a novel optimal control-based beamforming framework for phased antenna arrays, targeting advanced wireless communication and radar applications, including 5G systems. Unlike conventional beamforming techniques, such as Fourier-based methods and adaptive algorithms (e.g., LMS and RLS), the proposed approach formulates the beam synthesis problem as a discrete-time optimal control problem. The antenna array is modeled using a state-space representation, and a quadratic cost function is introduced to jointly minimize the deviation from a desired radiation pattern and the excitation power. The optimal excitation weights are derived using the Linear Quadratic Regulator (LQR) framework by solving the discrete-time algebraic Riccati equation. This formulation enables an effective trade-off between sidelobe suppression, main lobe accuracy, and power efficiency. Simulation results demonstrate that the proposed method achieves a well-focused main beam, significantly reduced sidelobe levels, and improved directivity compared to conventional approaches. Furthermore, the framework offers robustness and computational efficiency, making it a promising candidate for future FPGA and embedded implementations. Overall, the proposed optimal control-based beamforming approach provides a flexible, robust, and computationally efficient solution for next-generation antenna systems in 5G, beyond-5G (B5G), and radar applications. Full article

In this paper, a mechanically reconfigurable antenna is proposed to overcome the limitations of conventional patch antennas, particularly their static radiation patterns in millimeter-wave (mmWave) 5G applications. The proposed design integrates a physically rotating metasurface above a compact patch antenna, enabling dynamic beam steering through a simple mechanical rotation. A key contribution of this work is the clear and highly predictable relationship between the metasurface rotation angle and the resulting main lobe direction. By rotating the metasurface to specific positions, the main beam is precisely steered to $0^{\circ }$, ${90}^{\circ }$, ${180}^{\circ }$, and ${270}^{\circ }$ in direct correspondence with the metasurface rotation angle. For clarity and conciseness, four representative rotation states are selected and analyzed in this work, although the proposed antenna inherently supports continuous beam steering as a function of the metasurface rotation angle. Full-wave electromagnetic simulations, utilizing a RT/Duroid 5880 substrate, confirm a resonance frequency at 28 GHz with a bandwidth of 1.7 GHz, covering the frequency range from 27.15 GHz to 28.85 GHz. The results confirm notable performance improvements, with the antenna achieving a maximum realized gain of 8.66 dBi and its radiation efficiency increasing from $90\%$ to $94\%$ after metasurface integration. The proposed antenna offers a compact structure, high efficiency, and reliable beam steering without the need for complex feeding networks or active components, making it a promising solution for next-generation wireless communication systems. Full article

Smart microgrids combining photovoltaic arrays, wind turbines, and battery storage generate telemetry that existing open-source monitoring tools cannot process with per-mechanism energy loss visibility in real time. This paper presents the design, implementation, and evaluation of an IoT monitoring framework. The framework incorporates a physics-based microgrid simulator, a hierarchical MQTT communication architecture, and a React-based web-based user interface that supports WebSocket-based real-time data visualization. The framework consists of ten containerized microservices that can be started with a single command: docker compose up -d. All stack performance testing was conducted using a simulated 1 h test case based on a 100 kWp PV system, 10 kW wind turbine, and 50 kWh battery-powered campus microgrid. Median P50 publisher-to-subscriber latency was 27.2 ms and 99th percentile (P99) latency was 48.3 ms, with 100% message delivery across 5840 test messages, with per-topic analysis revealing a 25 ms serialization-order effect in sequential MQTT publishing. Comparative analysis against nine existing platforms including OpenEMS, VOLTTRON, Eclipse Ditto, and pymgrid confirms that, among the platforms surveyed, none unifies physics-based loss telemetry, IoT communication, time-series storage, and real-time visualization in a single reproducible deployment. Full article

Cloud computing and mobile edge computing address the growing demand for computing power driven by the rise in data-intensive applications, but they are prone to creating computing silos, resulting in unbalanced resource utilization. To address this issue, the computing power network (CPN) has been introduced to enable the centralized management and scheduling of resources across the entire network. However, task scheduling in the CPN requires joint selection of computation nodes and routing paths, which greatly increases the complexity of the scheduling problem. In existing studies, heuristic methods are difficult to satisfy real-time requirements, whereas deep reinforcement learning methods ignore the collaborative optimization of network resources, making them difficult to adapt to complex CPN scenarios. To this end, we propose a task scheduling method for the CPN, called TS-DQNF. First, the method uses the Deep Q-Network (DQN) to determine the computation node for the computation task. Then, it introduces a dynamic congestion-aware mechanism to determine a low-cost routing path. Finally, it gradually obtains an effective task scheduling scheme through multiple rounds of alternating iterations. Simulation results show that the TS-DQNF improves the task success rate by 2.47–60.71% and reduces the average processing delay by 1.92–16.94% compared with other methods, while demonstrating good convergence performance. Full article

The increasing frequency of social and emergency situations in modern cities has exposed the limitations of traditional cellular networks, which are often designed based on average traffic demands. These networks struggle to handle sudden demand peaks, leading to service blockages and degraded quality of service. To address this issue, the use of Unmanned Aerial Vehicles (UAV) as mobile base stations has been proposed as a temporary solution to expand network capacity during high-demand periods. However, existing traffic models, such as Erlang-B, fail to capture the dynamic entry, exit, and variability of dwelling times associated with UAVs, limiting their accuracy in real-world scenarios. To overcome these challenges, this work proposes the Erlang-U model, which extends classical traffic analysis by incorporating Markov chains and combining Erlang and Hyperexponential distributions to accurately model the heterogeneous and dynamic nature of UAV sojourn times. This novel approach enables both analytical and computational modeling of UAV mobility and dynamic availability, providing a more realistic estimation of blocking probabilities in cellular networks. Simulation results demonstrate that the adaptive deployment of UAVs, guided by the proposed model, can reduce blocking probability by over 25% compared to conventional solutions. These findings highlight the importance of selecting appropriate sojourn time models to optimize network resilience and efficiency in dynamic and high-demand environments. Full article

This paper presents a corrugated Vivaldi antenna architecture targeting sub-6 GHz and millimeter-wave frequency bands for 5G CubeSat applications, combining experimental validation at sub-6 GHz with a simulation-based scaling study at 26.5 GHz. Existing CubeSat antenna designs either target a single frequency band or rely on complex metamaterial structures incompatible with nanosatellite fabrication constraints. To address this gap, a single-element corrugated Vivaldi antenna measuring 90 mm × 80 mm is designed, fabricated on FR-4 substrate, and experimentally validated at 3.5 GHz, confirming a wide impedance bandwidth of 2.75 GHz and a peak gain of 9.6 dBi. The strong agreement between CST Studio Suite simulations and measurements validates the electromagnetic solver configuration, which is subsequently applied, as a simulation-based design study, to a geometrically scaled version on Taconic RF-60A substrate operating at 26.5 GHz. The miniaturized single-element version achieves a simulated 17 GHz ultra-wideband response and 6 dBi gain in a 7.32 mm × 6.32 mm footprint. Two- and four-element array configurations at 26.5 GHz demonstrate systematic simulated gain progression to 9 dBi and 13 dBi, respectively, with beamwidth narrowing from 49° to 30°. All 26.5 GHz designs are simulated with lossy copper metallization ($\sigma =5.8\times {10}^7$ S/m) and are entirely simulation-based; experimental mmWave validation is a designated target for future work. These results establish a validated design and scaling roadmap for corrugated Vivaldi antennas spanning sub-6 GHz and millimeter-wave bands, offering a cost-effective and CubeSat-compatible solution for high-data-rate inter-satellite communication links. Full article

Preserving perceptual quality and structural fidelity during image transmission remains challenging under bandwidth constraints and noisy channel conditions. Conventional compression standards often exhibit significant performance degradation under severe channel impairments, while integrated quantum-inspired compression and transmission frameworks remain largely underexplored. To address these limitations, this work proposes a simulation-based quantum-inspired image transmission framework that combines Quantum Chirp Transform (QCT)-based compression with a multi-stage U-Net reconstruction and denoising mechanism. In the proposed framework, image bitstreams are encoded using variable-dimensional representations with encoding dimension k, transformed into a chirp-structured domain, and transmitted through a numerically simulated composite quantum noise channel. The QCT exploits non-stationary quadratic phase characteristics to achieve efficient compression while preserving structurally significant image information. At the receiver, inverse processing and adaptive multi-stage U-Net enhancement are employed to suppress channel-induced distortions and improve reconstruction quality. Simulation results demonstrate compression ratios ranging from $2:1$ to $128:1$ depending on the selected encoding dimension, while maintaining high reconstruction fidelity. Compared with quantum Fourier transform (QFT) compression under identical transmission conditions, the proposed framework achieves superior robustness under noisy channels, with PSNR improvements of up to 4.9 dB over a QFT-based baseline and classification accuracy improvements from 84.3% to 90.4% at 10 dB SNR. Results further show that higher-dimensional encoding improves compression efficiency but increases sensitivity to channel impairments, which is effectively mitigated by the proposed multi-stage U-Net reconstruction strategy. These findings demonstrate the potential of chirp-structured quantum-inspired representations for robust image compression and transmission in bandwidth-constrained environments. Full article

Path-aware networking architectures, such as SCION, give endpoints explicit visibility into multiple inter-domain paths, opening new opportunities for data-driven path selection, reliability prediction, and automated diagnosis. However, the lack of standardized, machine learning-ready datasets collected from live path-aware deployments has slowed progress in this domain. We present ScionPathML, an open-source measurement and data-standardization pipeline that abstracts the complexity of SCION’s tooling to continuously collect longitudinal performance measurements (RTT, packet loss, jitter, bandwidth, and per-hop latency) in formats directly usable by ML pipelines. Using a four-week, multi-region campaign across four vantage points on the SCIONLab testbed, we release a public dataset capturing path availability, churn, lifetimes, and end-to-end performance across concurrently available paths. To demonstrate its application, we define four reproducible benchmark tasks, including short-horizon performance forecasting, path failure prediction, anomaly detection, and multi-objective path recommendation, each accompanied by baseline models and evaluation protocols. Our results show that live SCION path performance exhibits an exploitable temporal structure, enabling accurate short-term predictions and early detection of availability drops. Together, the dataset, benchmarks, and open tooling substantially lower the barrier for ML researchers and provide a reproducible foundation for accelerating innovation in path-aware networking. Full article

Conventional multiple-input multiple-output (MIMO) systems rely on static antenna placement. To exploit additional spatial degrees of freedom, the fluid antenna (FA) concept has emerged as a promising solution for improving data rates and diversity performance. Most existing FA studies assume Rayleigh fading, whereas analytical characterization under Nakagami-m fading is more challenging. This article investigates the ergodic capacity of FA-assisted MIMO systems over Nakagami-m fading channels. By applying majorization theory, upper and lower bounds on the ergodic capacity are derived. High signal-to-noise ratio (SNR) approximations are then obtained to clarify the role of the fading parameter and the number of propagation paths. The large-system behavior is also studied, and Monte Carlo simulations are used to assess the tightness of the proposed bounds. The results show that the upper bound closely tracks the simulated capacity, while the lower bound remains useful mainly in the low-SNR regime. Full article