The motivation behind the framework stems from a long-standing simplification in the field. Traditional mobility models, such as the classic Random Walk, treat movement as a planar problem, which makes trajectory planning and collision avoidance mathematically tractable but fails to capture the reality that drones must navigate around buildings, adjust altitude, and avoid collisions in all three spatial dimensions. Earlier attempts to extend models like the Random Walk, the Random Direction model, and the Gauss-Markov process into 3D produced smoother or more realistic trajectories, but they generally ignored two crucial ingredients: correlation between the movements of different drones, and avoidance of obstacles and of each other. Models that did address group behavior, such as the Particle Swarm Mobility Model, offered only static, two-dimensional collision avoidance and no obstacle handling at all. Meanwhile, sophisticated path-planning algorithms borrowed from robotics, including Optimal Reciprocal Collision Avoidance, artificial potential fields, and Rapidly-exploring Random Trees, offer strong theoretical guarantees, but their computational cost makes them impractical for generating mobility patterns for large numbers of simulated nodes.

Mo3D builds on the earlier Mo3 model, a lightweight, rule-based framework that had only partial 3D support. The key insight of the new work is that the framework’s five rules, each governing a different aspect of node movement, can be upgraded to three dimensions with targeted mathematical modifications rather than a complete redesign. Two of the rules, Individual Mobility and Correlated Mobility, already supported 3D in the original formulation. The real engineering challenge lay in the Collision Avoidance and Obstacle Avoidance rules, which required significant rework to handle the added complexity of volumetric space.

The 3D collision avoidance mechanism is a study in geometric pragmatism. Each drone’s trajectory is represented as a ray originating from its current position, defined by an azimuth angle and an elevation angle, with the drone’s future location expressed parametrically along that ray. When two drones come within a trigger radius, the framework performs a coplanarity check by computing the determinant of a matrix built from their positions and direction vectors. If the determinant is zero, the two trajectories lie in a common plane, and the analysis proceeds much as it would in 2D, with the lines either parallel, coinciding, or intersecting. If the trajectories are not coplanar, a direct crossing cannot occur, but danger can still lurk: the framework solves a system of two dot-product equations to find the points of minimum distance between the two lines, and flags a collision risk if that distance falls below a safety threshold and if both drones will reach those points in the future rather than having already passed them. This forward-looking check matters because trajectories change as avoidance rules are applied, so two drones skimming past each other today could still collide tomorrow.

Obstacle avoidance in 3D posed a different problem: how to represent solid objects without prohibitive computation. The framework models obstacles as vertical parallelepipeds or elliptic cylinders, in three configurations: resting on the floor, hanging from the ceiling, or filling the entire vertical extent of the environment. The elegant trick is projection. For any obstacle whose vertical span includes the drone’s current altitude, the obstacle is projected onto the drone’s horizontal plane, reducing it to a 2D shape that the original rule can handle directly. The drone’s heading is adjusted to circumvent the projected shape while its elevation angle is left untouched, avoiding unnecessary altitude changes. Obstacles outside the drone’s vertical span are generally ignored, except when a specific set of conditions signals vertical danger: the drone’s ground projection falls within the obstacle’s footprint, the vertical gap to the obstacle is smaller than a trigger distance, and the drone’s elevation angle indicates it is heading toward the hazard. In that case, the drone simply levels off, setting its elevation angle to zero to stabilize altitude and avert impact.

The software architecture reflects the same modularity that characterized the original model. Each drone’s velocity is described in spherical coordinates by magnitude, azimuth, and elevation, and five modules, Individual Mobility, Correlated Mobility, Collision Avoidance, Obstacle Avoidance, and Upper Bounds Enforcement, can be independently enabled or disabled through configuration flags, each running on its own update interval. The default Individual Mobility module uses the Boundless model, but the design allows any model capable of producing a velocity vector, including ones incorporating inertia or aerodynamics, to be swapped in without touching the other modules. A new memory feature stores the speed and direction set by the individual model and restores them if other modules modify them, preserving the drone’s original target destination. Correlated Mobility introduces group behavior through bindings between node pairs, a connectivity distance, and a grouping factor; when a drone’s fraction of connected bound partners falls below a threshold, it enters a Forced state, either steering toward its closest disconnected mate or, in a new option, toward the centroid of the group. Binding matrices can even change over the course of a simulation, allowing group structures to dissolve and reform dynamically.

Validation results demonstrate that the framework’s guarantees hold up in practice. In an ablation study with five nodes in a ten-meter cubic area, the collision avoidance mechanism consistently reduced the probability of two nodes coming within the safety threshold, even in extreme cases where the desired minimum distance approached the maximum possible separation in the volume. In a second test, five drones bound into a single tight group still maintained increased average inter-drone distances as the safety threshold grew, showing that collision avoidance works even when correlation rules are pulling the swarm together. Obstacle avoidance was tested with four drones navigating a grid of sixteen elliptic-cylinder obstacles: the minimum realized clearance rose monotonically from roughly 0.8 meters at the smallest trigger distance to about 12.3 meters at the largest, confirming that the trigger parameter provides effective, predictable control over safety margins even though the relationship is not strictly linear at small values.

Computational cost scales honestly with swarm size. With only the Upper Bounds Enforcement module active, execution time grows approximately linearly with the number of drones, matching the per-node cost of that rule. With all modules enabled, growth becomes superlinear, reaching roughly 7.8 times the normalized baseline time at six drones. The culprit is collision avoidance, which must recompute the pairwise distance matrix between all drones at every update, an operation whose cost grows with the square of the swarm size. The authors are candid that this measurement, taken for swarms of up to six, should be treated as indicative for larger fleets, and they note that collision avoidance is computationally heavy in essentially every model of this kind; optimization-based alternatives also scale quadratically. Future mitigations, such as spatial partitioning or neighbor-list approaches, are outlined in the paper, along with other limitations: obstacle shapes are restricted to two families, avoidance acts primarily through azimuth changes, and obstacles are static, though the architecture is designed so that dynamic obstacles would require only regenerating coordinates each update, not changing the avoidance logic itself.

The illustrative examples showcase the framework’s range. Four drones navigating a replica of a real industrial environment, complete with floor-mounted machinery in a room sixteen by thirty-three by six meters, maintained tight group cohesion while smoothly avoiding both obstacles and each other, with elevation angles varying to clear obstacles at different heights. With correlation disabled, the same drones scattered into independent trajectories yet still avoided every hazard. A second scenario demonstrated dynamic correlation, with drones alternating between independent wandering and convergence as two different binding matrices took effect in turn, while handling obstacles in all three vertical configurations. A third confirmed that full-height elliptic cylinders are circumvented as smoothly as box-shaped obstacles.

The broader implications reach into industrial automation and wireless network design. The work aligns with the RESTART Industrial Networks project, funded under the European Union’s NextGenerationEU program, which targets future factory communication systems involving mobile robots, automated guided vehicles, and drones in obstacle-rich environments. Because Mo3D supports both asynchronous integration, where trajectories are pre-generated for offline use, and synchronous integration, where a network simulator triggers each update in real time and can even reconfigure mobility based on network status, it plugs naturally into 5G and future 6G simulation pipelines. By lowering the barrier between abstract mobility mathematics and realistic deployment scenarios, the framework positions itself as a practical tool for the smart factories and safety-critical robotic systems now on the horizon, where movement, communication, and control are inseparably intertwined.

Subject of Research: 3D mobility modeling and simulation for UAVs in indoor environments

Article Title: Mo 3D – a mobility framework for mobility modeling in 3D indoor environments

Article References: Ferretti, D., De Nardis, L., & Di Benedetto, M.-G. (2026). Mo3D – a mobility framework for mobility modeling in 3D indoor environments. SoftwareX, 36, Article 103098. https://doi.org/10.1016/j.softx.2026.103098

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103098

Keywords: UAV mobility, 3D modeling, collision avoidance, obstacle avoidance, drone swarms, indoor simulation, open-source software, wireless networks, 5G, 6G, industrial automation, Python

Developed by Noah Kwaku Baah and Yingchi Mao of Hohai University in Nanjing, together with Portia Annabelle Opoku and Hans Oheneba Aduse Opoku, the approach tackles the core tension at the heart of edge computing. Offloading a task can save a device’s battery and speed up processing, but it consumes network bandwidth, adds transmission delay, and depends on the availability of edge resources that fluctuate from second to second. Most existing offloading strategies treat these factors with fixed priorities, which works well under one set of network conditions and poorly under others. WCOA instead adapts in real time, continuously reweighting the relative importance of energy consumption, bandwidth, latency, and computational resource availability as conditions change. The result is a decision process that tracks the shifting realities of a live network rather than optimizing for a static snapshot of it.

The technical heart of the framework is an Adaptive Weighting Algorithm, or AWA, which dynamically adjusts the weights assigned to each cost component in the optimization objective. When a device’s battery runs low, energy considerations gain weight; when the network is congested, bandwidth and latency dominate; when edge servers are heavily loaded, the cost of competing for computational resources rises in the calculation. This adaptive weighting is paired with two complementary offloading schemes, designated P-COM and G-COM, which model the decision problem in different configurations. Together they capture the trade-offs between executing tasks locally, transmitting them to edge nodes, and dividing work across the mobile-edge-cloud continuum, allowing the system to evaluate the true total cost of each option rather than optimizing a single metric in isolation.

What distinguishes WCOA from many heuristic offloading strategies is its use of exact mathematical optimization. The researchers formulated the offloading decision as a mixed-integer linear programming problem and solved it with Gurobi’s commercial MILP solver, employing Branch and Bound techniques to search the space of possible decisions efficiently. Mixed-integer programming is a class of optimization in which some variables must take whole-number values, such as a binary choice between offloading and not offloading, while others vary continuously, such as the fraction of resources allocated to a task. The Branch and Bound method systematically divides the problem into smaller subproblems, discarding branches that cannot contain better solutions than those already found. This rigor guarantees that the decisions produced are optimal with respect to the weighted cost function, not merely good approximations.

Speed is where the results become striking. Exact optimization methods are often dismissed in edge computing because solving them can take too long for real-time decisions, where offloading choices must be made in milliseconds. Yet WCOA reaches optimal offloading decisions within 0.1 to 1.05 milliseconds, which the authors report is up to five times faster than existing solvers applied to the same problem. That speed matters because the value of an offloading decision decays rapidly: a choice that is optimal for the network state of one moment may be stale by the next. By making exact optimization fast enough for live use, the framework bridges a long-standing gap between the theoretical guarantees of mathematical programming and the practical demands of latency-sensitive mobile applications.

The simulation results quantify the gains. Compared with popular baseline techniques, including LDROA, OONS, and Greedy strategies, WCOA reduced CPU and RAM usage by more than 70 percent and cut overall costs by 50 to 70 percent. Greedy approaches, which make locally sensible choices without considering the global picture, are a common benchmark in this field precisely because they are fast and simple, so outperforming them by such margins while retaining optimality guarantees is significant. The reductions in resource consumption also carry implications for scalability: if each offloading decision consumes far less processor time and memory on the edge infrastructure itself, then a single edge server can serve many more devices, which is essential as IoT deployments grow toward the tens of billions.

The study situates itself within a rich body of prior work on computation offloading. Earlier research has explored dynamic offloading for energy-harvesting devices, Markov decision process formulations of offloading timing, deep reinforcement learning agents that learn offloading policies from experience, and genetic and swarm-based heuristics for task scheduling. Each approach embodies a trade-off: learned policies can adapt to complex environments but offer no optimality guarantees and require extensive training data, while heuristics are fast but can be trapped by locally attractive yet globally poor decisions. WCOA’s contribution is to show that with the right problem formulation and a sufficiently fast solver, exact optimization can be competitive in real time, combining provable optimality with the adaptivity that heuristic and learning-based methods were designed to provide.

The broader context makes this work timely. Edge computing has become a foundational technology for 5G and future 6G networks, autonomous vehicles, industrial automation, and augmented reality, all of which demand millisecond-scale responsiveness that centralized clouds cannot deliver. Surveys of the field have repeatedly identified the offloading decision problem as a central bottleneck: the question of where computation should run determines whether the promise of edge computing is realized in practice. Strategies that reduce latency, energy use, and computational overhead simultaneously, as WCOA claims to do, directly improve both quality of service, measured in network performance terms, and quality of experience, measured in what users actually perceive. The authors frame their approach as a scalable and cost-effective solution for future MEC systems, and the reported resource savings support that framing.

There are, as with any simulation-based study, natural questions about how the framework will behave in physical deployments. Real networks introduce channel fading, user mobility, and hardware variability that simulators approximate imperfectly, and the authors note that no datasets were generated or analyzed during the study, meaning the evaluation rests on modeled scenarios. The work was published in Cluster Computing on 17 September 2026, received on 23 July 2025 and accepted on 3 September 2026, after revisions in March of that year. The authors report no competing interests and no external funding for the research. Corresponding author Noah Kwaku Baah led the conceptualization, methodology, software, and validation work, with co-authors contributing to supervision, software, visualization, and editing.

Even so, the direction of travel is clear. As connected devices multiply and the applications they run grow heavier, the intelligence that decides where computation happens will matter as much as the raw capacity of the servers themselves. WCOA demonstrates that the oldest tool in the optimization arsenal, exact mixed-integer programming, can be made fast enough to sit inside that decision loop, reweighting its priorities millisecond by millisecond as batteries drain, bandwidth fluctuates, and edge servers fill and empty. If the reported gains in resource efficiency and cost translate from simulation to production networks, the framework could help edge infrastructure keep pace with a device population heading toward 40.6 billion, ensuring that the smart devices of the next decade remain smart without draining their batteries or their users’ patience.

Subject of Research: Adaptive weighted cost-optimization for task offloading decisions in mobile edge computing

Article Title: An adaptive weighted cost-optimization approach (WCOA) for task offloading decision in mobile edge computing

Article References: Baah, N. K., Mao, Y., Opoku, P. A., & Aduse Opoku, H. O. (2026). An adaptive weighted cost-optimization approach (WCOA) for task offloading decision in mobile edge computing. Cluster Computing, 29(13), Article 754. https://doi.org/10.1007/s10586-026-06556-1

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06556-1

Keywords: mobile edge computing, task offloading, Internet of Things, mixed-integer linear programming, Gurobi, branch and bound, adaptive weighting, latency optimization, energy efficiency, cost optimization, 5G, distributed computing

The research team, led by Adnan M. Taha and including Mahdi Ghafourivayghan, Konstantin Burlakov, Sergey Shabunin, and Mohammad Alibakhshikenari, set out to address a persistent bottleneck in computational electromagnetics. Commercial full-wave solvers such as ANSYS HFSS, CST, and FEKO discretize an entire three-dimensional volume into a finite-element mesh, and their cost balloons when reflected waves must be tracked near a boundary. When the antenna operates in its near or intermediate zone, the amplitude and phase of the field cannot be inferred from a simple aperture distribution; every field component must be computed numerically, which dramatically inflates processing time and memory demands. For applications like radar sensors, subsurface radiolocation, and level gauges, where the far-zone condition is simply never met, this is more than an inconvenience.

The core of the new method is the Green’s tensor function, a mathematical object that encodes how an infinitesimal electric or magnetic current source radiates through a specified environment. In unbounded homogeneous space, the Green function takes a comparatively simple form. In a layered medium, however, it must absorb the thickness, number, and electromagnetic parameters of every layer, including permittivity, permeability, and conductivity. The authors build these properties into characteristic functions g(z, z’) and f(z, z’), which solve Sturm-Liouville-type differential equations and automatically satisfy all boundary conditions at each interface. Crucially, the modal conductances are recalculated recursively through the stack using an equivalent transmission-line model, in which spectral components of the field are mapped onto voltages and currents in equivalent circuits. Once this analytical groundwork is laid, the field can be evaluated only where it is actually needed, rather than everywhere in space.

To model a real aperture antenna, the team begins with the Huygens element, a pair of orthogonally crossed electric and magnetic dipoles that together reproduce the radiation of an elementary patch of wavefront. The electric field is obtained by integrating the external electric and magnetic currents over the source region, weighted by the appropriate Green’s tensor components. Because a Huygens element radiates a curved, non-uniform wavefront rather than a plane wave, its reflection from a nearby interface behaves nothing like the textbook plane-wave diffraction problem, and the Green’s-function framework captures this distinction exactly. The formulation also yields closed-form far-zone expressions via the saddle-point method, providing a convenient analytical check: the radial field component vanishes, and the radiation maximum points toward the interface, exactly as physics demands.

The extension from the elementary Huygens source to a practical pyramidal horn is where the study claims its principal advance over the authors’ earlier work. The horn flares in both principal planes, combining the properties of E-plane and H-plane sectoral horns, and its aperture field carries a quadratic phase variation that reflects the true curvature of the wavefront. The researchers derive a rigorous aperture integration in which the path-length difference across the aperture is expanded binomially, and the resulting integrals are evaluated in terms of cosine and sine Fresnel integrals. This captures interference structure near the reflector that a simple Huygens source fundamentally cannot represent, making the tool genuinely design-ready rather than a purely theoretical exercise.

Numerical stability is handled through a documented three-region spectral truncation strategy. The integration domain in wave-number space is split into an evanescent region of exponentially decaying near-field contributions, a narrow singularity gap around the branch point where split-path integration is applied, and a propagating region of oscillatory far-field radiation. Convergence analysis shows the truncation error decays exponentially with the upper limit, achieving one percent accuracy well within the adopted defaults, while the singularity treatment remains stable for gap sizes down to ten to the minus twenty-fifth. A parametric robustness study across horn heights from half a wavelength to two wavelengths, frequencies from 0.5 to 3 gigahertz, and substrate permittivities from 1 to 10 confirms that the chosen settings sit on a stable plateau and need no geometry-specific retuning.

The validation against ANSYS HFSS is the study’s most persuasive evidence. On identical observation grids, the proposed method required 30.9 seconds of wall-clock time against 88 seconds for HFSS, a roughly threefold speed-up, and used 200 megabytes of memory against 749 megabytes, a nearly fourfold reduction. The speed advantage stems from the region-of-interest strategy: the spectral integral is evaluated only at the user-specified observation points, whereas the finite-element solver must discretize the entire computational volume. Error budgets quantify the agreement precisely. The L2-norm relative error for the electric field magnitude is 2.78 percent, with maximum deviation of 2.29 percent, while the phase component shows an L2 error of 4.50 percent, a figure the authors attribute to the notorious sensitivity of phase near field nulls.

Even more telling is how the error scales with antenna height above the interface. At a separation of half a wavelength, deep in the reactive near field, the L2 error against HFSS is about 2.6 to 2.9 percent. At three-quarters of a wavelength it drops to roughly 0.65 percent, and at one and a half wavelengths it falls below 0.01 percent as the observation domain moves into the Fresnel region. Notably, the error shows only weak dependence on frequency and substrate permittivity, with higher permittivity actually marginally reducing the error at close separations because stronger field confinement diminishes the relative weight of the evanescent spectral tail. For radar-sensor and level-gauge designers, these numbers suggest the method can be trusted across a decade of bandwidth without recalibration.

The comparison also exposed an interesting divergence between the two approaches at a perfect electric conductor boundary, where the Green’s-function method delivered cleaner wave patterns than the finite-element reference. The authors point out that HFSS results depend on mesh symmetry and on the correct selection of the bounding box around the analyzed object; an asymmetric mesh around a structure symmetric with respect to the reference plane can introduce spurious field asymmetry. The analytical method, by contrast, is electrodynamically exact and automatically enforces all boundary conditions, eliminating such numerical artifacts. In benchmark runs over a dielectric boundary, the conventional solver took roughly twice as long at a comparable spatial step.

The authors are careful to delineate the method’s limits. Its computational advantage is specific to planar, stratified reflecting surfaces; for finite-sized, curved, or laterally inhomogeneous reflectors, edge diffraction and geometric scattering fall outside the stratified-media kernel, and full-wave solvers remain the appropriate tools. Substrates containing vias, patches, or etched patterns would require augmenting the transmission-line recursion with periodic or moment-method treatments. Within its domain, however, the framework offers a unified and efficient foundation that extends naturally to printed and slot antennas, transmitarrays, and reflectarrays, whose multiple functional layers are handled natively by stratified-media Green’s functions. As near-field applications multiply, from high-resolution electromagnetic imaging and secure short-range communication to over-the-air testing of 5G and 6G arrays, tools that trade brute-force meshing for analytical rigor are likely to become indispensable to the antenna community.

Subject of Research: Electromagnetic near-field analysis of pyramidal horn antennas above layered reflecting media using dyadic Green's functions

Article Title: Analysis of pyramidal horn antennas near a reflecting surface using Green’s functions

Article References: Taha, A. M., Ghafourivayghan, M., Burlakov, K., Shabunin, S., & Alibakhshikenari, M. (2026). Analysis of pyramidal horn antennas near a reflecting surface using Green’s functions. Results in Engineering, 32, Article 113033. https://doi.org/10.1016/j.rineng.2026.113033

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113033

Keywords: pyramidal horn antenna, Green's functions, near-field, reflecting surface, stratified media, ANSYS HFSS, electromagnetic modeling, radar sensors, spectral integration, computational electromagnetics, 5G, aperture antennas

The stakes are enormous. Fifth-generation technology, commercialized in 2019, can connect roughly one million devices in an area of 0.38 square miles, deliver peak data throughput of 20 gigabits per second, and achieve minimum latency of one millisecond. Yet the survey’s authors argue that 5G shows restricted flexibility in dynamic contexts. Vehicular services demand ultra-low latency and high reliability simultaneously, and the varied, rigorous requirements of Vehicle-to-Everything communication, known as V2X, often exceed what 5G can guarantee. Beyond 5G and especially sixth-generation networks are anticipated to close that gap by tapping higher frequency spectrum, enabling more network capacity with substantially reduced latency.

The core innovation the survey examines is network slicing, a technique that divides a single physical network into multiple logically separate networks, each tailored to the needs of a different service. In a vehicular context, one slice might serve safety-critical messages between cars, another might carry infotainment streams for passengers, and a third might handle bulk sensor uploads for autonomous driving algorithms. Because each slice can be designed with its own quality of service guarantees, network slicing has significantly enhanced the growth of vehicular networks. The approach relies on softwarization technologies such as software-defined networking and network functions virtualization, which let operators carve, configure, and reconfigure slices on demand rather than through fixed hardware.

But slicing alone is not enough, and this is where the survey’s central argument emerges. Vehicles are, by definition, mobile, and high mobility is one of the defining characteristics of vehicular network members. When a car crosses from one cell to another, or from one network domain to a completely different one, its slice relationships must travel with it. Mobility management is the set of techniques that dynamically preserves these slice relationships and quality of service guarantees while users or devices move. In advanced vehicular networks, the authors emphasize, network slicing and mobility management are two key components that must collaborate to ensure quality of service. A slice that cannot follow the vehicle is a broken promise.

The difficulty is that vehicular surroundings are dynamic and varied in ways that defeat traditional, rule-based mobility management. Handover decisions, the moments when a connection transfers between base stations or between slices, depend on signal quality, traffic load, vehicle speed, and prediction of future position, all changing from second to second. Conventional schemes with fixed parameters, such as static handover thresholds and time-to-trigger values, struggle to adapt. The survey reviews a range of critical techniques for mobility management in 6G vehicular networks, including handover optimization, dual connectivity, positioning and localization methods, and predictive approaches that anticipate where a vehicle will be before the connection needs to move.

Enter deep reinforcement learning, a machine learning technique that combines deep neural networks with the trial-and-error logic of reinforcement learning. In this framework, an agent observes the state of its environment, takes an action, and receives a reward or penalty, gradually learning a policy that maximizes long-term benefit. Deep neural networks allow the agent to handle enormous, high-dimensional state spaces, such as the full radio and traffic conditions of a highway segment, that would overwhelm tabular methods. Algorithms surveyed in the paper include deep Q-networks and their variants, actor-critic families such as proximal policy optimization and soft actor-critic, and multi-agent extensions in which several learning agents cooperate to manage shared resources.

The applications documented in the survey span the entire vehicular networking stack. Deep reinforcement learning has been applied to radio access network slicing for cellular V2X, to resource allocation among competing slices, to task offloading in vehicular edge computing, and to handover decisions in heterogeneous networks. Multi-agent deep reinforcement learning approaches have been used for network slicing in vehicular communications, where distributed agents coordinate slice resources without a central controller. The technique has also been applied to beam management in millimeter-wave systems, trajectory planning for unmanned aerial vehicles that assist ground networks, and joint optimization of caching, computing, and radio resources. In each case, the promise is the same: adaptive, experience-driven decisions that outperform static rules in environments no engineer could fully anticipate.

Why does this matter beyond the laboratory? The advancement of transportation, the authors note, affects multiple facets of people’s lives, encompassing the economy, tourism, and healthcare. Reliable V2X communication underpins emergency vehicle prioritization, platooning, cooperative perception for autonomous driving, and remote monitoring applications. A network that drops a safety message during a handover is not merely inconvenient; it can be dangerous. By combining network slicing, which guarantees per-service resources, with intelligent mobility management, which guarantees continuity of those guarantees under motion, 6G vehicular networks aim to make connected driving dependable enough for safety-critical deployment. The survey also situates this within a broader 6G toolkit that includes intelligent reflecting surfaces, integrated sensing and communication, digital twin networks, and non-terrestrial components such as low Earth orbit satellites.

The authors are careful about limitations. They state that the use of deep reinforcement learning is a beneficial method to improve mobility management in sliced vehicular networks, but that further studies are required. Open challenges include the exploration problem in reinforcement learning, the training cost and sample efficiency of deep agents, the interpretability of learned policies, and the security of learning-based slicing systems, an area where federated learning approaches are being explored to detect attacks across sliced networks. Standardization efforts, including the ITU’s IMT-2030 framework for 6G and the Open Radio Access Network architecture, will shape how intelligent mobility management is actually deployed.

To encourage further work, the survey outlines potential future research objectives, ranging from end-to-end slice mobility across heterogeneous domains to explainable AI for resource management in vehicular network slicing. The authors hope the survey will help researchers understand in depth the concept of network slicing in vehicular networks and the impact of mobility management in sliced environments. For the rest of us, the message is simpler: the self-driving future depends not just on smarter cars, but on networks smart enough to learn, adapt, and never let go of a moving vehicle’s connection, no matter how fast the road unfurls beneath it.

Subject of Research: Deep reinforcement learning for mobility management in sliced 6G vehicular networks

Article Title: Applying deep reinforcement learning to manage mobility in a sliced 6G vehicular network: a survey

Article References: Amaira, A., & Zarai, F. (2026). Applying deep reinforcement learning to manage mobility in a sliced 6G vehicular network: a survey. Cluster Computing, 29(14), Article 820. https://doi.org/10.1007/s10586-026-06606-8

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06606-8

Keywords: 6G, vehicular networks, network slicing, mobility management, deep reinforcement learning, V2X, 5G, handover, machine learning, quality of service, edge computing, beyond 5G

The device, which the authors call a Funnel Morph Antenna, gets its name from a distinctive hybrid geometry that combines a narrow tapered neck with a flared upper section, resembling the profile of an industrial funnel. That shape is not merely aesthetic. In millimeter-wave antenna design, one of the central challenges is impedance matching: the antenna must present a consistent 50-ohm load to the feeding circuitry across a wide range of frequencies, or else a large fraction of the captured signal simply reflects back instead of being delivered to the rectifier. The funnel profile solves this by providing a gradual electromagnetic transition from the narrow neck, which concentrates surface currents toward the radiating aperture, to the wide flared portion, which extends the effective radiating area. Because the current path changes smoothly rather than abruptly, discontinuities that would normally cause reflections are minimized, and the antenna maintains a reflection coefficient below minus 10 dB across the full operating band.

The physics behind the wideband behavior is a story of overlapping resonances. Simulations of the surface current distribution at 28, 33, and 38 GHz reveal that different parts of the structure dominate at different frequencies. At 28 GHz, current concentrates around the narrow neck and lower radiating sections, corresponding to a low-frequency resonant mode. By 33 GHz, the current spreads into the flared upper portion, indicating the formation of additional resonant modes, and at 38 GHz the maximum current density shifts to the upper flare and the cavity regions, where shorter current paths support the highest-frequency operation. The superposition of these multiple modes is what stretches the usable bandwidth to a full 14 GHz, an unusually wide span for a compact millimeter-wave element. A multilayered cavity embedded in the ground plane adds further control, confining the electromagnetic fields and stabilizing the directional radiation pattern.

Perhaps the most provocative engineering decision in the study is the choice of substrate. Millimeter-wave devices almost always use specialized low-loss laminates such as Rogers RT/duroid, because ordinary FR4, the fiberglass material found in countless consumer circuit boards, suffers from significant dielectric losses and dispersion at frequencies above 26 GHz. The team deliberately chose 1.6-millimeter FR4, with a dielectric constant of 4.3 and a loss tangent of 0.02, to test whether a genuinely low-cost design could still perform. Despite the higher insertion losses, careful optimization of the flare and feed geometry allowed the antenna to achieve wideband matching, and the geometry proved robust against the etching tolerances of standard printed circuit board fabrication, holding its performance even with dimensional deviations of plus or minus 0.05 millimeters. The single element measures only about 17.5 by 20.3 millimeters and delivers a simulated peak gain of roughly 5 dBi with radiation efficiency above 85 percent, figures that measurements closely confirmed.

A single antenna, however, captures only a modest amount of power, so the researchers replicated the element into a four-element array. To feed all four elements uniformly, they designed a 1-to-4 Wilkinson power divider, a classic microwave component built from three cascaded two-way dividers, each using quarter-wavelength transmission lines with a characteristic impedance of twice the system impedance and a 100-ohm isolation resistor between output ports. The divider performed impressively across the band, with insertion loss between roughly minus 6 and minus 6.2 dB, meaning each output port receives almost exactly one quarter of the input power, and port-to-port isolation exceeding minus 45 dB around 30 GHz, indicating negligible signal leakage between channels. The array elements were spaced 5 millimeters apart, half a wavelength at the operating frequency, to maximize gain while suppressing unwanted side lobes.

The assembled array, measuring 78.25 by 70.57 millimeters, delivered a measured peak gain of 10.25 dBi and an efficiency of about 93 percent at 28 GHz. The theoretical maximum gain for four ideal elements with a single-element gain of 4.8 dBi would be 10.82 dBi, so the measured value falls only 0.57 dB short, a gap the authors attribute to feed network losses, conductor losses in the microstrip lines, and the dielectric losses inherent to FR4. The aperture efficiency, a measure of how effectively the physical area of the array is used, remained a steady 78.5 percent despite mutual coupling between elements and phase errors across the wide band. The beam pattern is notably narrow, with a half-power beamwidth of about 9 degrees at 28 GHz and 10 degrees at 38 GHz in one plane, and side lobe levels between minus 7 and minus 9 dB, giving the array a focused, directional sensitivity well suited to harvesting energy from distant base stations.

Converting captured radio waves into usable electricity falls to the rectifier, and here the team navigated some practical constraints. They selected the 1SS351 Schottky diode from ON Semiconductor, chosen because its electrical characteristics closely match the widely used HSMS-2852 diode, which has recently become obsolete. Using Keysight Advanced Design System software, the researchers modeled the diode with harmonic balance and S-parameter simulations to extract its input impedance, finding a value of 47.4 minus j20 ohms near 28 GHz. Matching that impedance to the antenna required careful design work. The team compared half-wave and full-wave rectifier topologies and found that the full-wave bridge significantly raises the imaginary component of the input impedance, which would degrade efficiency without elaborate matching. A matched half-wave rectifier using microstrip lines emerged as the practical choice, especially after lumped-element matching proved infeasible because it required component values, such as a 53-ohm resistor and a 2.7-nanohenry inductor, that are difficult to source off the shelf.

The full system was validated experimentally in an anechoic chamber, with a broadband vector signal generator driving a standard gain horn antenna as the transmitter and the fabricated rectenna prototype as the receiver, separated by one meter, comfortably within the far-field region for these frequencies. Both antennas were carefully polarization-aligned, and the incident power density was calibrated with a broadband power sensor before characterization. When the array harvested 68.7 microwatts of incident RF power, the system produced an output voltage of 250 millivolts across a 2-kilohm load, corresponding to an overall RF-to-DC conversion efficiency of 45.5 percent. That figure is competitive with, and in several respects superior to, prior millimeter-wave rectennas, many of which required expensive substrates, waveguide structures, or far higher input power levels to reach comparable efficiency.

The comparison with earlier work underscores why the low-cost approach matters. Previous millimeter-wave rectennas have relied on Duroid substrates, substrate-integrated waveguides, liquid crystal polymer packaging, on-chip CMOS integration, and even air-filled waveguide Fabry-Perot resonators. Some achieved higher peak gains or higher conversion efficiencies, such as a waveguide-fed 35 GHz design reaching 68.5 percent, but typically with narrower bandwidth, more complex manufacturing, or substantially greater incident power. Others, like CMOS folded dipoles, achieved extreme miniaturization at the cost of negative gain. The new design occupies a distinctive middle ground: a full 26 to 40 GHz operating range, a respectable 10.25 dBi gain, a narrow 9-degree beamwidth, and 45.5 percent conversion efficiency, all on ordinary FR4 fabricated with standard PCB technology.

The implications reach beyond the laboratory. As 5G and future 6G networks densify urban radio environments, the ambient millimeter-wave energy available for harvesting will only grow, and devices that can scavenge it without batteries could power IoT sensors, wearables, and backscatter tags indefinitely. The authors suggest that future work will explore miniaturization techniques and adaptive beamforming to improve integration into compact real-world devices. If battery production costs, which are high relative to the energy those batteries actually store, continue to drive the push toward green electronics, a funnel-shaped patch of fiberglass that drinks from the 5G spectrum may prove to be one of the more elegant shortcuts to energy-autonomous wireless devices.

Subject of Research: A wideband funnel-geometry rectenna array for radio-frequency energy harvesting in the 26–40 GHz 5G millimeter-wave band

Article Title: Wideband funnel geometry rectenna array for RF energy harvesting in 26–40 GHz 5G networks

Article References: Megahed, A. A., Mousa, M. E., Al-Gburi, A., & Elabd, R. H. (2026). Wideband funnel geometry rectenna array for RF energy harvesting in 26–40 GHz 5G networks. Results in Optics, 25, Article 101178. https://doi.org/10.1016/j.rio.2026.101178

Image Credits: AI Generated

DOI: Not provided

Keywords: rectenna, RF energy harvesting, 5G, millimeter-wave, antenna array, Wilkinson power divider, Schottky diode, FR4 substrate, wireless power transfer, impedance matching, IoT, Results in Optics

The research, led by Tianming Feng and corresponding author Chenyu Wu, tackles a blind spot in how engineers evaluate multi-antenna, multi-user networks. Traditionally, network designers have leaned on a single headline statistic: the success probability, the average chance that a randomly chosen user’s transmission clears a minimum signal-to-interference ratio threshold. That number is useful, but it hides as much as it reveals. A network with a respectable average can still contain a significant minority of users stranded in interference shadows, suffering connections far worse than the mean implies. The new framework refuses to settle for the average. It layers on two additional metrics — the variance of link reliability across users and the so-called SIR meta distribution — to capture not just how well the network performs, but how fairly it performs and how reliably any individual link can be expected to behave.

The meta distribution deserves particular attention, because it is the metric most likely to reshape how next-generation networks are tuned. Rather than reporting the probability that a typical user succeeds, the meta distribution answers a sharper question: what fraction of users can achieve a given link reliability? The authors illustrate the concept with a concrete example — if operators focus on the 5th-percentile, the meta distribution tells them the link reliability that 95 percent of users in the network can attain. That is exactly the kind of cell-edge guarantee that matters for mission-critical applications, from industrial automation to emergency communications, where a good network average is cold comfort to the unlucky user whose connection keeps dropping.

To build this fine-grained picture, the team turned to stochastic geometry, the branch of mathematics that models randomly scattered transmitters and receivers as spatial point processes. Base stations and users are treated as points in a Poisson point process, and the interference each user experiences becomes a random variable whose statistics can be derived in closed form. The authors derive exact expressions for the success probability of each interference-nulling scheme, then obtain approximate expressions for the first and second moments of the link reliability, which in turn yield the variance and an approximation of the SIR meta distribution. The mathematical machinery is formidable — involving Laplace transforms of the interference field, gamma-distributed channel gains, and an elegant inversion technique dating back to a 1951 result by J. Gil-Pelaez — but the payoff is a set of tractable formulas that network planners can actually evaluate without brute-force simulation.

The study compares two distinct strategies for deploying interference nulling, and the contrast between them carries the paper’s most consequential finding. In the fixed scheme, abbreviated FxIN, each base station nulls interference toward users located within a fixed radius around it. Any user inside that protection zone who is being served by another base station gets a null steered in their direction, regardless of how far away their actual server is. In the flexible scheme, FlIN, the protection is adaptive: a user sends an interference-nulling request to a nearby base station only when that station is closer than a configurable multiple of the user’s serving distance. In other words, FlIN concentrates its nulling resources on the interferers that actually threaten a given link, rather than blanketing a fixed geographic area.

When the two schemes are run through the analytical framework, a clear pattern emerges on fairness. The flexible scheme always provides higher fairness among individual links than the fixed scheme, according to the study. This makes intuitive sense once the geometry is examined: FlIN adapts its protection to each user’s actual situation, so users in difficult positions receive proportionally more help, compressing the spread of link qualities across the network. The variance of link reliability — the framework’s fairness metric — comes out consistently lower under FlIN, meaning the gap between the best-connected and worst-connected users shrinks. For operators facing regulatory or commercial pressure to guarantee minimum service levels, that fairness advantage could prove decisive.

But here is the twist that prevents the story from ending with a simple verdict: the flexible scheme does not always win on raw performance. The study shows that the superiority of the success probability and the SIR meta distribution between the two schemes depends on the system parameter design. The fixed scheme, with its predictable geographic protection zone, can outperform the adaptive approach under certain configurations of antenna count, user loading, and nulling capacity. Each base station can only satisfy a limited number of nulling requests — the analysis models this capacity explicitly, deriving the mean number of requests each scheme generates. Under FxIN, the expected number of requests scales with the size of the protection circle and the user density, while under FlIN it scales with the square of the flexibility parameter minus one, multiplied by the number of users served per station. Choosing the parameter that balances request load against antenna resources is therefore the crux of the design problem.

The derivations themselves reveal how the antenna dimension enters the picture. When a base station satisfies a nulling request, it sacrifices one spatial degree of freedom, reducing the effective diversity order of its own served links — the analysis tracks this through the term D, equal to the number of antennas minus the number of served users plus one, minus the number of satisfied nulling requests. The desired channel gain under this reduced diversity follows a gamma distribution whose shape parameter shrinks with each null granted. The authors exploit a lower bound on the incomplete gamma function to derive tractable upper bounds on the conditional success probability, then aggregate over the random number of satisfied requests using the total probability theorem. The final expressions take the form of matrix exponentials and matrix inverses whose induced one-norms give the performance metrics directly — a compact and computationally efficient alternative to Monte Carlo simulation of large networks.

What makes this work timely is the trajectory of wireless technology. Multi-antenna systems have moved from research curiosity to the backbone of 5G and the blueprint for 6G, and interference nulling sits at the heart of techniques from coordinated multipoint transmission to user-centric network MIMO. Prior studies in the literature — including analyses of inter-tier interference nulling in heterogeneous networks and user-centric nulling in small-cell deployments — established that nulling improves average performance, but the meta-distribution lens shows that averages can mask deep inequities between users. As networks densify and the user experience becomes a marketed commodity, the difference between a network with a good average and a network with a good 5th-percentile is the difference between satisfied and frustrated customers.

The authors are candid about the framework’s boundaries. The analysis assumes perfect channel state information at the base stations, though they note the framework can accommodate imperfect CSI, leaving that extension to future work. No datasets were generated or analyzed in the study, which is purely theoretical. Yet the implications are practical: the closed-form results let engineers sweep through design parameters — antenna counts, user loads, protection radii, flexibility factors — and identify the operating points where fairness and performance trade off against each other. In a field where every antenna element and every nulling request carries a cost, a framework that quantifies exactly who benefits and who is left behind is not just an academic exercise. It is a map for building wireless networks that serve everyone, not just the average user.

Subject of Research: Fine-grained performance analysis of multi-antenna multi-user wireless networks using interference nulling

Article Title: A Fine-Grained Performance Analysis for Multi-Antenna Multi-User Networks with Interference Nulling

Article References: Feng, T., Wu, C., Wang, L., Lu, X., & Han, S. (2026). A Fine-Grained Performance Analysis for Multi-Antenna Multi-User Networks with Interference Nulling. Mobile Networks and Applications. https://doi.org/10.1007/s11036-026-02519-3

Image Credits: AI Generated

DOI: 10.1007/s11036-026-02519-3

Keywords: multi-antenna networks, interference nulling, SIR meta distribution, stochastic geometry, success probability, link reliability, fairness, MIMO, 5G, wireless networks, Poisson point process, Mobile Networks and Applications

The team, led by Noah Wickel and colleagues at the Chair of Medical Engineering, built a prototype wireless tracking system designed around the ISO/IEEE 11073 Service-oriented Device Connectivity standard, known as SDC. This interoperability standard, which has recently entered clinical use in patient monitoring for operating rooms and intensive care units, allows medical devices from different manufacturers to discover each other and exchange data over a network. The researchers mounted a surgical tracking camera, an embedded Linux computer, a lithium battery and power electronics on a mobile base with a medical holding arm, creating a self-contained unit that can run for more than 24 hours on a single charge. The embedded computer calculates surgical tool poses from the raw camera data and broadcasts them to navigation software over the network, using either a private 5G standalone network or an off-the-shelf Wi-Fi 6 router.

The choice of these two technologies reflects a broader debate in industrial and medical connectivity. Wi-Fi is cheap and ubiquitous, but its shared spectrum and collision-avoidance mechanisms make it difficult to guarantee the very low, bounded latencies that safety-critical applications demand. Fifth-generation cellular networks, by contrast, use licensed private spectrum and a strictly scheduled air interface, which has fuelled expectations that they could serve demanding medical control loops. The Aachen team operated their own private 5G campus network in the 3.7 to 3.8 GHz band licensed in Germany, running the open-source OpenAirInterface software for both the core network and the radio access network, connected to a low-power O-RAN radio unit over fibre. The tracking system was the only device on the network, giving 5G the most favourable possible conditions.

To measure end-to-end latency in a realistic way, the researchers devised an elegant experimental trick. The navigation computer controlled an infrared LED through a USB-to-serial adapter, and the tracking camera registered that LED as a fiducial marker. By timestamping the moment the LED was switched on and the moment the corresponding tracking message arrived at the navigation software, the team captured the entire acquisition and transmission chain, including the camera’s internal image processing that earlier measurements had omitted. Each configuration was measured for a full hour in an underground laboratory whose characteristics resemble a medium-sized operating room. As a benchmark for unimpeded surgical performance, the team adopted 100 milliseconds as an upper latency bound, drawing on studies of visual delay in laparoscopic and telesurgical settings showing that delays up to 100 milliseconds do not degrade task completion time or error rates, while longer delays double execution times and significantly increase errors.

The wired Ethernet baseline set a demanding standard. At a 100 Hz update rate, mean latencies of about 11 to 14 milliseconds were achieved with no packet loss, depending on whether the tracking system acted as the SDC provider sending reports or as a consumer triggering operations on the navigation computer. The Wi-Fi 6 configuration came remarkably close to this benchmark. At 50 Hz, mean latencies of 16.5 and 26.2 milliseconds were recorded in the conventional and reversed role set-ups respectively, with worst-case values of 36.7 and 97.2 milliseconds. Even at 100 Hz, the system operated without congestion or dropped messages, though maximum outliers grew larger. Crucially, the updated prototype consistently kept tracking events below 30 milliseconds on average and below 100 milliseconds in the worst case, a substantial improvement over the team’s earlier Wi-Fi 5 generation, which had shown unacceptable outliers exceeding 300 milliseconds.

The 5G results told a strikingly different story. Even at reduced update rates of 10, 20 and 50 Hz, the cellular link produced mean latencies roughly an order of magnitude higher than Ethernet, reaching 94.3 milliseconds at 10 Hz in the conventional configuration. Maximum delays approached 300 milliseconds, and at 100 Hz the system consistently descended into congestion, with messages arriving out of order, high drop rates of up to 16.85 percent, and subscription renewal failures that severed the SDC connection entirely. In the reversed role set-up, where the tracking system triggered operations on the navigation provider rather than streaming reports, the 5G link sustained 20 and 50 Hz without losses, but mean latencies of about 49 to 61 milliseconds still fell short of the Wi-Fi performance, and every 5G-based configuration saw at least one percent of data points arrive later than the critical 100-millisecond threshold.

Digging into the 5G uplink behaviour revealed why the cellular link struggled. In a series of controlled experiments using timestamped UDP packets, the researchers found that latency was highly sensitive to packet frequency: round-trip times changed little between 5 and 50 Hz but deteriorated sharply at 100 Hz and above. A clever mitigation strategy of bundling multiple tracking values into single packets, trading a small buffer delay for a lower packet rate, produced a remarkably stable transmission delay of 24 to 26 milliseconds and allowed simulated data rates of up to 800 Hz to be delivered within 45 milliseconds at the 99th percentile. That would be fast enough for the quickest clinical tracking systems on the market, which run at 400 to 500 Hz. However, maximum delays above 100 milliseconds reappeared whenever packet rates exceeded 20 Hz or background uplink traffic was present, underscoring the non-deterministic character of the link. Parallel downlink traffic was largely harmless, but even modest competing uplink traffic above 30 Mbit/s quickly congested the cell entirely.

The authors point to a likely culprit for the 5G shortcomings: the software-based radio access network. Their analysis of the literature shows that the OpenAirInterface implementation suffers from unpredictable jitter and frequent user-equipment disconnects, with comparable studies reporting outliers of 200 to 500 milliseconds, while alternative software stacks and dedicated hardware radio networks achieve far more stable results. The uplink also bears a structural handicap, since a device must first request a transmit grant from the network before sending, adding scheduling delay that downlink traffic avoids. Even so, neither the tested software radio network nor the dedicated hardware networks described in comparable studies currently meet the 3GPP standardisation target of 4 milliseconds user-plane latency, suggesting that the gap between cellular promise and surgical reality remains substantial.

The practical implications are significant. Wi-Fi 6, in this configuration, already delivers latencies compatible with the hand-eye coordination demands of navigated procedures such as neurosurgical spine instrumentation, meaning a wireless tracking camera could be prepped outside the sterile field, rolled in without cables, and repositioned freely to maintain line of sight to the instruments. That would ease set-up schedules, reduce trip hazards, and free mental capacity for the surgical team. Yet the researchers are careful not to write off 5G. Cellular networks handle dense user populations far better than Wi-Fi, and their behaviour in obstructed hospital environments remains an open question. Future work will explore higher 5G numerologies, pre-scheduled uplink grants, alternative radio network implementations, and deeper integration of SDC signalling into the 5G core, alongside Wi-Fi 7 features such as restricted target wake time that promise a further 30 percent latency reduction. For now, the cable-free operating room is closer than ever, and it will likely arrive first over Wi-Fi.

Subject of Research: Evaluation of 5G and Wi-Fi 6 wireless networks for latency-critical cable-free surgical navigation using the ISO/IEEE 11073 SDC standard

Article Title: Evaluation of 5G cellular and Wi-Fi 6 for cable-free surgical navigation using ISO/IEEE 11073 SDC

Article References: Wickel, N., Schollmaier, P., Radermacher, K., & Janß, A. (2026). Evaluation of 5G cellular and Wi-Fi 6 for cable-free surgical navigation using ISO/IEEE 11073 SDC. International Journal of Computer Assisted Radiology and Surgery. https://doi.org/10.1007/s11548-026-03774-1

Image Credits: AI Generated

DOI: 10.1007/s11548-026-03774-1

Keywords: 5G, Wi-Fi 6, surgical navigation, ISO/IEEE 11073 SDC, wireless medical devices, operating room, latency, tracking camera, private cellular networks, medical device interoperability, computer-assisted surgery, RWTH Aachen

The framework, called DDoSimu5G, is described in the journal SoftwareX by Karim Khalil, who reports support from the ELLIIT and WASP research programs. Version 2.0 of the tool is built as a layered extension of three established open-source platforms: the OMNeT++ discrete-event simulation kernel, the INET networking framework, and Simu5G, which models the 5G New Radio protocol stack including gNodeB base stations, NR-capable user equipment, and the User Plane Function with GTP-U tunneling. On top of that foundation, DDoSimu5G adds four new layers of its own: a common utilities layer, an application layer of benign and adversarial traffic generators, a controller layer that orchestrates attacks, and a configuration layer driven by declarative JSON files.

What makes the framework unusual is the care it takes with ground truth. Machine-learning intrusion detectors need to know, packet by packet, which traffic is benign and which is malicious, and existing approaches to obtaining that knowledge are awkward. Real 5G testbeds built on open-source stacks such as OpenAirInterface, Open5GS, or Free5GC offer high protocol fidelity but require external attack scripts, manual synchronization of attack periods, and post-hoc labeling. Standard simulators such as NS-3 and Simu5G are repeatable and controllable, but they do not natively support DDoS orchestration or integrated labeling. Tools like the Intrusion Detection Dataset Toolkit can inject malicious traffic into existing traces, but they depend on externally captured data rather than traffic generated inside a configurable 5G environment.

DDoSimu5G attacks the problem from both ends of the labeling pipeline. Every malicious packet carries an attack-type identifier directly in the IPv4 Type of Service field, a technique the author calls in-band labeling. Because the marker rides inside the packet header and survives GTP-U encapsulation, analysts can classify packets straight from the PCAP capture without relying on timestamp correlation, a process that becomes unreliable under scheduling variability or packet loss. In parallel, the framework writes out-of-band CSV annotations recording transmission direction, traffic type, spoofing status, and attack labels, preserving application-level context that cannot fit in a header. After the simulation, an offline script converts the TOS-marked captures into labeled CSVs and automatically zeroes the TOS, DSCP, and ECN fields, preventing the artificial ground-truth markers from leaking into the feature sets used to train detectors.

The framework also cleanly separates two concepts that are often conflated: attacks and infections. An attack is the traffic behavior executed by a compromised device, such as a UDP flood or a TCP SYN flood, while an infection is the moment a previously benign user equipment transitions to an adversarial state. Because these are configured independently, researchers can stage botnet-style campaigns in which devices turn hostile at staggered times, run multiple concurrent attack styles, and mix benign and adversarial traffic on the very same device. A centralized DataTrafficController reads an external infection timeline and schedules per-device state transitions at exact simulation times, instantiating the appropriate attack application from the device’s JSON profile.

Four attack models ship with the framework, reflecting behaviors observed in IoT malware families such as Bashlite and Satori: volumetric UDP floods, resource-exhausting TCP SYN floods, reflection-based DNS amplification, and application-layer HTTP floods. Each attack’s intensity over time is modeled as a configurable rate function, with constant, ramping, pulsing, and slow-rate temporal patterns, and transmission modes that determine whether benign traffic continues, stops, or is reduced during the attack. The framework even handles a subtle but important detail: it suppresses the artificial reply traffic that servers would otherwise generate in response to spoofed packets, such as DNS responses to forged queries or TCP reset packets to spoofed SYN segments, which would otherwise distort the captured traffic distributions.

To demonstrate the framework end to end, the paper walks through a 31-device scenario spanning five gNodeBs, a two-tier UPF architecture, and five backend servers, with devices playing roles ranging from industrial sensors and wearable health monitors to connected vehicles, drone controllers, and asset trackers. Thirteen of the devices carry both benign and adversarial profiles, while eighteen remain purely benign. Infections are staggered from 50 seconds to 340 seconds into the 600-second simulation, and each infected device is assigned one of the four attack types with a distinct temporal style and transmission mode. The result is a dataset of 336,751 packets and roughly 161 megabytes of traffic, of which 27.8 percent is malicious, all generated in 205 seconds of wall-clock time, about 2.93 times faster than real time, with peak memory usage below 132 megabytes.

The consistency checks are where the framework earns its credibility. Packet counts in the UPF capture matched application-layer label records to within 2.8 percent overall, with discrepancies attributable to ordinary TCP control behavior, retransmissions, and timing differences between capture points. More strikingly, because the gNodeB and the UPF observe the same uplink packets before and after GTP-U decapsulation, per-attack-type counts must agree across the two vantage points, and they did exactly: a ratio of 1.000 and a cosine similarity of 1.0000 across all four attack classes. The dual-vantage capture design itself is a research asset, letting analysts study how identical attack traffic appears at the radio edge and at the core network simultaneously.

The framework’s authors also showed that the output plugs directly into conventional intrusion-detection workflows. After stripping the PPP framing that Simu5G’s packet recorder emits, the sanitized UPF capture was processed with the Argus flow tool, yielding 21,125 bidirectional flows, every one of which was successfully matched to its ground-truth label using 5-tuple matching, for 100 percent label-mapping coverage. The per-class flow statistics tell intuitive stories: TCP SYN floods produce short single-packet flows with no return traffic, while HTTP, UDP, and DNS attacks produce distinctive packet counts, durations, and byte volumes, and benign communication shows traffic in both directions. Crucially, because the internal TOS marker is sanitized before feature extraction, the resulting flow records carry no trace of the framework’s artificial labeling channel.

The tool has honest limitations. Source-address spoofing is represented semantically in the CSV labels rather than by rewriting IPv4 headers, since the underlying network configurator binds each module’s address to its interface, a constraint that mirrors real 5G networks where the User Plane Function enforces uplink source verification; researchers who want spoofed headers must apply them as a separate post-processing step. The framework currently covers only unencrypted user-plane traffic and does not model 5G control-plane attacks such as PFCP exploitation or network-slicing abuse. Planned extensions include MQTT, CoAP, and QUIC traffic models and systematic quality comparisons against established benchmark datasets such as 5G-NIDD, CIC-DDoS2019, and UNSW-NB15. Even so, the release fills a genuine gap: rather than forcing security researchers to choose between fixed public datasets and laboriously orchestrated testbed experiments, DDoSimu5G lets them generate controlled, reproducible, and endlessly variable labeled DDoS datasets on demand, all under an LGPL-3.0 license with the code and a reproducible capsule publicly available.

Subject of Research: A simulation framework for generating labeled DDoS traffic datasets in 5G networks

Article Title: DDoSimu5G: A simulation framework for generating labeled DDoS traffic datasets in 5G network

Article References: Khalil, K. (2026). DDoSimu5G: A simulation framework for generating labeled DDoS traffic datasets in 5G network. SoftwareX, 36, Article 103053. https://doi.org/10.1016/j.softx.2026.103053

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103053

Keywords: DDoS, 5G, network security, intrusion detection, simulation, OMNeT++, Simu5G, IoT, botnet, machine learning, open source, labeled datasets

The motivation is straightforward and, for city governments, increasingly urgent. Tile detachment on building exteriors is a genuine public hazard; falling facade tiles have injured and killed pedestrians in dense cities across Asia and elsewhere, and most municipalities still rely on periodic manual inspections conducted from scaffolding, cherry pickers or the ground with binoculars and cameras. Those inspections are slow, expensive, weather-dependent and inherently sporadic, meaning defects can develop and worsen in the long intervals between surveys. Prior research has explored automated alternatives, including deep-learning systems trained on images of heritage buildings in Portugal, climbing robots that crawl across walls to photograph tiles at close range, and fixed-camera crack detection on bridges using earlier generations of the YOLO detector family. Each approach removes some human labor but introduces its own constraints, whether limited mobility, narrow coverage or slow turnaround between image capture and analysis.

The Taiwanese team’s answer is to put the camera and the detector on a drone and the network in between. The aircraft carries high-resolution imaging sensors pointed at building facades, and the video frames it captures are fed into YOLOv8, the newest major iteration of the You Only Look Once single-stage detection architecture. Single-stage detectors process an entire image in one pass through a neural network, rather than first proposing candidate regions and then classifying them as two-stage systems do, which is precisely why they are favored for real-time applications. YOLOv8 improves on its predecessors, YOLOv5 and YOLOv7, through architectural refinements in its anchor-free detection head and its training recipe, and it ships in multiple sizes so operators can trade detection accuracy against the computational load on the onboard or edge processor. In the reported system, the detector classifies two defect categories of direct safety relevance: cracks in tiles and tiles that have loosened or detached from the wall surface.

The 5G link is the element that turns a smart camera into an inspection service. Captured images and detection results must travel from the drone to wherever engineers or municipal systems will act on them, and the authors argue that the characteristics of 5G, ultra-low latency and high data throughput, make this transmission seamless enough for genuine real-time operation. That matters because defect detection is only useful if the report arrives while the defect is fresh and locatable; a hazard alert that lands minutes later, or drops frames over a congested network, undermines the point of automated surveillance. The study does not treat the radio link as a given, either. The researchers explicitly evaluated system performance under varying 5G network conditions alongside varying YOLOv8 configurations, analyzing both detection accuracy and data transmission efficiency, so the reported results reflect the coupled behavior of vision model and network rather than the best case of each in isolation.

That joint evaluation is the methodological heart of the paper. Running a heavier YOLOv8 configuration generally buys better precision and recall on defect classes, but it also generates more computation and can change how much data flows over the link; meanwhile, degraded radio conditions can delay or lose frames entirely, degrading effective system performance even when the model itself is flawless. By sweeping both dimensions, the authors map the operating envelope within which a city could actually deploy such a service, for example by choosing a mid-sized model on a day with strong signal and a lighter model when throughput is constrained. The experimental results, according to the abstract, confirm the feasibility and effectiveness of the approach, demonstrating that detection and transmission can be balanced well enough for real-time AI-powered UAV surveillance of urban facades.

The broader context is the emerging field of cellular-connected drones, in which UAVs use commercial mobile networks rather than dedicated radio links for command and payload data. A recent comprehensive survey of 5G-and-beyond networks with UAVs catalogues applications ranging from delivery and emergency response to infrastructure monitoring, along with the regulatory and technical challenges of flying drones on networks designed for ground users. Earlier engineering work has already demonstrated real-time transmission of UAV video and control signals over 5G, so the Taiwanese study extends an established pipeline into a specific, safety-critical domain. It also complements parallel efforts to push intelligence onto the drone itself, such as CrackScopeNet, a lightweight neural network designed to run crack detection on resource-constrained drone platforms without offloading to the network at all. The two strategies, edge inference on the aircraft and network-assisted inference with rapid transmission, will likely coexist, with 5G connecting whichever processing tier hosts the model.

The authors situate the work explicitly in the smart city and remote monitoring landscape, and the fit is plausible. A municipality could schedule routine drone passes over districts with aging building stock, with the detection system flagging facades that need closer human examination, prioritizing emergency responses where tiles are actively detaching, and building a longitudinal record of each building’s condition over time. Because the drone covers facades from the air without scaffolding or road closures, the cost per surveyed building could fall dramatically compared with conventional methods, and coverage could extend to tall or awkwardly shaped structures that ground-based photography handles poorly. The framework’s emphasis on efficient defect detection in urban environments also suggests applicability beyond tiles; the same detection-plus-transmission architecture could in principle host models trained on other facade pathologies, and the literature already shows YOLO-family detectors succeeding on tasks from bridge cracks to potholes to construction-site helmet compliance.

Limitations deserve honest weighting. The published abstract reports a comprehensive experimental evaluation under varying model configurations and network conditions but does not specify deployment scale, so questions about battery endurance, flight regulations, weather tolerance, and performance across diverse building materials and lighting conditions remain open engineering problems. Object detection in adverse conditions is a known weak point of vision systems generally, as work on pothole detection in bad weather has shown, and facade imagery adds its own complications in specular reflections, repetitive tile patterns and shadow edges that can mimic cracks. The authors themselves frame the current system as a foundation, noting that future advances in 5G and edge computing will expand applicability and pointing toward autonomous infrastructure maintenance and intelligent urban management as the longer-term destination. They also note that no datasets were generated or analyzed beyond the study’s own experiments, which modestly constrains immediate reproducibility by outside groups.

Even with those caveats, the study lands at an inflection point worth watching. Deep learning-based defect detection has been validated on individual structures and specific materials; cellular-connected drones have been validated as a transport layer; and now a credible systems paper has bolted the pieces together for one of the most visible hazards in dense urban fabric. If the operating envelope mapped by Tsai, Tai and Lin holds up in field deployments, the archetype of the building inspector may shift from a person on scaffolding with a camera to a fleet of drones that quietly sweep the city’s walls each month, with neural networks and 5G radios doing the tedious, dangerous first pass and humans handling the judgment calls. That is a modest revolution in a mundane but consequential corner of urban engineering, and exactly the kind of quiet systems integration from which smart cities are actually built.

Subject of Research: Real-time detection of building facade tile defects using UAVs, deep learning and 5G networks

Article Title: 5G-Driven UAV Intelligence: Real-Time Tile Defect Detection in Mobile Networks

Article References: 5G-Driven UAV Intelligence: Real-Time Tile Defect Detection in Mobile Networks. (n.d.). https://doi.org/10.1007/s11036-026-02529-1

Image Credits: AI Generated

DOI: 10.1007/s11036-026-02529-1

Keywords: UAVs, 5G, YOLOv8, deep learning, building facade inspection, structural defect detection, smart cities, mobile networks, computer vision, infrastructure monitoring, edge computing, aerial robotics

The research, led by Nikita Goel and Pankaj Kumar of Manipal Institute of Technology together with Vrinda Gupta of the National Institute of Technology Kurukshetra, tackles a scenario known as drone-assisted cooperative communication, or DACC. In such systems, a source node on the ground cannot reach the destination directly with sufficient quality, so a drone hovering between them receives the signal, strengthens it, and retransmits it. Because the drone is the linchpin of the link, draining its battery quickly collapses the entire connection, which is why the authors focused their energy-harvesting design on the relay itself.

What distinguishes this work from earlier energy-harvesting schemes is the way the drone decides how much of each received signal to divert into its battery. Conventional designs use a fixed power-splitting factor, dividing every incoming signal by a constant fraction regardless of conditions. The researchers instead introduce a statistical, height-dependent splitting factor. At any given altitude and set of environmental parameters, the drone computes the probability that a line-of-sight path exists between itself and the ground node, then sets its scavenging ratio accordingly. When the line-of-sight probability is high and received power is strong, the drone banks more energy; when the path is obstructed, it shifts the balance back toward information transmission.

The physics behind that decision rests on the air-to-ground channel model. Rather than assuming the idealized extremes used in much of the prior literature, the team modeled the link between the drone and ground users with Nakagami-m fading, a flexible statistical model that can capture mixtures of line-of-sight and non-line-of-sight propagation. The direct ground link between source and destination, assumed to be purely non-line-of-sight in the dense scenario they study, uses Rayleigh fading. Crucially, because the drone moves vertically, the channels in this hybrid environment are not independent: the correlation between the source-drone, drone-destination, and direct links varies with altitude, and the mathematical framework explicitly tracks this height-dependent correlation.

Within this correlated hybrid fading environment, the researchers derived closed expressions for two key performance metrics: outage probability, the chance that the link fails to deliver a target data rate, and achievable rate at the destination. The destination node combines the direct signal received in the first time slot with the relayed signal received in the second using maximum ratio combining, extracting the best of both paths. The drone can operate in either amplify-and-forward mode, which scales and retransmits the analog received signal, or decode-and-forward mode, which decodes, re-encodes, and retransmits it. Two algorithms were developed: one governing the scavenging and information-splitting decisions at the drone, and another computing rate and outage probability across the correlated channel environment, implemented in MATLAB simulations.

The simulation results reveal a nuanced trade-off. Outage probability rises with drone altitude in every scenario considered, an effect the authors attribute to the Nakagami-m shaping parameters being treated as height-independent, so that path loss eventually dominates any line-of-sight gain. When the drone transmits using only the power it has harvested, performance is worst, because the harvested energy fluctuates with channel conditions from one transmission session to the next. The best configuration lets the drone transmit at a fixed, relatively high power while simultaneously scavenging energy to sustain it, effectively replenishing the battery that would otherwise deplete steadily.

That sustained battery translates directly into longevity. Compared with a system in which the drone draws all relay power from its primary battery, the power-scavenging scheme completes significantly more communication cycles for the same initial charge, and substantially more packets arrive successfully at the destination. Although harvesting does slightly worsen outage performance in some regimes, because power siphoned into the battery is unavailable for retransmission, the authors show that this drawback is outweighed by the dramatic increase in total delivered data over the life of the network. In a crisis scenario, that difference is measured not in abstract metrics but in the number of messages that get through before the aerial relay falls silent.

Environment matters as well. The team compared outage behavior across dense urban, urban, and suburban settings, finding the worst results in dense urban terrain, where tall buildings suppress the probability of a line-of-sight connection and depress the received signal-to-noise ratio, while suburban environments, with clearer sightlines, performed best. The height-dependent splitting factor adapts across all of these contexts, adjusting scavenging intensity to the environment-specific line-of-sight probability in a way that a static design cannot.

The study also contributes a sharper account of channel correlation than most prior drone-relay analyses. At low altitudes, the source-drone and source-destination links, as well as the drone-destination and direct links, exhibit strong correlation because the geometry of the moving drone couples them; as the drone climbs, that coupling weakens and different link pairs take on the stronger relationship. Because most of the literature treats relay channels as fixed and independent, this height-driven correlation dynamic has been largely overlooked, even though it materially affects outage and rate predictions in real deployments.

The authors position the work within the march toward 5G and beyond-5G networks, where users demand higher throughput, better reliability, and lower energy consumption from battery-constrained devices, and where drones are increasingly folded into cooperative communication architectures. They note that the scheme applies to infrastructure-less wireless networks generally, and that extending it to multi-user scenarios and deriving closed-form performance expressions are the next research steps. For disaster response teams weighing how long an aerial relay can keep a shattered network breathing, the message is that the drone’s own下行 data stream can double as a fuel line, and that tuning how much of that stream to bottle up, altitude by altitude, can stretch mission endurance considerably.

Subject of Research: A height-dependent radio-frequency energy harvesting scheme for drone-assisted cooperative communication in correlated hybrid fading channels.

Article Title: Power Scavenging for Strengthening the Life Cycle of Cooperative Devices in Correlated Hybrid Fading Environment

Article References: Goel, N., Gupta, V., & Kumar, P. (2026). Power Scavenging for Strengthening the Life Cycle of Cooperative Devices in Correlated Hybrid Fading Environment. Mobile Networks and Applications. https://doi.org/10.1007/s11036-026-02526-4

Image Credits: AI Generated

DOI: 10.1007/s11036-026-02526-4

Keywords: drone relays, energy harvesting, cooperative communication, Nakagami-m fading, channel correlation, outage probability, power splitting, UAV communications, network lifetime, wireless networks, 5G, disaster response