Engineering

Abstract: Federated learning (FL) is an attractive learning paradigm for privacy-preserving edge intelligence because it allows distributed devices to train a shared model without moving raw data to a central server. This feature is especially relevant to 5G and emerging 6G networks, where ultra-low latency, dense connectivity, and edge-native computing are expected to support large-scale intelligent services. Nevertheless, practical FL deployment remains difficult in heterogeneous wireless environments because client devices differ in processing capability, battery budget, data volume, and channel quality. These differences create stragglers, increase round latency, and waste scarce communication resources when client participation is scheduled naively. This study develops a deployment-oriented framework for dynamic client selection and resource allocation in heterogeneous edge environments. We formulate each FL round as a latency-constrained optimization problem that jointly captures computation time, uplink transmission time, and minimum participation requirements. On this basis, we propose a Dynamic Client Selection and Resource Allocation (DCS-RA) method that ranks clients using a weighted score combining computational capability, channel quality, and a fairness term, followed by a greedy radio-resource allocation procedure that prioritizes the largest marginal reduction in estimated completion time. Using the reported simulation setting with 100 clients and 20 resource blocks, DCS-RA reduces average round-completion time from 1.92 s to 1.55 s on MNIST and from 2.02 s to 1.57 s on CIFAR-10, corresponding to improvements of 19.39% and 22.47%, respectively. The results indicate that lightweight joint scheduling can substantially improve wall-clock efficiency for FL over heterogeneous 5G/6G edge networks.

Abstract: The combination of ultra-dense network deployments and high mobility results in an unfavorable outcome, rendering the task of handover more difficult than in environments typical of previous generations. 5G and 6G necessitate the deployment of heterogeneous networks and small cells to meet the demand, which at the same time introduces certain challenges. This scenario introduces small cells (such as femtocells, picocells, and microcells) that have very limited coverage areas, which, combined with the high speed of user equipment, create an excessive number of handover triggers, leading to the “ping-pong effect,” which wastes network resources and degrades the overall Quality of Service. Furthermore, high mobility means that a user might enter and exit a cell in less time than the mobile terminal’s dwell time, dropping the connection and resulting in handover failures and radio link failures. The conventional handover methods that rely on thresholds of certain factors such as the received signal strength could be insufficient for these environments. Different criteria should be balanced to avoid the drop, such as the user’s velocity, dwell time, target cell load, available bandwidth, device battery, and application latency requirements. Predictive methods could be a more efficient alternative to the existing reactive ones. This paper presents a decision-tree-based algorithm as one predictive method that learns the patterns among all the criteria mentioned and is particularly useful for avoiding ping-pongs and limiting handover failures. The classifier is trained on real multi-operator drive-test data with ping-pong events excluded from the positive class, and evaluated under Leave-One-Trace-Out cross-validation on 16 traces covering UMTS, HSUPA, HSPA+, and LTE cells. The proposed system achieves F1=0.642 and AUC =0.797 under LOTO, with a +0.052F1 lift over the best threshold-based baseline, while remaining interpretable and deployable in real time. The paper aims to present a solution applicable also to 5G NR and 6G.

Abstract: Channel estimation is important for Orthogonal Frequency-Division Multiplexing (OFDM) in wireless channel communication and requires algorithms that offer the best accuracy while at the same time have very low computational and runtime complexities. Newtonised Orthogonal Matching Pursuit (NOMP) is a promising algorithm for channel estimation; however, it suffers from high computational complexity due to repeated refinement and least-squares updates. In this paper, we propose a low complexity NOMP variant that reduces the dominant computational cost through three modifications: (i) a residual energy-based stopping criterion for NOMP to avoid expensive CFAR evaluation, (ii) a partial cyclic refinement with frozen atoms, and (iii) approximate one-sweep per atom least-squares updates. Complexity analysis shows a reduction from O(K³) to O(KN) in the gain update and from O(K²N) to O(KN) in refinement. Simulation results show that the proposed method achieves ∼87% reduction in runtime, while the symbol error rate (SER) performance is comparable to classical NOMP and outperforms Oversampled OMP at high signal-to-noise ratio (SNR). These results show that NOMP can be computationally efficient for OFDM systems without sacrificing estimation accuracy.

Abstract: The integration of satellite and terrestrial networks within the same spectrum is a key enabler for extending mobile connectivity in future communication systems. In this context, the Direct Connectivity between Mobile Satellite Service and International Mobile Telecommunications user equipment (DC-MSS-IMT) paradigm, currently under study within the International Telecommunication Union [1], foresees the use of terrestrial IMT frequency bands by satellite systems to directly serve conventional mobile devices. This paper presents an experimental study to assess the coexistence between a terrestrial 5G-NR receiver and a co-channel interfering signal representative of a Low Earth Orbit (LEO) satellite downlink. A controlled laboratory setup in conducted configuration was implemented to ensure repeatability and accurate control of interference conditions. Measurements were performed over four carrier frequencies representative of IMT bands (763 MHz, 1482 MHz, 2150 MHz, and 2635 MHz) [2], considering different traffic load conditions (100% and 50%) and Doppler shifts associated with satellite motion. The interference impact was evaluated in terms of receiver desensitization, defined as the increase in the total received power relative to the baseline noise level [3]. The results show that a 1 dB desensitization threshold is consistently reached when the interfering signal power is approximately 5–6 dB below the receiver noise floor, corresponding to an interference-to-noise ratio (I/N) of about −6 dB. This behavior is observed across all tested frequency bands, traffic conditions, and Doppler scenarios, indicating limited sensitivity to frequency offsets within the considered range. The findings confirm the validity of commonly adopted coexistence criteria and provide experimentally derived reference values to support ongoing regulatory and technical studies on spectrum sharing between satellite and terrestrial IMT systems.

Abstract: Digital agriculture employs a wide range of sensing, actuation, and analytics technologies to optimize productivity, sustainability, and decision-making in farming operations. However, rural and remote regions face persistent barriers, including limited network coverage and insufficient support for both low- and high-throughput applications, which hinder the deployment of conventional and broadband-intensive Internet of Things solutions. A central challenge is the lack of adequate field-level network infrastructure, with connectivity often unavailable or unreliable. This article presents a comprehensive survey of Broadband-based IoT as a solution for supporting both low- and high-data-rate digital agriculture applications, including UAVs, computer vision, and extended reality, even in settings without continuous internet connectivity. It examines how technologies such as 5G/6G, dynamic spectrum access, non-terrestrial networks, and edge computing can help address connectivity and infrastructure gaps in underserved agricultural areas. Furthermore, we introduce and analyze the concept of Evolved-Variety Technologies, which combines modified state-of-the-art modules with next-generation networks to create flexible, modular, and scalable system designs adaptable to diverse topographical and operational conditions. Beyond technical evaluations, the article examines economic feasibility, environmental sustainability, and policy implications, emphasizing the need for coordinated roles among governments, telecom providers, and agribusiness stakeholders. Our findings advocate for hybrid telecom architectures that integrate terrestrial and non-terrestrial components, leveraging emerging technologies to reduce the rural–urban digital divide and enable scalable, data-driven agriculture in underserved regions.

Abstract: Large carrier frequency offsets (CFOs) can severely distort the correlation response of the Physical Random Access Channel (PRACH), generating multiple significant peaks even for a single transmitting user equipment (UE), such that CFO-induced pseudo-peaks may exceed the detection threshold and be erroneously identified as valid peaks. This work addresses the problem of peak disambiguation under such conditions by formulating peak selection as a model-consistency validation problem under mismatch. A generalized likelihood ratio test (GLRT) is first formulated to provide a principled statistical validation of each detected candidate peak based on the estimated timing advance (TA) and CFO parameters. While theoretically grounded, this approach is shown to be insufficient under realistic large-CFO conditions, where CFO-induced peak ambiguity is further complicated by multipath-induced model mismatch. To address this limitation, a complementary residual-energy-based criterion is introduced, along with a weighted combination of both metrics, interpreted as a penalized consistency criterion for robust peak selection under model mismatch. The proposed framework enables the selection of a single reliable TA/CFO pair among multiple candidates, improving receiver robustness and reducing spurious updates. Performance is evaluated using precision, recall, and F1-score for both short and long PRACH formats under 3GPP-aligned channel models, including high-CFO and high-Doppler scenarios. Results demonstrate that the proposed weighted strategy generally provides a more robust trade-off than the individual GLRT-only and residual-only criteria.

Abstract: The fast growth of wireless communication systems and the growing need for very high data rates have been the driving force behind the creation of sixth-generation (6G) technologies that operate in the terahertz (THz) frequency region. This research represents the design and analysis of a small compact microstrip patch antenna that works in the terahertz (THz) frequency range for 6G cellular connectivity. The Rogers RT5880 substrate and annealed copper are used in the design of the suggested antenna, which aims for a 593 GHz resonance frequency. A progressive design technique that incorporates slotting and geometric optimization has been used to develop a castle shaped antenna which improve impedance matching and bandwidth to overcome the inherent constraints of traditional microstrip antennas. Excellent impedance matching is shown by the final design's near-ideal voltage standing wave ratio (VSWR) and return loss (S11) of –48.76 dB. It achieves a broad impedance bandwidth of 154.88 GHz, which far outperforms many current systems. The antenna exhibits consistent radiation characteristics in the broadside direction, a gain of 8.005 dBi, a directivity of 8.727 dBi, and an efficiency of around 91.73%. The proposed design performs very well in terms of bandwidth and efficiency, while also preserving compact dimensions and structural simplicity, as shown by a comparative comparison with most current literature. These results validate the suitability of the proposed antenna for high-speed, short-range THz communication systems in future 6G networks.

Abstract: The proliferation of Unmanned Aerial Vehicles (UAVs) in various applications has created a pressing need for robust and efficient communication systems. Fifth generation (5G) networks, with their high bandwidth and low latency, are poised to support the massive connectivity requirements of UAVs. However, the high mobility of drones presents significant challenges for handover management, leading to frequent service interruptions and degraded performance. This paper proposes a novel, first-of-its-kind framework that integrates multi-UAV trajectory prediction with proactive handover optimization in 5G networks. Our approach utilizes a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to predict the future flight path of each UAV. The predicted trajectories are then fed into a Deep Reinforcement Learning (DRL) agent, which makes optimal handover decisions to ensure seamless connectivity and high Quality of Service (QoS). Unlike existing solutions that primarily rely on simulated data, our framework is validated using a real-world drone trajectory dataset. The experimental results demonstrate that our proposed method significantly outperforms traditional and existing machine learning-based handover schemes in terms of handover success rate, average Signal-to-Interference-plus-Noise Ratio (SINR), and handover delay. The proposed framework paves the way for more reliable and efficient drone operations in 5G and beyond networks.

Abstract: We investigate the problem of stable signal estimation under irregular observation conditions characterized by missing samples, heteroscedastic noise, and reportability constraints. In such environments, the primary engineering challenge arises from the severe curvature distortion of the objective landscape, which renders traditional Euclidean gradient methods numerically unstable. To address this, we propose a layered information-geometric framework for stable estimation on a distorted statistical manifold.The architecture consists of three integrated components: (i) a deterministic basin-safe front-end that resolves global navigation on the non-convex information landscape; (ii) a diagonal-Fisher local refiner that performs local metric normalization to correct for geometric scaling under irregular weighting; and (iii) a PT-even gatekeeper that acts as an engineering feasibility constraint by restricting the optimization trajectory to the reportable subspace. As a concrete structured path within this layered estimator, the QFRT branch—a Quaternionic Fourier–Ramanujan representation used here as an arithmetic-periodicity anchor under shared whitening and weighting—serves as a structured source of robustness under gappy observations without altering the bounded scope of the present technical claim.Across a locked stress matrix, the resulting hybrid estimator exhibits a two-layer gain structure. First, a general refinement gain in RMSE_ω is achieved via curvature-aware updates in the classical-tangent regime. Second, a specific PT-sensitive gain emerges when nuisance-coupled sectors become observable, effectively suppressing non-reportable "ghost-mode" leakage (|z| ≈ 0) where unprojected baselines suffer from substantial parameter drift. Mechanism diagnostics support a seed-path shielding interpretation: the classical front-end resolves the ω-dominant basin selection problem, shielding the downstream PT-aware refinement from unfavorable seed geometry. The resulting contribution is a technical methods framework for auditable stable estimation under missing and heteroscedastic observations.

Abstract: The rapid advancement of AI is fundamentally disrupting research and engineering. While much attention is given to how AI may optimize wireless systems, this article explores a different question: how will AI impact the ecosystem and community developing future wireless technology? We trace this transformation across the entire lifecycle, from education and core research to technical publication and production-ready network deployments. As AI increasingly automates routine tasks, the primary value of the human researcher will shift from problem-solving to problem-finding, research orchestration, and oversight of trade-off management. By actively preserving spaces for deep, unplugged thinking and steering AI toward genuine discovery rather than mere recombination, we can navigate this profound shift to ultimately elevate human ingenuity and the future evolution of the researcher.

Abstract: This study investigates the vulnerability of target signals to co-channel Linear Frequency Modulation (LFM) interference in automotive Frequency-Modulated Continuous-Wave (FMCW) radar systems. It analyzes the limitations of conventional adaptive noise cancellation (ANC) techniques, particularly slow convergence and performance degradation under intense interference. To address these issues, an improved ANC algorithm is proposed. The method generates reference signals through single-channel self-delay processing and adopts a joint optimization framework for weight adaptation, which integrates normalized variable-step-size Least Mean Squares (LMS) adaptation with a leakage factor. Notably, the algorithm achieves robust performance in high-interference scenarios without requiring additional hardware or complex signal transformations. Simulation results verify that the proposed algorithm significantly improves the signal-to-interference-plus-noise ratio (SINR) preserves signal fidelity, and enhances detection probability under strong LFM interference.

Abstract: The problem of Reduced Capability (RedCap) User Equipment (UE) positioning within indoor 5G networks is addressed. While conventional approaches rely on time-domain ranging, the limited signal bandwidth associated with RedCap devices often prevents these methods from satisfying stringent accuracy requirements. As an alternative, this paper proposes a positioning framework based on Angle-of-Arrival (AoA) measurements. The framework incorporates a low-complexity AoA estimation algorithm derived from the analysis of the spatial covariance matrix. This procedure inherently generates a link quality metric which, alongside the AoA estimate, is utilized for final UE localization. The proposed localization algorithm belongs to the class of Weighted Least Squares (WLS) estimators and provides a unified approach to UE positioning in both 2D and 3D physical space. Simulation results demonstrate the effectiveness of the proposed framework under the challenging high-multipath conditions inherent to 5G indoor deployments.

Abstract: Mobile network operators are increasingly adopting 5G Fixed Wireless Access (FWA) to meet the growing demand for high performance services in households. This study evaluated the adoption and Quality of Experience (QoE) of 5G FWA through a multi-phase study. First phase, utilized a systematic literature review to develop a structural equation modeling (SEM) framework, identifying Quality of Service (QoS) and User Experience (UX) factors. A questionnaire survey was then conducted with 42 industry experts and 52 end-users. The SEM analysis shows that UX is not transferable between FTTx and 5G FWA, as the correlation (y = - 0.052, t value = -0.100) was statistically insignificant. The technical QoS FTTx does not influence how users perceive the technical QoS 5G FWA (y = - 0.02, t value = -0.122). Bandwidth and Quality are the most critical drivers for 5G FWA success regarding UX, whereas latency, MoS, and throughput are vital for QoS. Exploratory Factor Analysis for the UX and QoS parameters of 5G FWA showed strong internal consistency across all identified factors. The framework with fit indices reflected excellent model QoS (RMSEA = 0.08, CFI = 0.973, TLI = 0.965, CMINDF = 1.254 and GFI = 0.782) and UX (RMSEA = 0.08, CFI = 0.895, TLI = 0.881, CMINDF = 1.377 and GFI = 0.655). The mathematical SEM model provides empirical evidence of the role of the service factor as observed parameters and introduces a validated theoretical framework QoE-SEM. This research contributes to the academic and telecommunications industries, to deliver a fit observe model for upcoming new technology 5G FWA and assist decision makers in formulating strategic QoE models.

Abstract: UAV-based Multi-Access Edge Computing (MEC) systems are vital solutions in disaster scenarios by providing temporary radio and processing resources to rescue teams and survivors. However, recent schemes in the literature typically treat offloaded tasks as one indivisible units and fail to account for heterogeneous reliability requirements, leading to non-optimal resource utilization and performance deterioration under such emergency conditions. Moreover, the joint optimization of communication, computation, and UAV path planning in cooperative setup remains inadequately addressed. This paper proposes a priority-aware layered task offloading framework for cooperative UAV-MEC networks based on Superposition Coding (SPC) and non-orthogonal access. The proposed design separates tasks into reliability-critical base-layer (BL) and enhancement-layer (EL) components, to assure reliable and timely transmission and execution. BL data is prioritized via intra- and inter-user constraints, while EL data is adaptively processed locally or offloaded via a cooperative UAVs. A joint latency minimization problem is formulated and tackled using an alternating optimization framework with successive convex approximation (AO-SCA). Simulation results demonstrate that the proposed scheme significantly outperforms baseline methods. For 20 users, it achieves a processing efficiency of 92.3%, compared to over 83% for baseline schemes. As the number of users increases to 120, the proposed method maintains superior efficiency at 63.1%, outperforming NC-SPC (40.3%), FT-Coop (51.3%), SL-NOMA (52.4%), and L-OMA (45.3%), highlighting its robustness and scalability in meeting reliability and low-latency requirements in post-disaster scenarios.

Abstract: Heterogeneous sensor systems generate measurements in incompatible physical units, which complicates their direct integration with photonic stochastic processors. This study proposes a universal edge frontend that converts heterogeneous sensor channels into unified event-oriented probabilities and then into Bernoulli bitstreams compatible with polarization-encoded optical interfaces. The framework combines sensor-to-probability mapping, weighted event-level fusion, stochastic bitstream generation, and system-level control of correlation and synchronization. Its performance was investigated through reproducible Colab-based modeling using baseline validation, weighting-strategy comparison, static and time-varying decorrelation/synchronization studies, and robustness/scaling analysis. The results show that the stochastic event estimate converges toward the float reference with increasing bitstream length, reliability-aware weighting outperforms equal and tested data-driven weighting in the benchmark, independent stream generation provides the best inference quality, and synchronization mismatch becomes measurable in time-varying fusion. The frontend also demonstrates graceful degradation under channel corruption and favorable scaling under mixed informative, weak, redundant, conflicting, and noisy channel configurations. These findings indicate that heterogeneous sensors can be interfaced with photonic stochastic systems through a common event-level representation and that weighting, decorrelation, synchronization, and robustness must be treated as core frontend design variables.

Abstract: UAV-assisted data collection often suffers from spatial data holes and communication unfairness, a challenge exacerbated in Wireless Powered Communication Networks (WPCNs) by the inherent doubly near-far problem. To bridge these gaps, this paper proposes a novel Spatio-Temporal Trajectory-Driven Dynamic Time-Division Multiple Access (STD-TDMA) scheduling strategy. Deviating from conventional discrete hovering paradigms, we introduce a continuous-flight framework that exploits the UAV's mobility to provide seamless spatial coverage. By jointly optimizing the UAV's flight speed and dynamic time-slot allocation, the proposed strategy ensures that each sensor node can interact with the UAV at its optimal channel condition along the trajectory, thereby effectively mitigating the doubly near-far effect and ensuring absolute nodal fairness. To solve the formulated non-convex optimization problem, we develop a low-complexity algorithm that integrates Binary Search for speed optimization with the Hungarian algorithm for spatio-temporal mapping. Extensive simulations demonstrate that our STD-TDMA strategy significantly enhances nodal fairness and boosts overall task execution efficiency compared to conventional baseline schemes.

Abstract: This article presents the design and analysis of microstrip MIMO antennas intended for operation in the 5G high-frequency band (High-Band). The proposed antenna structures include 2 and 4 element MIMO configurations operating in the millimeter-wave spec-trum with a center frequency of 38 GHz. The aim of the article was to develop compact antenna systems with performance parameters suitable for 5G mmWave applications, while addressing the limitations of microstrip technology. This article describes the development process of a single radiating element and its subsequent integration into multi-antenna structures. Particular attention is paid to impedance matching, port isolation, and mutual coupling mitigation, which represent key challenges in implementing MIMO antennas within the millimeter-wave band. Furthermore, the impact of the number of antenna elements on the radiation pattern, gain, and overall efficiency of the MIMO system is analyzed. The obtained results confirm that MIMO microstrip antennas in 2- and 4-element con-figurations can be an effective solution for 5G High-Band applications, providing ade-quate radio parameters while maintaining small dimensions and the ability to integrate with RF front-end systems. The presented solutions can be used in user terminals, CPE modules, and compact access stations of 5G systems operating in the millimeter-wave band.

Abstract: Mobile Reliable Opportunistic Routing (MROR) protocol improves the reliability in data forwarding in Cognitive Radio Sensor Networks (CRSNs) by mobility-conscious virtual contention groups and handover zoning. Regardless of its advantages, the problem-solving essence of heuristic decision-making in MROR is poor both in highly dynamic spectrum access and random node mobility. To address this shortcoming, we present DRR-MROR, which is a refined framework that incorporates Deep Reinforcement Learning (DRL) to provide smart routing, adaptive functionality. The users in DRAOMR are autonomous agents that are referred to as secondary users (SUs), and they constantly observe their own local state - including primary user activity, link quality, residual energy and neighbor mobility patterns. These agents acquire an ideal routing policy through a Deep Q-Network (DQN), optimised to expand the long-term network utility in throughput, delay, and energy efficiency. We define the routing problem as a Markov Decision Process (MDP) and use experience replay whereby prioritized sampling is used to guarantee convergence of learning. Extensive simulations show that DRL-MROR has better performance in comparison to the original MROR protocol and modern AI-based solutions (AIRoute) under various conditions. Our results show vast improvements: up to 38% increased throughput, 42% increased goodput, 29% decreased in energy consumed per packet, and about 18% improvement in network lifetime, all and at the same time ensuring high route stability and fairness. Also, the DRL-MROR minimizes control reduces both overhead by 30% and average end-to-end delay by 32% , maintaining high performance even when under stress at elevated PU rates and velocity of nodes. The transformation of the non-adaptive opportunistic routing to a cognitive and self-adaptative one can be successfully achieved by learning makes it compatible with the requirements of the next-generation IoT and smart infrastructure by making it more paradigm-driven.

Abstract: Reliable and resilient communication systems are indispensable for first responders, enabling rapid coordination and effective emergency response. However, traditional communication networks frequently encounter congestion, interoperability failures, and infrastructure collapse during large-scale disasters. To address these deficiencies, specialized networks such as the First Responder Network Authority (FirstNet) have been developed, leveraging advancements in Long-Term Evolution (LTE), Fifth-Generation New Radio (5G NR), and priority access mechanisms to enhance reliability and coverage. This comprehensive review examines the technological evolution of first-responder communication systems from legacy Land Mobile Radio (LMR) and Project 25 (P25) systems through modern broadband solutions. We systematically analyze key enablers including network prioritization with Quality of Service (QoS), Priority, and Preemption (QPP); dedicated spectrum allocation on Band 14 (758–768/788–798 MHz); Mission Critical Push-to-Talk (MCPTT), Mission Critical Video (MCVideo), and Mission Critical Data (MCData) standards defined across 3GPP Releases 13–18; network slicing for dedicated emergency virtual networks; and Multi-access Edge Computing (MEC) for ultra-low-latency field processing. Additionally, this study assesses the integration of artificial intelligence and machine learning for predictive network management, digital twin technology for infrastructure resilience simulation, Internet of Things (IoT) sensor ecosystems for enhanced situational awareness, and satellite communication systems—including emerging Low Earth Orbit (LEO) constellations—for connectivity in infrastructure-denied environments. We further examine real-world deployments through case studies encompassing Hurricane Katrina (2005), the September 11 attacks (2001), Hurricane Harvey (2017), the California wildfires (2018–2025), and the 2011 Great East Japan Earthquake, alongside global initiatives including the United Kingdom’s Emergency Services Network (ESN), the European Union’s Public Protection and Disaster Relief (PPDR) framework, and South Korea’s PS-LTE SafeNet. By synthesizing recent advancements across more than 120 scholarly and technical sources, this review provides a forward-looking roadmap addressing Sixth-Generation (6G) networks, terahertz communications, holographic situational awareness, augmented and extended reality for field operations, blockchain-secured data sharing, and cybersecurity frameworks. We conclude with policy recommendations and identify critical research gaps necessary to ensure a seamless, intelligent, and globally interoperable communication infrastructure for first responders.

Abstract: The sixth-generation (6G) mobile networks are envisioned to deliver seamless 3D coverage from ground to sky and vice versa. In parallel, aerial corridors are emerging to elevate ground-based transportation into the air, enabling smart air mobility for unmanned aerial vehicles (UAVs). The convergence of this intelligent transportation system (ITS) with 6G introduces new challenges: how to ensure reliable, efficient connectivity within aerial corridors, and how these corridors can serve as foundational sky infrastructure to advance the 6G ecosystem. This paper presents the first comprehensive survey on aerial corridors. It conceptualizes the aerial corridor as a tube-shaped, multi-lane, bidirectional structure to manage drone-based roles, including user equipment (UE), base stations (BS), and communication relays. To support this vision, key enablers such as air-to-ground channel modeling and integrated sensing and communication (ISAC) are investigated. The proposed infrastructure aligns with the IMT-2030 vision, supporting machine-type communication, ubiquitous connectivity, and immersive services in regulated aerial space.