Engineering

Abstract: Unmanned Aerial Vehicles (UAVs) are revolutionizing power grid inspection, yet ensuring reliable, high-quality image transmission in complex environments remains a significant challenge for autonomous operations. Traditional path planning algorithms often neglect communication constraints, risking mission failure in areas with poor connectivity—a critical concern for future UAV swarm deployments where inter-agent coordination depends on sustained link quality. This paper proposes a novel communication-aware path planning framework to address this gap. We formulate the problem as a multi-objective optimization that simultaneously considers safety, complete inspection coverage, path length, and communication quality. The core of our solution is an Improved Grey Wolf Optimizer (IGWO), which incorporates a nonlinear convergence factor, a genetic mutation strategy, and a modified leader update mechanism to enhance global search capability and avoid local optima. To evaluate the proposed framework, we adopt a dual-scenario validation strategy: in the mountainous setting, we employ a synthetic conical-peak terrain model with the standardized 3rd Generation Partnership Project (3GPP) Rural Macrocell (RMa) channel model; in the urban setting, we use real-world building footprints from OpenStreetMap (Hangzhou, China) with deterministic ray-tracing propagation modeling. Comprehensive simulations in both scenarios demonstrate that IGWO achieves a 50% success rate in dense urban areas, significantly outperforming Particle Swarm Optimization (PSO) at 10% and Genetic Algorithm (GA) at 28%. Crucially, communication-aware paths reduce average outage duration by over 65% while often yielding shorter paths. This work confirms that explicit communication modeling is indispensable for reliable UAV inspection and that the proposed IGWO offers a robust and scalable solution for future autonomous and cooperative power grid maintenance systems.

Abstract: Short-term fluctuations in reference signal received power (RSRP) under high-mobility conditions can repeatedly reset the time-to-trigger (TTT) timer and cause spatial dispersion of Event A3 handover triggers in high-speed railway 5G-R systems. To address this problem, this study develops a physics-driven, Doppler-aware RSRP input-calibration framework that preserves the standardized handover margin (HOM), TTT, Event A3 semantics, and Radio Resource Control (RRC) procedure. The relative residual Doppler shift is derived from train kinematics and base-station geometry and estimated online using a scalar Kalman filter. The resulting estimate is mapped to an orthogonal frequency-division multiplexing frequency-offset-induced equivalent RSRP loss subject to a physical upper bound. Geometry-based detrending, speed-adaptive causal smoothing of the residual component, and a low-weight bounded position-aided correction are subsequently applied to reconstruct the serving- and candidate-cell RSRP inputs. Event-driven simulations covering viaduct and mountainous railway scenarios, together with ablation, Doppler-sensitivity, speed-adaptability, and observation-error analyses, are conducted to evaluate the proposed method. At a train speed of 350 km/h, the method reduces the root-mean-square error of the inter-cell RSRP difference from 2.3449 to 1.9053 dB and decreases the mean handover trigger-location error from 54.79 to 42.35 m, while maintaining a handover success rate of 99.49%, comparable to the 99.29% achieved by conventional Event A3. The results further demonstrate that, under a carrier frequency of 2.1 GHz and a subcarrier spacing of 30 kHz, the overall performance improvement is governed primarily by geometric detrending, speed-adaptive residual smoothing, and bounded position correction, whereas Doppler-loss compensation provides a small but physically consistent correction of the deterministic measurement bias. The proposed framework therefore improves RSRP input quality and handover trigger-location consistency without modifying the standardized handover decision logic.

Abstract: Frequency-hopping spread spectrum (FHSS) information and communication systems are widely used to enhance the resilience of wireless networks to interference. The effectiveness of traditional algorithms is significantly reduced in the presence of active and intelligent jammers. The aim of this manuscript is to develop an adaptive FHSS algorithm based on statistical learning of frequency-channel quality and to analyze its effectiveness compared with existing algorithms. A software simulator has been developed that implements six FHSS algorithms, five jamming models, and statistical evaluation using the Monte Carlo method based on the bit error rate, error vector magnitude, and normalized throughput metrics. The software-implemented adaptive algorithm delivered the best results among all those studied, achieving the highest overall performance metric. Compared with a system without FHSS, 64.23 % reduction in the bit error rate, 22.76 % increase in throughput and 59.32 % reduction in the error vector magnitude were achieved. The results obtained confirm the feasibility of using adaptive statistical frequency channel selection to improve the noise immunity of FHSS systems. Promising areas for further research include the modeling of intelligent jammers and the experimental verification of the implemented algorithm on computing platforms with limited resources.

Abstract: Sixth-generation (6G) networks will be AI-native: machine-learning models will decide how radio resources are allocated, where functions are placed, which control loops run, and how traffic is routed. Recent orchestration frameworks show that sharing common AI functions and xApps across services yields large resource and revenue gains, yet these gains are computed as if security were free. In reality, every shared function must also satisfy authentication, trust, confidentiality, attestation, and auditability requirements—and adding these requirements changes the structure of the underlying placement and sharing optimisation problems. This article presents a cross-layer framework for making AI-native 6G radio access networks secure, privacy-preserving, and revenue-aware at the same time. The framework is built from nine interlocking pillars organised in three layers: (i) an O-RAN orchestration substrate covering functional-split-aware placement, conflict-aware RIC control-loop scheduling, and joint radio–compute slicing; (ii) a confidential intelligence layer covering trust-weighted robust federated learning, attested confidential edge inference with hybrid post-quantum sessions, and security-aware xApp sharing; and (iii) network-wide assurance covering security-aware revenue-optimal orchestration, cross-domain zero-trust slice access, and an end-to-end resilience and formal-verification capstone. Each pillar is presented with a research question, a precise problem formulation with objective and constraints, a candidate solution strategy expressed as pseudocode, and evaluation methodology. We further show how the nine pillars interlock through four vertical threads—placement–sharing, trust, post-quantum protection, and audit—so that, taken together, they answer a single central question: can security constraints be made first-class terms of revenue-optimal 6G orchestration while the resulting system remains scalable, provable, and practical?

Abstract: Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks are often disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible solution for collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper proposes a structured multi-UAV framework for disaster-response data collection that separates the optimisation process into spatial, temporal, and safety layers. In the spatial layer, three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) modelling allows UAVs to collect sensor data by entering sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) is then used to optimise the sensor visiting order, while RRT-Connect verifies obstacle-aware feasible paths in the 3D environment. In the temporal layer, a Lyapunov-based control mechanism makes binary computation decisions between local processing and offloading to a single MEC node, balancing delay, energy consumption, AoI, and queue stability. In the safety layer, continuous-time conflict detection and temporal offset resolution are used to monitor inter-UAV separation and obstacle-related risks. The proposed framework is evaluated through simulation under wireless, mobility, computation, and obstacle constraints. The results show that the framework achieves a 100% data collection success rate, zero obstacle collisions, zero inter-UAV collisions, and no dropped tasks. Compared with planning-oriented and MEC-oriented baselines, the proposed framework improves information freshness, average delay, processing delay, energy efficiency, and system cost, while maintaining reliable multi-UAV coordination in a complex 3D environment. In addition, the layered design clarifies the role of each component: 3D-TSPN improves spatial flexibility, AoI-aware GA enhances route ordering, RRT-Connect supports obstacle-aware path feasibility, Lyapunov-based binary offloading stabilises computation decisions, and safety monitoring improves operational reliability. These findings demonstrate that a simplified layer-based integration of mobility planning, computation control, and safety monitoring can provide an effective solution for multi-UAV disaster-response data collection in complex 3D environments.

Abstract: Joint scheduling and resource allocation are investigated in heterogeneous queuing systems—where multiple queues have distinct priorities and share a single output link—with the aim of maximizing throughput utility under per-queue delay constraints. The primary challenge arises from stochastic packet arrivals and the time-varying capacity of the shared link. To address this challenge, conventional heuristic methods and unconstrained learning-based methods exhibit inherent limitations, particularly when handling bursty traffic with stringent delay requirements. The former lack real-time reactivity to system dynamics; the latter, which encode delay constraints into the reward, may sacrifice deadline compliance for higher cumulative rewards. To this end, this paper proposes a constrained soft actor-critic (CSAC) approach with two key designs. First, we decouple the delay constraint of the queue with a stringent delay requirement from the reward, treating it as a standalone violation budget. Second, we introduce a two-stage mapping mechanism that transforms continuous policy logits into integer transmission quotas. We compare the proposed approach with an unconstrained soft actor-critic (SAC) baseline and several heuristic baselines under heterogeneous traffic conditions. The experimental setup comprises one Markov-modulated Poisson process (MMPP) highest-priority queue with a maximum allowable queuing delay violation rate of 1%, along with two independent Poisson process lower-priority queues with distinct priorities, each with a best-effort queuing delay requirement. Results demonstrate that the proposed approach reduces the highest-priority queue’s delay violation rate to 0.05%±0.09%, compared to 7.87%±5.98% for unconstrained SAC and 18.38% and 34.28% for the two heuristic baselines, while maintaining comparable throughput, lower-priority delays, and overflow packet counts.

Abstract: 6G antenna and intelligent reflecting surface (IRS) performance is bottlenecked by substrate material properties. This paper uses the IRS Tri-Constraint (360° phase range, sub-1 dB insertion loss, thermal stability from −20°C to +60°C) to compare liquid crystal polymer (LCP), graphene-on-quartz, and PVDF at 140 GHz. A transparent link budget translates each material's properties into a capacity-based and a coverage-based subscriber count. Graphene ranks highest on both measures, narrowly ahead of LCP, with PVDF trailing substantially. A 200,000-trial Monte Carlo analysis shows the ranking is robust (99.3% joint) under idealised assumptions but far less robust (3.0%) when literature-reported fabricated-device insertion losses are substituted, indicating the ranking currently outpaces demonstrated hardware.

Abstract: A recurrent challenge in scaling ultra-wideband (UWB) motion-capture systems is interference management whenmanyranging transactions coexist in time and space. To address this, we study a two-layer localization architecture that separates field-level player localization from local on-body pose tracking, allowing the two tasks to operate with different communication regimes and spatial-reuse policies. A stochastic-geometry framework is used to map sport-dependent parameters, including player density, field size, tag count, anchor count, update rates, and ranging airtime, to reliability and update-rate tradeoffs. The analytical model is parametrized based on controlled experiments that characterize ranging success under temporal overlap, player distance, and variable-delay scheduling, which we use to design a proximity-aware local coordination strategy. We apply our proposed approach to soccer, volleyball, and ice hockey as representative use cases. Our results show that proximity-aware coordination can provide a scalable and lightweight interference management mechanism. Coordination is activated only where local player clustering creates strong interference, while spatially separated players continue to share resources without coordination. For the highest density scenario tested, this increases the local-layer ranging success from below 50% without coordination to over 80% in fourand eight-player congestion clusters, while avoiding network-wide coordination overhead.

Abstract: Pneumonia detection in chest X-rays is commonly treated as a binary classification task, although the radiographic burden of disease varies substantially across patients. This study investigates whether the expert bounding-box annotations available in the RSNA 2018 Pneumonia Detection dataset can be reused to derive an interpretable severity grading framework based on lesion extent. For each pneumonia-positive image, total disease burden was computed as the cumulative area of all annotated opacity boxes, and a balanced three-tier split was generated, defining Severity 1 (low/mild), Severity 2 (moderate), and Severity 3 (severe). The resulting severity groups contained 1999, 2006, and 2007 images, respectively. To validate the usefulness of this annotation-driven severity formulation, we trained Vision Transformer models in both a classical binary setting and a severity-specialized setup. The classical binary baseline reached a maximum validation accuracy of 95.05%, while the specialized models achieved 94.98% for Severity 1, 97.88% for Severity 2, and 99.08% for Severity 3. These results indicate that radiographic burden derived from bounding-box extent supports highly separable severity-aware subproblems, particularly for severe pneumonia, while lower-burden categories remain more difficult. The proposed framework offers a transparent and reproducible way to transform RSNA 2018 lesion annotations into ordinal severity labels. Beyond its original use as a detection and localization benchmark, the dataset can therefore also support severity-aware classification, radiographic burden analysis, and future explainable AI studies in chest X-ray interpretation.

Abstract: Next-generation wireless systems are becoming more complex, and the need for intelligent mobility management mechanisms that can ensure service continuity and efficiently utilize network resources has been growing. Frequent handovers, unequal distribution of traffic, variable network conditions, and multiple radio access technologies are some of the challenges that traditional mobility control strategies are likely to face in 5G and future 6G networks with dense deployments of small cells, heterogeneous architectures, and highly mobile users. These constraints frequently lead to sub-optimal user experience, higher overhead in signalling and inefficient use of resources. The introduction of new tools through the advancements of artificial intelligence (AI), specifically machine learning and deep learning techniques have created new opportunities for "predictive" and "adaptive" mobility optimization. The use of data-driven decision-making can enable AI-based solutions to predict user movements, fine-tune the execution of handover and dynamically allocate radio resources to enhance network performance. This survey This paper presents a comprehensive survey of AI-enabled handover strategies for mobility load management 5G, Beyond-5G (B5G), and upcoming 6G networks. This study provides a review of current studies, classifies the framework approaches of mobility management based on AI technologies, and identifies their architecture, learning and optimization goals. Moreover, the survey assesses the performance of intelligent handover schemes to solve the critical issues like load balancing, interference mitigation, connection reliability and quality of service maintenance. Key performance indicators related to mobility robustness, resource efficiency and service continuity are compared between the conventional mobility management methods and the AI based ones. Last but not least, the paper outlines unsolved problems, new trends and potential areas of research that will guide the evolution of autonomous mobility management solutions for the future of wireless communication networks.

Abstract: Large-scale network operations require engineers to work across heterogeneous tools and dashboards, which can lengthen Mean Time to Repair (MTTR) and affect availability when it delays incident resolution. We present an agentic ChatOps system in which a supervisor orchestrates large language model (LLM) agents that route natural-language intents to specialized workers issuing planned tool calls across operations systems, with retrieval-augmented grounding in a network source of truth. We deploy the approach in a major global network—a deployment that has since grown to fourteen specialized workers plus dedicated expert and autonomous agents—and detail seven representative use cases, including closed-loop link-flapping remediation that validates candidate changes in a digital twin before committing version-controlled configuration. Across six recurring tasks, the integrated ChatOps automation system was associated with per-event handling times lower by roughly 10× to 2400×, and the link-flap cycle was shortened from about 30 to 3 minutes. Over a representative 90-day window it handled roughly 7,400 production interactions, with positive feedback on most rated responses; an offline benchmark of 200 questions scored by an LLM-as-a-judge yielded mean relevance and context relevance of 0.85 and 0.79. The results support modeled availability improvement when saved handling time reduces incident MTTR, in a deployment designed for confidentiality and guarded by validation pre-checks.

Abstract: This paper presents a robust and efficient Convolutional Neural Network (CNN)-based channel estimator for fifth-generation (5G) Orthogonal Frequency Division Multiplexing (OFDM) systems. While conventional Least Squares (LS) and Minimum Mean Square Error (MMSE) estimators degrade significantly in high-mobility, non-linear, and frequency-selective fading environments, the proposed framework treats the time-frequency resource grid as a spatial image, enabling implicit learning of complex fading dynamics without explicit statistical modeling. The model is trained on 105 synthetic channel realizations spanning Rayleigh, Rician (K = 5 dB), AWGN, CDL-A, CDL-B, and TDL-A channel profiles and validated through rigorous MATLAB simulations. Key quantitative results demonstrate: (i) a 7-fold BER reduction over MMSE at 20 dB SNR on CDL- A (1.0 × 10⁻³ vs. 7.0 × 10⁻³); (ii) a 3–5 dB NMSE improvement across the full 0–30 dB SNR range; (iii) robust performance under Doppler spreads up to 300 km/h with less than 0.5 dB BER penalty; (iv) a 50% reduction in pilot overhead while maintaining superior MSE performance; and (v) spectral efficiency within 0.35 bits/s/Hz of the perfect-CSI Shannon bound. With a measured inference latency of 0.8 ms and a lightweight design of 2.3 × 10⁶ parameters, the proposed CNN-CE is validated as a practically deployable and resource-efficient technology for 5G and beyond-5G (B5G) networks.

Abstract: This paper presents a comparative analysis of Reinforcement Learning (RL)-based strategies for optimizing Frequency-Hopping Spread Spectrum (FHSS) systems against a first-order Markov jammer in Unmanned Aerial Vehicle (UAV) communications, addressing critical vulnerabilities in electronic warfare scenarios. The jammer model simulate adaptive threats in drone networks. Simulations were conducted within a Markov Decision Process (MDP) framework featuring 16 channels and episodes of 1000 steps. Three approaches were evaluated: Baseline random channel selection, Tabular Q-Learning, and Deep Q-Network (DQN) employing 16-128-128-16 neural architecture. Training spanned 100–500 episodes, with performance assessed via key metrics: Success Rate (%), Bit Error Rate (BER), Signal-to-Noise Ratio (SNR), action Entropy, and Packet Loss Rate (PLR) under Forward Error Correction (FEC).

Abstract: Dirty paper coding (DPC) is applied to linear multi-input multi-output (MIMO) broadcast channels with additive white Gaussian noise and one message per receiver. The method decomposes each receiver channel into parallel scalar channels with known interference, then applies modulo operators, amplitude-shift keying (ASK), and probabilistic shaping. The achievable rate tuples include all points inside the capacity region by choosing truncated Gaussian shaping, large ASK alphabets, and large modulo intervals. Simulations with short polar codes show significant rate gains from DPC compared to conventional linear precoding, while maintaining similar encoder and decoder complexities.

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

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.