link budget analysis

To address coverage imbalance, capacity limitations, and load heterogeneity in 3D heterogeneous cellular networks serving both ground users (GUEs) and unmanned aerial vehicles (UAVs), this paper pioneers the integration of quantization theory into base station (BS) deployment optimization, establishing a deterministic node modeling-based joint optimization framework. Methodologically, it unifies 3D channel modeling, nonlinear optimization, and a co-design algorithm jointly optimizing BS locations, antenna orientations, and radiation parameters. Departing from conventional single-dimensional optimization paradigms, our approach achieves the first holistic performance trade-off between GUEs and UAVs: UAV average capacity increases by 42%, while GUE performance degradation remains below 3%. This outperforms antenna-only tuning schemes significantly. The work provides a theoretically grounded and empirically verifiable methodology for 3D air-ground integrated network deployment.

Designing cellular networks to support unmanned aerial vehicle (UAV) dedicated aerial corridors while maintaining ground user performance remains challenging due to high-dimensional, coupled air–ground propagation dynamics. Method: This paper proposes a data-driven high-dimensional Bayesian optimization (HD-BO) framework that jointly optimizes base station antenna downtilt and half-power beamwidth (HPBW) to enhance aerial coverage without degrading terrestrial service. It is the first to apply HD-BO to joint 3D air–ground network parameter optimization; incorporates transfer learning for cross-scenario generalization; and supports multi-objective air–ground trade-off optimization. SINR-based modeling and 3D channel simulations underpin the approach. Results: Evaluated on a real-world network deployment, the method achieves over 2× average throughput gain in UAV corridors and median SINR improvement exceeding 20 dB, demonstrating both efficacy and engineering feasibility.

This study addresses the challenge of high energy consumption and the difficulty of implementing energy-saving base station shutdowns in dense urban cellular networks. To overcome this, the authors propose leveraging High-Altitude Platform Stations (HAPS) to form a “Hypercell” that replaces the coverage of multiple terrestrial macrocells, thereby enabling coordinated shutdown of both coverage-layer and capacity-layer base stations for network-wide energy savings. Innovatively repositioning HAPS from its conventional role in non-terrestrial networks (NTN) as a mere coverage extender to an enabler of joint coverage-and-capacity shutdown, the work introduces two HAPS–Hypercell pairing architectures to support distributed carrier shutdown mechanisms. Evaluations based on 3GPP-compliant modeling and realistic channel simulations demonstrate substantial reductions in network power consumption while also revealing limitations of direct HAPS integration, offering critical insights for future green communication strategies.

To address the low efficiency and poor security of conventional network measurements in low-density, topographically complex rural areas, this paper proposes and implements the first programmable UAV-based measurement platform integrating commercial cellular modems with an onboard computing unit. The platform incorporates a line-of-sight (LoS)-optimized high-altitude flight strategy, geospatial mapping, and multidimensional metric statistical analysis to overcome coverage blind spots inherent in ground-based measurements. Experimental results reveal significant inconsistency between received signal strength and actual service coverage; although transmit power improves with altitude, co-channel and adjacent-channel interference intensifies. Nevertheless, most regions maintain acceptable throughput and stable latency. This work establishes a novel paradigm for wide-area cellular network assessment from the air and delivers a reusable, open technical framework for aerial radio measurement.

This study addresses the challenges of spectrum and power resource allocation for multi-connected unmanned aerial vehicles (UAVs) in integrated space-air-ground networks. Focusing on three heterogeneous link types—UAV-to-terrestrial base station (UAV-RBS), UAV-to-UAV, and UAV-to-high-altitude platform (UAV-HAP)—the work proposes a joint resource allocation framework under dynamic channel conditions and diverse quality-of-service (QoS) constraints. Two novel algorithms are developed: the first maximizes the aggregate throughput of non-UAV-UAV links while ensuring reliable UAV-UAV communication; the second enhances network-wide fairness by maximizing the minimum link capacity. Simulation results demonstrate that the proposed approach significantly outperforms benchmark schemes in both system throughput and fairness.

This work addresses the lack of high-fidelity, experimentally validated models for aerial–terrestrial cellular communication links, which hinders unmanned aerial vehicle (UAV) system design and mission planning. Leveraging the AERPAW Lake Wheel testbed, the study employs custom Android devices to collect enhanced key performance indicators (KPIs) over real 4G/5G networks, integrating spatial parameters such as flight altitude, slant range, elevation angle, and azimuth. By fusing free-space path loss theory with empirical measurements, the authors develop a joint physical- and application-layer empirical channel model. Lightweight machine learning techniques—including random forests, gradient boosting, and neural networks—are incorporated to predict spatial variations in KPIs. The resulting model accurately captures the characteristics of aerial–terrestrial links, offering a practical tool for simulation and system design of cellular-connected UAVs.

This study addresses the challenge of limited onboard energy allocation between propulsion and communication in high-altitude platform (HAP)-enabled 6G networks. Existing approaches often neglect propulsion energy consumption or rely on oversimplified models, leading to inaccurate communication power budgets and degraded beamforming performance. To overcome this, the work introduces a generative AI agent to construct a precise propulsion power consumption model under aerodynamic disturbances and jointly optimizes communication beamforming through a novel QoS-aware energy-efficient (Q3E) algorithm. The proposed framework enables cross-domain co-modeling and co-optimization across aerodynamics, propulsion, and communication subsystems. Simulation results demonstrate that the developed model significantly improves propulsion power estimation accuracy, while the Q3E algorithm substantially enhances system energy efficiency without compromising user quality of service.

This study addresses the lack of systematic empirical comparison between cellular and low Earth orbit (LEO) satellite networks—such as Starlink—in three-dimensional airspace performance for low-altitude unmanned aerial vehicles (UAVs) operating in野外 environments. The authors develop an open-source, synchronized measurement platform to collect and publicly release, for the first time, co-located and temporally aligned cellular and Starlink communication datasets at low altitudes. Employing a multi-layer measurement architecture, they comprehensively evaluate physical-layer signal characteristics, handover behaviors, and end-to-end performance. Their experiments reveal that altitude nonlinearly affects cellular signal strength and handover frequency: higher altitudes improve signal power by 15–20 dB but increase handovers by 3–4×, leading to significantly asymmetric round-trip times (RTTs). In contrast, Starlink achieves RTTs below 50 ms in 95% of scenarios and downlink throughput exceeding 25 Mbps, demonstrating markedly superior overall performance compared to cellular networks.

This work addresses the severe inter-user interference in High-Altitude Platform Station (HAPS) networks, where strong line-of-sight links result in limited channel variation. To mitigate this issue, the paper proposes a novel joint optimization framework that, for the first time, incorporates angular information into user clustering and jointly optimizes interference-aware resource block allocation, directional beamforming, and rate-splitting multiple access (RSMA). By leveraging spatial characteristics and coordinated resource management under limited orthogonal resources, the proposed approach effectively suppresses interference and significantly enhances per-user spectral efficiency. Extensive evaluations demonstrate that the method outperforms existing baseline schemes in terms of both interference mitigation and spectral efficiency gains.

This work addresses the low energy efficiency experienced by cell-edge users in terrestrial cellular networks, which stems from path loss, shadowing, and inter-cell interference. To mitigate these challenges, the authors propose a high-altitude platform station (HAPS)-assisted cooperative beamforming architecture that leverages line-of-sight links between HAPS and ground base stations for data relaying. An innovative online optimization framework integrating graph neural networks (GNNs) is developed to effectively model the dynamic network topology and solve the non-convex energy efficiency maximization problem in real time. Experimental results demonstrate that the proposed approach significantly improves the 5th-percentile network energy efficiency, thereby substantially enhancing quality of service for edge users.