Accurate radio environment map (REM) construction proves critical for efficient wireless spectrum management. Although conventional 2D REMs have demonstrated effectiveness in wireless network optimization, they inherently overlook vertical signal strength variations, which are vital for UAV operations, particularly in urban landscapes with skyscrapers or diverse terrain features. Current estimation approaches, including ground-based crowdsourcing, random sampling, and predetermined trajectory measurements, show limited capability in generating high-fidelity 3D REMs. This study proposes a joint optimization framework for UAV-enabled adaptive 3D radio mapping, integrating 3D REM construction with adaptive aerial sampling. At the heart of the construction module, a dual-branch encoder-decoder architecture fuses multi-scale features from sparse aerial measurements with building structural data, explicitly modeling obstruction effects through offline pre-training and online refinement to enhance generalization. For adaptive sampling, a diffusion-based trajectory planner dynamically optimizes UAV measurement paths by integrating environmental priors (e.g., building layouts), effectively overcoming the sparse-reward limitations inherent in reinforcement learning methods. Experimental validation demonstrates significant performance improvements across all evaluation metrics. Compared to 2D per-layer estimation methods, our 3D estimator achieves 49% superior structural similarity (SSIM) in construction accuracy, while the feature fusion module yields a 37% reduction in mean squared error (MSE). The diffusion-based planner outperforms reinforcement learning approaches by achieving 45% lower MSE and 18% higher SSIM in resultant map quality after 5,000 step iterations.

The deployment of high-frequency carriers in 5G networks and beyond introduces significant challenges, particularly due to increased path loss and physical limitations of user equipment (UE). In particular, these challenges lead to uplink/downlink coverage imbalances, critically affecting applications that rely on robust uplink capacity such as autonomous driving, telemedicine, and live streaming. While downlink-uplink decoupling (DUDe) and supplementary uplink (SUL) have emerged as promising solutions for uplink enhancement, their flexible cell association mechanisms disrupt conventional handover strategies by introducing multidimensional, decoupled decision-making. To address this, we propose a heterogeneous network architecture that integrates DUDe into SUL, supporting non-co-located deployment of supplementary uplink carriers and enabling dual decoupling at both the base station and carrier levels. Building on this architecture, we introduce Counterfactual Multi-agent fuzzy neural network (COMA-FNN), a multi-agent reinforcement learning (MARL) algorithm based on centralized training with decentralized execution (CTDE). COMA-FNN incorporates fuzzy neural networks to model the complex, nonlinear relationships among multiple communication attributes, improving the precision and adaptability of handover decisions. The algorithm also employs a centralized critic with counterfactual baselines to effectively resolve credit assignment issues among competing agents. To accommodate varying service requirements, COMA-FNN incorporates service-specific reward functions aligned with four 3GPP-standardized service types. These rewards are weighted using the Analytic Hierarchy Process (AHP), enabling the algorithm to support different QoS policies. Simulation results demonstrate that COMA-FNN significantly improves handover efficiency, reduces latency, and enhances throughput, making it a robust solution for intelligent mobility management in decoupled uplink/downlink architectures.

Satellite clustering has the potential to enhance inter-satellite cooperation, resist satellite malfunction, and enable more agile space-air-ground integrated applications. This paper investigates a promising model for clustered low Earth orbit (LEO) systems, in which one typical unmanned aerial vehicle (UAV) can assist one satellite cluster to serve one random terrestrial user. Particularly, intra-cluster satellites can communicate user, while inter-cluster satellites are regarded as interference. Two types of satellites and users are randomly deployed at three visible spherical spaces by adopting three independent spherical Poisson point processes. In the modeling, an adaptive selecting mechanism is proposed to pick the strongest received signal between direct and cooperative transmissions. To facilitate a simpler analysis, we firstly transform the three spaces into the three planes through modifying their respective density. Next, assuming that the shadowed-Rician fading is employed in the satellite channel, two Gamma random variables are utilized to approximately express the aggregated power of interference and noise received by the UAV and user, respectively. Subsequently, the exact conditional user association and approximate Laplace transform of the accumulated signal power are derived to further investigate the conditional coverage probability. Finally, simulation results illustrate that: 1) Moderate satellite cluster sizes combined with a UAV altitude of about 200m are beneficial for achieving higher coverage probability; and 2) The adaptive selection mechanism generally achieves comparable or better performance than traditional transmissions by leveraging spatial diversity.

Abstract Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites (CCS) system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control, fundamentally enhancing network resilience. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, new waveform design, grant-free massive access, nonorthogonal multicast, distributed phased array antennas, high-speed inter-satellite communication, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.

This article proposes a downlink/uplink decoupled (DUDe) access scheme for cellular-enabled unmanned aerial vehicle (UAV) communication systems. To minimize interference, the proposed scheme separates the control and data links of UAVs, as well as the uplinks (ULs) and downlinks (DLs) of ground users (GUEs), onto different serving base stations and operating frequencies. Since power availability is a major constraint in UAV communications, two power allocation schemes based on$Q$-learning (QL) and deep$Q$-learning (DQL) are proposed to optimize the communication energy efficiency (EE) of this DUDe network. To quantify the improvements achieved, the proposed schemes are compared with the fractional power control (FPC) scheme used in 4G and 5G networks, as well as a convex optimization-based optimal power allocation scheme. The results demonstrate that the proposed DUDe scheme can achieve up to several times higher sum rates and EE in the UL direction than its coupled counterparts. Moreover, it is shown that the EE performance of the QL and DQL power allocation schemes approach the optimal performance and surpass the conventional FPC scheme by 80%–100% in the UHF band, and by 160%–170% in the mmWave band.

The global market for unmanned aerial vehicles (UAVs) has grown substantially over the past decade and has become a high point for economic growth in many countries. Hence integrating UAVs with cellular networks is considered pivotal to tapping into new business opportunities for cellular operators, especially as the smartphone market is almost saturated. In this paper, we propose a new scheme for efficiently integrating UAVs within 5G cellular systems. To this end, we separate the UAV control and non-payload communication links (CNPC) from high-capacity data communication links-by decoupling the uplink (UL) and downlink (DL) access-to allow user equipments (UEs) to connect to different base-stations (BSs) and transmit over different frequency bands in the UL and DL, such that the interference between ground UEs (GUEs) and UAVs is significantly reduced. The ground UE-BS links are also decoupled in a similar fashion to further minimize interference and maximize energy-efficiency. The numerical results validate the effectiveness of the proposed scheme and quantify the improvement in terms of the data rate of UAVs and GUEs in comparison to coupled benchmarks.

Millimeter-wave communication is limited by high penetration and pathloss but can be used in base stations closer to the user terminals through heterogeneous networks to maximize spectrum usage and enrich coverage. This paper compares the performance of single and dual connectivity from both perspectives of pathloss and capacity. We also expand our capacity based association approach proposed in [12] to work in UHF-mmWave hybrid networks where mobile users can connect to different ultra high frequency small cells, millimeter-wave small cells and ultra high frequency macro cells with the option of decoupling. The results provide an insight into the performance of such techniques and network typologies for various performance metrics of interest. It will be shown that dual connectivity is not always superior and that single and dual hybrid connectivity can be a better choice. It will also be shown that wireless networks with both ultra high frequency and millimeter-wave small cells achieve higher capacity than those with only one kind of small cells.