Three-dimensional (3D) radio environment maps (REMs) can visually represent the spectrum situation over the geographical maps, which facilitates the management of spectrum resources. Recently, the utilization of unmanned aerial vehicles (UAVs) for 3D REM construction has emerged as a promising solution. In this paper, we address the UAV sampling path planning problem by maximizing the information collection along dynamically optimized paths. On this basis, we propose an efficient informative path planning (IPP)-based scheme for autonomous spectrum mapping in unknown environments. Firstly, an effective spectrum data spatial distribution prediction method based on the scalable Gaussian process is introduced. It generates estimated REMs and quantifies associated uncertainty, which serve as the basis for UAV path planning. Then, we develop a hierarchical path planning method. Specifically, an uncertainty-aware target decision strategy is initially designed, which selects informative targets by maximizing the negative integrated posterior variance utility. Then, a local path planner driven by the upper confidence bound criterion is introduced, which utilizes the dynamic programming method to refine the UAV’s path toward the target. The proposed method is validated by a simulated dataset in the campus scenario. Compared to state-of-the-art approaches, simulation results demonstrate that the proposed scheme achieves reductions of 20.37%, 14.85%, and 57.29% in mean absolute error, mean negative log loss, and mean uncertainty, respectively. Moreover, the time consumption is reduced by 81.65% compared to conventional IPP-based method.

Drones are expected to be promising aerial platforms in air-to-ground (A2G) integrated communication networks, where the A2G propagation channel is fundamental for reliable communication links. This paper proposes a hybrid parameter generation framework combining the deterministic and statistical methods for a cluster-based A2G channel model. In this framework, the map-based deterministic method is used to generate delay and angle parameters, which can achieve great scenario consistency. However, it is difficult for users to provide precise material information of scatterers, which would cause deviation of power parameters. To tackle this issue, a measurement-driven power generation method is proposed. Firstly, a bandwidth-dependent clustering method is developed to group the rays into clusters. Then, the cluster power is generated by measurement-driven statistical models with a power-decomposition idea. It decomposes the power parameter into several parts that are less dependent on the scenario. Moreover, it can avoid massive measurement campaigns and is more robust when applied in unmeasured scenarios. Finally, a new channel measurement campaign in a street canyon scenario is performed for validations. The proposed method is also compared with a ray-tracing (RT) method and the 3rd Generation Partnership Project (3GPP) channel model. It is shown that the proposed framework and power generation method are great alternatives for accurate and robust modeling requirements under specific A2G communication scenarios.

Unmanned aerial vehicle (UAV)-based interference source localization is increasingly important in dense 5G-Advanced and emerging 6G networks, where unknown interferers degrade communication reliability and bias measuremen-tdriven channel modeling. However, propagation uncertainty caused by multipath, blockage, and non-line-of-sight (NLOS) propagation makes the measurement-to-location relationship highly nonlinear and time-varying, which limits the reliability of conventional localization methods. To address this challenge, we propose a single-UAV localization framework that uses spatial received signal strength indicator (RSSI) measurements collected along an adaptive flight trajectory to localize a dominant interference source. A robust channel-aware localization algorithm estimates the source location together with its associated uncertainty. A Gaussian process (GP) Bayesian optimization method selects waypoints by maximizing the expected reduction in localization uncertainty. By integrating channel-aware localization with uncertainty-aware waypoint selection, the proposed closed-loop received signal strength difference (RSSD) interference localization framework iteratively updates the source estimate and plans subsequent waypoints. At a signal-to-noise ratio (SNR) of 20 dB under multipath propagation, the proposed framework reduces the flight distance for a given localization root mean square error (RMSE) by about 60% and the localization RMSE at a given flight distance by about 70% compared with the baseline methods.

In this study, we consider large-scale wireless-powered Internet of Things networks in which sensor nodes (SNs) harvest energy from distributed power beacons equipped with multiple antennas during the energy-harvesting downlink (EH DL) phase and then transmit their sensing data to associated information receivers during the information-transfer uplink (IT UL) phase. In the IT UL phase, two different resource utilization strategies are considered for transmitting sensing data. First, each active node requires one time slot to transmit its sensing data during the UL phase, which consists of multiple time slots (Type I). Second, all active SNs share the time slots available for IT (Type II). Considering a practical EH model, we aim to analyze both the harvested energy in the EH DL phase and transmission capacity in the IT UL phase. Based on the analytical results, we demonstrate that the transmission capacity is unimodal with respect to the number of EH time slots and transmit power for IT. We also propose a network parameter optimization algorithm in which the number of EH time slots and transmit power for IT are jointly optimized to maximize the transmission capacity.

Received signal strength indicator (RSSI)-based localization methods have been widely applied due to their low costs and easy installation. However, their positioning accuracy is often undermined in environments with multipath fading and spatially varying noise. In this paper, we propose a multi-unmanned aerial vehicle (UAV) cooperative localization method for dynamic multipath environments. A new dual-level fusion framework is presented, which can simultaneously improve the local estimation robustness and the global localization adaptability. At the single-agent estimation level, we develop an enhanced unscented Kalman filter (UKF) with a time-decaying sliding window. This structure adaptively reweights historical RSSI measurements, and thus, can mitigate multipath effects and improve individual estimation accuracy. At the fusion level, a residual-informed adaptive fusion strategy is proposed. According to short-term Gaussian residuals and long-term reliability metrics, the fusion strategy evaluates and adjusts the contribution of each UAV to guarantee the localization consistency in highly dynamic environments. Extensive simulation results demonstrate that the proposed cooperative localization method can improve positioning accuracy by approximately 70% over the prior art.

Reinforcement learning (RL) has achieved unprecedented success on making complex decisions encountered in various applications. However, RL has two well-recognized technical challenges, namely, weak scene generalization and local-optima trapping; both lead to degraded performance in terms of navigation accuracy and efficiency. To tackle these issues, a novel RL framework is proposed in this paper, called the cognitive escape reinforcement learning (CERL), which is inspired by the cognition ability inherited in the human beings. Our developed CERL framework consists of two sequential stages: the semantic cognition module (SCM) and the cognitive escape module (CEM), corresponding to the roles played by the perception layer and the decision layer in traditional RL, respectively. The SCM exploits semantic information extracted from the scenes for facilitating the CEM on making complex decisions. Our developed SCM is based on the semantic information extracted from the scenes that is quite capable of adapting to new environments for improving the agent's navigation performance. The CEM proactively identifies the occurrences of local-optima trappings and escapes from such incidences responsively. Extensive experiments have demonstrated the superiority of our proposed RL method, CERL, when compared to the state-of-the-art methods.

Constructing three-dimensional (3D) radio environment maps (REMs) has emerged as a promising solution to visualize the spectrum information over the geographical map. In this paper, we propose a novel online path planning method for unmanned aerial vehicle (UAV)-based 3D spectrum mapping in unknown environments. The UAV can effectively collect spectrum data along dynamically planned paths while adhering to budget constraints. We formulate the path planning problem by a surrogate objective that maximizes the information gain along the sampling path. Specifically, a Gaussian process (GP) is adopted to estimate the spatial distribution of received signal strength (RSS) based on observations. A goal location decision algorithm based on the negative integrated posterior variance (NIPV) criterion is developed, which identifies high-value locations by maximizing the reduction in uncertainty. Besides, an uncertainty-aware local path planner is introduced to optimize sampling paths during flight. Simulation results demonstrate that it achieves at least a 66.49% improvement in REM construction accuracy and 42.74% reduction in mapping uncertainty compared to traditional methods.

Radio environment maps (REMs) have been used to visualize the information of invisible electromagnetic spectrum. Although in the past there have been many research activities dealing with the reconstruction of static REMs, they did not consider the time variation of the dynamic spectrum operational environment. In this article, we present a novel time-variant REM reconstruction methodology based on sparsely distributed sensors which jointly considers sensor layout optimization, propagation model improvement, and missing spectrum data recovery. First, a low complexity and computationally efficient method is proposed to improve the sampling efficiency. The proposed method jointly employs the gradient descent method and an upgraded greedy matching algorithm to optimize the sensor positions even when large-scale scenarios are considered. Then, by using the sampled spectrum data obtained from these sensors, the accuracy of commonly employed propagation models is improved and subsequently used to construct a channel dictionary for such time-varying environments. By exploring the heterogeneity of dynamic spectrum operational environments, an improved optimal reconstruction method is designed to recover the spectrum data using their spatial-temporal correlation. By considering a typical university campus environment as a case study, simulation and measurement data are obtained to reconstruct the time-variant REM. Through the simulation data, the reconstruction performance results are compared with those obtained from other state-of-the-art methods showing that the proposed methodology outperforms the others with respect to the sampling scheme and missing rate. Additionally, field measurement results have demonstrated that the proposed approach can effectively reconstruct time-variant REMs under dynamic scenarios.

Trust region optimization-based received signal strength indicator (RSSI) interference source localization methods have been widely used in low-altitude research. However, these methods often converge to local optima in complex environments, degrading the positioning performance. This paper presents a novel unmanned aerial vehicle (UAV)-aided progressive interference source localization method based on improved trust region optimization. By combining the Levenberg-Marquardt (LM) algorithm with particle swarm optimization (PSO), our proposed method can effectively enhance the success rate of localization. We also propose a confidence quantification approach based on the UAV-to-ground channel model. This approach considers the surrounding environmental information of the sampling points and dynamically adjusts the weight of the sampling data during the data fusion. As a result, the overall positioning accuracy can be significantly improved. Experimental results demonstrate the proposed method can achieve high-precision interference source localization in noisy and interference-prone environments.

This letter addresses the problem of unmanned aerial vehicles (UAVs)-based localization for a directional emitter with unknown position and transmission orientation. We propose an efficient three-step alternating estimator with iteratively reweighted least square (TA-IRLS). Based on linearized measurement models, the initial estimation of emitter position and orientation are obtained using low-complexity least squares (LS) approaches. To further enhance localization accuracy, IRLS is employed to refine measurement weights based on estimation residuals. Numerical results validate the superiority of the proposed TA-IRLS in terms of both localization efficiency and accuracy.

Specific emitter identification (SEI), known as radio frequency fingerprint (RFF) identification, is one of the key techniques to provide effective protection for the low-altitude security. However, most existing SEI methods cannot achieve satisfactory identification performance in low signal-to-noise ratio (SNR) environments. By fusing background information of the environment, this paper presents a new deep learning-based SEI method that can accurately identify emitters in the environments with severe noises. We first construct a dual convolutional neural network (DCNN) and a U-shaped Convolutional Network (UNet) to extract the RFFs and background information features, respectively. Then, a background-fingerprint attention fusion network (BFAFN) is designed to fuse the background information with RFF features. Using this network, we can obtain detailed emitters information through the fused signals, improving the identification accuracy. Experimental results show that our proposed SEI method outperforms other methods in performance on both open-source and collected unmanned aerial vehicles (UAVs) datasets, with an improvement in identification accuracy of 2% to 5%.

With the deep integration of the Internet of Vehicles and air-ground collaborative communication networks, urban spectrum management faces multidimensional challenges, including an increasingly prominent supply-demand imbalance of spectrum resources. Conventional spectrum coordination methods suffer from low utilization efficiency in the presence of heterogeneous network environments and dynamic service demands. To address this issue, this paper proposes a dynamic spectrum prediction method based on temporal knowledge graphs. By integrating multidimensional knowledge including electromagnetic spectrum data, equipment parameter features, and environmental data, we construct a knowledge graph for communication spectrum coordination with fusion of static and dynamic knowledge (SDKG). By employing a knowledge graph embedding (KGE) model based on the recurrent evolution network via graph convolution network (RE-GCN) combined with graph neural networks (GCNs) and a long short-term memory (LSTM) reasoning algorithm, a GCN-LSTM with RE-GCN dynamic spectrum prediction algorithm is proposed. Simulation results demonstrate that the GCN-LSTM with RE-GCN algorithm can effectively enhance frequency prediction accuracy.

Low-Earth orbit (LEO) satellite communication is poised to be a key enabler in the burgeoning 6G era. However, the emergence of unauthorized LEO satellite user terminals (LSUTs) poses significant threats to communication security, creating an urgent need for effective LSUT surveillance. Nevertheless, LSUT surveillance is challenging since the transmit beamforming of LSUT leads to the highly directional uplink beam, which complicates the reception of high signal-to-noise ratio (SNR) signals necessary for accurate geolocation and monitoring by surveillance equipment. To handle the above issues, we develop a dual-grained recursive Bayesian filtering scheme for unmanned aerial vehicle (UAV)-enabled spectrum surveillance. Specifically, we construct an LSUT uplink signal detection probability map, enabling a coarse-grained search to identify the surveillance region of interest (SROI). This procedure enhances the SNRs of received signals and improves the initialization of LSUT monitoring. Furthermore, we propose a particle filter with beam motion mode tracker for fine-grained monitoring, which facilitates accurate geolocation and robust monitoring despite dynamic beam pointing direction and satellite handovers. Numerical results demonstrate that our dual-grained trajectory planning approach significantly benefits LSUT surveillance, yielding superior cumulative detection probability, monitoring accuracy, SNRs of received signals, and geolocation accuracy

This paper presents a measured spectrum strength dataset in the urban scenario with multiple radiation sources, aiming to address the limitation of open datasets for spectrum environment map (SEM) in realistic multi-source dynamic scenarios. The dataset was collected through high-precision measurements, covering the 30 MHz, 115 MHz, and 2 GHz frequency bands, with a spatial resolution of 1m×1 m. It includes spectrum strength or received signal strength (RSS) data in dBm for 80×105 grids. Each grid includes the information such as longitude, latitude, altitude, and time. The experiment utilizes three radiation sources with isotropic antennas and a mobile signal receiving system equipped with a spectrum analyzer and a GPS module. It collects data along a pre-defined path at a constant speed. The key feature of this dataset is its realistic representation of nonline ar characteristics of propagation channel in a multi-radiation source coexistence scenario. Its applications include the verification of spectrum map completion algorithms, wireless channel modelling, deep learning-driven signal prediction, and the optimization of Wi-Fi/cellular networks.

Due to its fast processing time and robustness against harsh environmental conditions, the frequency modulated continuous waveform (FMCW) multiple-input multiple-output (MIMO) radar is widely used for target localization. For high-accuracy localization, the two-dimensional multiple signal classification (2D MUSIC) algorithm can be applied to signals received by a single FMCW MIMO radar, achieving high-resolution positioning performance. To further enhance estimation accuracy, received signals or MUSIC spectra from multiple FMCW MIMO radars are often collected at a data fusion center and processed coherently. However, this approach increases data communication overhead and implementation complexity. To address these challenges, we propose an efficient high-resolution target localization algorithm. In the proposed method, the target position estimates from multiple FMCW MIMO radars are collected and combined using a weighted averaging approach to determine the target’s position within a unified coordinate system at the data fusion center. We first analyze the achievable resolution in the unified coordinate system, considering the impact of local parameter estimation errors. Based on this analysis, weights are assigned according to the achievable resolution within the unified coordinate framework. Notably, due to the typically limited number of antennas in FMCW MIMO radars, the azimuth angle resolution tends to be relatively lower than the range resolution. As a result, the achievable resolution in the unified coordinate system depends on the placement of each FMCW MIMO radar. The performance of the proposed scheme is validated using both synthetic simulation data and experimentally measured data, demonstrating its effectiveness in real-world scenarios.

With the rapid advancement of 5 G communication and the Internet of Things (IoT), unmanned aerial vehicles (UAVs) has shown its potential to play a pivotal role as aerial base stations within the evolving landscape of future wireless networks and edge computing systems. However, in such UAV-assisted communication networks, the complexity of multi-agent scenarios creates challenges due to the problem of learning and decision-making in complex tasks. To address this issue, we propose a novel algorithm that optimizes UAV trajectory planning and autonomous clustering of user equipment (UE) for communication. The proposed algorithm leverages hierarchical reinforcement learning and multi-agent cognitive consistency to improve UE communication decisions in interference-prone environments. Simulation results confirm the convergence of the proposed algorithm and reveal special strength in coordinating spectrum utilization in complex air-ground environments.

Mobile edge computing (MEC) deployed in unmanned aerial vehicles (UAVs) has shown special strength by enhancing computational capacity and prolonging the battery lives of terrestrial user equipment (UE). Nevertheless, current research lacks studies of robust offloading scheme scheduling and trajectory planning using terrestrial random channels. The state-of-the-art joint task-offloading and trajectory-planning optimization techniques for UAV-mounted MEC are focused on scenarios where only air–ground channels exist rather than time-varying terrestrial channels. By contrast, this paper considers the scenario where both the time-varying/random terrestrial channels and the line-of-sight air–ground channels occur. Aiming at robust resource scheduling for energy-efficient UAV-assisted MEC, we formulate a novel joint optimization of UAV trajectory planning and task offloading, which, however, is highly nonconvex. As a countermeasure, the original optimization is recast as subproblems related to task offloading and trajectory planning and solved by a novel robust iterative optimization algorithm that combines the methods of weighted minimum mean square error, S-procedure, successive convex approximation, etc. Numerical results indicate that, compared to various baselines, the proposed algorithm can effectively reduce energy consumption and optimize the trajectory in the presence of a large number of input tasks. In addition, in terms of stability and effectiveness, the proposed robust iterative optimization algorithm can reduce energy consumption more stably in time-varying/random channels compared to non-robust schemes.

The spectrum environment map (SEM), which can visualize the information of invisible electromagnetic spectrum, is vital for monitoring, management, and security of spectrum resources in cognitive radio (CR) networks. In view of a limited number of spectrum sensors and constrained sampling time, this paper presents a new three-dimensional (3D) SEM construction scheme based on sparse Bayesian learning (SBL). Firstly, we construct a scenario-dependent channel dictionary matrix by considering the propagation characteristic of the interested scenario. To improve sampling efficiency, a maximum mutual information (MMI)-based optimization algorithm is developed for the layout of sampling sensors. Then, a maximum and minimum distance (MMD) clustering-based SBL algorithm is proposed to recover the spectrum data at the unsampled positions and construct the whole 3D SEM. We finally use the simulation data of the campus scenario to construct the 3D SEMs and compare the proposed method with the state-of-the-art. The recovery performance and the impact of different sparsity on the constructed SEMs are also analyzed. Numerical results show that the proposed scheme can reduce the required spectrum sensor number and has higher accuracy under the low sampling rate.

In this paper, we initiate the study of rate-splitting multiple access (RSMA) for a mono-static integrated sensing and communication (ISAC) system, where the dual-functional base station (BS) simultaneously communicates with multiple users and detects multiple moving targets. We aim at optimizing the ISAC waveform to jointly maximize the max-min fairness (MMF) rate of the communication users and minimize the largest eigenvalue of the Cram\'er-Rao bound (CRB) matrix for unbiased estimation. The CRB matrix considered in this work is general as it involves the estimation of angular direction, complex reflection coefficient, and Doppler frequency for multiple moving targets. Simulation results demonstrate that RSMA maintains a larger communication and sensing trade-off than conventional space-division multiple access (SDMA) and it is capable of detecting multiple targets with a high detection accuracy. The finding highlights the potential of RSMA as an effective and powerful strategy for interference management in the general multi-user multi-target ISAC systems.

Metaverse, which enables the combination of the virtual and the physical worlds, requires mobile networks with high-capacity and reliable connectivity. Radio maps (RMs) can offer the knowledge of the wireless environments to improve the connectivity, by charactering the spatial distribution of received signal strength (RSS) throughout physical spaces. This paper investigates a collaborative RM reconstruction scheme, where client unmanned aerial vehicles (UAVs) collect RSS samples measured by mobile users for local training, while a server UAV performs model aggregation to optimize the global model. Unfortunately, RSS samples measured in practice can be sparse, non-uniformly distributed and non-independent and identically distributed (non-iid), such that reconstructing a complete RM is intractable. Therefore, we propose a novel RM reconstruction scheme based on federated learning (FL) with generative adversarial network (GAN), where GAN is exploited to generate a RM with sparsely and non-uniformly distributed RSS data. In order to tackle with non-iid RSS data, the FL is integrated with an adaptive client UAV selection strategy with model similarity evaluation, as well as a model weight assignment method with earth mover’s distance evaluation for model aggregation. Simulation results reveal that benefiting from the aforementioned design, the proposed scheme can significantly enhance the reconstruction accuracy and convergence speed compared to the conventional algorithms.