The radio environment map (REM), 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) REM 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 REM. We finally use the simulation data of the campus scenario to construct the 3D REMs and compare the proposed method with the state-of-the-art. The recovery performance and the impact of different parameters on the constructed REMs are also analyzed. Numerical results show that the proposed scheme can reduce the required spectrum data and has higher accuracy under the low sampling rate.

A realistic channel model is vital for designing, optimizing, and evaluating unmanned aerial vehicle (UAV)-to-ground (U2G) communication systems. This paper presents a comprehensive U2G ultra-wideband (UWB) channel model by considering the large-scale fading (LSF), including path loss (PL), shadow fading (SF), and airframe shadowing (AS). The effects of carrier frequency, bandwidth, building distribution, and UAV airframe structure on the LSF are fully studied. Different from the traditional bandwidth-independent PL calculation method, an extended closed-form expression for calculating PL is proposed to capture the new characteristics of ultra-wide bandwidth. The SF part is statistically described by a parametric lognormal model, and the built-up scenario-dependent parameters are predicted by a hybrid method with the ray-tracing and virtual scenario technologies. In addition, the AS part with respect to the airframe structure and posture variation is derived. It consists of the direct and reflected path components which are obtained by the deterministic and statistical approaches, respectively. The numerical simulations show that bandwidth, building distribution, and airframe structure have great influence on the LSF characteristics. For example, the fluctuation of received power can reach 30 dB due to the AS. The proposed model also shows its effectiveness and reliability by comparing with the RT and measured data, as well as the good compatibility with standardized models for some typical scenarios.

Line-of-sight (LoS) path is essential for the reliability of air-to-ground (A2G) communications, but the existence of the LoS path is difficult to predict due to random obstacles on the ground. Based on the statistical geographic information and Fresnel clearance zone, a general stochastic LoS probability model for 3-D A2G channels under urban scenarios is developed. By considering the factors, that is, building height distribution, building width, building space, carrier frequency, and transceiver’s heights, the proposed model is suitable for different frequencies and altitudes. Moreover, in order to get a closed-form expression and reduce the computational complexity, an approximate parametric model is also built with the machine-learning (ML) method to estimate the model parameters. The simulation results show that the proposed model has good consistency with existing models at low altitude. When the altitude increases, it has a better performance by comparing with that of the ray-tracing (RT) Monte-Carlo simulation data. The analytical results of the proposed model are helpful for the channel modeling and performance analyses such as cell coverage, outage probability, and bit error rate in A2G communications.

Unmanned aerial vehicle (UAV) aided millimeter wave (mmWave) technologies have a promising prospect in the future wireless communication networks. By considering the factors of three-dimensional (3D) scattering space, 3D trajectory, and 3D antenna array, a non-stationary ray-based channel model for UAV-to-vehicle (U2V) mmWave communications is proposed. The computation and generation methods of channel parameters including inter-path and intra-path are developed and illustrated in detail. The inter-path parameters are calculated in a deterministic way based on the given geometric information. The parameters of intra-path rays are generated in a stochastic way based on the statistical properties, which can be obtained in advance by applying a Gaussian mixture model (GMM) on the massive ray tracing (RT) data of different typical scenarios. Meanwhile, a modified method of equal areas (MMEA) is developed to generate the random intra-path variables. To speed up the RT algorithm, the 3D propagation scenario is reconstructed based on the user-defined digital map. Moreover, the theoretical statistical properties of the proposed channel model, i.e., power delay profile (PDP), autocorrelation function (ACF), Doppler power spectrum density (DPSD), cumulative distribution function (CDF), and level crossing rate (LCR) are derived. Finally, a typical U2V channel under urban scenario at 28 GHz is generated and validated. Simulation results demonstrate that the PDP and DPSD of the proposed channel model are accorded well with the theoretical ones, and the ACF, CDF, and LCR are consistent with the measured ones as well.

To address the issue of limited application scenarios associated with connectivity assurance based on two-dimensional (2D) trajectory planning, this paper proposes an improved deep reinforcement learning (DRL) -based three-dimensional (3D) trajectory planning method for cellular unmanned aerial vehicles (UAVs) communication. By considering the 3D space environment and integrating factors such as UAV mission completion time and connectivity, we develop an objective function for path optimization and utilize the advanced dueling double deep Q network (D3QN) to optimize it. Additionally, we introduce the prioritized experience replay (PER) mechanism to enhance learning efficiency and expedite convergence. In order to further aid in trajectory planning, our method incorporates a simultaneous navigation and radio mapping (SNARM) framework that generates simulated 3D radio maps and simulates flight processes by utilizing measurement signals from the UAV during flight, thereby reducing actual flight costs. The simulation results demonstrate that the proposed approach effectively enable UAVs to avoid weak coverage regions in space, thereby reducing the weighted sum of flight time and expected interruption time.

Dual-functional radar and communication (DFRC) techniques are promising in the next generation wireless communication network. An intelligent reflecting surface (IRS) can improve the energy efficiency (EE) of the DFRC system, which is of crucial importance in practical applications. However, the EE achieved by using the sum-rate can be limited by the channel conditions and the radar performance requirement. Thus, in this paper, the EE maximization problem is studied in an IRS-assisted DFRC system. The EE is maximized by jointly optimizing the transmit beamforming and the IRS phase shift matrix under both the perfect channel state information (CSI) and the imperfect CSI cases. To tackle the challenging nonconvex fractional optimization problem, an alternating optimization algorithm is proposed. The high-order challenging radar performance constraint is tackled by using properties of the convex function and Hermitian matrix involved in the analysis. In the imperfect CSI case, the ${\mathcal{S}}$ -Procedure is applied to tackle the semi-infinite inequality arising from the channel uncertainty. Simulation results demonstrate that our proposed design enjoys fast convergence and achieves a higher EE compared to the benchmark schemes, even under imperfect CSI case. Furthermore, simulation results demonstrate that the EE of the DFRC system can be significantly improved by applying IRS.