Vortex electromagnetic waves are conveniently produced by a uniform circular array (UCA). The mutual coupling matrix (MCM) of UCAs presents rotational symmetry, which can be employed to cut down the parameters in the circuit model. The impedance matrix also does not rely on a specific port. This work uses the rotational symmetry to expand the MCM into orbital angular momentum (OAM) mode domain. The OAM domain phase sequence is derived by the Discrete Fourier Transform (DFT) of the first row of the MCM. The connection between the number of OAM modes and excitation of elements is described. Furthermore, an improved propagator method (PM) for OAM based decoupled two-dimensional (2-D) angle estimation is proposed. This method employs original steering vector of OAM waves to process the receiving signal, which avoids error caused by the Bessel function approximation. The estimation error is also reduced by redefining the propagator matrix. In terms of computational complexity, the proposed method avoids spectral search and element level eigenvalue decomposition (EVD). The results demonstrate that the coupling is compensated effectively by the OAM mode domain MCM. The error of proposed method is less than 1 degree under the condition of snr = 20dB and 1000 snapshots. The computational complexity is reduced by 1 to 2 orders of magnitude compared to the origin propagator method. The method is also suitable for one-dimensional estimation scenarios.

Space–air–ground integrated network (SAGIN) has shown strong communication and computation abilities in various Internet of Things (IoTs) applications with the assistance of artificial intelligence (AI), such as emergency communication and remote sensing. However, resource heterogeneity of aerial devices is always the bottleneck of the performance of AI models and energy efficiency. In this paper, a collaborative federated learning (FL) scheme based on device-to-device (D2D) communication in unmanned aerial vehicle (UAV)-assisted SAGIN is proposed to address the issue of heterogeneity. Aerial devices with limited communication and computation resource can offload partial nonprivacy data samples to proximity D2D pair, which can assist to train FL models. An optimization problem is proposed to minimize the total energy consumption and the loss function of local FL models. In order to solve the mixed integer nonlinear problem (MINLP), a data offloading selection strategy based on proximity discovery and an iterative method-based resource allocation algorithm (IRA) are proposed. In addition, the closed-form solutions of the optimized variables are obtained. Simulation results demonstrate that the proposed collaborative training scheme based on D2D can reduce the impact of heterogeneity on FL model performance and IRA can effectively reduce energy consumption while simultaneously enhancing training efficiency of FL.

As the Internet of Things (IoT) technology and artificial intelligence (AI) technology continue to evolve, many envisaged concepts regarding smart cities are gradually becoming a reality. However, the proliferation of numerous IoT devices in smart cities has led to several challenges. The existing 5G networks are incapable of meeting the requirements of these devices in terms of channel capacity and network coverage. Additionally, traditional cloud-based centralized machine-learning methods fail to ensure the privacy of user data. At this juncture, space-air–ground information network, along with federated learning (FL), are perceived as viable solutions to address these issues. This article focuses on addressing FL challenges in smart cities using the space-air–ground information network. Here, data distribution heterogeneity leads to increased federated training time and higher energy costs. This article begins by analyzing the reasons for the nonindependent and nonidentically distributed (Non-IID) data collected by devices in this scenario. Subsequently, from the perspective of device selection, this article proposes a node selection model based on near-edge strategy optimization, termed “low node selection in FL” (LCNSFL). Finally, the LCNSFL algorithm is compared with federated averaging algorithms based on random selection strategies and the FedProx algorithm. Experimental results demonstrate that the FL model aided by the LCNSFL algorithm achieves the target accuracy with fewer communication rounds, considerably reducing the required training time and energy costs compared to the other two algorithms.

The space–air–ground information network (SAGIN) has been widely used due to its excellent performances including wide coverage and high flexibility. However, the dynamic network topology of SAGIN presents challenges for traditional protocols. The statistical priority-based multiple access (SPMA) control protocol has received widespread attention because it effectively allocates resources in networks with heterogeneous terminals and dynamic topology. However, the existing SPMA protocols suffer from issues like large errors and low prediction accuracy in channel load statistics. Therefore, this paper proposes an improved SPMA based on the bi-directional long short-term memory (BiLSTM) neural network. First, we analyze and correct errors in channel load statistics at the physical layer, then develop a BiLSTM-based channel load prediction model, and finally simulated the improved SPMA using Matlab. Experimental results show that the proposed channel load prediction model achieves good prediction accuracy, and the improved SPMA protocol markedly improves channel utilization, providing differentiated services for multi-priority businesses.

Direction of arrival (DOA) estimation for conformal arrays is challenging due to non-omnidirectional element patterns and shadow effects. Conical conformal array (CCA) can avoid the shadow effect at small elevation angles. So CCA is suitable for DOA estimation on both azimuth and elevation angles at small elevation angles. However, the element pattern in CCA cannot be obtained by conventional directional element coordinate transformation. Its local element pattern also has connection with the cone angle. The paper establishes the CCA radiation pattern in local coordinate system using 2-D coordinate transformation. In addition, in the case of large elevation angle, only half elements of the CCA can receive signal due to the shadow effect. The array degrees of freedom (DOF) are reduced by halves. We introduce the difference coarray method, which increases the DOF. Moreover, we propose a more accurate propagator method for 2-D cases. This method constructs a new propagation matrix and reduces the estimation error. In addition, this method reduces computational complexity by using linear computations instead of eigenvalue decomposition (EVD) and avoids spectral search. Simulation and experiment verify the estimation performance of the CCA. Both demonstrate the CCA model established in this paper is corresponding to the designed CCA antenna, and the proposed algorithms meet the needs of CCA angle detection. When the number of array elements is 12, the estimation accuracy is about 5 degrees.