This article considers a low Earth orbit (LEO) satellite communication system integrated with 5G new radio, which performs conventionally initial access beam management through multiple measurements and reports. However, such management scheme may lead to outdated measurement results and large terminal access delay, in the presence of significant propagation losses, high dynamics, and limited number of antennas on LEO satellites. Addressing these issues, we propose an initial access beam management framework based on the dynamic coordination of signaling and service beams. This framework includes the optimization of the beam alignment process and the analysis of the coordination mechanism of signaling and service beams with a queueing model. Specifically, this queueing model is further used to assist in modeling an optimization problem with respect to the number and direction of signaling and service beams, to achieve the dynamic balance between coverage capability and service efficiency. Furthermore, a Lyapunov-based initial access beam management (LYP-IABM) algorithm is specially designed to find the optimized solution of the modeled problem in a low complexity and online manner. The mathematical derivation is also provided to prove the correctness of the designed algorithm. Simulation results show that, the proposed online LYP-IABM algorithm not only outperforms the traditional offline algorithms, but also demonstrates the superiority comparing with the online benchmarking algorithms, in terms of reducing the time complexity by more than 90%.

NB-IoT是由3GPP标准化组织定义的一种LPWAN技术标准,广泛应用于地面物联网系统中。但地面物联网在沙漠、海洋、偏远地区由于建设维护基站成本高,展现出需求和提供的服务不相匹配的特征。本文对地面物联网NB-IoT体制在低轨卫星物联网系统中适应性做了一定的分析,重点研究了NB-IoT空口信号在低轨卫星系统中的峰均比、多普勒频域以及循环前缀长度对于系统性能的影响。仿真结果表明地面物联网NB-IoT空口信号需要对发射信号峰均比、高动态环境下抗多普勒性能以及高时延下循环前缀过长做一定的适应性改造才能应用于低轨卫星物联网系统。

本文在深度学习方法应用下提出一种雷达干扰信号分类以及抑制方法。首先在卷积神经网络应用下实现对雷达干扰信号的自动分类，分别为卷积层、池化层以及全连接层，训练完成后得到分类模型。基于此实现关于去干扰网络的设计，进而在重构损失函数应用下去干扰信号。完成设计后，进行实验研究，共选取4类干扰信号以及雷达信号，结果显示本文方法在雷达信号分类中的准确率可以达到96.3%,同时能够保留有效信号，信噪比可以提升到5 dB以上，降噪后数据准确率达到98.2%,可见这一方法不但能够实现雷达干扰信号的自动分类，同时也能够产生有效抑制作用，可以取得良好的降噪效果，更有助于实施雷达信号处理。

We explore a satellite internet of things (SIoT) system, wherein multiple IoT users are allowed to access the SIoT in a grant-free manner. Such a manner may incur the severe co-frequency interference (CFI), and decease the user capacity in terms of the number IoT users successfully accessing the SIoT. To this end, we first design two beam preprocessing oriented user grouping methods, called user distribution based user grouping (UDUG) and user position based user grouping (UPUG), respectively. With respect to the UDUG, we propose the regional statistical channel state information based multi-beam processing (RsCSI-MBP). For the UPUG, we conceive the user statistical channel state information with position error based MBP (UsCSIPe-MBP). Both the RsCSI-MBP and the UsCSIPe-MBP schemes can mitigate inter-beam CFI relying on MBP, wherein the optimized beamforming vectors are obtained by using generalized Rayleigh quotient. Furthermore, the user capacity, preamble collision probability and packet loss probability are analyzed for the RsCSI-MBP and UsCSIPe-MBP schemes. Numerical results demonstrate that the UsCSIPe-MBP scheme outperforms the RsCSI-MBP and the conventional seven-color frequency-reuse multibeam schemes in terms of a higher user capacity and a lower packet loss probability, even considering the satellite mobility, phase and amplitude inconsistence of the antenna array used.

单星多波束干扰源定位方法需要在3个以上的同频波束都能接收到干扰源信号的情况下才能实现定位。由于同频波束的数量有限以及干扰源位置的随机性，往往会出现能接收到干扰源信号的同频波束少于3个，或接收增益过低影响定位精度的情况。【目的】针对只有两个可用波束的定位问题以及进一步提高干扰源的定位精度，提出了通过邻星与主星建立基于多波束天线与到达时间差（TDOA）技术的双星联合定位模型。【方法】通过建立双星联合定位模型，并联立出定位方程组证明了该模型的可行性。同时，基于定位几何稀疏精度因子（GDOP）对定位模型的接收增益误差、波束指向误差、到达时间差误差以及位置预测误差4个方面进行了定位误差分析，并使用Matlab进行仿真对比。【结果】仿真实验表明，基于多波束天线与TDOA技术的双星联合定位方法的定位精度明显优于传统的单星多波束定位方法。【结论】基于多波束天线与TDOA技术的双星联合定位方法利用了TDOA对定位精度影响较小的优势，使其代替部分波束参与到卫星定位中，不仅减少了对定位波束数量的需求，而且减少了测量增益的误差与波束指向的误差，大大提高了定位的精度并提升了可靠性，较单星多波束定位更具优势。

针对低成本和小型化低轨卫星的监测角度和方向分辨率相对较低、处理能力和峰值功率受限等因素造成单颗低轨卫星频谱认知能力弱的问题，提出了融合Stackelberg博弈和联邦学习的多星协作频谱认知方法。首先，结合各频谱认知卫星的可用算力、认知性能、处理与传输时延等特性，建立面向多频谱认知任务的协作卫星选择与算力资源分配算法；其次，基于所选择的节点和所分配的算力，设计低复杂度的多星协作频谱认知策略，其可自动辨识频谱空洞、检测干扰和识别调制模式。仿真实验结果表明，相比于单节点认知方法，所设计多星协作认知方法能显著提升认知性能，且相比于对比模型，所设计策略中的模型可在不损失性能时，模型的参数数量和浮点运算量降低分别可达96.69%和93.32%。

To solve the problem of the weak spectrum-cognitive ability caused by monitoring angle, direction resolution, limited processing ability and peak power for a low-earth-orbit (LEO) satellite, a multi-satellite cooperative spectrum cognitive method integrating Stackelberg game and federated learning was proposed. Firstly, considering the available computing resource, cognitive performance, processing and transmission delay of each spectrum cognitive satellite, a cooperative-satellite selection and computing-resource allocation algorithm was built for multiple spectrum-cognitive tasks. Secondly, based on the selected satellites and the allocated computing resources, a low-complexity multi-satellite cooperative spectrum cognitive strategy was further designed, which could automatically sense the spectrum holes, and detect interference as well as identify the modulation mode. Simulation results demonstrate that compared to the single-node cognitive method, the designed multi-satellite cooperative spectrum cognitive strategy can obtain a better cognitive performance. Moreover, comparing with the existing model, the model utilized in the designed strategy can effectively achieve 96.69% and 93.32% lower number of parameters and required floating point operations per second, whilst maintaining the performance. © 2024 Editorial Board of Journal on Communications. All rights reserved.

The main geolocation technology currently used in COSPAS-SARSAT system is TDOA/FDOA or three-star TDOA, the principle is to determine the location of the signal source by using the difference in arrival time and frequency of the wireless signal between different receivers. Therefore, ground monitoring stations need to be equipped with more than two antenna receiving stations, and multiple satellites should be able to simultaneously relay the distress signal from the target source in order to achieve the geolocation function. However, when the ground receiving system has only one antenna receiving station, or the target source is in a heavily obscured environment, the ground side is unable to receive the forwarded signals from multiple satellites at the same time, which will make it impossible to locate. To address these problems, in this paper, a time-sharing single satellite geolocations method based on different orbits is proposed for the first time. This method uses one or several low-earth orbit satellites (LEO) and medium-earth orbit satellites (MEO) in the visible area, and the receiving station only needs one pair of receiving antennas to complete the positioning. It can effectively compensate for the shortcomings of the traditional TDOA using the same moment and have better positioning accuracy compared with the single satellite in the same orbit. Due to the limited experimental conditions, this paper tests the navigation satellite using different orbit time-sharing single satellite geolocations, and proves that the positioning method has high positioning accuracy and has certain promotion and application value.

The main geolocation technology currently used in COSPAS-SARSAT system is TDOA/FDOA or three-star TDOA, the principle is to determine the location of the signal source by using the difference in arrival time and frequency of the wireless signal between different receivers. Therefore, ground monitoring stations need to be equipped with more than two antenna receiving stations, and multiple satellites should be able to simultaneously relay the distress signal from the target source in order to achieve the geolocation function. However, when the ground receiving system has only one antenna receiving station, or the target source is in a heavily obscured environment, the ground side is unable to receive the forwarded signals from multiple satellites at the same time, which will make it impossible to locate. To address these problems, in this paper, a time-sharing single satellite geolocations method based on different orbits is proposed for the first time. This method uses one or several low-earth orbit satellites (LEO) and medium-earth orbit satellites (MEO) in the visible area, and the receiving station only needs one pair of receiving antennas to complete the positioning. It can effectively compensate for the shortcomings of the traditional TDOA using the same moment and have better positioning accuracy compared with the single satellite in the same orbit. Due to the limited experimental conditions, this paper tests the navigation satellite using different orbit time-sharing single satellite geolocations, and proves that the positioning method has high positioning accuracy and has certain promotion and application value.

针对传统卫星重叠通信中单个掩护信号带宽以及功率容限不够的问题，利用卫星转发器频谱环境中多个掩护信号提出了一种频域分割-子谱功率控制联合优化的多掩护信号重叠通信方法，建立了隐蔽通信信号传输性能和隐蔽性能的双目标优化问题，信关站侧采用感知的历史频谱数据训练生成支持向量机回归预测模型，用来预测不同转发器频谱环境下隐蔽信号的通信性能和隐蔽性能，并将训练好的预测模型下载到通信终端；终端侧利用双目标背包算法将支持向量机回归预测模型预测的隐蔽信号的通信性能和隐蔽性能作为价值因素、掩护信号个数作为背包重量来选择转发器频谱环境中的掩护信号，并且求解出隐蔽信号的频域分割和子频谱的功率控制参数，从而实现终端通信信号隐藏在卫星转发器的频谱环境中的目的。