In emitter localization, the synthetic aperture positioning technique can achieve high-precision positioning even at a low signal-to-noise ratio (SNR). However, existing methods overlook the impact of receiver motion errors on the phase history of the received signal, leading to a reduction in localization accuracy. In this study, we propose a motion compensation (MoCo) technique for synthetic aperture passive localization using weather radar signals. Weather radar stations are chosen as reference stations due to their widespread coverage, high transmission power, and continuous signal transmission. The proposed method involves estimating and compensating for phase errors in the received signal, enabling phase coherency accumulation and achieving high-precision emitter localization. First, pulse compression is applied to the received weather radar signals to extract the phase information containing motion error details. Subsequently, we estimate motion errors using the extracted radar signal phase and apply phase compensation to the emitter target signal. Finally, the existing synthetic aperture positioning method is used to estimate the position of the target. Simulation results demonstrate that the method proposed in this paper offers superior accuracy in emitter localization compared to relying solely on real-time kinematic (RTK) for MoCo. The effectiveness of our proposed method is validated through actual unmanned aerial vehicle (UAV) experiments.

In the non-cooperative electromagnetic environment, new radar emitters will emerge unexpectedly during the test phase, which brings the “Open-Set” Radar Emitter Recognition (OS-RER). Conventional classifiers cannot identify new radar emitters that do not exist in the training dataset. Therefore, in this paper, a novel Deep Metric Auto-Encoder (DMAE) is proposed for OS-RER. In DMAE, deep metric learning learns new non-linear mappings in the metric space to measure the similarity between instances. The dual-path deep auto-encoder is designed to reduce the open space risk by learning a low-dimensional manifold and a discriminative representation of known instances. Specifically, DMAE models known classes, and measures class belongingness through the reconstruction error of the AE and the entropy of the classifier. The deep metric network learns a more precise distance metric by minimizing the distance between the known class instances and the corresponding reconstruction. To accurately detect unknown instances, the classifier and the deep metric network are used together to preliminarily detect unknown instances. Finally, the detected unknown instances are used to further train the classifier to recognize the radar emitter in the open-set scenarios. The DMAE learns the discriminative representation through end-to-end learning. Extensive experiments conducted on real radar datasets and simulated radar datasets show that DMAE can identify unknown emitters and significantly outperforms existing open-set classification methods.

In emitter localization, the synthetic aperture positioning (SAP) technique can achieve high-precision positioning even at a low signal-to-noise ratio (SNR). However, existing methods require 2-D search implementation, which produces a huge amount of computation. In this study, the synthetic aperture map drift (MD) positioning method aided by an interferometer is proposed. This method reduces the amount of computation while achieving high-precision positioning. First, the exact azimuth position and approximate range position can be determined by a double-interference antenna via least-squares estimation. Doppler history is then used to correct range positioning error via an MD algorithm, which avoids the search of range and reduces the computational complexity. Simulation results show that the positioning accuracy of this method is close to the Cramer–Rao lower bound (CRLB). The effectiveness of the proposed method is verified through actual unmanned aerial vehicle (UAV) experiments.

Due to the high mobility of vehicles, service migration is inevitable in vehicular edge computing (VEC) networks. Frequent service migrations incur prohibitive migration cost including the computing cost (e.g., increased computing delay) and communication cost (e.g., occupied backhaul bandwidth). Yet existing service migration schemes are usually designed without considering the impact of the computing cost. This paper considers the impact of computing and communication cost jointly, and proposes a computing and communication cost-aware service migration scheme for VEC networks (i.e., CA-migration). Taking the service delay as a QoS metric for VEC networks, this paper formulates a migration optimization problem aiming to maximize the services' satisfaction degree of delay (i.e., the probability that the service delay is smaller than the service delay requirement), where both the communication cost and computing cost affect the services' satisfaction degree. Since the optimization problem is a constrained non-linear integer programming problem, it is difficult to solve. Moreover, the VEC networks are highly dynamic. Thus, a fast transfer reinforcement learning (fast-TRL) method combining transfer learning and reinforcement learning is proposed to provide an adaptive service migration scheme in dynamic VEC networks. Simulation results show that compared with existing schemes, the proposed CA-migration scheme can increase the satisfaction degree by up to 30%, and needs 25% less training time to obtain the optimal service migration policy.

Federated learning (FL) over mobile networks has attracted intensive attention recently. User mobility is a fundamental feature of mobile networks, which leads to dynamic network topology and wireless connectivity losses. As such, user mobility is usually considered a “trouble maker” and a great challenge to FL over mobile networks. Interestingly, we found that small user mobility can positively contribute to improving FL performance. This is because the total dataset size and the data diversity that the FL can utilize are increased by user mobility. Based on this observation, we aim to tame and exploit mobility instead of treating it as a hostile “trouble maker”. To this end, we first investigate how the FL performance changes with user mobility theoretically by jointly taking into account the positive and negative aspects of mobility. Specifically, a closed-form expression to quantify the impact of mobility on the FL loss is derived, which explains when negative or positive aspects of mobility dominate the FL performance. Next, a joint FL and communication optimization problem is formulated based on theoretical analyses to minimize the FL loss function by optimizing wireless resource allocation. Finally, we propose a two-step optimization algorithm to solve the formulated problem. The simulation results verify the theoretical analyses. It is also shown that the proposed method can significantly enhance learning performance considering users with high mobility. When the average velocity is larger than 150 km/h, the proposed method achieves more than 80% accuracy in the MNIST dataset, while the existing methods may fail during training.

Deploying 1-bit analog-to-digital converters (ADCs) in massive multiple-input multiple-output (MIMO) systems is promising to reduce the energy consumption. However, serious quantization errors limit the feasibility of high order quadrature amplitude modulation (QAM). This paper focuses on the performance analysis of 1-bit ADC massive MIMO systems with high order QAM. Firstly, we theoretically analyze the relationship between the quantization error and various quantization parameters. We reveal that there exists an overlapping of constellations at high signal to noise ratio (SNR) with symmetric quantization. This is because the amplitude information in the detected signal at high SNR is lost which is multiplied by the zero quantization threshold in the symmetric quantization. To overcome this problem, asymmetric quantization should be considered. Based on the analysis of the quantization error, we propose two approaches to recover the amplitude information. One is a near unbiased detection (NUD) by scaling the traditional linear detector and optimizing the quantization threshold. The other is a sub-array detection based on non-uniform quantization (SAD-NQ) to correct the deviation of the detection result by averaging over sub-arrays. Simulation results show that the proposed approaches can significantly improve the detection performance of high order QAM signal in the 1-bit ADC massive MIMO systems.

Emitter localization has been an active research subject in electronic reconnaissance, target tracking, emergency response, and satellite interference source localization. Recently, researchers applied passive synthetic aperture in the emitter localization to improve the positioning accuracy. Passive synthetic aperture uses Doppler rate and zero Doppler point to target range and azimuth locations, respectively. In spaceborne model, effective satellite velocity is used in the range equation to fit the passive synthetic aperture model. Therefore, this study proposed a convex optimization approach to calculate the effective satellite velocity. We established an objective function by calculating the relationship among beam footprint velocity, satellite velocity, and effective satellite velocity. The optimal effective satellite velocity and range distance are obtained through iterating different range distances. Finally, the experimental results show that the positioning accuracy of proposed method is one order of magnitude higher than that of traditional FOA and FDOA method.

In highly dynamic LEO satellite communication systems, the grade of service (QoS) is severely degraded by the frequent handover. Existing handover schemes focusing on the overall system QoS cannot ensure single user’s QoS, which is not desirable for important users, such as emergency communication users. Exploiting the fact that the movement of LEO satellites are periodic and predictable, this paper proposes a channel reservation based load aware (CRLA) handover algorithm. On one hand, channel can be reserved for important users to ensure their QoS. On the other hand, the load status of each satellite is considered in CRLA handover of the whole system. Simulation result shows that CRLA can reduce handover failure rates while ensure the load balance of the system. Comparing to existing schemes, the CRLA handover algorithm can reduce the handover failure rate by 20% and improve the QoS by 16%.

This paper considered a data rate maximization problem of relay satellite network and aimed to overcome the difficulty of direct transmission between the satellites and ground station. The proposed optimization problem was first formulated as a mixed integer non-convex programming which is difficult to solve. Then, to design an efficient solving method, the proposed optimization problem were approximately decoupled into two subproblems including relay selection problem and power allocation problem. Through a node virtualization method, the relay selection problem was converted to a maximum weighted matching problem. However, the power allocation problem was still a non-convex problem but could be approximated as convex problem by using difference of convex (DC) programming. Moreover, an iterative algorithm was designed to acquire the optimal solution of approximated convex problem based on the Lagrangian dual method. Finally, simulation results are provided to demonstrate the superiority of the algorithm.