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.

Satellite communication (SatCom) has attracted much attention due to its inherent characteristics. Security issues have gained severe concerns in SatCom since it is susceptible to be illegally eavesdropped by malicious ground stations within large-scale wireless coverage. In this paper, we investigate the physical layer security of a multiuser SatCom system in the presence of multiple eavesdroppers. Particularly, we propose a threshold-based scheduling scheme, where the geographically clustered eavesdroppers with both the colluded and collaborated eavesdropping scenarios are assumed. Specifically, closed-form expression for the secrecy outage probability (SOP) is derived for the passive eavesdropping scenario when the channel state information (CSI) of the eavesdroppers is unavailable. Moreover, we obtain a closed-form expression for the average secrecy capacity (ASC) of the considered system under the proposed user scheduling scheme. In order to get further insights of the proposed scheduling scheme at high signal-to-noise ratios (SNRs), the asymptotic analysis for the SOP and ASC is also demonstrated. Moreover, the reduced percentage with respect to number of user examination is also given, which validates the simplicity and efficiency of our proposed scheme compared to the traditional approaches. Numerical results deduce that with the proposed scheme, a comparable system performance with regard to the maximal selection (MS) scheme can be achieved.

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.

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.

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.

Two-dimensional direction of arrival (2D-DOA) estimation, or estimating the azimuth and elevation angles of sources simultaneously, is an increasingly important area in array signal processing. This paper presents a 2D-DOA algorithm together with a novel structure of array named Arc-Array (ArcA). The ArcA is enlarged to a virtual Uniform Circular Array (UCA) through a Deep-Neural-Network (DNN) based covariance matrix completion strategy; afterward, the MUSIC algorithm is performed with the completed covariance matrix. The proposed method is named as ArcA-DNN, and the performance of ArcA-DNN is evaluated by computer simulations. The simulation results indicate that the performance of 2D-DOA estimation in ArcA is able to approach that of a complete UCA; meanwhile, the number of physical elements is substantially reduced compared to the UCA. Moreover, the proposed ArcA-DNN algorithm gives access to implementing underdetermined 2D-DOA estimation with reasonable results.