With the growing reliance on Global Navigation Satellite System (GNSS) for navigation and positioning in autonomous systems, the vulnerability to spoofing attacks has emerged as a critical concern. The increasing deployment of dual-frequency GNSS receivers in vehicular applications has drawn attention to their potential for efficient spoofing detection. Dual-Frequency Doppler Consistency Checking (DFDCC) offers an intuitive approach by assessing the Doppler consistency between two frequency signals. This method not only enables the identification of spoofed signals but also exhibits robustness and flexibility in vehicular scenarios. Despite its practical advantages, DFDCC currently lacks a comprehensive theoretical framework and detailed performance analysis, particularly in the context of vehicular applications. To bridge these gaps, this study aims to develop a theoretical foundation and evaluate the real-world applicability and effectiveness of DFDCC in vehicular scenarios. The paper begins by deriving a Doppler observation model for both authentic and spoofing signals, deriving key sources of Doppler bias. These include the deviation between false and real user states, the spoofer clock bias, and the relative velocity between the spoofer and the user. Besides, extensive simulation experiments are conducted to analyze the influence of these factors on DFDCC performance, revealing an offset phenomenon when multiple factors coexist. This method is further validated through field tests under three typical vehicular motion patterns, demonstrating its feasibility across diverse vehicular scenarios. The results confirm that DFDCC is a viable solution for spoofing detection for autonomous vehicles, offering valuable insights for its practical implementation and serving as a reference for future research.

This paper considers the spectrum resource and computing resource allocation in an mobile edge computing (MEC) network. The problem is formulated as an optimization problem with an objective to maximize the average successful processing ratio of users’ service requests while satisfying users’ service delay requirements. To solve the formulated problem, a federated learning (FL) and deep-Q-network (DQN) based cooperative resource allocation (CoRA) algorithm is proposed to adaptively select optimal offloading nodes and allocate spectrum and computing resources for users’ service requests by training DQNs in a master node and slave nodes in the form of federated learning. Simulation results show that the proposed CoRA algorithm outperforms two benchmark algorithms in terms of the average successful processing ratio.

This paper studies the joint spectrum and computing resource allocation problem in a mobile edge computing (MEC) network, where mobile users can offload computing tasks to an edge node for processing. We formulate the joint resource allocation problem as an optimization problem with an objective to maximize the system throughput while satisfying the service delay requirements of as many service requests as possible. A novel DQN-based resource allocation algorithm is proposed to solve the formulated problem. The proposed algorithm consists of a resource pre-allocation process and a resource allocation process. The former performs joint spectrum and computing resource allocation for each service request based on a deep-Q-network (DQN) model, which attempts to make an optimal decision on resource allocation for each service request by learning online to better adapt to dynamic traffic arrivals and diverse service requirements. The latter performs resource allocation for each service request according to a resource pre-allocation decision and service requirements. Simulation results show that the proposed DQN-based resource allocation algorithm outperforms three benchmark algorithms in terms of system throughput.

This paper considers the network resource allocation problem in multi-service MEC networks, and in particular considers joint spectrum and computing resource allocation in an edge network service system. The resource allocation problem under consideration is formulated as an optimization problem, which takes into account users’ minimum service requirements in terms of the service delay, transmission rate, and computation rate, with an objective to minimize the average service delay of a user’s service request. To solve the problem, we propose a deep-Q-network (DQN) based joint spectrum and computing resource allocation algorithm, which attempts to optimize the resource allocation for each service request by learning a more efficient allocation scheme so as to better adapt to dynamic traffic arrivals, diverse service requirements, and complex environmental conditions. Simulation results show that the proposed resource allocation algorithm can efficiently reduce the average service delay compared with two benchmark algorithms.

This paper studies the power allocation problem in device-to-device (D2D) communications underlaying cellular networks. A Q-learning based distributed power allocation (Q-PA) algorithm is first proposed, which attempts to obtain optimal power allocation through iterative updating of Q-value tables. Based on the Q-PA algorithm, a neural network (NN) based power allocation (N-PA) algorithm is further proposed, in which training data obtained using the Q-PA algorithm are used to train an NN model, and the trained NN model is used to perform power allocation for D2D users. Simulation results show that both the Q-PA algorithm and the N-PA algorithm can improve the system throughput as compared with two traditional algorithms. The N-PA algorithm can significantly reduce the time cost for power allocation at a small expense of the system throughput as compared to the Q-PA algorithm.