The mobile edge computing (MEC) technology can simultaneously provide high-speed computing services for multiple vehicular users (VUs) in vehicular edge computing (VEC) networks. Nevertheless, due to the open feature of the wireless offloading channels and the high mobility of the vehicles, the security and stability of the offloading process would be seriously degraded. In this paper, by utilizing the physical layer security (PLS) technique and spectrum sharing architecture, we propose a deep reinforcement learning based joint secure offloading and resource allocation (SORA) scheme to improve the secrecy performance and resource efficiency of the multi-user VEC networks, where the VU offloading links share the frequency spectrum preoccupied with the vehicle-to-vehicle (V2V) communication links. We use Wyner’s wiretap coding scheme to obtain the achievable secrecy rate and guarantee that confidential information cannot be decoded by multiple mobile eavesdroppers. We aim at minimizing the system processing delay while securing the wireless offloading process, by jointly optimizing the transmit power, the frequency spectrum selection and the computation resource allocation. We formulate the optimization problem as a multi-agent collaborative optimal decision problem and solve it with a double deep Q-learning algorithm. Besides, we set a punishment mechanism for the rate degradation to guarantee the communication quality of each V2V link. Simulation results demonstrate that multiple VU agents adopting the SORA scheme can rapidly adapt to the highly dynamic VEC networks and cooperate to improve the system delay performance while increasing the secrecy probability.

The space-air-ground integrated network(SAGIN) is considered to be a significant frame-work for realizing the vision of “6 G intelligent connection of all things”. A typical SAGIN consists of three parts: a space-based network composed of various orbiting satellites, an air-based network composed of aircraft, and a traditional ground-based network. Considering the cost of satellite launch, the network needs to be flexible and controllable. In order to ensure that the ground can handle satellite anomalies in real time by program, it is necessary to introduce in-orbit programmable networks, such as software defined network(SDN). In the network management architecture, if the controller plane in SDN adopts flat management scheme, the expansion of the control plane is limited due to low efficiency of data synchronization among controllers. Compared to controller deployments on terrestrial networks, multi-controller deployments in SAGIN face the following problems: the dynamic change of satellite network topology, the large-scale network nodes, the increase or decrease of aerial vehicles, and the unbalanced distribution of ground users. Therefore, it is of great significance to study how to optimize the deployment of multiple controllers in the SDN-enabled SAGIN. This paper introduces SDN into the SAGIN and designs hierarchical domain-based SDN-enabled SAGIN architecture. And a multi-controller deployment strategy for hierarchical domain-based SDN-enabled SAGIN was proposed. Firstly, divide the SDN control plane into two layers, the primary controller layer was deployed on the ground network, and the secondary on the space-based network. The SDN data plane was composed of space-based, air-based, and ground-based networks. Secondly, considering the average network delay and the controller load, a multi-objective optimization model was constructed. To determine the number of controllers and the relative positions of switch nodes and controllers, the clustering algorithm based on k-mean was adopted to initially divide the data plane. Finally, to improve the global search ability of the algorithm, a multi-objective optimization algorithm based on a genetic algorithm (GA) is adopted. The simulation results show that the proposed strategy is effective in reducing the average network delay and improving the controller load balance. Compared with other algorithms, the average network delay is reduced by 13.3% and the controller load is improved by 10.33%.

Mobile edge computing (MEC) offers promising solutions for various delay-sensitive vehicular applications by providing high-speed computing services for a large number of user vehicles simultaneously. In this paper, we investigate non-orthogonal multiple access (NOMA) assisted secure offloading for vehicular edge computing (VEC) networks in the presence of multiple malicious eavesdropper vehicles. To secure the wireless offloading from the user vehicles to the MEC server at the base station, the physical layer security (PLS) technology is leveraged, where a group of jammer vehicles is scheduled to form a NOMA cluster with each user vehicle for providing jamming signals to the eavesdropper vehicles while not interfering with the legitimate offloading of the user vehicle. We formulate a joint optimization of the transmit power, the computation resource allocation and the selection of jammer vehicles in each NOMA cluster, with the objective of minimizing the system energy consumption while subjecting to the computation delay constraint. Due to the dynamic characteristics of the wireless fading channel and the high mobility of the vehicles, the joint optimization is formulated as a Markov decision process (MDP). Therefore, we propose an asynchronous advantage actor-critic (A3C) learning algorithm-based energy-efficiency secure offloading (EESO) scheme to solve the MDP problem. Simulation results demonstrate that the agent adopting the A3C-based EESO scheme can rapidly adapt to the highly dynamic VEC networks and improve the system energy efficiency on the premise of ensuring offloading information security and low computation delay.

Exploiting millimeter wave is an effective way to meet the data traffic demand in the 5G wireless communication system. In this paper, we study secure transmissions under slow fading channels with multipath propagation in millimeter wave systems. Concerning the new propagation characteristics of millimeter wave, we investigate three transmission schemes, namely, maximum ratio transmitting (MRT) beamforming, artificial noise (AN) beamforming, and partial MRT (PMRT) beamforming. We evaluate the secrecy performance by analyzing both the secrecy outage probability (SOP) and the secrecy throughput for each scheme. Particularly, for the AN scheme, we derive a closed-form expression for the optimal power allocation ratio of the information signal power to the total transmit power that minimizes the SOP, as well as obtain an explicit solution on the optimal transmission parameters that maximize the secrecy throughput. By comparing the secrecy performances achieved by different strategies, we demonstrate that the secrecy performance of the millimeter wave system is significantly influenced by the relationship between the legitimate user's and the eavesdropper's spatially resolvable paths, which is different from the wireless systems with statistically independent channel models. In the absence of the common path between the legitimate user and the eavesdropper, MRT beamforming is the best scheme. In the presence of common paths, AN beamforming and PMRT beamforming show their respective superiorities depending on the transmit power and the number of common paths. Numerical results are provided to verify our theoretical analysis.

Millimeter wave offers a sensible solution to the capacity crunch faced by 5G wireless communications. This paper comprehensively studies physical layer security in a multi-input single-output (MISO) millimeter wave system where multiple single-antenna eavesdroppers are randomly located. Concerning the specific propagation characteristics of millimeter wave, we investigate two secure transmission schemes, namely maximum ratio transmitting (MRT) beamforming and artificial noise (AN) beamforming. Specifically, we first derive closed-form expressions of the connection probability for both schemes. We then analyze the secrecy outage probability (SOP) in both non-colluding eavesdroppers and colluding eavesdroppers scenarios. Also, we maximize the secrecy throughput under a SOP constraint, and obtain optimal transmission parameters, especially the power allocation between AN and the information signal for AN beamforming. Numerical results are provided to verify our theoretical analysis. We observe that the density of eavesdroppers, the spatially resolvable paths of the destination and eavesdroppers all contribute to the secrecy performance and the parameter design of millimeter wave systems.

In this paper, we investigate physical-layer security of the uplink millimeter wave communications for a cellular vehicle-to-everything (C-V2X) network comprised of a large number of base stations (BSs) and different categories of V2X nodes, including vehicles, pedestrians, and road side units. Considering the dynamic change and randomness of the topology of the C-V2X network, we model the roadways, the V2X nodes on each roadway, and the BSs by a Poisson line process, a 1D Poisson point process (PPP), and a 2D PPP, respectively. We propose two uplink association schemes for a typical vehicle, namely, the smallest-distance association (SDA) scheme and the largest-power association (LPA) scheme, and we establish a tractable analytical framework to comprehensively assess the security performance of the uplink transmission, by leveraging the stochastic geometry theory. Specifically, for each association scheme, we first obtain new expressions for the association probability of the typical vehicle, and then derive the overall connection outage probability and secrecy outage probability by calculating the Laplace transform of the aggregate interference power. Numerical results are presented to validate our theoretical analysis, and we also provide interesting insights into how the security performance is influenced by various system parameters, including the densities of V2X nodes and BSs. Moreover, we show that the LPA scheme outperforms the SDA scheme in terms of secrecy throughput.