Adversarial examples can evaluate the robustness and safety of deep neural networks effectively. Aiming at the problem of low success rate of adversarial attacks in black-box scenarios and to improve the transferability of adversarial examples， a Multi-space Probability Enhancement Adversarial example generation Method （MPEAM） was proposed. The transferability of the adversarial examples was improved by the proposed method through introduction of two pieces of random data enhancement branches in the adversarial example generation method. In this process， random image Cropping and Padding （CP） based on the pixel space， as well as random Color Changing （CC） based on HSV color space， were implemented， respectively， by each branch. At the same time， the returned image examples were controlled by constructing a probability model， which increased the diversity of the original examples while decreasing the dependence of the adversarial examples on the original dataset， thereby enhancing the transferability of adversarial examples. On this basis， the proposed method was introduced into the integration model to further improve the success rate of the adversarial example attack in black-box scenarios. After extensive experiments on ImageNet dataset， the experimental results show that the proposed method improves the black-box attack success rate by 28.72 and 8.44 percentage points， averagely and respectively， compared to the benchmark methods Iterative Fast Gradient Sign Method （IFGSM） and Momentum Iterative Fast Gradient Sign Method （MIFGSM）， and improves the black-box attack success rate by up to 6.81 percentage points compared to the attack methods based on single-space probability enhancement. The above indicates that the proposed method can improve the transferability of adversarial examples at a small cost of complexity and achieve effective attacks in black-box scenarios.

In response to the resource allocation problem in multi-beam scenarios of Low Earth Orbit （LEO） satellite， as the factors such as interference and noise between wave beams in actual satellite communication environments are complex and variable， conventional subcarrier dynamic allocation algorithms cannot adjust parameters dynamically to adapt to changes in the communication environment. By combining traditional communication scheduling algorithms with reinforcement learning techniques， with the goal of minimizing user packet loss rate， user’s scheduling situations were adjusted dynamically and resources of the entire satellite communication system were allocated dynamically to adapt to environmental changes. The dynamic characteristic model of LEO satellite was discretized by time slot division， and a Deep Reinforcement Learning （DRL）-based resource allocation strategy was proposed on the basis of the modeling of LEO satellite resource allocation scenarios. In this strategy， the scheduling opportunities for users with high latency were increased by adjusting the satellite scheduling queue situation， that is， adjusting the resource blocks in each beam of a single LEO satellite to correspond to qualifications of users， thereby ensuring a certain level of fairness and reducing the user packet loss rate at the same time. Simulation results show that under the condition meeting total power constraints， the user transmission fairness and system throughput are stable in the proposed Deep Reinforcement Learning based Resource Allocation algorithm （DRL-RA）， and users with large latency obtain more scheduling opportunities in DRL-RA due to priority improvement. Compared with Proportional Fairness （PF） algorithm and Maximum Carrier/Interference （Max C/I） algorithm， DRL-RA has the data packet loss rate reduced by 13.9% and 15.6% respectively. It can be seen that the proposed algorithm solves the problem of packet loss effectively during data transmission.

Orthogonal Time Sequency Multiplexing （OTSM） achieves transmission performance similar to Orthogonal Time Frequency Space （OTFS） modulation with lower complexity， providing a promising solution for future high-speed mobile communication systems that require low complexity transceivers. To address the issue of insufficient efficiency in existing time-domain based Gauss-Seidel （GS） iterative equalization， a secondary signal detection algorithm was proposed. First， Linear Minimum Mean Square Error （LMMSE） detection with low complexity was performed in the time domain， and then Successive Over Relaxation （SOR） iterative algorithm was used to further eliminate residual symbol interference. To further optimize convergence efficiency and detection performance， the SOR algorithm was linearly optimized to obtain an Improved SOR （ISOR） algorithm. The simulation experimental results show that compared with SOR algorithm， ISOR algorithm improves detection performance and accelerates convergence while increasing lower complexity. Compared with GS iterative algorithm， ISOR algorithm has a gain of 1.61 dB when using 16 QAM modulation with a bit error rate of {\mathrm{10}}^{-\mathrm{4}} .