Edge computing has become indispensable in the context of wireless network communication, particularly in scenarios with strained computing resource such as remote areas or areas where ground communication disruptions are prevalent. It significantly enhances the service quality of ground terminal devices. This paper proposes a Space-Air-Ground Integrated Network(SAGIN) edge computing model. However, the limited computational resources pose challenges to crucial aspects such as edge server deployment, edge server computing resource allocation, and offloading strategies in such scenarios, becoming pivotal in boosting network service capabilities. To address this, we explore different deep reinforcement learning algorithms to train the model towards optimizing system objectives. Among them, the Soft-Actor-Critic (SAC) deep reinforcement learning algorithm demonstrates superior applicability to such model problems, improving training effectiveness by approximately 18% compared to the DDPG algorithm (calculated using average reward value). The edge computing offloading technique also serves as a crucial foundation for future edge computing cross-domain interconnections and distributed task collaboration.
Encryption algorithm is the basis for securing satellites, achieving reliable user access and secure data transmission. However, since the electromagnetic information generated by satellite encryption operation has certain regularities, the risk of leaking plaintext is high when the side channel is attacked, especially using a single algorithm. To solve this problem, this paper analyzes encryption algorithms with different types, summarizes four coarse-grained operators, and proposes a coarse-grained reconfigurable encryption module (CGREM) for satellites based on FPGA. Based on the coarse-grained reconfigurable architecture (CGRA), we implement a reconfigurable configuration subsystem and a computing array on a 0.15 μm CMOS FPGA. The result of the performance test show that our module can reconstruct encryption algorithms in 1.76 us. Compared with the previous method, our design is three orders of magnitude faster and more efficient. Our method can obtain the resource consumption rate of LUT and FF less than 60%, which means it can achieve a derating effect on satellite components.
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