# DNNs Based Computation Offloading for LEO Satellite Edge Computing
## 2 Related Works
In deep learning, a deep neural network (DNN) is a very classical and effective network structure [28]. The biggest difference between it and other neural network structures is that its layers are fully connected. For the problem of the shortage of edge computing resource, [PERSON] et al. [29] propose an algorithm based on adaptive DNN partition, which has obtained the best overall cost. In order to solve the problem of limited resource in the servers in the edge computing system, [PERSON] et al. [30] jointly optimize the problem of DNN partition and resource allocation, and obtain ideal efficiency through iterator alternating optimization algorithm. In order to solve the problem of offloading decisions and bandwidth allocation in the edge computing system, [PERSON] et al. [31] propose a distributed DNN offloading algorithm (DDOA) algorithm to generate offloading decisions through multiple parallel DNNs and jointly optimize the weighted sum of delay and energy consumption. To solve the bandwidth allocation problem, the DDOA algorithm uses the orthogonal frequency division multiple access (OFDMA) technique to divide the bandwidth, and the simulation results show the superiority of the performance. The DDOA algorithm is one of the comparative algorithms in our paper. The differences between the algorithm proposed in this paper and the DDOA algorithm are as follows:  
The DDOA algorithm considers the mobile fog computing scenario in the ground network, while this paper considers placing MEC server on LEO satellite to provide computing services for users anywhere on the ground. Although the channel gain is mentioned in the DDOA algorithm, the specific channel condition is not given. In this paper, the free space loss in the satellite-earth link is considered. In the DDOA algorithm, each user's tasks are executed sequentially, so the total delay of each user's task processing in the DDOA algorithm is the sum of the processing delay of all tasks. However, in this paper, we consider that the offloading decisions for each user's tasks are executed in parallel. While the tasks are processed locally, other tasks can also be offloaded to LEO satellite for processing. Therefore, the total delay of each user's task processing in this paper is the maximum value of each user's total local processing delay and each user's total edge processing delay. In the DDOA algorithm, bandwidth allocation is to use the orthogonal frequency division multiple access (OFDMA) technique to divide bandwidth, and the bandwidth allocated to each user is proportional to the size of the offloading tasks. In this paper, we consider the size of the tasks, the generated offloading decisions, the channel gain, the transmit power and the noise power, and derive the optimal bandwidth allocation scheme using the Lagrangian multiplier method. In order to compare the pros and cons of the related works and compare them with the current work, we summarize the related works in Table 1.  
Compared with the above references, the proposed algorithm jointly optimizes the system delay and energy consumption, generates near-optimal offloading decisions through DNNs, derives the optimal bandwidth allocation scheme through Lagrangian multiplier method, and has stable convergence effect and low time cost. Compared with the comparative algorithms, the proposed algorithm obtains the optimal system utility function values.