###### Abstract  
Low Earth orbit (LEO) satellites that can be used as computing nodes are an important part of future communication networks. However, growing user demands, scarce channel resources and unstable satellite-ground links result in the challenge to design an efficient channel allocation algorithm for the LEO satellite network. Edge computing (EC) provides sufficient computing power for LEO satellite networks and makes the application of reinforcement learning possible. In this paper, an adaptive dynamic channel allocation algorithm based on a temporal-spatial correlation analysis for LEO satellite networks is proposed. First, according to the user mobility model, the temporal-spatial correlation of handoff calls is analyzed. Second, the dynamic channel allocation process in the LEO satellite network is formally described as a Markov decision process. Third, according to the temporal-spatial correlation, a policy for different call events is designed and online reinforcement learning is used to solve the channel allocation problem. Finally, the simulation results under different traffic distributions and different traffic intensities show that the proposed algorithm can greatly reduce the rejection probability of the handoff call and then improve the total performance of the LEO satellite network. dynamic channel allocation; temporal-spatial correlation analysis; LEO satellite network; reinforcement learning; edge computing +
Footnote †: journal: Applied  
channel allocation algorithm can reduce the rejection probability of calls and improve the total performance of LEO satellite networks [12]. In recent years, scholars have researched channel allocation algorithms in LEO satellite networks [13; 14; 15; 16; 17; 18]. A fixed channel allocation (FCA) algorithm can allocate unchanged channels in each specified cell. [PERSON] et al. [19] analyzed the performance of an FCA for LEO satellite networks. An FCA is simple to implement, but it has a poor adaptability to variations in the demands of terminal users. A dynamic channel allocation (DCA) algorithm is superior to an FCA in performance. [PERSON] et al. [20] used a DCA to improve the resource utilization in a satellite network. However, the computational complexity of a DCA is higher than that of an FCA. Reinforcement learning (RL) is suitable for solving the DCA problem [21]. [PERSON] et al. [22] used Q-learning to solve the DCA problem and reduce the computational complexity. [PERSON] et al. [23] proposed a deep RL framework to solve the DCA problem and further improve the resource utilization in a satellite network. [PERSON] et al. [24] considered the temporal correlation of a satellite network and used deep RL to further improve the resource utilization. [PERSON] et al. [25] extracted the state features through a convolution neural network and used deep learning to solve the DCA problem. The above scholars optimized a DCA with RL, which effectively improved the resource utilization in LEO satellite networks. However, LEO satellite networks have the problem of frequent handoffs. The existing dynamic channel allocation algorithms based on RL rarely evaluate the performance of handoff calls. The channel reservation technique is an effective way to resolve the problem of frequent handoffs [26]. [PERSON] et al. [27] designed a channel locking mechanism for handoff users with a successful handoff. [PERSON] et al. [28] proposed different handoff queueing strategies with dynamic and fixed channel allocation techniques. However, it is difficult to balance the complexity and performance. A channel allocation algorithm combined with RL technology can be implemented more flexibly and efficiently [29; 30]. The traffic prediction of the calls of terminal users plays a decisive role in the resource allocation [31]. Due to LEO satellites moving along their orbit regularly and periodically, a handoff call has the characteristic of a temporal-spatial correlation.