# Toward Low Time Fragmentation of Equipment: A Double-DQN Based TT&C Task Planning Approach
## 1 Introduction  
The satellite tracking, telemetry, and command (TT&C) system is utilized to transmit the telemetry and telecommand data and determine the satellite orbit [1]. With the development and construction of prominent satellite constellations such as Hongyun and Hongyan in China [2], the number of satellites in space has rapidly increased [3; 4], resulting in an increasing TT&C demand for space networks. On the other hand, due to the long construction period, high cost, and constraints of national borders, the number of ground stations is relatively limited, resulting in a lack of available TT&C resources. Therefore, more severe resource conflicts will be encountered in TT&C task planning, which also brings more significant challenges to the TT&C resource management of space networks. The TT&C tasks can be divided into two categories: common tasks and emergency tasks. Common tasks [5] request the daily tracing of satellites in space and form a majority of the TT&C tasks. Emergency tasks [6] are the real-time tasks that take place during emergencies. As satellites play an increasingly important role in disaster relief and emergency search and rescue, more and more emergency TT&C tasks are emerging. During the planning stage, the emergency task often needs to be inserted into the idle time of the TT&C equipment or preempt the planned resources that are allocated to common tasks. However, the existing traditional TT&C task-planning methods only aim to maximize the sum revenue of the completed tasks, which generates many short time intervals (i.e., time fragments) between the TT&C windows, which cannot be used for planning tasks, significantly affecting the planning of emergency tasks. The resources of common TT&C tasks are preempted so that the common TT&C tasks cannot be successfully executed. Therefore, it is necessary to study the TT&C task-planning method toward low timefragments, which facilitate the planning of emergency TT&C tasks to reduce the response time of emergency TT&C tasks. The existing methods for common TT&C task planning mainly include deterministic algorithms [7; 8; 9], heuristic algorithms [10; 11; 12], and intelligent algorithms [13; 14; 15; 16; 17]. Among them, the deterministic algorithm is mainly suitable for solving TT&C task scheduling problems on a small scale, but it is difficult to apply this algorithm to solve problems on a large scale. The heuristic algorithm is mainly based on certain heuristic rules to obtain the optimal solution within a certain time. However, the heuristic rules of this type of algorithm are often derived from engineering experience and have great uncertainty. Intelligent algorithms seek optimal results while taking into account efficiency and accuracy. With the gradual increase in satellites in recent years, traditional deterministic and heuristic algorithms have not been the most suitable for solving TT&C task planning problems. Therefore, most of the current research focuses on the research into intelligent algorithms. References [13; 14; 15] all established mathematical models of TT&C resource planning models to minimize the workload and use different ant colony algorithms to solve them. Reference [16] designed an emergency task-planning algorithm based on a simulated annealing algorithm with the goal of maximizing the total task revenue. The algorithm reduces the probability of falling into local optimum through an iterative neighborhood search and instability strategy to realize the reasonable allocation of multi-dimensional TT&C resources. Reference [17] established a mathematical model of TT&C task planning with a minimized overall task satisfaction rate as the optimization goal. It proposed an improved DQN (Deep Q-Network)-based TT&C resource optimization configuration method to effectively solve the joint planning problem of heterogeneous TT&C network resources. This algorithm effectively improves the satisfaction rate of the TT&C tasks.