## 1 Introduction  
With the rapid development of communication network technology, the demand for network by users is exploding. However, under certain special circumstances (such as ground base station failure) [1, 2], existing communication facilities are not sufficient to satisfy the demands of users for communication networks. Due to traffic congestion and other reasons, it is difficult to deploy fixed base stations or emergency communication vehicles in a short period of time, which poses a huge challenge to traditional networks. The UAV-assisted wireless network, as an emerging technology, is being applied in the challenging scenarios mentioned above. For example, China Telecom has built UAV bases in multiple cities, such as Suzhou, Sujian, and Chongqing, to provide network coverage services. To combat the problem of the excessive energy consumption during flight and the short service duration of ordinary UAVs [3, 4], this article uses mobile base stations deployed on multiple SP-UAVs to provide communication network coverage for users in the target area [5, 6]. The SP-UAV is a drone equipped with solar cells, which is able to collect solar energy to power itself during flight. Although SP-UAVs could alleviate the problem of the high power consumption of traditional UAVs and has advantages such as high flexibility and ease of deployment [7; 8; 9], using SP-UAVs to provide network coverage also poses many problems. Firstly, for SP-UAVs to collect enough solar energy requires increasing flight altitude, which leads to a decrease in the coverage range of the SP-UAV [10]. Moreover, providing network coverage to the target area often requires the use of multiple SP-UAVs, and since SP-UAVs are expensive, it is usually not possible to deploy enough of them. Therefore, it is necessary to plan a set of optimal 3D flight trajectories to prevent a single UAV from consuming too much energy and causing the entire system's lifetime to end quickly, while simultaneously cover more users with limited SP-UAVs. In addition, when multiple SP-UAVs are used to provide coverage to ground users, it is possible that the majority of users will receive long-term coverage while a few users will never be covered. This phenomenon is caused by the fact that the majority of users are densely distributed, while a few users may be located in remote areas. SP-UAVs are reluctant to fly to these users' surroundings as they are designed to save flight energy. This results in geographical unfairness, even if the overall coverage rate is high. In scenarios such as earthquakes and tsunamis, however, we would like all users to be able to communicate equally. Therefore, we also consider the issue of multiple SP-UAVs providing geographically fair coverage to users. To solve the above problems, we modeled the flight energy consumption and solar energy collection of SP-UAVs and introduced a fairness index in order to characterize the geographical fairness of UAVs providing coverage to ground users. The research object of this paper is defined as a multi-objective optimization problem, with constraints on UAV flight altitude, speed, and direction. To solve this problem, we proposed a DRL-based multiple SP-UAV 3D trajectory optimization algorithm, which aims to find a set of optimal flight trajectories that maximize coverage rates and fairness indexes and minimize energy consumption. The algorithm can also prevent a single UAV from consuming too much power and quickly running out of energy, thus extending the entire system's lifetime. The contributions of our works are as follows:  
* We establish a model that utilizes multiple SP-UAVs to provide communication coverage for ground users and characterize the research problem as a multi-objective joint optimization problem. * We propose a new trajectory optimization algorithm based on DRL to study the multi-objective optimization problem in this paper, in which the state space, observation space, action space, and reward function are clearly defined.