# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 1 Introduction  
With the rapid development of spatial information networks (SIN), space-air-ground integrated networks (SAGIN) have received extensive attention in recent years [1]. In many application scenarios, such as resource exploration and environment monitoring, the terrestrial networks cannot be deployed due to topographical reasons [2]. Acting as a backbone architecture in SIN, the satellite network can efficiently transfer the data randomly generated in these special scenarios due to its wide coverage and high communication capacity [3]. In view of the small size and low cost, low earth orbit (LEO) satellites can be deployed in large numbers to collect high-quality data from the earth. However, some types of LEO satellites (e.g., remote sensing satellites, meteorological satellites) are not networked in satellite cluster [4]. These satellites can only download the on-board data for a few minutes when they are within the coverage of ground stations [5], which brings significant challenges in downloading spatial data. To tackle the data transmission problem, the relay satellite network (RSN) is considered a prominent solution. In RSN, geostationary earth orbit (GEO) satellites are dedicated to supplying data forwarding services for user satellites (i.e., LEO satellites) through the seamless switching of inter-satellite links (ISLs) [6]. In addition, GEO satellites with stable orbits can download received data to ground stations via satellite-ground links (SGLs). It is of paramount importance to design reliable and stable transmission strategies for the stochastic arrived data in RSN. Different from the terrestrial network with static properties, the topology of RSN changes dynamically due to the periodic operation of user satellites in their own orbits. Furthermore, limited resources of RSN (e.g., battery capacity, transmission bandwidth, and transceivers) may complicate the data transmission decisions. Generally, solar energy harvesting has been utilized to power LEO satellites [7]. However, the LEO satellites harvest energy only when exposed to the sun. The satellite is mainly in a discharging state during the eclipse phase [8], which further limits the data acquisition and transmission. Moreover, only background knowledge about dynamic network topology, channel transmission capacity, and solar exposure duration is available. This may pose great challenges in stable transmission and sustainable energy supply over the whole network operation process. This can be manifested in the following two aspects:  
* **Random data arrivals and fluctuating energy supply. ** First, spatial data arrives at user satellites randomly, which leads to the data buffer varying over time. A larger data cache will prolong the transmission time due to limited network capacity, which may result in transmission delay. Second, the remaining battery capacity fluctuates dynamically due to the energy consumption required for data acquisition and transmission [9]. Furthermore, satellite networks face dynamic energy supply due to unstable illumination. Each satellite charges itself by receiving solar energy only when the satellites move to the sunny side. Particularly, the satellite may fall to a resting state due to insufficient residual energy, which would decrease transmission performance [10]. Thus, an optimization control method with sustainable energy is required to transmit the stochastic arrived data. * **Time-variant inter-satellite link. ** Due to the intermittent connection between GEO satellites and LEO satellites, ISLs have time-varying characteristics. Although potential ISLs are predictable, technical research on inter-satellite routing is required to enhance transmission performance [11]. In addition, the number of antennas that can be used to establish ISLs is limited, which will directly affect the transmission state of satellites [12]. This challenges the resource allocation and link contact plan design (CPD).