# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 2 Related Work  
As we know, the research about data transmission schemes in satellite networks has been intensively studied. Mainly, RSN relays space data from LEO satellites at the user satellite layer to ground stations directly, which attracts many researchers to improve network transmission performance. In the literature, there have been some data transmission scheduling schemes for executing tasks with predictable data arrival [16; 19; 20]. In [16], a two-stage task scheduling scheme has been designed, which decomposes task scheduling into a power allocation (PA) problem and a PA-based task scheduling optimal problem. The authors in [19] presented a task scheduling algorithm composed of two stages, i.e., an initial scheduling stage and a dynamic scheduling stage. In [20], a heuristic mission scheduling algorithm has been given, in which the dynamic setting of antennas based on visibility window constraints is considered. To solve the problem of scheduling hybrid tasks, a stochastic optimization framework has been proposed [21], which can maximize the average time of tasks by joint optimization of the scheduling period and antenna allocation. The authors in [22] suggested a task scheduling schema considering breakpoint transmission. In the scheme, a single task is split into multiple sub-tasks and rationally arranged in multiple transmission time windows. Some existing efforts have studied resource optimization strategies in data transmission [23; 24; 25]. To schedule stochastic arrived data, [PERSON] et al. proposed a queue stability model and a dynamic contact capacity optimization scheme [23]. [PERSON] et al. [24] considered different strategies, to obtain optimal power and adaptive transmission rate in a relay satellite network. In [25], the authors designed a resource optimization model to realize the continuity of radio resources for mapping with packets. In addition, several studies explore reinforcement learning methods to address the resource allocation problem. In [26], the authors focused on power allocation, power splitting, and time allocation optimization strategies corresponding to the static and dynamic schemes. To optimize relay and transmission power resources, a deep Q-network-based approach is proposed in underwater acoustic sensor networks with energy harvesting module [27]. [PERSON] et al. [28] studied the optimization of resource utilization and proposed a Markov decision process algorithm considering the energy status of the relay nodes. The authors in [29] utilize a deep rein forcement learning model to improve the effectiveness of computational task offloading in SAGIN. For the time-varying dynamic topology of satellite networks, the ISLs between satellites are not persistent, which exhibits the characteristics of a delay tolerant network (DTN) [30; 31]. To make effective use of limited network resources, some works focus on routing (i.e., link allocation) in the literature, such as [32; 33]. Limited by the number of transceivers, [PERSON] et al. obtained the maximum feasible contacts between satellites based on conflict graphs (CGs) with the same edge weight [18]. To avoid a large-scale routing table caused by calculating all routes, the authors in [34] proposed a routing calculation schema based on contact graph routing (CGR), employing four strategies. To overcome the problem that CGR is limited in large-capacity transmission, [PERSON] et al. designed a multi-flow maximizing routing strategy based on a storage time aggregation graph (STAG) [35]. To send the same information to multiple users, the authors proposed an energy-efficient routing scheme with a delay guarantee based on the multicast time-expanded graph (MTEG) [36]. In [37], [PERSON] et al. exploited uncertain contact plans to enhance data transmission under which routing is described as the Markov Decision Process (MDP) with multiple copies.