# Deep Reinforcement Learning-Based Joint Satellite Scheduling and Resource Allocation in Satellite-Terrestrial Integrated Networks

Authors: Yabo Yin; Chuanhe Huang; Dong-Fang Wu; Shidong Huang; M. Wasim Abbas Ashraf; Qianqian Guo; Lin Zhang
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Satellite-terrestrial integrated networks (STINs) are considered to be a new paradigm for the next generation of global communication because of its distinctive merits, such as wide coverage, high reliability, and flexibility. When the satellite associates with different base stations (BSs) and adopts different channels for communication, the utility of offloading data to BSs is different. In our work, we study how to jointly associate satellites with appropriate BSs and allocate channels to satellites. Our purpose is to maximize the utility of the data offloaded from satellites to BSs while considering the load balance of BSs. However, some satellites are often unable to connect to BSs because of their periodic flight characteristic, which makes the joint satellite-BS association and channel allocation more challenging. To solve the problem that satellites sometimes cannot connect to BSs, we abstract the communication model between satellites and BSs into a bipartite graph and add a virtual BS to ensure that all satellites can connect to at least one BS. Then, in the constructed joint optimization problem, we solve the assignment of satellites and channels simultaneously. Considering that the joint optimization problem is nonconvex, we use double deep Q-Network (DDQN) for achieving the optimal strategy of satellite association and channel allocation. Furthermore, the reward value in most state transition information generated by satellites is 0, which leads to the low learning efficiency of DDQN. Aiming at enhancing the learning efficiency of DDQN, the priority sampling-based DDQN (PSDDQN) algorithm is proposed. Experimental results demonstrate that PSDDQN gets better utility and achieves the load balance of BSs compared with other algorithms.


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## Body

## 1. Introduction

Recently, 5G technology has developed rapidly, and many 5G base stations have been deployed at the same time. Therefore, 5G network communication services can be provided in many places, such as urban and metropolitan areas. However, for those remote areas without communication facilities, such as deserts, oceans, and other places, it is difficult to provide people with communication services. LEO satellite network has attracted researchers’ attention due to its characteristics of wide coverage, low delay, and high bandwidth, and it can provide communication services for those places which are not covered by base stations [1]. Therefore, the STINs become a new paradigm for providing seamless communication. Meanwhile, people’s demands for high quality video, voice, and other multimedia services grow explosively. Therefore, how to efficiently manage resources in STINs for providing people with better Internet services has become a new challenge [2, 3].Resource allocation is a key technology that affects the performance of STINs. But most of researches focus on radio spectrum allocation [4, 5], power allocation [6, 7], and other issues. And there is little research that studies the joint satellite association and channel allocation. Hu et al. [8] proposed a competitive market scheme to solve user association issue of satellite-drone networks. To address the user association issue of heterogeneous networks, a distributed belief propagation method was proposed [9]. Khalili et al. [10] investigated user association of the uplinks in heterogeneous networks and used maximization-minimization theory and augmented Lagrange method to guarantee users’ data transmission rate. Zhao et al. [11] used multiagent RL approach for achieving the optimal user association strategy while considering the service requirements of users in heterogeneous cellular networks. However, these approaches are not suitable in STINs. The main reasons are as follows: (1) In the network scenarios mentioned above, when optimizing user association problem, users are within the communication range of BSs. That is, users are covered by at least one BS at any time. However, in STINs, many satellites cannot connect to BSs because satellites communicate with BSs by the Line-of-Sight (LoS) way. (2) Because of the periodic movement of satellites, the communication process between satellites and BSs is discontinuous rather than continuous. (3) The load balance of BSs seriously affect the performance of STINs. If the load balance of BSs is not considered, the capacity of some BSs will be exhausted in advance. And this degrades the performance of STINs. However, the above literature ignores the load balance of BSs.In our research, the joint satellite scheduling and resource allocation in STINs are investigated. When satellites communicate with BSs, allocating appropriate BSs and channels to satellites for transmitting data has a great impact on the utility of STINs. Because the problem of joint satellite association and channel allocation is coupled and nonconvex, conventional methods cannot effectively obtain the optimal satellite association and channel allocation strategy.Fortunately, in recent years, artificial intelligence algorithm such as reinforcement learning (RL) has developed rapidly and applied to many fields. RL has been used to solve system control problems [12]. Moreover, it has been applied in combinatorial optimization [13, 14], capacity management [15], resource management [16, 17], etc., especially in games and chess. Furthermore, RL also has good performance in dynamic network environment. Inspired by this, we try to use RL approach to achieve the optimal strategy in STINs.This paper mainly studies the joint satellite scheduling and resource allocation in STINs. Our purpose is to maximize the utility of data offloaded from satellites to BSs as much as possible while considering the load balance of BSs. The joint optimization problem of the satellites and channel assignments is constructed. Then, a priority sampling-based double DQN (PSDDQN) method is used to address the aforementioned problem. We give the main contributions in the following:(1)
We focus on the joint satellite association and channel allocation while considering the load balance of BSs in STINs. We formulate the problem of joint satellite association and channel allocation as a joint optimization problem. And, we use RL approach to solve it(2)
We propose a bipartite graph with virtual BS to describe the communication model between satellites and BSs. By adding a virtual BS, we ensure that the action space of all satellites is the same. So we can easily determine the action space of our proposed RL model(3)
Considering that the reward value in most state transition information is 0, we propose a priority sampling-based DDQN (PSDDQN) to enhance the performance of DDQN. Moreover, to reduce the consumption of computing resources caused by sorting state transition information, we use SumTree structure to store state transition information. Experimental results demonstrate that PSDDQN can converge quickly and get better performance than the selected baseline approachesThe rest of our paper unfolds below. The recent research developments are summarized in Section2. In Section 3, the system framework and problem formulation are illustrated. Section 4 describes our PSDDQN method. Section 5 evaluates our PSDDQN method and discusses the experimental results. We conclude this article and give the future research in Section 6.


## 2. Related Work

Many works investigated user association in wireless networks. Feng et al. [18] proposed a repeated game-based user association scheme while considering spectrum allocation to maximize users’ data transmission rate of the MIMO system. Liu et al. [19] used Lagrange dual decomposition approach to address the user association issue in two different access schemes. Liu et al. [20] jointly optimized user association and power control and designed a semidistributed solution to obtain the optimal results. Considering that users’ mobility affects the performance of heterogeneous networks, Cheng et al. [21] introduced the users’ mobility and proposed a multiagent RL approach to maximize the system capacity. Based on users’ preferences information, Zalghout et al. [22] proposed a priority-based user association approach to maximize user’s QoS in heterogeneous wireless networks.There are many works on the resource allocation of satellite networks. Zhu et al. [23] used multidimensional knapsack theory to minimize energy consumption while considering user’s delay constraint. To improve the spectrum utilization of satellites, Zuo et al. [24] jointly optimized allocation of time, spectrum, power, and beam and solved it by heuristic algorithm. Mai et al. [25] used Stackelberg game method to reduce the transmission delay of remote sensing data. Deng et al. [26] jointly optimized virtual machine assignment and power allocation to minimize energy consumption in cloud-based satellite communication networks. Shahid et al. [27] used radio utilization as load metric and proposed a load balancing-based resource management method for improving STINs’ performance. Ji et al. [28] presented a data offloading method for solving the energy overhead issue in multicell STINs. Deng et al. [29] designed a satellite constellation deployment solution with minimum number of satellites while meeting backhaul requirements of users in STINs. However, none of them paid attention to the joint satellite association and channel allocation.In recent years, researchers have used artificial intelligence algorithms to solve network problems. He et al. applied deep learning algorithm to memory optimal detection [30] and optimal strategy search [31] in MIMO system. Lai [32] used federated learning to select outdated access point in MEC networks. Tang and Chen [33] designed a federated edge learning framework to reduce the computational latency of tasks in IoT networks. RL has been applied to ubiquitous computing [34], network security [35], and resource allocation. For reducing the task execution time, the Q-learning approach was adopted to manage computing resources in IoT [36]. The Q-learning-based task offloading scheme was used to maximize the utility of system [37]. The designed deep RL-based radio resource management method was used to improve the radio utilization [38]. Zhang et al. [39] adopted deep RL approach to allocate resource while meeting users’ reliability requirements. The multiagent RL-based hierarchical task management strategy can satisfy users’ communication requirements [40]. Luong et al. [41] jointly optimized UAV position and transmitted beamforming and UAV-UE association in UAV-assisted wireless networks. Considering that this optimization problem was nonconvex, a method based on deep Q-learning was proposed for allocating resources.


## 3. System Model and Problem Formulation

### 3.1. Network Model

Figure1 shows the structure of STINs. The STINs consist of N satellites and L BSs. For the terrestrial networks, it can connect to LEO satellite networks through BSs with satellite gateways. Because the LEO satellite network topology changes dynamically over time, we use snapshot method to discretize the satellite network topology according to reference [42]. During a snapshot, the network topology is fixed. The whole operation time is T, and the number of snapshots is NT; then, we can easily get
(1)∑t=1NTTt=T.Figure 1
The structure of STINs. The dotted lines (including red and blue) indicate that the satellite is capable of connecting to the BSs under its coverage. The blue dotted line means the communication link established between the satellite and the BS. And each satellite is able to associate with one BS for communication in a time slice.The more snapshots, the higher the precision of the representation of satellite network topology. But extensive storage resources and computing resources are needed to deal with these snapshots. To ensure the precision of satellite network topology and reduce the consumption of storage resources, the duration of the snapshot should be less than the communication time between satellites and BSs. We define it as(2)Tt≤minti,j,∀i∈1,2,3,⋯,N,j∈1,2,3,⋯,L,where variable ti,j expresses the communication time between ith satellite and jth BS. The structure of STINs is expressed by G=V,E. And graph G is undirected graph. The node set in STINs is denoted by V. And E represents the communication links between satellites and BSs.


### 3.2. Communication Model

When BSs are not within the coverage of satellites, satellites cannot communicate with BSs. Moreover, each satellite may cover many BSs sometimes. Therefore, the communication model between satellites and BSs can be abstracted into a bipartite graph. We show the structure of bipartite graph.In Figure2, grey nodes represent satellites, and orange nodes represent BSs. The grey lines indicate the communication links between satellites and BSs. From Figure 2, we observe that only four satellites can connect to BSs, and other satellites of the satellite networks cannot connect to any BSs. Considering the BSs covered by different satellites are different, the action space of different satellites is different. Therefore, the RL method cannot be directly applied to STINs. To solve this problem, we introduce the virtual BS. By adding a virtual BS, each satellite can be connected to a BS, and the action space of each satellite is same. We show the bipartite graph with virtual BS.Figure 2
Structure of bipartite graph.In Figure3, the blue node is the added virtual BS. When a satellite cannot connect to any BS, we connect this satellite to the added virtual BS. Then, each satellite in STINs can be connected to at least one BS at any snapshot. Hence, the satellite can select one BS from L+1 BSs to establish a communication link at any time. This operation ensures that the action space of each satellite is same, which is convenient for the definition of action space of RL in subsequent section.Figure 3
Bipartite graph with virtual BS.


### 3.3. Channel Model

Considering that we have added a virtual BS to STINs, we use setL=0,1,2,3,⋯,L to represent the BSs of STINs. And the index of the added virtual BS is 0. Only when the satellite is associated with one BS and occupies one channel of this BS can the satellite communicate with this BS. We use binary decision variable si,lt that indicates whether satellite i associate with BS l at time t. If satellite i is assigned to BS l, then si,lt=1; otherwise, si,lt=0. For convenience, the satellite can only connect to one BS during one snapshot. So we can get
(3)∑l=0Lsi,lt≤1,∀i∈1,2,3,⋯,N.Moreover, we assume that each BS hasM channels, and each channel can only be assigned to one satellite at any time. Therefore, each BS can connect at most M satellites simultaneously; we can get
(4)∑i=1Nsi,lt≤M,∀l∈1,2,3,⋯,L.Here, we assume that the number of satellites connected to the virtual BS is not limited. So the added virtual BS can connect to all satellites. Considering that the index of the virtual BS is 0, we can get(5)∑i=1Nsi,0t≤N.Assigning different channels to satellites has an important impact on the data transmission rate between satellites and BSs. Assumed that different channels of the BS are orthogonal. Here, we use binary channel-allocation variableci,mt that indicates whether satellite i communicate with BS on channel m. If satellite i uses channel m for communication at time t, ci,mt=1; otherwise, ci,mt=0. Here, one channel can only be allocated to one satellite. We can easily get
(6)∑m=1Mci,mt≤1,∀i∈1,2,3,⋯,N.Considering that the satellite may cover many BSs, when satellitei communicates with BS l, other BSs will also interfere with the communication process. Therefore, the cochannel influence of other BSs should be considered. We obtain the SINR by
(7)SINRi,l,mt=pi,l,mtgi,l,mtsi,ltci,mt∑b∈L\0,lLpi,b,mtgi,b,mtsi,btci,mt+BN0.The virtual BS does not interfere with the communication process of other BSs. Variablepi,l,m represents the transmit power operating on channel m between satellite i and BS l, and variable gi,l,m indicates the channel gain of channel m. The channel m is between ith satellite and lth BS. The variable N0 indicates Gaussian white noise. B expresses the channel bandwidth.Let the longitude and latitude of BSl be λl and φl, respectively. And λi and φi represent the longitude and latitude of satellite i. The height of satellite orbit is h, so the distance between satellite i and BS l is given by
(8)di,l=RE+h1+RERE+h2−2RERE+hcosλl−λicosφicosφl+sinφisinφl,where variable RE denotes the earth’s radius. Thus, we can get the path loss of the signal in the atmosphere
(9)Li,lt=32.44+20logdi,l+20logf,where variable f represents the carrier frequency. Thus, we can get the channel gain of satellite i(10)pi,l,mt=Ai,m−Li,lt,where variable Ai,m represents the antenna gain operating on channel m of satellite i. Based on the computed SINR, we can get the data transmission rate by
(11)ri,l,mt=Blog21+SINRi,l,mt.Variable B represents the channel bandwidth. Considering that the communication time between satellite i and BS l is different in a time slice, we can obtain the valid communication time between satellite i and BS l(12)τi,l=minTt,ti,l.


### 3.4. Problem Formulation

Each satellite wants to get its maximum utility. Our object is to obtain the maximum utility of the data offloaded from satellites to BSs while considering the load balance of BSs. The utility of the data offloaded by satellitei is ui. And it consists of the utility generated by the offloaded data and the cost of the BS storing these offloaded data. We define it as
(13)uit=ωpi−ξflri,l,mtτi,l,where variables ω and ξ are weight coefficients. Variable fl represents the cost of storing unit data in BS l. As the storage capacity of BSs is consumed, the storage cost will gradually increase. Here, we define it as
(14)fl=1Cl−∑i=1Nri,l,mτi,l+Q,where variable Cl represents the remaining capacity of BS l and variable Q is a constant. Based on the abovementioned, we can get the utility generated by the data offloaded from all satellites to BSs in a time slice,
(15)Γt=∑i=1N∑l=0L∑m=1Muit.The running timeT of satellites is composed of a series of time slices. Therefore, we can get the utility generated by the data offloaded from all satellites to BSs during the whole operation time T. We define it as
(16)ϒ=max∑t=1NTΓt,s.t.∑l=0Lsi,lt≤1,∀i∈1,2,3,⋯,N,∑i=1Nsi,lt≤M,∀l∈1,2,3,⋯,L,∑i=1Nsi,lt≤N,l=0,∑m=1Mci,mt≤1,∀i∈1,2,3,⋯,N,where variable NT denotes the number of snapshots. Considering that this problem is nonconvex, traditional approaches cannot be directly used to obtain the optimal results. Therefore, we adopt deep RL for achieving the optimal satellite association and channel allocation strategy.


## 3.1. Network Model

Figure1 shows the structure of STINs. The STINs consist of N satellites and L BSs. For the terrestrial networks, it can connect to LEO satellite networks through BSs with satellite gateways. Because the LEO satellite network topology changes dynamically over time, we use snapshot method to discretize the satellite network topology according to reference [42]. During a snapshot, the network topology is fixed. The whole operation time is T, and the number of snapshots is NT; then, we can easily get
(1)∑t=1NTTt=T.Figure 1
The structure of STINs. The dotted lines (including red and blue) indicate that the satellite is capable of connecting to the BSs under its coverage. The blue dotted line means the communication link established between the satellite and the BS. And each satellite is able to associate with one BS for communication in a time slice.The more snapshots, the higher the precision of the representation of satellite network topology. But extensive storage resources and computing resources are needed to deal with these snapshots. To ensure the precision of satellite network topology and reduce the consumption of storage resources, the duration of the snapshot should be less than the communication time between satellites and BSs. We define it as(2)Tt≤minti,j,∀i∈1,2,3,⋯,N,j∈1,2,3,⋯,L,where variable ti,j expresses the communication time between ith satellite and jth BS. The structure of STINs is expressed by G=V,E. And graph G is undirected graph. The node set in STINs is denoted by V. And E represents the communication links between satellites and BSs.


## 3.2. Communication Model

When BSs are not within the coverage of satellites, satellites cannot communicate with BSs. Moreover, each satellite may cover many BSs sometimes. Therefore, the communication model between satellites and BSs can be abstracted into a bipartite graph. We show the structure of bipartite graph.In Figure2, grey nodes represent satellites, and orange nodes represent BSs. The grey lines indicate the communication links between satellites and BSs. From Figure 2, we observe that only four satellites can connect to BSs, and other satellites of the satellite networks cannot connect to any BSs. Considering the BSs covered by different satellites are different, the action space of different satellites is different. Therefore, the RL method cannot be directly applied to STINs. To solve this problem, we introduce the virtual BS. By adding a virtual BS, each satellite can be connected to a BS, and the action space of each satellite is same. We show the bipartite graph with virtual BS.Figure 2
Structure of bipartite graph.In Figure3, the blue node is the added virtual BS. When a satellite cannot connect to any BS, we connect this satellite to the added virtual BS. Then, each satellite in STINs can be connected to at least one BS at any snapshot. Hence, the satellite can select one BS from L+1 BSs to establish a communication link at any time. This operation ensures that the action space of each satellite is same, which is convenient for the definition of action space of RL in subsequent section.Figure 3
Bipartite graph with virtual BS.


## 3.3. Channel Model

Considering that we have added a virtual BS to STINs, we use setL=0,1,2,3,⋯,L to represent the BSs of STINs. And the index of the added virtual BS is 0. Only when the satellite is associated with one BS and occupies one channel of this BS can the satellite communicate with this BS. We use binary decision variable si,lt that indicates whether satellite i associate with BS l at time t. If satellite i is assigned to BS l, then si,lt=1; otherwise, si,lt=0. For convenience, the satellite can only connect to one BS during one snapshot. So we can get
(3)∑l=0Lsi,lt≤1,∀i∈1,2,3,⋯,N.Moreover, we assume that each BS hasM channels, and each channel can only be assigned to one satellite at any time. Therefore, each BS can connect at most M satellites simultaneously; we can get
(4)∑i=1Nsi,lt≤M,∀l∈1,2,3,⋯,L.Here, we assume that the number of satellites connected to the virtual BS is not limited. So the added virtual BS can connect to all satellites. Considering that the index of the virtual BS is 0, we can get(5)∑i=1Nsi,0t≤N.Assigning different channels to satellites has an important impact on the data transmission rate between satellites and BSs. Assumed that different channels of the BS are orthogonal. Here, we use binary channel-allocation variableci,mt that indicates whether satellite i communicate with BS on channel m. If satellite i uses channel m for communication at time t, ci,mt=1; otherwise, ci,mt=0. Here, one channel can only be allocated to one satellite. We can easily get
(6)∑m=1Mci,mt≤1,∀i∈1,2,3,⋯,N.Considering that the satellite may cover many BSs, when satellitei communicates with BS l, other BSs will also interfere with the communication process. Therefore, the cochannel influence of other BSs should be considered. We obtain the SINR by
(7)SINRi,l,mt=pi,l,mtgi,l,mtsi,ltci,mt∑b∈L\0,lLpi,b,mtgi,b,mtsi,btci,mt+BN0.The virtual BS does not interfere with the communication process of other BSs. Variablepi,l,m represents the transmit power operating on channel m between satellite i and BS l, and variable gi,l,m indicates the channel gain of channel m. The channel m is between ith satellite and lth BS. The variable N0 indicates Gaussian white noise. B expresses the channel bandwidth.Let the longitude and latitude of BSl be λl and φl, respectively. And λi and φi represent the longitude and latitude of satellite i. The height of satellite orbit is h, so the distance between satellite i and BS l is given by
(8)di,l=RE+h1+RERE+h2−2RERE+hcosλl−λicosφicosφl+sinφisinφl,where variable RE denotes the earth’s radius. Thus, we can get the path loss of the signal in the atmosphere
(9)Li,lt=32.44+20logdi,l+20logf,where variable f represents the carrier frequency. Thus, we can get the channel gain of satellite i(10)pi,l,mt=Ai,m−Li,lt,where variable Ai,m represents the antenna gain operating on channel m of satellite i. Based on the computed SINR, we can get the data transmission rate by
(11)ri,l,mt=Blog21+SINRi,l,mt.Variable B represents the channel bandwidth. Considering that the communication time between satellite i and BS l is different in a time slice, we can obtain the valid communication time between satellite i and BS l(12)τi,l=minTt,ti,l.


## 3.4. Problem Formulation

Each satellite wants to get its maximum utility. Our object is to obtain the maximum utility of the data offloaded from satellites to BSs while considering the load balance of BSs. The utility of the data offloaded by satellitei is ui. And it consists of the utility generated by the offloaded data and the cost of the BS storing these offloaded data. We define it as
(13)uit=ωpi−ξflri,l,mtτi,l,where variables ω and ξ are weight coefficients. Variable fl represents the cost of storing unit data in BS l. As the storage capacity of BSs is consumed, the storage cost will gradually increase. Here, we define it as
(14)fl=1Cl−∑i=1Nri,l,mτi,l+Q,where variable Cl represents the remaining capacity of BS l and variable Q is a constant. Based on the abovementioned, we can get the utility generated by the data offloaded from all satellites to BSs in a time slice,
(15)Γt=∑i=1N∑l=0L∑m=1Muit.The running timeT of satellites is composed of a series of time slices. Therefore, we can get the utility generated by the data offloaded from all satellites to BSs during the whole operation time T. We define it as
(16)ϒ=max∑t=1NTΓt,s.t.∑l=0Lsi,lt≤1,∀i∈1,2,3,⋯,N,∑i=1Nsi,lt≤M,∀l∈1,2,3,⋯,L,∑i=1Nsi,lt≤N,l=0,∑m=1Mci,mt≤1,∀i∈1,2,3,⋯,N,where variable NT denotes the number of snapshots. Considering that this problem is nonconvex, traditional approaches cannot be directly used to obtain the optimal results. Therefore, we adopt deep RL for achieving the optimal satellite association and channel allocation strategy.


## 4. Priority Sampling-Based DDQN Algorithm for Joint Satellite Scheduling and Resource Allocation

### 4.1. Reinforcement Learning Model

#### 4.1.1. State Space

The state space mainly includes the real-time status of all satellites and the status of BSs. For the satellite, the status information includes its position at timet and valid communication time with different BSs. For the BS, the status information includes its remaining storage capacity and channel allocation state. We get the state space of satellite i by
(17)Si=λi,φi,τi,0,τi,1,τi,2,⋯,τi,L,si,0,si,1,si,2,⋯,si,L,ci,1,ci,2,⋯,ci,M,C0,C1,C2,⋯,CL,∀i∈1,2,3,⋯,N,where variables λi and φi are introduced in Equation (8). Variable Cl represents the remaining capacity of BS l. Variable si,l indicates whether satellite i is associated with BS l. Variable ci,m indicates whether satellite i adopts channel m for communication.


#### 4.1.2. Action space

When the satellite communicates with the BS, the satellite must be associated with one BS and occupy one channel of this BS. Therefore, the action of satellite mainly includes two parts: satellite association and channel allocation. We define the action space of satellitei as
(18)Ai=si,0,si,1,si,2,⋯,si,L,ci,0,ci,1,ci,2,⋯,ci,M,∀i∈1,2,3,⋯,N.From Equation (18), we can see that satellite association and channel allocation are coupled. For convenience, we use matrix E that represents the action space of the satellite
(19)E=e0,1,e0,2,e0,3,⋯,e0,Me1,1,e1,2,e1,3,⋯,e1,M⋮⋮⋮eL,1,eL,2,eL,3,⋯,eL,M,∀l∈0,1,2,3,⋯,L,m∈1,2,⋯,M.We use binary variableel,m that represents the joint satellite association and channel allocation of the satellite. For a satellite, when BS l and its channel m are assigned to this satellite simultaneously, el,m=1; otherwise, el,m=0.


#### 4.1.3. Reward value

Reward value seriously affects the performance of RL, and the agent use it to obtain the optimal strategy. When satellites are associated with different BSs and adopt different channels for communication, the utility of data offloaded from satellites to BSs is different. Moreover, the storage cost of the BS also changes with the consumption of its capacity. In our research, our purpose is to obtain the maximum utility of offloaded data while considering the load balance of BSs. The reward value consists of the utility generated by the offloaded data and the cost of BS for storing these data. We define it as(20)ri=ωp−ξflri,l,mtτi,l,where variables ω and ξ are the weight coefficients, which are initialized by analytic hierarchy process method. And variable ri,l,mt denotes the downlink data transmission rate between satellite i and BS l operating on channel m. Variable τi,l denotes the communication time between satellite i and BS l. Furthermore, we also normalize the utility of offloaded data and the cost of BSs.


### 4.2. Double Deep Q-Network (DDQN) Method

DQN is used widely in discrete scenarios. Meanwhile, theQ value is approximated by deep neural network. After the iterative training process, the obtained Q value is close to the true Q value
(21)Q∗s,a≈Qs,a;θ.Moreover, the experience relay buffer scheme and gradient descent method are used for updating the parameterθ of neural network
(22)Lθ=yi−Qs,a;θ2,(23)∇θLθ=yi−Qs,a;θ∇θQs,a;θ,where variable yi represents the Q value obtained by DQN; we can get it by
(24)yi=ri,is_endiistrueri+γmaxa′Qs′,a′;θ,is_endiisfalse.Because greedy strategy is adopted for selecting action in DQN, theQ value obtained by DQN is over estimated. To solve this problem, double DQN (DDQN) selects actions and calculates the target Q value by two independent network structures. These two independent network structures are online Q-Network and target Q-Network, respectively, and they have the same network structure. The target Q-Network is used to decouple the action selection and the Q value estimation, which can solve the over estimation problem of Q value in DQN. When updating the parameters of DDQN, we select the action with the largest Q value by
(25)amaxs′;θ=argmaxa′Qs′,a;θ.Then, the targetQ value in DDQN is calculated by target Q-Network
(26)yiDDQN=ri,is_endiistrueri+γQ′s′,amaxs′;θ;θ′,is_endiisfalse,whereri is the instant reward value. Variable γ denotes the discount rate. And the discount rate indicates the importance of instant reward and future reward. γ=0 indicates that the agent is myopic, and the agent only considers the instant reward. When the discount rate is 1, the agent is farsighted and only considers the future reward. In practice, the value of γ is generally between 0 and 1.Then, we use the targetQ value to calculate the TD-error. Finally, we calculate the gradients according to the obtained TD-error and update the weights of online Q-Network. In the process of training, online Q-Network assigns its weights to target Q-Network every certain number of training steps to update the parameters of target Q-Network.The last layer of DDQN is a fully connected layer. However, the action space of the satellite is a two-dimensional matrix. In order to train DDQN, we transform the action space of the satellite into a one-dimensional vector with sizeL+1∗M. The specific transformation process is shown in Figure 4.Figure 4
Action space transformation process.We assume the index of actionel,m is indexel,m, and we can get the location of el,m in the last layer of DDQN
(27)indexel,m=l∗M+m,∀i∈1,2,3,⋯,L,m∈1,2,3,⋯,M.By transforming the action space into a one-dimensional vector, we can use the state transition information to train DDQN.


### 4.3. Priority Sampling-Based DDQN Algorithm for Joint Satellite Scheduling and Resource Allocation

In DDQN, the uniform sampling method is used to select samples when calculating the gradient and updating the model’s parameters. If samples in experience replay buffer are evenly distributed, uniform sampling method is effective. However, because satellites communicate with BSs by LoS, few satellites can communicate with BSs at any time. Moreover, most satellites are connected to virtual BS. Therefore, most of state transition information generated by satellites is from virtual BS to virtual BS because the communication time between satellites and BSs is short. At this time, the reward received by the satellite is 0. Thus, the reward of most state transition information in experience replay buffer is 0. If these samples are uniformly sampled, the learning performance of DDQN model will be degraded. Therefore, we use priority sampling method to enhance the performance of DDQN.For a sample, the greater its priority, the greater the probability of being sampled. We define the sampling probability of the sample as(28)Pj=pj∑ipi,where pj and Pj are the priority and sampling probability of sample j, respectively. Considering that sorting these samples wastes extensive computing resources, we use SumTree structure to store these samples. The specific structure of PSDDQN is shown in Figure 5.Figure 5
Structure of PSDDQN.When training PSDDQN model, we firstly selectM samples from SumTree structure and compute their loss functions and gradients,
(29)Lossθ=∑j=1MwjyjDDQN−Qsj,aj;θ2,(30)∇θLossθ=wjyjDDQN−Qsj,aj;θ∇Qsj,aj;θ,(31)wj=N∗Pj−βmaxiwi=N∗Pj−βmaxiN∗Pi−β=Pj−βmaxiPi−β,where wj represents the weight of priority of sample j for calculating the loss value. And variable β represents the hyperparameter, which is determined by empirical value.Then, the weights of DDQN are updated according to the calculated gradients. Moreover, we also need to update the priorities of samples in the SumTree structure. For the sample, the greater the TD-error, the better the effect of training the DDQN model. Here, we use TD-error value to represent the priority of the sample. We get the TD-error value of the sample and update its priority by(32)TD−errorj=yj−Qsj,aj;θ,(33)pj=TD‐errorj.The ground control centre has rich computing resources and can directly obtain the real-time state information of BSs and satellites. In the process of communication, BSs and satellites send their real-time state information to the ground control centre for updating their state information. The agent is able to obtain the latest state information of STINs from the ground control centre in real time. In our research, we deploy PSDDQN algorithm in the ground control centre. The specific details of PSDDQN is described in the following:Algorithm 1: PSDDQN for joint satellite association and channel allocation.
Input: number of episodes, Num_Episodes; number of time slices, Num_Timeslices; number of leaves of Sumtree structure, B; exploration rate, ε; update frequency, F; learning rate, α ; number of satellites, N;Output: the weight of online Q-Network, θ;1. Initialize the state of STINs, including the capacityC of BSs, antenna model, channel model, satellite orbit parameters, and the initial positions of satellites;2. Randomly initialize the weightθ of online Q-Network; for the weight in target Q-Network, θ'=θ;3.Forepisode =1 to Num_Episodes do4. Fortime =1 to Num_Timeslices do5.  Fori =1 to N do6.   Get the state information of satellitei from the ground control centre at time t, si;7.  End for8.  Get the sate information of BSs from the ground control centre at timet, H;9.  Obtain the state information of STINs at timet, S=(s1,s2,s3,…,sN , H);10.  Get the next state of STINsS’ from the ground control centre at t+1 and its termination flag;11.  Fori =1 to N do12.   Useε-greedy strategy to select an action, ai;13.   The agent execute actionai and obtain the instant reward ri by Equation (20);14.   Store state transition information (si, ai, ri, si’ ) in the Sumtree structure;15.  End for16.S= S’;17.  The BSs and satellites send their state information to the ground control centre for updating the state information of STINs;18.  Sample samples from the Sumtree structure, and compute the loss of Q-value of each sample according to Equation (29);19.  Compute the gradient of each sample according to Equation (30);20.  Update the weight of online Q-Network according to the back propagation algorithm;21.  Compute theTD-error value of each sample according Equation (32) and update its priority by Equation (33);22.  Update the parameters of target Q-Network every frequencyF, let θ'=θ;23. End for24.End for


## 4.1. Reinforcement Learning Model

### 4.1.1. State Space

The state space mainly includes the real-time status of all satellites and the status of BSs. For the satellite, the status information includes its position at timet and valid communication time with different BSs. For the BS, the status information includes its remaining storage capacity and channel allocation state. We get the state space of satellite i by
(17)Si=λi,φi,τi,0,τi,1,τi,2,⋯,τi,L,si,0,si,1,si,2,⋯,si,L,ci,1,ci,2,⋯,ci,M,C0,C1,C2,⋯,CL,∀i∈1,2,3,⋯,N,where variables λi and φi are introduced in Equation (8). Variable Cl represents the remaining capacity of BS l. Variable si,l indicates whether satellite i is associated with BS l. Variable ci,m indicates whether satellite i adopts channel m for communication.


### 4.1.2. Action space

When the satellite communicates with the BS, the satellite must be associated with one BS and occupy one channel of this BS. Therefore, the action of satellite mainly includes two parts: satellite association and channel allocation. We define the action space of satellitei as
(18)Ai=si,0,si,1,si,2,⋯,si,L,ci,0,ci,1,ci,2,⋯,ci,M,∀i∈1,2,3,⋯,N.From Equation (18), we can see that satellite association and channel allocation are coupled. For convenience, we use matrix E that represents the action space of the satellite
(19)E=e0,1,e0,2,e0,3,⋯,e0,Me1,1,e1,2,e1,3,⋯,e1,M⋮⋮⋮eL,1,eL,2,eL,3,⋯,eL,M,∀l∈0,1,2,3,⋯,L,m∈1,2,⋯,M.We use binary variableel,m that represents the joint satellite association and channel allocation of the satellite. For a satellite, when BS l and its channel m are assigned to this satellite simultaneously, el,m=1; otherwise, el,m=0.


### 4.1.3. Reward value

Reward value seriously affects the performance of RL, and the agent use it to obtain the optimal strategy. When satellites are associated with different BSs and adopt different channels for communication, the utility of data offloaded from satellites to BSs is different. Moreover, the storage cost of the BS also changes with the consumption of its capacity. In our research, our purpose is to obtain the maximum utility of offloaded data while considering the load balance of BSs. The reward value consists of the utility generated by the offloaded data and the cost of BS for storing these data. We define it as(20)ri=ωp−ξflri,l,mtτi,l,where variables ω and ξ are the weight coefficients, which are initialized by analytic hierarchy process method. And variable ri,l,mt denotes the downlink data transmission rate between satellite i and BS l operating on channel m. Variable τi,l denotes the communication time between satellite i and BS l. Furthermore, we also normalize the utility of offloaded data and the cost of BSs.


## 4.1.1. State Space

The state space mainly includes the real-time status of all satellites and the status of BSs. For the satellite, the status information includes its position at timet and valid communication time with different BSs. For the BS, the status information includes its remaining storage capacity and channel allocation state. We get the state space of satellite i by
(17)Si=λi,φi,τi,0,τi,1,τi,2,⋯,τi,L,si,0,si,1,si,2,⋯,si,L,ci,1,ci,2,⋯,ci,M,C0,C1,C2,⋯,CL,∀i∈1,2,3,⋯,N,where variables λi and φi are introduced in Equation (8). Variable Cl represents the remaining capacity of BS l. Variable si,l indicates whether satellite i is associated with BS l. Variable ci,m indicates whether satellite i adopts channel m for communication.


## 4.1.2. Action space

When the satellite communicates with the BS, the satellite must be associated with one BS and occupy one channel of this BS. Therefore, the action of satellite mainly includes two parts: satellite association and channel allocation. We define the action space of satellitei as
(18)Ai=si,0,si,1,si,2,⋯,si,L,ci,0,ci,1,ci,2,⋯,ci,M,∀i∈1,2,3,⋯,N.From Equation (18), we can see that satellite association and channel allocation are coupled. For convenience, we use matrix E that represents the action space of the satellite
(19)E=e0,1,e0,2,e0,3,⋯,e0,Me1,1,e1,2,e1,3,⋯,e1,M⋮⋮⋮eL,1,eL,2,eL,3,⋯,eL,M,∀l∈0,1,2,3,⋯,L,m∈1,2,⋯,M.We use binary variableel,m that represents the joint satellite association and channel allocation of the satellite. For a satellite, when BS l and its channel m are assigned to this satellite simultaneously, el,m=1; otherwise, el,m=0.


## 4.1.3. Reward value

Reward value seriously affects the performance of RL, and the agent use it to obtain the optimal strategy. When satellites are associated with different BSs and adopt different channels for communication, the utility of data offloaded from satellites to BSs is different. Moreover, the storage cost of the BS also changes with the consumption of its capacity. In our research, our purpose is to obtain the maximum utility of offloaded data while considering the load balance of BSs. The reward value consists of the utility generated by the offloaded data and the cost of BS for storing these data. We define it as(20)ri=ωp−ξflri,l,mtτi,l,where variables ω and ξ are the weight coefficients, which are initialized by analytic hierarchy process method. And variable ri,l,mt denotes the downlink data transmission rate between satellite i and BS l operating on channel m. Variable τi,l denotes the communication time between satellite i and BS l. Furthermore, we also normalize the utility of offloaded data and the cost of BSs.


## 4.2. Double Deep Q-Network (DDQN) Method

DQN is used widely in discrete scenarios. Meanwhile, theQ value is approximated by deep neural network. After the iterative training process, the obtained Q value is close to the true Q value
(21)Q∗s,a≈Qs,a;θ.Moreover, the experience relay buffer scheme and gradient descent method are used for updating the parameterθ of neural network
(22)Lθ=yi−Qs,a;θ2,(23)∇θLθ=yi−Qs,a;θ∇θQs,a;θ,where variable yi represents the Q value obtained by DQN; we can get it by
(24)yi=ri,is_endiistrueri+γmaxa′Qs′,a′;θ,is_endiisfalse.Because greedy strategy is adopted for selecting action in DQN, theQ value obtained by DQN is over estimated. To solve this problem, double DQN (DDQN) selects actions and calculates the target Q value by two independent network structures. These two independent network structures are online Q-Network and target Q-Network, respectively, and they have the same network structure. The target Q-Network is used to decouple the action selection and the Q value estimation, which can solve the over estimation problem of Q value in DQN. When updating the parameters of DDQN, we select the action with the largest Q value by
(25)amaxs′;θ=argmaxa′Qs′,a;θ.Then, the targetQ value in DDQN is calculated by target Q-Network
(26)yiDDQN=ri,is_endiistrueri+γQ′s′,amaxs′;θ;θ′,is_endiisfalse,whereri is the instant reward value. Variable γ denotes the discount rate. And the discount rate indicates the importance of instant reward and future reward. γ=0 indicates that the agent is myopic, and the agent only considers the instant reward. When the discount rate is 1, the agent is farsighted and only considers the future reward. In practice, the value of γ is generally between 0 and 1.Then, we use the targetQ value to calculate the TD-error. Finally, we calculate the gradients according to the obtained TD-error and update the weights of online Q-Network. In the process of training, online Q-Network assigns its weights to target Q-Network every certain number of training steps to update the parameters of target Q-Network.The last layer of DDQN is a fully connected layer. However, the action space of the satellite is a two-dimensional matrix. In order to train DDQN, we transform the action space of the satellite into a one-dimensional vector with sizeL+1∗M. The specific transformation process is shown in Figure 4.Figure 4
Action space transformation process.We assume the index of actionel,m is indexel,m, and we can get the location of el,m in the last layer of DDQN
(27)indexel,m=l∗M+m,∀i∈1,2,3,⋯,L,m∈1,2,3,⋯,M.By transforming the action space into a one-dimensional vector, we can use the state transition information to train DDQN.


## 4.3. Priority Sampling-Based DDQN Algorithm for Joint Satellite Scheduling and Resource Allocation

In DDQN, the uniform sampling method is used to select samples when calculating the gradient and updating the model’s parameters. If samples in experience replay buffer are evenly distributed, uniform sampling method is effective. However, because satellites communicate with BSs by LoS, few satellites can communicate with BSs at any time. Moreover, most satellites are connected to virtual BS. Therefore, most of state transition information generated by satellites is from virtual BS to virtual BS because the communication time between satellites and BSs is short. At this time, the reward received by the satellite is 0. Thus, the reward of most state transition information in experience replay buffer is 0. If these samples are uniformly sampled, the learning performance of DDQN model will be degraded. Therefore, we use priority sampling method to enhance the performance of DDQN.For a sample, the greater its priority, the greater the probability of being sampled. We define the sampling probability of the sample as(28)Pj=pj∑ipi,where pj and Pj are the priority and sampling probability of sample j, respectively. Considering that sorting these samples wastes extensive computing resources, we use SumTree structure to store these samples. The specific structure of PSDDQN is shown in Figure 5.Figure 5
Structure of PSDDQN.When training PSDDQN model, we firstly selectM samples from SumTree structure and compute their loss functions and gradients,
(29)Lossθ=∑j=1MwjyjDDQN−Qsj,aj;θ2,(30)∇θLossθ=wjyjDDQN−Qsj,aj;θ∇Qsj,aj;θ,(31)wj=N∗Pj−βmaxiwi=N∗Pj−βmaxiN∗Pi−β=Pj−βmaxiPi−β,where wj represents the weight of priority of sample j for calculating the loss value. And variable β represents the hyperparameter, which is determined by empirical value.Then, the weights of DDQN are updated according to the calculated gradients. Moreover, we also need to update the priorities of samples in the SumTree structure. For the sample, the greater the TD-error, the better the effect of training the DDQN model. Here, we use TD-error value to represent the priority of the sample. We get the TD-error value of the sample and update its priority by(32)TD−errorj=yj−Qsj,aj;θ,(33)pj=TD‐errorj.The ground control centre has rich computing resources and can directly obtain the real-time state information of BSs and satellites. In the process of communication, BSs and satellites send their real-time state information to the ground control centre for updating their state information. The agent is able to obtain the latest state information of STINs from the ground control centre in real time. In our research, we deploy PSDDQN algorithm in the ground control centre. The specific details of PSDDQN is described in the following:Algorithm 1: PSDDQN for joint satellite association and channel allocation.
Input: number of episodes, Num_Episodes; number of time slices, Num_Timeslices; number of leaves of Sumtree structure, B; exploration rate, ε; update frequency, F; learning rate, α ; number of satellites, N;Output: the weight of online Q-Network, θ;1. Initialize the state of STINs, including the capacityC of BSs, antenna model, channel model, satellite orbit parameters, and the initial positions of satellites;2. Randomly initialize the weightθ of online Q-Network; for the weight in target Q-Network, θ'=θ;3.Forepisode =1 to Num_Episodes do4. Fortime =1 to Num_Timeslices do5.  Fori =1 to N do6.   Get the state information of satellitei from the ground control centre at time t, si;7.  End for8.  Get the sate information of BSs from the ground control centre at timet, H;9.  Obtain the state information of STINs at timet, S=(s1,s2,s3,…,sN , H);10.  Get the next state of STINsS’ from the ground control centre at t+1 and its termination flag;11.  Fori =1 to N do12.   Useε-greedy strategy to select an action, ai;13.   The agent execute actionai and obtain the instant reward ri by Equation (20);14.   Store state transition information (si, ai, ri, si’ ) in the Sumtree structure;15.  End for16.S= S’;17.  The BSs and satellites send their state information to the ground control centre for updating the state information of STINs;18.  Sample samples from the Sumtree structure, and compute the loss of Q-value of each sample according to Equation (29);19.  Compute the gradient of each sample according to Equation (30);20.  Update the weight of online Q-Network according to the back propagation algorithm;21.  Compute theTD-error value of each sample according Equation (32) and update its priority by Equation (33);22.  Update the parameters of target Q-Network every frequencyF, let θ'=θ;23. End for24.End for


## 5. Experimental Results and Analysis

We firstly introduce the experimental environment. Then, we validate PSDDQN algorithm in the simulation data and analysis the experimental results.


### 5.1. Experimental Environment

The communication system of STINs in this paper is mainly composed of 120 LEO satellites and 6 BSs with satellite gateways. The index of virtual BS is 0. And we use Walker delta model to construct satellite constellation by satellite tool kit (STK). The satellite constellation has 12 orbits, and each orbit has 10 satellites. The inclination and height of each orbit are 45 degrees and 550 km, respectively. Moreover, every satellite is equipped with same antenna. The specific parameters of simulation are described by Table1.Table 1
Parameters of simulation.
ParametersValueChannel bandwidthB41.67 kHzTransmitter EIRPp51.6 dBWAntenna gaing53.7 dBiAntenna diameter of BSD3.5 mCarrier frequencyf20 GHzPath loss modelLoss32.44+20logd+20logf (f:MHz)Noise powerN0-174 dBm/HzNumber of satellitesN120Number of BSsL6Number of channelsM4Number of time slicesNum_timeslices30Weight of utilityω0.7Weight of storage costξ0.3Considering that the estimated CSI is inaccurate and there is feedback propagation delay during the information transmission, the CSI information obtained by BSs is imperfect. Therefore, it is inaccurate to use the obtained CSI information to calculate the data transmission rate between satellites and BSs. To solve this problem, in practice, we use the estimated CSI value and the error value which follows circular symmetric complex Gaussian distribution to approximate the CSI value.We conduct experiments on a computer with Win 10 OS, 16G RAM, and 3.2 GHz CPU. The programming language used is Python, and we select TensorFlow framework to construct PSDDQN model. Additionally, each BS contains 4 channels in this paper. Therefore, each BS can connect to 4 satellites simultaneously. Considering that the number of BSs is 6, the fully connected layer of PSDDQN has 24 neurons. Moreover, the two independent network structures of PSDDQN are same. And each neural network structure consists of the input layer, two hidden layers, and the output layer. In addition, the number of neurons in the input layer, hidden layer, and output layer is 156, 64 and 32, and 24, respectively. The specific parameters of PSDDQN are given by Table2.Table 2
Parameters of PSDDQN algorithm.
ParametersValueBatch size32Learning rateα0.005Number of leaves of SumTree structure2000Number of neurons in input layer156Number of neurons in hidden layers64, 32Number of neurons in output layer24Discount rateγ0.9Activation functionReLUOptimizerGradient descent optimizerConsidering that most operations in PSDDQN are dot product operations, we use FLOPS to represent its computation time. Because we use the full connection way to build the neural network, the FLOPs in the first layer, the second layer, and the third layer are (2∗156-1) ∗ 64, (2 ∗ 64-1) ∗ 32, and (2 ∗ 32-1) ∗ 24, respectively. In addition, the activation function used in the first and second layers is ReLU; the required FLOPS of ReLU in these two layers are 64 and 32. The activation function used in the third layer is Softmax; the required FLOPS of Softmax in the third layer is 24 ∗ 4. Therefore, the total required FLOPs to execute one sample is 25672. Considering that the batch size we used is 32, the total FLOPS of PSDDQN is 25672 ∗ 32. For the parameters of PSDDQN, the parameters required in the first layer, the second layer, and the third layer are 156 ∗ 64+ 64, 64 ∗ 32+ 32, and 32 ∗ 24+ 24. Therefore, the total number of parameters in PSDDQN is 12920.The weight of utilityω and storage cost ξ in Equation (20) is set to 0.7 and 0.3, respectively. The utility of unit data offloaded by the satellite is 1. Moreover, the duration of each time slice is 3.5 minutes, and there are 30 time slices in total.


### 5.2. Experimental Result Analysis

We firstly verify the convergence of PSDDQN. Then, we compare PSDDQN algorithm with random allocation algorithm, greedy allocation algorithm, DQN [37], DDQN [43], and Dueling-DQN [44], respectively. Finally, we show the experimental results and give the discussion.Random allocation algorithm: for the satellite, one BS is randomly selected from the BSs within its coverage. Then, the satellite is associated with the selected BS, and a channel is randomly selected from the selected BS and assigned to the satellite.Greedy allocation algorithm: for the satellite, the BS with the largest amount of offloaded data is selected from the BSs within its coverage and associated with the satellite. If the BS has no free channels, the suboptimal BS is selected.


#### 5.2.1. Evaluation of Reinforcement Learning Model

Figure6 shows the rewards of PSDDQN algorithm with different episodes. We note from Figure 6 that in the beginning, the rewards obtained by PSDDQN algorithm fluctuate sharply. The reason is that the parameters of PSDDQN algorithm are initialized randomly. As the number of episodes increases, the fluctuation of rewards obtained decreases gradually and finally tends to be stable. It is obvious from Figure 6 that PSDDQN algorithm converges at about 50 episodes.Figure 6
Rewards of PSDDQN with different episodes.Figure7 plots the loss value of PSDDQN algorithm with different episodes. We observe that the loss value of PSDDQN is large in the beginning. With the increasing of episodes, the loss value of PSDDQN decreases gradually. Finally, the loss value tends to be stable. In addition, from Figure 7, we know that PSDDQN algorithm learns useful knowledge from state transition information.Figure 7
Loss value of PSDDQN with different episodes.From Figures6 and 7, we can see that PSDDQN algorithm converges at about 50 episodes. The convergence speed of PSDDQN in STINs is relatively fast. In the training process, the satellite selects one BS from the BSs within its coverage as an action. Because the communication time between satellites and BSs is very short, most of state transition information is from virtual BS to virtual BS. Therefore, when state transition is from virtual BS to virtual BS, the satellite can only select virtual BS as an effective action. In PSDDQN, we directly set that the satellite at this time can only connect to virtual BS, which effectively reduces the convergence time of PSDDQN. This explains why PSDDQN converges fast.When training PSDDQN algorithm, we use gradient descent method to update its parameters. If the learning rate is large, the step of parameter update is large. The convergence speed of PSDDQN is fast, but its performance fluctuates sharply in the convergence process. In addition, PSDDQN often cannot find the optimal solution. If the learning rate is small, the step of parameter update is small. And the convergence speed of PSDDQN is slow. Therefore, setting an appropriate learning rate in the experiment is very important for the convergence of PSDDQN.Figure8 depicts the rewards obtained by PSDDQN with different learning rates. With the increasing of episodes, PSDDQN algorithm with different learning rates can converge to a steady state. In addition, we also find that when α=0.01, the fluctuation of rewards obtained by PSDDQN is relatively large. However, if the value of α is too large, the agent often obtains the local optimal result rather than the optimal solution. When α=0.001, the convergence speed of PSDDQN is slower than that of α=0.005. Therefore, in this paper, we set α to be 0.005 to train PSDDQN.Figure 8
Rewards of PSDDQN with different learning rates.


#### 5.2.2. Evaluation of System Utility and Capacity Performance of BSs

We compare the system utility obtained by different algorithms with different number satellites in Figure9. The increases of satellites number lead to the gradually increases of utility for all algorithms. This is because that the more satellites exist, the more connections between satellites and BSs in a time slice. Therefore, the amount of data offloaded from satellites to BSs increases, which increases the system utility. Moreover, we also find that the utility obtained by random allocation algorithm is the least, and the utility obtained by PSDDQN is the largest. For random allocation algorithm, it randomly assigns BSs and channels to satellites without considering the channel state and their communication time. As a result, the utility obtained by random allocation algorithm is the least. For greedy allocation algorithm, satellites prefer to associate with BSs that can offload more data. Therefore, greedy allocation algorithm achieves better performance than random allocation algorithm.Figure 9
System utility with different number of satellites.Furthermore, the performance of RL-based methods (e.g., DQN, DDQN, and Dueling-DQN) is similar. The reasons are as follows. First, RL-based algorithms consider the load of BSs in joint satellite association and channel allocation, so BSs can store offloaded data at a lower cost. Second, when offloading data, they not only consider the utility generated by satellites in the current time slice but also consider the utility generated by satellites in the subsequent time slices. Therefore, RL-based algorithms obtain more utility than random allocation algorithm and greedy allocation algorithm. In addition, DQN, DDQN, and Dueling-DQN algorithms use uniform sampling method to train their models. And PSDDQN adopts priority sampling method to train its model, which enhance the learning efficiency of uniform sampling. This explains why PSDDQN obtains the best performance.In Figure10, we compare the system utility obtained by all algorithms. We from Figure 10 note that with the increasing of time slices, the utility of all algorithms shows a gradual upward trend. And PSDDQN algorithm has the greatest utility, and random allocation algorithm has the least utility. Moreover, we also find that in the beginning, each algorithm obtains almost the same utility. However, with the increasing of time slices, the utility obtained by different algorithms is different. The main reason is that in satellite scheduling and resource allocation, RL-based algorithms pay more attention to the expected cumulative utility rather than the immediate utility. Therefore, they get better performance. In contrast, random allocation algorithm and greedy allocation algorithm only consider the utility generated by satellites in the current time slice and ignore the utility generated by satellites in the subsequent time slices.Figure 10
System utility with different time slices.Moreover, we can also see that for RL-based algorithms, the performance of PSDDQN is the best and that of DQN is the worst. The reason is that DQN is trained by a neural network structure, leading to the over estimation of theQ value. In addition, the training process of DQN is unstable, while DDQN adopts two independent network structures to select the action and calculate the Q value, which alleviate the over estimation problem. This explains why DDQN performs better than DQN. When calculating the Q value, Dueling-DQN pays more attention to the action with large advantage value. Thus, it achieves better results than DDQN. For PSDDQN, it not only adopts two independent network structures to calculate the Q value but also uses priority sampling method to sample samples with larger TD-error value for training model. Therefore, compared with DQN, DDQN, and Dueling-DQN algorithms, PSDDQN algorithm gets better performance.Here, we show the capacity change of BSs. In STINs, BS0 is virtual BS, and its capacity does not change at any time. So we do not show the change of its capacity.Figures11–15 show the capacity of BSs of different algorithms with different time slices. When the time slices increase, the remaining capacity of BSs decreases gradually. The reason is that with the increasing of time slices, more satellites can offload data to BSs. In addition, we find that the remaining capacity of different BSs is different. Particularly, we observe from Figures 11 and 12 that random allocation algorithm and greedy allocation algorithm offload most of data to BS1 and BS2, leading to the significant reduction in the capacity of BS1 and BS2. The remaining capacities of BS1 and BS2 are 90 and 128, respectively. From Figures 13–15, we also note that random allocation algorithm and greedy allocation algorithm offload less data to BS3, BS4, and BS5. The remaining capacities of BS3, BS4, and BS5 are 158, 185, and 195, respectively. Therefore, we know that random allocation algorithm and greedy allocation algorithm cause the capacities of BSs to be used unevenly.Figure 11
Capacity change of BS1.Figure 12
Capacity change of BS2.Figure 13
Capacity change of BS3.Figure 14
Capacity change of BS4.Figure 15
Capacity change of BS5.Furthermore, we find that when RL-based algorithms are adopted, the remaining capacities of BS1, BS2, BS3, BS4, and BS5 are 160, 155, 140, 145, and 150, respectively. These algorithms basically ensure that the capacity of each BS is used evenly. When satellite association and channel allocation are performed, greedy allocation algorithm only considers the amount of offload data and ignores the load of BSs, which causes the capacity of some BSs to be overused and the storage cost of some BSs becomes larger. In contrast, RL-based algorithms consider not only the amount of offloaded data but also the capacity of BSs. Therefore, the results obtained by RL-based algorithms are better than those of greedy allocation algorithm and random allocation algorithm. In addition, considering that PSDDQN offloads more data to BSs, it consumes more capacity compared with other RL algorithms.


## 5.1. Experimental Environment

The communication system of STINs in this paper is mainly composed of 120 LEO satellites and 6 BSs with satellite gateways. The index of virtual BS is 0. And we use Walker delta model to construct satellite constellation by satellite tool kit (STK). The satellite constellation has 12 orbits, and each orbit has 10 satellites. The inclination and height of each orbit are 45 degrees and 550 km, respectively. Moreover, every satellite is equipped with same antenna. The specific parameters of simulation are described by Table1.Table 1
Parameters of simulation.
ParametersValueChannel bandwidthB41.67 kHzTransmitter EIRPp51.6 dBWAntenna gaing53.7 dBiAntenna diameter of BSD3.5 mCarrier frequencyf20 GHzPath loss modelLoss32.44+20logd+20logf (f:MHz)Noise powerN0-174 dBm/HzNumber of satellitesN120Number of BSsL6Number of channelsM4Number of time slicesNum_timeslices30Weight of utilityω0.7Weight of storage costξ0.3Considering that the estimated CSI is inaccurate and there is feedback propagation delay during the information transmission, the CSI information obtained by BSs is imperfect. Therefore, it is inaccurate to use the obtained CSI information to calculate the data transmission rate between satellites and BSs. To solve this problem, in practice, we use the estimated CSI value and the error value which follows circular symmetric complex Gaussian distribution to approximate the CSI value.We conduct experiments on a computer with Win 10 OS, 16G RAM, and 3.2 GHz CPU. The programming language used is Python, and we select TensorFlow framework to construct PSDDQN model. Additionally, each BS contains 4 channels in this paper. Therefore, each BS can connect to 4 satellites simultaneously. Considering that the number of BSs is 6, the fully connected layer of PSDDQN has 24 neurons. Moreover, the two independent network structures of PSDDQN are same. And each neural network structure consists of the input layer, two hidden layers, and the output layer. In addition, the number of neurons in the input layer, hidden layer, and output layer is 156, 64 and 32, and 24, respectively. The specific parameters of PSDDQN are given by Table2.Table 2
Parameters of PSDDQN algorithm.
ParametersValueBatch size32Learning rateα0.005Number of leaves of SumTree structure2000Number of neurons in input layer156Number of neurons in hidden layers64, 32Number of neurons in output layer24Discount rateγ0.9Activation functionReLUOptimizerGradient descent optimizerConsidering that most operations in PSDDQN are dot product operations, we use FLOPS to represent its computation time. Because we use the full connection way to build the neural network, the FLOPs in the first layer, the second layer, and the third layer are (2∗156-1) ∗ 64, (2 ∗ 64-1) ∗ 32, and (2 ∗ 32-1) ∗ 24, respectively. In addition, the activation function used in the first and second layers is ReLU; the required FLOPS of ReLU in these two layers are 64 and 32. The activation function used in the third layer is Softmax; the required FLOPS of Softmax in the third layer is 24 ∗ 4. Therefore, the total required FLOPs to execute one sample is 25672. Considering that the batch size we used is 32, the total FLOPS of PSDDQN is 25672 ∗ 32. For the parameters of PSDDQN, the parameters required in the first layer, the second layer, and the third layer are 156 ∗ 64+ 64, 64 ∗ 32+ 32, and 32 ∗ 24+ 24. Therefore, the total number of parameters in PSDDQN is 12920.The weight of utilityω and storage cost ξ in Equation (20) is set to 0.7 and 0.3, respectively. The utility of unit data offloaded by the satellite is 1. Moreover, the duration of each time slice is 3.5 minutes, and there are 30 time slices in total.


## 5.2. Experimental Result Analysis

We firstly verify the convergence of PSDDQN. Then, we compare PSDDQN algorithm with random allocation algorithm, greedy allocation algorithm, DQN [37], DDQN [43], and Dueling-DQN [44], respectively. Finally, we show the experimental results and give the discussion.Random allocation algorithm: for the satellite, one BS is randomly selected from the BSs within its coverage. Then, the satellite is associated with the selected BS, and a channel is randomly selected from the selected BS and assigned to the satellite.Greedy allocation algorithm: for the satellite, the BS with the largest amount of offloaded data is selected from the BSs within its coverage and associated with the satellite. If the BS has no free channels, the suboptimal BS is selected.


### 5.2.1. Evaluation of Reinforcement Learning Model

Figure6 shows the rewards of PSDDQN algorithm with different episodes. We note from Figure 6 that in the beginning, the rewards obtained by PSDDQN algorithm fluctuate sharply. The reason is that the parameters of PSDDQN algorithm are initialized randomly. As the number of episodes increases, the fluctuation of rewards obtained decreases gradually and finally tends to be stable. It is obvious from Figure 6 that PSDDQN algorithm converges at about 50 episodes.Figure 6
Rewards of PSDDQN with different episodes.Figure7 plots the loss value of PSDDQN algorithm with different episodes. We observe that the loss value of PSDDQN is large in the beginning. With the increasing of episodes, the loss value of PSDDQN decreases gradually. Finally, the loss value tends to be stable. In addition, from Figure 7, we know that PSDDQN algorithm learns useful knowledge from state transition information.Figure 7
Loss value of PSDDQN with different episodes.From Figures6 and 7, we can see that PSDDQN algorithm converges at about 50 episodes. The convergence speed of PSDDQN in STINs is relatively fast. In the training process, the satellite selects one BS from the BSs within its coverage as an action. Because the communication time between satellites and BSs is very short, most of state transition information is from virtual BS to virtual BS. Therefore, when state transition is from virtual BS to virtual BS, the satellite can only select virtual BS as an effective action. In PSDDQN, we directly set that the satellite at this time can only connect to virtual BS, which effectively reduces the convergence time of PSDDQN. This explains why PSDDQN converges fast.When training PSDDQN algorithm, we use gradient descent method to update its parameters. If the learning rate is large, the step of parameter update is large. The convergence speed of PSDDQN is fast, but its performance fluctuates sharply in the convergence process. In addition, PSDDQN often cannot find the optimal solution. If the learning rate is small, the step of parameter update is small. And the convergence speed of PSDDQN is slow. Therefore, setting an appropriate learning rate in the experiment is very important for the convergence of PSDDQN.Figure8 depicts the rewards obtained by PSDDQN with different learning rates. With the increasing of episodes, PSDDQN algorithm with different learning rates can converge to a steady state. In addition, we also find that when α=0.01, the fluctuation of rewards obtained by PSDDQN is relatively large. However, if the value of α is too large, the agent often obtains the local optimal result rather than the optimal solution. When α=0.001, the convergence speed of PSDDQN is slower than that of α=0.005. Therefore, in this paper, we set α to be 0.005 to train PSDDQN.Figure 8
Rewards of PSDDQN with different learning rates.


### 5.2.2. Evaluation of System Utility and Capacity Performance of BSs

We compare the system utility obtained by different algorithms with different number satellites in Figure9. The increases of satellites number lead to the gradually increases of utility for all algorithms. This is because that the more satellites exist, the more connections between satellites and BSs in a time slice. Therefore, the amount of data offloaded from satellites to BSs increases, which increases the system utility. Moreover, we also find that the utility obtained by random allocation algorithm is the least, and the utility obtained by PSDDQN is the largest. For random allocation algorithm, it randomly assigns BSs and channels to satellites without considering the channel state and their communication time. As a result, the utility obtained by random allocation algorithm is the least. For greedy allocation algorithm, satellites prefer to associate with BSs that can offload more data. Therefore, greedy allocation algorithm achieves better performance than random allocation algorithm.Figure 9
System utility with different number of satellites.Furthermore, the performance of RL-based methods (e.g., DQN, DDQN, and Dueling-DQN) is similar. The reasons are as follows. First, RL-based algorithms consider the load of BSs in joint satellite association and channel allocation, so BSs can store offloaded data at a lower cost. Second, when offloading data, they not only consider the utility generated by satellites in the current time slice but also consider the utility generated by satellites in the subsequent time slices. Therefore, RL-based algorithms obtain more utility than random allocation algorithm and greedy allocation algorithm. In addition, DQN, DDQN, and Dueling-DQN algorithms use uniform sampling method to train their models. And PSDDQN adopts priority sampling method to train its model, which enhance the learning efficiency of uniform sampling. This explains why PSDDQN obtains the best performance.In Figure10, we compare the system utility obtained by all algorithms. We from Figure 10 note that with the increasing of time slices, the utility of all algorithms shows a gradual upward trend. And PSDDQN algorithm has the greatest utility, and random allocation algorithm has the least utility. Moreover, we also find that in the beginning, each algorithm obtains almost the same utility. However, with the increasing of time slices, the utility obtained by different algorithms is different. The main reason is that in satellite scheduling and resource allocation, RL-based algorithms pay more attention to the expected cumulative utility rather than the immediate utility. Therefore, they get better performance. In contrast, random allocation algorithm and greedy allocation algorithm only consider the utility generated by satellites in the current time slice and ignore the utility generated by satellites in the subsequent time slices.Figure 10
System utility with different time slices.Moreover, we can also see that for RL-based algorithms, the performance of PSDDQN is the best and that of DQN is the worst. The reason is that DQN is trained by a neural network structure, leading to the over estimation of theQ value. In addition, the training process of DQN is unstable, while DDQN adopts two independent network structures to select the action and calculate the Q value, which alleviate the over estimation problem. This explains why DDQN performs better than DQN. When calculating the Q value, Dueling-DQN pays more attention to the action with large advantage value. Thus, it achieves better results than DDQN. For PSDDQN, it not only adopts two independent network structures to calculate the Q value but also uses priority sampling method to sample samples with larger TD-error value for training model. Therefore, compared with DQN, DDQN, and Dueling-DQN algorithms, PSDDQN algorithm gets better performance.Here, we show the capacity change of BSs. In STINs, BS0 is virtual BS, and its capacity does not change at any time. So we do not show the change of its capacity.Figures11–15 show the capacity of BSs of different algorithms with different time slices. When the time slices increase, the remaining capacity of BSs decreases gradually. The reason is that with the increasing of time slices, more satellites can offload data to BSs. In addition, we find that the remaining capacity of different BSs is different. Particularly, we observe from Figures 11 and 12 that random allocation algorithm and greedy allocation algorithm offload most of data to BS1 and BS2, leading to the significant reduction in the capacity of BS1 and BS2. The remaining capacities of BS1 and BS2 are 90 and 128, respectively. From Figures 13–15, we also note that random allocation algorithm and greedy allocation algorithm offload less data to BS3, BS4, and BS5. The remaining capacities of BS3, BS4, and BS5 are 158, 185, and 195, respectively. Therefore, we know that random allocation algorithm and greedy allocation algorithm cause the capacities of BSs to be used unevenly.Figure 11
Capacity change of BS1.Figure 12
Capacity change of BS2.Figure 13
Capacity change of BS3.Figure 14
Capacity change of BS4.Figure 15
Capacity change of BS5.Furthermore, we find that when RL-based algorithms are adopted, the remaining capacities of BS1, BS2, BS3, BS4, and BS5 are 160, 155, 140, 145, and 150, respectively. These algorithms basically ensure that the capacity of each BS is used evenly. When satellite association and channel allocation are performed, greedy allocation algorithm only considers the amount of offload data and ignores the load of BSs, which causes the capacity of some BSs to be overused and the storage cost of some BSs becomes larger. In contrast, RL-based algorithms consider not only the amount of offloaded data but also the capacity of BSs. Therefore, the results obtained by RL-based algorithms are better than those of greedy allocation algorithm and random allocation algorithm. In addition, considering that PSDDQN offloads more data to BSs, it consumes more capacity compared with other RL algorithms.


## 5.2.1. Evaluation of Reinforcement Learning Model

Figure6 shows the rewards of PSDDQN algorithm with different episodes. We note from Figure 6 that in the beginning, the rewards obtained by PSDDQN algorithm fluctuate sharply. The reason is that the parameters of PSDDQN algorithm are initialized randomly. As the number of episodes increases, the fluctuation of rewards obtained decreases gradually and finally tends to be stable. It is obvious from Figure 6 that PSDDQN algorithm converges at about 50 episodes.Figure 6
Rewards of PSDDQN with different episodes.Figure7 plots the loss value of PSDDQN algorithm with different episodes. We observe that the loss value of PSDDQN is large in the beginning. With the increasing of episodes, the loss value of PSDDQN decreases gradually. Finally, the loss value tends to be stable. In addition, from Figure 7, we know that PSDDQN algorithm learns useful knowledge from state transition information.Figure 7
Loss value of PSDDQN with different episodes.From Figures6 and 7, we can see that PSDDQN algorithm converges at about 50 episodes. The convergence speed of PSDDQN in STINs is relatively fast. In the training process, the satellite selects one BS from the BSs within its coverage as an action. Because the communication time between satellites and BSs is very short, most of state transition information is from virtual BS to virtual BS. Therefore, when state transition is from virtual BS to virtual BS, the satellite can only select virtual BS as an effective action. In PSDDQN, we directly set that the satellite at this time can only connect to virtual BS, which effectively reduces the convergence time of PSDDQN. This explains why PSDDQN converges fast.When training PSDDQN algorithm, we use gradient descent method to update its parameters. If the learning rate is large, the step of parameter update is large. The convergence speed of PSDDQN is fast, but its performance fluctuates sharply in the convergence process. In addition, PSDDQN often cannot find the optimal solution. If the learning rate is small, the step of parameter update is small. And the convergence speed of PSDDQN is slow. Therefore, setting an appropriate learning rate in the experiment is very important for the convergence of PSDDQN.Figure8 depicts the rewards obtained by PSDDQN with different learning rates. With the increasing of episodes, PSDDQN algorithm with different learning rates can converge to a steady state. In addition, we also find that when α=0.01, the fluctuation of rewards obtained by PSDDQN is relatively large. However, if the value of α is too large, the agent often obtains the local optimal result rather than the optimal solution. When α=0.001, the convergence speed of PSDDQN is slower than that of α=0.005. Therefore, in this paper, we set α to be 0.005 to train PSDDQN.Figure 8
Rewards of PSDDQN with different learning rates.


## 5.2.2. Evaluation of System Utility and Capacity Performance of BSs

We compare the system utility obtained by different algorithms with different number satellites in Figure9. The increases of satellites number lead to the gradually increases of utility for all algorithms. This is because that the more satellites exist, the more connections between satellites and BSs in a time slice. Therefore, the amount of data offloaded from satellites to BSs increases, which increases the system utility. Moreover, we also find that the utility obtained by random allocation algorithm is the least, and the utility obtained by PSDDQN is the largest. For random allocation algorithm, it randomly assigns BSs and channels to satellites without considering the channel state and their communication time. As a result, the utility obtained by random allocation algorithm is the least. For greedy allocation algorithm, satellites prefer to associate with BSs that can offload more data. Therefore, greedy allocation algorithm achieves better performance than random allocation algorithm.Figure 9
System utility with different number of satellites.Furthermore, the performance of RL-based methods (e.g., DQN, DDQN, and Dueling-DQN) is similar. The reasons are as follows. First, RL-based algorithms consider the load of BSs in joint satellite association and channel allocation, so BSs can store offloaded data at a lower cost. Second, when offloading data, they not only consider the utility generated by satellites in the current time slice but also consider the utility generated by satellites in the subsequent time slices. Therefore, RL-based algorithms obtain more utility than random allocation algorithm and greedy allocation algorithm. In addition, DQN, DDQN, and Dueling-DQN algorithms use uniform sampling method to train their models. And PSDDQN adopts priority sampling method to train its model, which enhance the learning efficiency of uniform sampling. This explains why PSDDQN obtains the best performance.In Figure10, we compare the system utility obtained by all algorithms. We from Figure 10 note that with the increasing of time slices, the utility of all algorithms shows a gradual upward trend. And PSDDQN algorithm has the greatest utility, and random allocation algorithm has the least utility. Moreover, we also find that in the beginning, each algorithm obtains almost the same utility. However, with the increasing of time slices, the utility obtained by different algorithms is different. The main reason is that in satellite scheduling and resource allocation, RL-based algorithms pay more attention to the expected cumulative utility rather than the immediate utility. Therefore, they get better performance. In contrast, random allocation algorithm and greedy allocation algorithm only consider the utility generated by satellites in the current time slice and ignore the utility generated by satellites in the subsequent time slices.Figure 10
System utility with different time slices.Moreover, we can also see that for RL-based algorithms, the performance of PSDDQN is the best and that of DQN is the worst. The reason is that DQN is trained by a neural network structure, leading to the over estimation of theQ value. In addition, the training process of DQN is unstable, while DDQN adopts two independent network structures to select the action and calculate the Q value, which alleviate the over estimation problem. This explains why DDQN performs better than DQN. When calculating the Q value, Dueling-DQN pays more attention to the action with large advantage value. Thus, it achieves better results than DDQN. For PSDDQN, it not only adopts two independent network structures to calculate the Q value but also uses priority sampling method to sample samples with larger TD-error value for training model. Therefore, compared with DQN, DDQN, and Dueling-DQN algorithms, PSDDQN algorithm gets better performance.Here, we show the capacity change of BSs. In STINs, BS0 is virtual BS, and its capacity does not change at any time. So we do not show the change of its capacity.Figures11–15 show the capacity of BSs of different algorithms with different time slices. When the time slices increase, the remaining capacity of BSs decreases gradually. The reason is that with the increasing of time slices, more satellites can offload data to BSs. In addition, we find that the remaining capacity of different BSs is different. Particularly, we observe from Figures 11 and 12 that random allocation algorithm and greedy allocation algorithm offload most of data to BS1 and BS2, leading to the significant reduction in the capacity of BS1 and BS2. The remaining capacities of BS1 and BS2 are 90 and 128, respectively. From Figures 13–15, we also note that random allocation algorithm and greedy allocation algorithm offload less data to BS3, BS4, and BS5. The remaining capacities of BS3, BS4, and BS5 are 158, 185, and 195, respectively. Therefore, we know that random allocation algorithm and greedy allocation algorithm cause the capacities of BSs to be used unevenly.Figure 11
Capacity change of BS1.Figure 12
Capacity change of BS2.Figure 13
Capacity change of BS3.Figure 14
Capacity change of BS4.Figure 15
Capacity change of BS5.Furthermore, we find that when RL-based algorithms are adopted, the remaining capacities of BS1, BS2, BS3, BS4, and BS5 are 160, 155, 140, 145, and 150, respectively. These algorithms basically ensure that the capacity of each BS is used evenly. When satellite association and channel allocation are performed, greedy allocation algorithm only considers the amount of offload data and ignores the load of BSs, which causes the capacity of some BSs to be overused and the storage cost of some BSs becomes larger. In contrast, RL-based algorithms consider not only the amount of offloaded data but also the capacity of BSs. Therefore, the results obtained by RL-based algorithms are better than those of greedy allocation algorithm and random allocation algorithm. In addition, considering that PSDDQN offloads more data to BSs, it consumes more capacity compared with other RL algorithms.


## 6. Conclusion

In our research, we investigated the joint satellite scheduling and resource allocation in STINs. We added a virtual BS to solve the problem that satellites cannot connect to BSs and reconstructed the communication model between satellites and BSs. Then, we formulated the joint satellite association and channel allocation as a joint optimization problem about utility and proposed PSDDQN algorithm to obtain the optimal strategy. When assigning appropriate BSs and channels to satellites, PSDDQN algorithm also considers the load balance of BSs. The simulation results demonstrate that PSDDQN obtains the maximum utility generated by offloaded data and achieves the load balance of BSs.For some applications, data generated by satellites need to be transmitted to the terrestrial networks in real time. However, most satellites cannot communicate with BSs during the time of a snapshot. Therefore, it is a challenge to transmit the real-time data generated by satellites to the terrestrial networks. Considering that the satellites connected to BSs can be used as gateways, in the future work, we mainly study the traffic scheduling of satellites and try to design a traffic scheduling scheme for reasonably and dynamically transmitting the data of satellites to these gateways. Finally, these gateways are used to transmit these data to the terrestrial networks.



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*Source: 1177544-2022-02-24.xml*