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Resources scheduling for data relay satellite with microwave and optical hybrid links based on improved niche genetic algorithm
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ARTICLE IN PRESS Optik xxx (2014) xxx–xxx
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Resources scheduling for data relay satellite with microwave and optical hybrid links based on improved niche genetic algorithm Wei-hu Zhao ∗ , Jing Zhao, Shang-hong Zhao, Yong-jun Li, yi Dong, chen Dong, Xuan Li Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
a r t i c l e
i n f o
Article history: Received 5 July 2013 Accepted 19 December 2013 Available online xxx Keywords: Optical satellite communication Microwave satellite communication Data relay satellite Resources scheduling Niche genetic algorithm
a b s t r a c t The formation of the Space-based Information System with the technology of high performance optical inter-satellite communication and the realization of global seamless coverage and mobile terminal accessing are the necessary trend of the development of optical and microwave hybrid communication. Considering the resources, missions and restraints of data relay satellite optical and microwave hybrid links system, a model of hybrid links resources scheduling is established and a scheduling algorithm based on an improved niche genetic algorithm is put forwarded. According to the multi-user-satellite, multi-time-window, multi-class-antenna and a number of missions with priority weight, the scheduling schemes are generated randomly to begin with and evaluated with the fitness functions. To obtain an optimal scheduling result, an improved niche genetic algorithm is adopted to optimize the scheduling schemes. The simulation result reveals that a satisfactory result is obtained and the improved niche genetic algorithm has advantages in both efficiency and performance in a scenario including a relay satellite with 2 optical antennas and 1 microwave antenna for user satellites connection, 8 user satellites with 64 missions. The simulation indicates that the model and the optimization algorithm are suitable for multi-user, multi-mission and multi-class-antenna hybrid communication recourses scheduling problem. © 2014 Elsevier GmbH. All rights reserved.
1. Introduction With the increased military and commercial interest in deep space probing, remote sensing and telemetering, high-resolution earth observing, an efficient means for information flow must be found. Future the high-resolution earth observation satellites call for satellite-to-ground links with high availability to make their data immediately available to the users. High availability is usually achieved by GEO relay links [1]. The existing microwave communication limits the GEO relay’s data rate to roughly 1Gbps [2,3], which will not meet the requirements of satellites data relay in future. Basic performance features of an optical GEO relay were verified in the SILEX program by ESA and JAXA demonstrating LEO-to-GEO [4], and GEO-to-Ground links [5,6] followed by the French LOLA program demonstrating optical links from an aircraft to the SILEX GEO satellite ARTEMIS. Verified in-orbit since January 2008 optical communication terminals now are applied in EDRS for commercial operation [7–10], at first for the LEO-to-GEO link, later on for the GEO-to-Ground link. The JAXA’s next generation inter-orbit optical communication system is adopted to data relay satellites as a data
∗ Corresponding author. E-mail address: [email protected] (W.-h. Zhao).
rate of 2.5Gbps communication link which connects user earth observation satellites and GEO data relay satellite and expected to be a key element of the future space data infrastructure from 2008 [11–13]. Consequently, optical communication will extend GEO relay’s data rate into the 10Gbps range in the future easily [14]. Therefore, the developed trends in the future data relay satellite system is the combination of microwave and optical hybrid communication. As for the satellite communications system with insufficient resources, the efficiency of utilizing the satellite communications resources is extremely important. With the increased interest in multi-satellites networking, various missions need to be transferred to the ground station. Many constraints need be considered to make optimal use of those resources by optimizing the satellite scheduling with artificial intelligent algorithm. Duo to the satellites moving in orbit around the earth, the missions of data relay are restricted by the view period windows, and always conflict with each other. Scheduling and optimizing satellite communication resources is an enormously complex mission [15]. Considered the factors above, an algorithm of Resources Scheduling Problem based on Data Relay Satellite with microwave and optical hybrid links (RSP-DRSMO) is studied. The remainder of the paper is organized as follows. Section 2 describes features of satellite resources scheduling. In Section 3, the mathematical
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Please cite this article in press as: W.-h. Zhao, et al., Resources scheduling for data relay satellite with microwave and optical hybrid links based on improved niche genetic algorithm, Optik - Int. J. Light Electron Opt. (2014), http://dx.doi.org/10.1016/j.ijleo.2013.12.042
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3.1. Constraints description (i = 1,2,. . .,N; N: number of missions)
Fig. 1. Data relay satellite with microwave and optical hybrid links.
formulation of RSP-DRSMO and a constrained programming model is proposed. In Section 4, The artificial intelligent optimization algorithm based on modified niche genetic algorithm is presented. In Section 5, some numerical experiments are conducted to demonstrate the ability of the proposed approach in applications to RSP-DRSMO. In section VI, we conclude our work and put forward some open issues for future research.
2. Features of satellite resources scheduling The data relay satellite with microwave and optical hybrid links is shown in Fig. 1. In Fig. 1, several kinds of user satellites with microwave or optical link need the GEO satellite to relay data, while the links resources of data relay satellite are insufficient to satisfy all the requirements. Besides, the user satellites move in orbit around the earth, the missions of data relay are restricted by the view period windows, which makes the resource scheduling problem much more complicated. The resource scheduling problem of data relay satellite with hybrid links has the following characteristics: (1) Relay satellite resource scheduling is constrained by the view period windows. The view period window is a collection of periods when relay and user satellites are visible. (2) Relay satellite resource scheduling is constrained by the priorities of the missions. (3) The rate of optical transmission is faster than which of microwave transmission an order of magnitude. Therefore, in the scheduling preprocessing stage, optical transmission in large mission has a great advantage. (4) The divergence angle of the laser beam is smaller than which of microwave beam 3 to 5 orders of magnitude. Spatial alignment of the laser beam between satellites is difficult. To ensure establishment of reliable inter-satellite laser link, the complex technology of space optical Acquisition, Tracking and Pointing (ATP) is needed. As a result of that, the duration of link establishment is usually up to tens of seconds or even reaches 100 s about. Therefore, the microwave communication has advantages in small size of mission transmission.
(1) J = {j1 ,j2 ,. . .,jN }, Set of independent missions, and each mission should choose an antenna to transmit. (2) Set the collection of antennas that mission ji could choose as Mi , and xi is the decision variable of the mission ji . Consequently, xi ∈Mi . (3) Mission scheduling identifier: Flag = {flag1 , flag2 ,. . .,flagN }, fi ∈{0,1}, flagi = 1 if mission i is accomplished and flagi = 0 otherwise. (4) Priority weights of missions: wi ∈{1,2,. . .,wmax }, The greater wi is, the more important the mission ji is. (5) The mission ji must satisfy the constraint of the view period windows Wi . STWi k and ETWi k are the starting time and the ending time of kth view period window of the mission ji , respectively. So Wi = ∪[STWi k , ETWi k ]. Suppose that the mission start to be transmitted at the time sti and set the transmission time as dti (which include the ready time of antenna switch and the user connection). Accordingly, sti must satisfy: STWi k ≤ sti ≤ ETWi k dti . (6) All missions must be accomplished in the scheduling period. Set the starting time and the ending time of the scheduling period as TS and TE , respectively. Accordingly, sti and dti must satisfy: TS ≤ sti ≤ TE , TS ≤ sti + dti ≤ TE . In the real scheduling preprocessing stage, there are many other constraints should be considered as well, like the power consumption of terminal, and the link capacity of GEO satellite to ground station, etc. However, we just considered the most important factors of satellite resources scheduling to simplify the problem. 3.2. Problem formulation The scheduler creates a scheduling scheme X first, which consists of an array of decision variables xi , i.e. X = {x1 ,x2 ,. . .,xi ,. . .,xN }, where xi ∈Mi . Then the scheduling algorithm arranges the timewindow for missions, according to scheduling scheme X and the constraints above. Therefore, the mission scheduling identifier (Flag) can be obtained. Objective function is defined as Max : N flagi wi to ensure the system to complete as many highi=1 priority missions as possible. The problem formulation defines as follows: Max :
f =
S.T. :
J = {j1 , j2 , . . ., jN } flagi ∈ {0, 1},
i = 1, 2, . . ., N
wi ∈ {1, 2, . . ., w max}, Wi ∈ {W1 , W2 , . . ., WN },
i = 1, 2, . . ., N
(1)
where :
TWi
Wi =
IF
Resources scheduling problem of relay satellite with microwave and optical hybrid links can be seen as a class of Constraint Satisfaction Problem (CSP). CSP in computer science and artificial intelligence research is one of the core issues, in real life many combinations, scheduling optimization problems can be described as a CSP. This consists of a set of variables. Each variable value is limited in a collection of constraints. The objective of the CSP is to find an assignment within the value ranges of all the variables.
flagi wi
i=1
[STWik , ETWik ],
k=1
3. Problem formulation
N
i = 1, 2, . . ., N
STWik ≤ sti ≤ ETWik − dti ,
TS ≤ sti ≤ TE ,
flagi = 1
TS ≤ sti + dti ≤ TE ,
i = 1, 2, . . ., N
4. Algorithm description An improved genetic algorithm is adopted to solve the data relay satellite optical communication resource scheduling, which is divided into two steps. First, calculate fitness value of the scheduling scheme consisting of decision variables for all missions, i.e. evaluation of its scheduling effect (the priority weights
Please cite this article in press as: W.-h. Zhao, et al., Resources scheduling for data relay satellite with microwave and optical hybrid links based on improved niche genetic algorithm, Optik - Int. J. Light Electron Opt. (2014), http://dx.doi.org/10.1016/j.ijleo.2013.12.042
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accomplished). Second, employ an improved niche genetic algorithm to search the optimal scheduling scheme.
4.1. Scheduling scheme implementation Suppose a total of N mission requests are made by the user satellites, each mission request with a priority weight. At the beginning, several scheduling schemes are generated randomly, i.e. each decision variable xi in each scheduling scheme X = {x1 , x2 ,. . .,xi ,. . .,xN } is generated randomly and satisfies xi ∈Mi . Then, the scheduling algorithm arranges the time-window for missions, according to scheduling scheme X and the constraints, with two processes: “Ascertain Current Mission Scheduling Time” and “Refresh Latter Mission Time-Window”. At last, the algorithm calculates the fitness function of the scheduling scheme X based on the scheduling result to evaluate the scheduling scheme.
4.1.1. Refreshing view period window All the missions are sorted in descending order by priority weights, for the mission request with same priority weight, according to the mission duration ascending sort. According to the antenna categories selected by the mission, the mission requests can be classed into different sets. For the same requests set, missions are scheduled in turn based on the mission orders in the set. To meet a mission request consists of two processes: “Ascertain Current Mission Scheduling Time” and “Refresh Latter Mission Time-Window”. Set the current scheduling mission is the i-th mission. To scheduling it, first step is to determine whether there is view period window available. The mission ji can be arranged scheduling within the kth view period window while dti ≤ ETWi k STWi k . Set the starting time of mission transmission sti = STWi k , and set the ending time of mission transmission eti = sti + dti and set flagi = 1. The second step is to refresh the latter mission time window. According to the different relationships between missions, different modes of refreshment operation are adopted. There are 5 kinds of modes, which are showed in Fig. 2.
4.1.2. Scheduling procedures The specific scheduling procedures are shown in Fig. 3. There are two processes: “Ascertain Current Mission Scheduling Time” and “Refresh Latter Mission Time-Window” in Fig. 3. The main process of the operation “Ascertain Current Mission Scheduling Time” includes: determining whether there is view period window available for the current scheduling mission. If the current scheduling mission request is still unable to be meet after all the view period windows traversed, the current scheduling mission join in the queue US (the queue of unscheduled missions). The main process of the operation “Refresh Latter Mission Time-Window” refers the modes in Fig. 2.
Fig. 3. Scheduling procedures of missions in same resource.
4.2. Excellent reservation-based niche genetic algorithm optimization scheduling scheme Each scheduling scheme Xi is regarded as an individual in a population, then, the artificial intelligent optimization algorithm is adopted to search an optimal scheduling scheme. Genetic algorithms set the value of objective function as the information for search. It is excellent in local optimization, but its global search capability is not satisfactory. The premature phenomenon occurs easily, which reduces the optimization capabilities severely. Considering the factors above, the niche idea is introduced in genetic algorithm and a method of niche genetic algorithm optimization based on excellent reservation and experiences remember strategy is designed, which maintains the diversity of the population properly and improves the global search ability. 4.2.1. Niche distance design To maintain the diversity of population and improve global search efficiency, the dynamic niche distance parameter is adopted in the algorithm. The individuals located in the “niche distance” from the excellent individuals will be severely punished, i.e. the fitness of individual located in the “niche distance” multiplies by a very small number (much less than 1), and thus its probability of being selected into the next generation is decreased. Definition 1:Set the minimum Euclidean distance from Xi to all individuals as the difference between Xi and the population, as shown in Eq. (2) below:
N
2 1 Di = min Xi − Xj = min xik − xjk j= / i
N
where i = 1, 2, . . ., Nscale
k=1
(2)
The variable Nscale is the population size. Definition 2: Define the mean differences between Nniche excellent individuals and population as the “niche distance”, and set the variable L as the “niche distance”. When the “niche distance” reduced to unit distance, the unit distance is set as “niche distance”, as shown in Eq. (3) below: L = max
Fig. 2. Operation of view period windows refreshment.
j= / i
mean {Di }, 1
(3)
i∈idx niche
4.2.2. Excellent reservation and experiences remember 4.2.2.1. Excellent reservation. To ensure that the algorithm converges at an optimal result quickly, a strategy of excellent reservation is designed. Suppose the population size is Nscale . Therefore, the children population with the size of Nscale is obtained with the algorithm executing the operation of crossover and mutation.
Please cite this article in press as: W.-h. Zhao, et al., Resources scheduling for data relay satellite with microwave and optical hybrid links based on improved niche genetic algorithm, Optik - Int. J. Light Electron Opt. (2014), http://dx.doi.org/10.1016/j.ijleo.2013.12.042
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4 Table 1 Basic situation of user satellites. Satellite
LEO 01
LEO 02
LEO 03
LEO 04
LEO 05
LEO 06
LEO 07
LEO 08
Altitude Orbit inclination angle
400 km 30◦
400 km 45◦
400 km 60◦
600 km 45◦
600 km 75◦
800 km 45◦
1000 km 60◦
1500 km 75◦
550
540
500
530 520
Fitness
Fitness
550
Improved Niche GA Standard GA
510
10
20
30
40
50
60
70
80
90
100
450
548 547 546 545
400
Times of repetition 350
55 57 59 61 63 65
Fig. 4. Repeated tests for two optimization algorithms.
300
Then, the algorithm will execute the operation of excellent reservation in a large population consisting of the parent and the children population. Nscale excellent individuals would be selected in the large population to make up the parent population in the next generation. 4.2.2.2. Experiences remember. Because of the heavy computation burden of fitness function calculation, the algorithm gets rid of the same individuals generated by the operation of crossover and mutation in each generation, and the same individuals are regenerated. Besides, the individuals near by the optimal one are usually generated repeatedly in different generations and their fitness functions are repetitive computed. Therefore, a strategy of “experiences remember” is proposed to overcome the problem. Definition 3: Define “experiences array” to remember the individuals near by the optimal one, considering of repetitive computation of the history individuals’ fitness functions. The history individuals near by the optimal one are regarded as the “experiences” of the algorithm, and these individuals are not necessary to take part in propagation. Each new individual generated in every generation would contrast with the history individuals in the “experiences array”. While a new individual is same as some individual in the “experiences array”, the new individual will be abandoned and regenerated. If the “experiences array” is oversized, the computation burden of contract will be too heavy for the algorithm. Therefore, the size of the “experiences array” should be set as a proper value. 4.2.3. Optimization algorithm procedure • Step 1: Parameter initialization. Set the population size Nscale , the maximum number of generations Gmax , crossover probability
Best Mean
0
20
40
60
Generation
80
100
Fig. 6. Improved niche genetic algorithm.
•
• •
• •
•
•
•
Pc and mutation probability Pm . Set the “experiences array” size EAsize and the niche population size Nniche . Step 2: Generate the initial population. Nscale individuals generated randomly as the parent population. Set the iterations counter: count = 1, and set “experiences array” = ϕ. Step 3: Make the crossover and the mutation operation. The children population is generated. Step 4: Contrast the children individuals with the parent population and the “experiences array” to judge whether there are same individuals, and if so, the same individuals would be regenerated. Step 5: Calculate the fitness value of all the children individuals, the mean fitness value favg and maximal fitness value fbest . Step 6: Experiences remember. Sort the large population with the children and the parent individuals according to the fitness value, and select EAsize excellent individuals as the “experiences array”. Step 7: Niche operations. Select Nniche excellent individuals as the niche population. Calculate the “niche distance” L according to the Eqs. (2) and (3), and punish the individuals located in the “niche distance” severely, i.e. the fitness value multiply by a number much less than 1. Step 8: Produce the new parent population. Sort the large population again according to the new fitness value obtained in Step 7, and select Nscale excellent individuals as the parent population in next generation. Step 9: Judge whether the end condition is satisfied. If not, iterations counter updates as count = count + 1, and return to Step 3; if the condition is satisfied, then output the result and the algorithm ends.
550
Fitness
500
5. Simulation and result analysis
450
5.1. Simulation scenario
400
Best Mean
350 300
0
20
40
60
80
Generation Fig. 5. Standard genetic algorithm.
100
Many international organizations of aerospace and astronomy are providing parameters of satellites in orbit, this paper uses data from a global satellite orbit database released by the U.S. Company AGI in June 2010, select the GEO satellite located at longitude of 10◦ as the data relay satellite. Suppose the relay satellite has two optical antennas and a microwave antenna (Ka-band) for LEO satellites connection. Hypothesis the links between GEO satellite and ground stations are stable and continual, so all the data could
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Fig. 7. Gantt chat of scheduling result.
be relay from LEO satellites to ground station. Set the transmission rates of optical antenna and Ka-band antenna are 2 Gbps and 0.5 Gbps, respectively, and the switch time of optical antenna and Ka-band antenna as 100 s and 1 s, respectively. Simulation time is 00:00:00 – 06:00:00, the user satellites orbital parameters import in STK, the specific parameters are shown in Table 1. Acquire the view period windows between the relay satellite and the user satellites with the software of STK, and input data in Matlab. Suppose each user satellite requests 8 missions, therefore there are a total of 64 missions required to schedule. Each mission has a different priority weight. 5.2. Result analysis Schedule the missions Based on the different optimization algorithm (population size Nscale = 60, “experiences array” size EAsize = 500, niche population size Nniche = 30, whole generations Gmax = 100, crossover probability Pc = 0.5 and mutation probability Pm = 0.2). Fig. 4 shows the results of repeated tests for two optimization algorithms (standard and improved niche genetic algorithm). The search processes of one test are shown in Fig. 5 (based on the standard genetic algorithm) and Fig. 6 (based on the improved niche genetic algorithm), and the optimal scheduling result is shown in Fig. 7. Fig. 4 shows the results of different optimization algorithm for the resource scheduling problem. It is obvious that the improved niche genetic algorithm is much better than the Standard Genetic Algorithm. Figs. 5 and 6 shows the best and the mean fitness values change in each generation with different optimization algorithm. The figures reveal that two algorithms have fast convergence both (converged in 30–40 generation both). Comparing the best value curves in Figs. 5 and 6 show that the best value derived by the standard genetic algorithm (Best Fitness = 539) is worse than the best value derived by the Improved niche genetic algorithm the optimal value (Best Fitness = 548), which indicates that the search process of the standard genetic algorithm fall into local optimum value easily. Comparing the mean fitness value curves in two figures indicates that there are some gaps between the mean fitness value and the best value of the population in the improved niche genetic algorithm, while the population of the standard genetic algorithm concentrated around the best value, which indicates that the improved niche genetic algorithm performs well in both population diversity and global search ability. Fig. 7 shows the specific scheduling time and scheduling order of the missions arranged on three antennas resource. Different color blocks represent different missions and the length of color block represents the duration of the mission transmission. The labels on
the color blocks represent the serial numbers and priority weights of the missions. Because of optical antenna with high speed transmission rates, the scheduling algorithm will try to arrange the large-capacity mission on optical antenna, and the small-capacity mission will be arranged flexibly in accordance with the scheduling state, which can be seen from the figure. The figure shows the high utilization rate of three antennas, especially the optical antenna. The whole scheduling time period is 21,600 s (6 h). The last mission scheduled ends at the time 21,569.586 s, and the completion rate of priority weights is 96.85%. Because of resource conflict, some missions with smaller priority weights unable to be arranged scheduling. Taking into account that the mission must be arranged to transmit within a specific time window, the results show that the scheduling algorithm has advantages in the convergence rate, the scheduling efficiency, and which is adaptive to multi-user, multimission and multi-class-antenna recourses scheduling problem. 6. Conclusions The efficient and reliable data relay satellite system resource scheduling is a key technology of Space-based Integrated Information Network construction in future. A scheduling algorithm for data relay satellite resource with microwave and optical links coexisting based on artificial intelligence optimization is studied. A model about microwave and optical hybrid links data relay satellite system resources scheduling constraints programming is introduced, and a scheduling algorithm based on improved niche genetic algorithm is proposed. The simulation results show that the improved niche genetic algorithm is better obviously than the Standard Genetic Algorithm in the performance of search for the global optimal solution to the problem. The optimal scheduling results show that the model is applicable to describe the multi-user, multi-mission and multi-class-antenna data relay satellite resource scheduling, and improved optimization algorithm can guarantee results in fast convergence while maintaining the diversity of the population. Next step the other factors affecting scheduling schemes will be considered, such as the capacity of the buffer memory on data relay satellite and the capacity of the links between GEO satellite and ground stations, the power consumption and the of data relay satellite, etc. Besides, the artificial intelligence optimization algorithm will be further modified to improve the optimization efficiency. Acknowledgement This work is financially supported by the National Science Foundation of China (Grant No. 61108068 and No. 61205002).
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References [1] J. Teles, M.V. Samii, C.E. Doll, Overview of TDRSS, Adv. Space Res. 16 (12) (1995) 67–76. [2] Harry C. Shaw, Michael W. Rackley, TDRSS Space ground link terminal user services subsystem replacement and upgrades, Proc. Space Oper. (2010) 1–13. [3] Jun-hui Wang, Kun Li, Elementary research and simulation of the TDRSS constellation’s construction, J. Syst. Simul. 16 (5) (2004) 974–977. [4] M. Toyoshima, et al., Ground-to-satellite optical link tests between the Japanese laser communication terminal and the Eurpean geostationary satellite ARTEMIS, Proc. SPIE 5338 (2004). [5] T. Nielsen, G. Oppenhäuser, In-orbit test result of an operational inter-satellite link between ARTEMIS and SPOT-4, SILEX, Proc. SPIE 4635 (2002). [6] B. Smutny, H. Kaempfner, G. Muehlnikel, 5.6 Gbps optical inter-satellite communication link, Proc. SPIE 7199 (2009) 1–6. [7] Böhmer Knut, Gregory Mark, Heine Frank, et al., Laser communication terminals for the European data relay system, Proc. SPIE 8246 (2012) 1–7. [8] R.J. Cesarone, D.S. Abraham, S. Shambayati, Deep-space optical communications, Int. Conf. Space Opt. Syst. Appl. 8 (2011) 410–423.
[9] Otakar Wilfert, Hennes Henniger, Zdenek Kolk, Optical communication in free space, Proc. SPIE 7141 (2008) 1–12. [10] Sodnik Zoran, Lutz Hanspeter, Furch Bernhard, et al., Optical satellite communications in Europe, Proc. SPIE 7587 (2010) 1–9. [11] Yamakawa Shiro, Hanada Tatsuyuki, Kohata Hiroki, et al., JAXA’s efforts toward next generation space data-relay satellite using optical inter-orbit communication technology, Proc. SPIE 7587 (2010) 1–6. [12] Hanada Tatsuyuki, Yamakawa Shiro, Kohata Hiroki, Study of optical inter-orbit communication technology for next generation space data-relay satellite, Proc. SPIE 7923 (2011) 1–6. [13] A. Löscher, Atmospheric influence on a laser beam observed on the OICETSARTEMIS communication demonstration link, Atmos. Meas. Tech. 3 (2010) 1233–1239. [14] Shang-hong Zhao, Ji-li Wu, Yong-jun Li, Wang Xiang, Ma Li-hua, Zhong-xiang Han, Present status and developing trends of satellite laser communication, Laser Optoelectron. Prog. 092801 (2011) 7–21. [15] Yan Zhang, Zhan-jun Sun, Jian Li, TDRS dynamic scheduling problem, J. Syst. Simul. 23 (7) (2011) 1461–1468.
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Contents lists available at ScienceDirect
Optik journal homepage: www.elsevier.de/ijleo
Resources scheduling for data relay satellite with microwave and optical hybrid links based on improved niche genetic algorithm Wei-hu Zhao ∗ , Jing Zhao, Shang-hong Zhao, Yong-jun Li, yi Dong, chen Dong, Xuan Li Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
a r t i c l e
i n f o
Article history: Received 5 July 2013 Accepted 19 December 2013 Available online xxx Keywords: Optical satellite communication Microwave satellite communication Data relay satellite Resources scheduling Niche genetic algorithm
a b s t r a c t The formation of the Space-based Information System with the technology of high performance optical inter-satellite communication and the realization of global seamless coverage and mobile terminal accessing are the necessary trend of the development of optical and microwave hybrid communication. Considering the resources, missions and restraints of data relay satellite optical and microwave hybrid links system, a model of hybrid links resources scheduling is established and a scheduling algorithm based on an improved niche genetic algorithm is put forwarded. According to the multi-user-satellite, multi-time-window, multi-class-antenna and a number of missions with priority weight, the scheduling schemes are generated randomly to begin with and evaluated with the fitness functions. To obtain an optimal scheduling result, an improved niche genetic algorithm is adopted to optimize the scheduling schemes. The simulation result reveals that a satisfactory result is obtained and the improved niche genetic algorithm has advantages in both efficiency and performance in a scenario including a relay satellite with 2 optical antennas and 1 microwave antenna for user satellites connection, 8 user satellites with 64 missions. The simulation indicates that the model and the optimization algorithm are suitable for multi-user, multi-mission and multi-class-antenna hybrid communication recourses scheduling problem. © 2014 Elsevier GmbH. All rights reserved.
1. Introduction With the increased military and commercial interest in deep space probing, remote sensing and telemetering, high-resolution earth observing, an efficient means for information flow must be found. Future the high-resolution earth observation satellites call for satellite-to-ground links with high availability to make their data immediately available to the users. High availability is usually achieved by GEO relay links [1]. The existing microwave communication limits the GEO relay’s data rate to roughly 1Gbps [2,3], which will not meet the requirements of satellites data relay in future. Basic performance features of an optical GEO relay were verified in the SILEX program by ESA and JAXA demonstrating LEO-to-GEO [4], and GEO-to-Ground links [5,6] followed by the French LOLA program demonstrating optical links from an aircraft to the SILEX GEO satellite ARTEMIS. Verified in-orbit since January 2008 optical communication terminals now are applied in EDRS for commercial operation [7–10], at first for the LEO-to-GEO link, later on for the GEO-to-Ground link. The JAXA’s next generation inter-orbit optical communication system is adopted to data relay satellites as a data
∗ Corresponding author. E-mail address: [email protected] (W.-h. Zhao).
rate of 2.5Gbps communication link which connects user earth observation satellites and GEO data relay satellite and expected to be a key element of the future space data infrastructure from 2008 [11–13]. Consequently, optical communication will extend GEO relay’s data rate into the 10Gbps range in the future easily [14]. Therefore, the developed trends in the future data relay satellite system is the combination of microwave and optical hybrid communication. As for the satellite communications system with insufficient resources, the efficiency of utilizing the satellite communications resources is extremely important. With the increased interest in multi-satellites networking, various missions need to be transferred to the ground station. Many constraints need be considered to make optimal use of those resources by optimizing the satellite scheduling with artificial intelligent algorithm. Duo to the satellites moving in orbit around the earth, the missions of data relay are restricted by the view period windows, and always conflict with each other. Scheduling and optimizing satellite communication resources is an enormously complex mission [15]. Considered the factors above, an algorithm of Resources Scheduling Problem based on Data Relay Satellite with microwave and optical hybrid links (RSP-DRSMO) is studied. The remainder of the paper is organized as follows. Section 2 describes features of satellite resources scheduling. In Section 3, the mathematical
0030-4026/$ – see front matter © 2014 Elsevier GmbH. All rights reserved. http://dx.doi.org/10.1016/j.ijleo.2013.12.042
Please cite this article in press as: W.-h. Zhao, et al., Resources scheduling for data relay satellite with microwave and optical hybrid links based on improved niche genetic algorithm, Optik - Int. J. Light Electron Opt. (2014), http://dx.doi.org/10.1016/j.ijleo.2013.12.042
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3.1. Constraints description (i = 1,2,. . .,N; N: number of missions)
Fig. 1. Data relay satellite with microwave and optical hybrid links.
formulation of RSP-DRSMO and a constrained programming model is proposed. In Section 4, The artificial intelligent optimization algorithm based on modified niche genetic algorithm is presented. In Section 5, some numerical experiments are conducted to demonstrate the ability of the proposed approach in applications to RSP-DRSMO. In section VI, we conclude our work and put forward some open issues for future research.
2. Features of satellite resources scheduling The data relay satellite with microwave and optical hybrid links is shown in Fig. 1. In Fig. 1, several kinds of user satellites with microwave or optical link need the GEO satellite to relay data, while the links resources of data relay satellite are insufficient to satisfy all the requirements. Besides, the user satellites move in orbit around the earth, the missions of data relay are restricted by the view period windows, which makes the resource scheduling problem much more complicated. The resource scheduling problem of data relay satellite with hybrid links has the following characteristics: (1) Relay satellite resource scheduling is constrained by the view period windows. The view period window is a collection of periods when relay and user satellites are visible. (2) Relay satellite resource scheduling is constrained by the priorities of the missions. (3) The rate of optical transmission is faster than which of microwave transmission an order of magnitude. Therefore, in the scheduling preprocessing stage, optical transmission in large mission has a great advantage. (4) The divergence angle of the laser beam is smaller than which of microwave beam 3 to 5 orders of magnitude. Spatial alignment of the laser beam between satellites is difficult. To ensure establishment of reliable inter-satellite laser link, the complex technology of space optical Acquisition, Tracking and Pointing (ATP) is needed. As a result of that, the duration of link establishment is usually up to tens of seconds or even reaches 100 s about. Therefore, the microwave communication has advantages in small size of mission transmission.
(1) J = {j1 ,j2 ,. . .,jN }, Set of independent missions, and each mission should choose an antenna to transmit. (2) Set the collection of antennas that mission ji could choose as Mi , and xi is the decision variable of the mission ji . Consequently, xi ∈Mi . (3) Mission scheduling identifier: Flag = {flag1 , flag2 ,. . .,flagN }, fi ∈{0,1}, flagi = 1 if mission i is accomplished and flagi = 0 otherwise. (4) Priority weights of missions: wi ∈{1,2,. . .,wmax }, The greater wi is, the more important the mission ji is. (5) The mission ji must satisfy the constraint of the view period windows Wi . STWi k and ETWi k are the starting time and the ending time of kth view period window of the mission ji , respectively. So Wi = ∪[STWi k , ETWi k ]. Suppose that the mission start to be transmitted at the time sti and set the transmission time as dti (which include the ready time of antenna switch and the user connection). Accordingly, sti must satisfy: STWi k ≤ sti ≤ ETWi k dti . (6) All missions must be accomplished in the scheduling period. Set the starting time and the ending time of the scheduling period as TS and TE , respectively. Accordingly, sti and dti must satisfy: TS ≤ sti ≤ TE , TS ≤ sti + dti ≤ TE . In the real scheduling preprocessing stage, there are many other constraints should be considered as well, like the power consumption of terminal, and the link capacity of GEO satellite to ground station, etc. However, we just considered the most important factors of satellite resources scheduling to simplify the problem. 3.2. Problem formulation The scheduler creates a scheduling scheme X first, which consists of an array of decision variables xi , i.e. X = {x1 ,x2 ,. . .,xi ,. . .,xN }, where xi ∈Mi . Then the scheduling algorithm arranges the timewindow for missions, according to scheduling scheme X and the constraints above. Therefore, the mission scheduling identifier (Flag) can be obtained. Objective function is defined as Max : N flagi wi to ensure the system to complete as many highi=1 priority missions as possible. The problem formulation defines as follows: Max :
f =
S.T. :
J = {j1 , j2 , . . ., jN } flagi ∈ {0, 1},
i = 1, 2, . . ., N
wi ∈ {1, 2, . . ., w max}, Wi ∈ {W1 , W2 , . . ., WN },
i = 1, 2, . . ., N
(1)
where :
TWi
Wi =
IF
Resources scheduling problem of relay satellite with microwave and optical hybrid links can be seen as a class of Constraint Satisfaction Problem (CSP). CSP in computer science and artificial intelligence research is one of the core issues, in real life many combinations, scheduling optimization problems can be described as a CSP. This consists of a set of variables. Each variable value is limited in a collection of constraints. The objective of the CSP is to find an assignment within the value ranges of all the variables.
flagi wi
i=1
[STWik , ETWik ],
k=1
3. Problem formulation
N
i = 1, 2, . . ., N
STWik ≤ sti ≤ ETWik − dti ,
TS ≤ sti ≤ TE ,
flagi = 1
TS ≤ sti + dti ≤ TE ,
i = 1, 2, . . ., N
4. Algorithm description An improved genetic algorithm is adopted to solve the data relay satellite optical communication resource scheduling, which is divided into two steps. First, calculate fitness value of the scheduling scheme consisting of decision variables for all missions, i.e. evaluation of its scheduling effect (the priority weights
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accomplished). Second, employ an improved niche genetic algorithm to search the optimal scheduling scheme.
4.1. Scheduling scheme implementation Suppose a total of N mission requests are made by the user satellites, each mission request with a priority weight. At the beginning, several scheduling schemes are generated randomly, i.e. each decision variable xi in each scheduling scheme X = {x1 , x2 ,. . .,xi ,. . .,xN } is generated randomly and satisfies xi ∈Mi . Then, the scheduling algorithm arranges the time-window for missions, according to scheduling scheme X and the constraints, with two processes: “Ascertain Current Mission Scheduling Time” and “Refresh Latter Mission Time-Window”. At last, the algorithm calculates the fitness function of the scheduling scheme X based on the scheduling result to evaluate the scheduling scheme.
4.1.1. Refreshing view period window All the missions are sorted in descending order by priority weights, for the mission request with same priority weight, according to the mission duration ascending sort. According to the antenna categories selected by the mission, the mission requests can be classed into different sets. For the same requests set, missions are scheduled in turn based on the mission orders in the set. To meet a mission request consists of two processes: “Ascertain Current Mission Scheduling Time” and “Refresh Latter Mission Time-Window”. Set the current scheduling mission is the i-th mission. To scheduling it, first step is to determine whether there is view period window available. The mission ji can be arranged scheduling within the kth view period window while dti ≤ ETWi k STWi k . Set the starting time of mission transmission sti = STWi k , and set the ending time of mission transmission eti = sti + dti and set flagi = 1. The second step is to refresh the latter mission time window. According to the different relationships between missions, different modes of refreshment operation are adopted. There are 5 kinds of modes, which are showed in Fig. 2.
4.1.2. Scheduling procedures The specific scheduling procedures are shown in Fig. 3. There are two processes: “Ascertain Current Mission Scheduling Time” and “Refresh Latter Mission Time-Window” in Fig. 3. The main process of the operation “Ascertain Current Mission Scheduling Time” includes: determining whether there is view period window available for the current scheduling mission. If the current scheduling mission request is still unable to be meet after all the view period windows traversed, the current scheduling mission join in the queue US (the queue of unscheduled missions). The main process of the operation “Refresh Latter Mission Time-Window” refers the modes in Fig. 2.
Fig. 3. Scheduling procedures of missions in same resource.
4.2. Excellent reservation-based niche genetic algorithm optimization scheduling scheme Each scheduling scheme Xi is regarded as an individual in a population, then, the artificial intelligent optimization algorithm is adopted to search an optimal scheduling scheme. Genetic algorithms set the value of objective function as the information for search. It is excellent in local optimization, but its global search capability is not satisfactory. The premature phenomenon occurs easily, which reduces the optimization capabilities severely. Considering the factors above, the niche idea is introduced in genetic algorithm and a method of niche genetic algorithm optimization based on excellent reservation and experiences remember strategy is designed, which maintains the diversity of the population properly and improves the global search ability. 4.2.1. Niche distance design To maintain the diversity of population and improve global search efficiency, the dynamic niche distance parameter is adopted in the algorithm. The individuals located in the “niche distance” from the excellent individuals will be severely punished, i.e. the fitness of individual located in the “niche distance” multiplies by a very small number (much less than 1), and thus its probability of being selected into the next generation is decreased. Definition 1:Set the minimum Euclidean distance from Xi to all individuals as the difference between Xi and the population, as shown in Eq. (2) below:
N
2 1 Di = min Xi − Xj = min xik − xjk j= / i
N
where i = 1, 2, . . ., Nscale
k=1
(2)
The variable Nscale is the population size. Definition 2: Define the mean differences between Nniche excellent individuals and population as the “niche distance”, and set the variable L as the “niche distance”. When the “niche distance” reduced to unit distance, the unit distance is set as “niche distance”, as shown in Eq. (3) below: L = max
Fig. 2. Operation of view period windows refreshment.
j= / i
mean {Di }, 1
(3)
i∈idx niche
4.2.2. Excellent reservation and experiences remember 4.2.2.1. Excellent reservation. To ensure that the algorithm converges at an optimal result quickly, a strategy of excellent reservation is designed. Suppose the population size is Nscale . Therefore, the children population with the size of Nscale is obtained with the algorithm executing the operation of crossover and mutation.
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4 Table 1 Basic situation of user satellites. Satellite
LEO 01
LEO 02
LEO 03
LEO 04
LEO 05
LEO 06
LEO 07
LEO 08
Altitude Orbit inclination angle
400 km 30◦
400 km 45◦
400 km 60◦
600 km 45◦
600 km 75◦
800 km 45◦
1000 km 60◦
1500 km 75◦
550
540
500
530 520
Fitness
Fitness
550
Improved Niche GA Standard GA
510
10
20
30
40
50
60
70
80
90
100
450
548 547 546 545
400
Times of repetition 350
55 57 59 61 63 65
Fig. 4. Repeated tests for two optimization algorithms.
300
Then, the algorithm will execute the operation of excellent reservation in a large population consisting of the parent and the children population. Nscale excellent individuals would be selected in the large population to make up the parent population in the next generation. 4.2.2.2. Experiences remember. Because of the heavy computation burden of fitness function calculation, the algorithm gets rid of the same individuals generated by the operation of crossover and mutation in each generation, and the same individuals are regenerated. Besides, the individuals near by the optimal one are usually generated repeatedly in different generations and their fitness functions are repetitive computed. Therefore, a strategy of “experiences remember” is proposed to overcome the problem. Definition 3: Define “experiences array” to remember the individuals near by the optimal one, considering of repetitive computation of the history individuals’ fitness functions. The history individuals near by the optimal one are regarded as the “experiences” of the algorithm, and these individuals are not necessary to take part in propagation. Each new individual generated in every generation would contrast with the history individuals in the “experiences array”. While a new individual is same as some individual in the “experiences array”, the new individual will be abandoned and regenerated. If the “experiences array” is oversized, the computation burden of contract will be too heavy for the algorithm. Therefore, the size of the “experiences array” should be set as a proper value. 4.2.3. Optimization algorithm procedure • Step 1: Parameter initialization. Set the population size Nscale , the maximum number of generations Gmax , crossover probability
Best Mean
0
20
40
60
Generation
80
100
Fig. 6. Improved niche genetic algorithm.
•
• •
• •
•
•
•
Pc and mutation probability Pm . Set the “experiences array” size EAsize and the niche population size Nniche . Step 2: Generate the initial population. Nscale individuals generated randomly as the parent population. Set the iterations counter: count = 1, and set “experiences array” = ϕ. Step 3: Make the crossover and the mutation operation. The children population is generated. Step 4: Contrast the children individuals with the parent population and the “experiences array” to judge whether there are same individuals, and if so, the same individuals would be regenerated. Step 5: Calculate the fitness value of all the children individuals, the mean fitness value favg and maximal fitness value fbest . Step 6: Experiences remember. Sort the large population with the children and the parent individuals according to the fitness value, and select EAsize excellent individuals as the “experiences array”. Step 7: Niche operations. Select Nniche excellent individuals as the niche population. Calculate the “niche distance” L according to the Eqs. (2) and (3), and punish the individuals located in the “niche distance” severely, i.e. the fitness value multiply by a number much less than 1. Step 8: Produce the new parent population. Sort the large population again according to the new fitness value obtained in Step 7, and select Nscale excellent individuals as the parent population in next generation. Step 9: Judge whether the end condition is satisfied. If not, iterations counter updates as count = count + 1, and return to Step 3; if the condition is satisfied, then output the result and the algorithm ends.
550
Fitness
500
5. Simulation and result analysis
450
5.1. Simulation scenario
400
Best Mean
350 300
0
20
40
60
80
Generation Fig. 5. Standard genetic algorithm.
100
Many international organizations of aerospace and astronomy are providing parameters of satellites in orbit, this paper uses data from a global satellite orbit database released by the U.S. Company AGI in June 2010, select the GEO satellite located at longitude of 10◦ as the data relay satellite. Suppose the relay satellite has two optical antennas and a microwave antenna (Ka-band) for LEO satellites connection. Hypothesis the links between GEO satellite and ground stations are stable and continual, so all the data could
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Fig. 7. Gantt chat of scheduling result.
be relay from LEO satellites to ground station. Set the transmission rates of optical antenna and Ka-band antenna are 2 Gbps and 0.5 Gbps, respectively, and the switch time of optical antenna and Ka-band antenna as 100 s and 1 s, respectively. Simulation time is 00:00:00 – 06:00:00, the user satellites orbital parameters import in STK, the specific parameters are shown in Table 1. Acquire the view period windows between the relay satellite and the user satellites with the software of STK, and input data in Matlab. Suppose each user satellite requests 8 missions, therefore there are a total of 64 missions required to schedule. Each mission has a different priority weight. 5.2. Result analysis Schedule the missions Based on the different optimization algorithm (population size Nscale = 60, “experiences array” size EAsize = 500, niche population size Nniche = 30, whole generations Gmax = 100, crossover probability Pc = 0.5 and mutation probability Pm = 0.2). Fig. 4 shows the results of repeated tests for two optimization algorithms (standard and improved niche genetic algorithm). The search processes of one test are shown in Fig. 5 (based on the standard genetic algorithm) and Fig. 6 (based on the improved niche genetic algorithm), and the optimal scheduling result is shown in Fig. 7. Fig. 4 shows the results of different optimization algorithm for the resource scheduling problem. It is obvious that the improved niche genetic algorithm is much better than the Standard Genetic Algorithm. Figs. 5 and 6 shows the best and the mean fitness values change in each generation with different optimization algorithm. The figures reveal that two algorithms have fast convergence both (converged in 30–40 generation both). Comparing the best value curves in Figs. 5 and 6 show that the best value derived by the standard genetic algorithm (Best Fitness = 539) is worse than the best value derived by the Improved niche genetic algorithm the optimal value (Best Fitness = 548), which indicates that the search process of the standard genetic algorithm fall into local optimum value easily. Comparing the mean fitness value curves in two figures indicates that there are some gaps between the mean fitness value and the best value of the population in the improved niche genetic algorithm, while the population of the standard genetic algorithm concentrated around the best value, which indicates that the improved niche genetic algorithm performs well in both population diversity and global search ability. Fig. 7 shows the specific scheduling time and scheduling order of the missions arranged on three antennas resource. Different color blocks represent different missions and the length of color block represents the duration of the mission transmission. The labels on
the color blocks represent the serial numbers and priority weights of the missions. Because of optical antenna with high speed transmission rates, the scheduling algorithm will try to arrange the large-capacity mission on optical antenna, and the small-capacity mission will be arranged flexibly in accordance with the scheduling state, which can be seen from the figure. The figure shows the high utilization rate of three antennas, especially the optical antenna. The whole scheduling time period is 21,600 s (6 h). The last mission scheduled ends at the time 21,569.586 s, and the completion rate of priority weights is 96.85%. Because of resource conflict, some missions with smaller priority weights unable to be arranged scheduling. Taking into account that the mission must be arranged to transmit within a specific time window, the results show that the scheduling algorithm has advantages in the convergence rate, the scheduling efficiency, and which is adaptive to multi-user, multimission and multi-class-antenna recourses scheduling problem. 6. Conclusions The efficient and reliable data relay satellite system resource scheduling is a key technology of Space-based Integrated Information Network construction in future. A scheduling algorithm for data relay satellite resource with microwave and optical links coexisting based on artificial intelligence optimization is studied. A model about microwave and optical hybrid links data relay satellite system resources scheduling constraints programming is introduced, and a scheduling algorithm based on improved niche genetic algorithm is proposed. The simulation results show that the improved niche genetic algorithm is better obviously than the Standard Genetic Algorithm in the performance of search for the global optimal solution to the problem. The optimal scheduling results show that the model is applicable to describe the multi-user, multi-mission and multi-class-antenna data relay satellite resource scheduling, and improved optimization algorithm can guarantee results in fast convergence while maintaining the diversity of the population. Next step the other factors affecting scheduling schemes will be considered, such as the capacity of the buffer memory on data relay satellite and the capacity of the links between GEO satellite and ground stations, the power consumption and the of data relay satellite, etc. Besides, the artificial intelligence optimization algorithm will be further modified to improve the optimization efficiency. Acknowledgement This work is financially supported by the National Science Foundation of China (Grant No. 61108068 and No. 61205002).
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References [1] J. Teles, M.V. Samii, C.E. Doll, Overview of TDRSS, Adv. Space Res. 16 (12) (1995) 67–76. [2] Harry C. Shaw, Michael W. Rackley, TDRSS Space ground link terminal user services subsystem replacement and upgrades, Proc. Space Oper. (2010) 1–13. [3] Jun-hui Wang, Kun Li, Elementary research and simulation of the TDRSS constellation’s construction, J. Syst. Simul. 16 (5) (2004) 974–977. [4] M. Toyoshima, et al., Ground-to-satellite optical link tests between the Japanese laser communication terminal and the Eurpean geostationary satellite ARTEMIS, Proc. SPIE 5338 (2004). [5] T. Nielsen, G. Oppenhäuser, In-orbit test result of an operational inter-satellite link between ARTEMIS and SPOT-4, SILEX, Proc. SPIE 4635 (2002). [6] B. Smutny, H. Kaempfner, G. Muehlnikel, 5.6 Gbps optical inter-satellite communication link, Proc. SPIE 7199 (2009) 1–6. [7] Böhmer Knut, Gregory Mark, Heine Frank, et al., Laser communication terminals for the European data relay system, Proc. SPIE 8246 (2012) 1–7. [8] R.J. Cesarone, D.S. Abraham, S. Shambayati, Deep-space optical communications, Int. Conf. Space Opt. Syst. Appl. 8 (2011) 410–423.
[9] Otakar Wilfert, Hennes Henniger, Zdenek Kolk, Optical communication in free space, Proc. SPIE 7141 (2008) 1–12. [10] Sodnik Zoran, Lutz Hanspeter, Furch Bernhard, et al., Optical satellite communications in Europe, Proc. SPIE 7587 (2010) 1–9. [11] Yamakawa Shiro, Hanada Tatsuyuki, Kohata Hiroki, et al., JAXA’s efforts toward next generation space data-relay satellite using optical inter-orbit communication technology, Proc. SPIE 7587 (2010) 1–6. [12] Hanada Tatsuyuki, Yamakawa Shiro, Kohata Hiroki, Study of optical inter-orbit communication technology for next generation space data-relay satellite, Proc. SPIE 7923 (2011) 1–6. [13] A. Löscher, Atmospheric influence on a laser beam observed on the OICETSARTEMIS communication demonstration link, Atmos. Meas. Tech. 3 (2010) 1233–1239. [14] Shang-hong Zhao, Ji-li Wu, Yong-jun Li, Wang Xiang, Ma Li-hua, Zhong-xiang Han, Present status and developing trends of satellite laser communication, Laser Optoelectron. Prog. 092801 (2011) 7–21. [15] Yan Zhang, Zhan-jun Sun, Jian Li, TDRS dynamic scheduling problem, J. Syst. Simul. 23 (7) (2011) 1461–1468.
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