Optimizing Satellite Handover Rate Using Particle Swarm Optimization (PSO) Algorithm

Tahir Rehman, Faizullah Khan, Surat Khan, Amjad Ali


Low earth orbital satellites are able to provide access to worldwide wireless or regional mobile services. The Low Earth Orbit (LEO) satellite arrangement generally has a cellular type of contact, similar to the cellular telephone structure. Neighboring satellites are linked with each other through Inter Satellite Links (ISLs). The user device interconnects with the satellite through a user mobile link (UML) while Gateway Link (GWL) is used to link a satellite with an earth station. The consumer’s call duration may exceed than the service duration of a satellite necessitating hand over of the call to another detectible satellite to avoid disruption of the call. At the time of handover a user may be covered by more than single satellite. Either the mobile stations at earth or the serving satellite must be intelligent enough to handover the call to a satellite having longest coverage time. In this paper, we suggest an algorithm which decreases the predictable hand over rate by selecting a satellite with the largest service time. Using the Particle Swarm Optimization (PSO) algorithm, the serving satellite itself calculates the service time of neighboring satellites and optimally handover the call to a satellite with a maximum coverage time. Simulations are performed using MATLAB and various results are obtained for two prominent LEO satellite constellations, IRIDIUM and GLOBALSTAR. The effects of the satellite parameters (elevation angle, height, angular velocity and trace angle) on service time are calculated mathematically by drawing different scenarios.


Satellite constellation; Satellite handover; Link layer handover; Particle swarm optimization

Full Text:



Bai Q. (2010). Analysis of Particle Swarm Optimization Algorithm. Computer and Information Science

(1): 180- 184

Chini P, Giambene, Kota. (2012). Development status and comparative analysis of satellite mobile

communication system. Satell. Netw. IEEE 5:38–46.

Fenglin. (2015). A simple real-time handover management in the mobile satellite communication

networks 175 - 179.

Forouzan BA. (2013). Data Communications and Networking. McGraw-Hill Higher Education. 3rd Ed.

California, de anza college.

Lee WN, Park JB. (2007). Development of an Educational Simulator for Particle Swarm Optimization

and Economic Dispatch Applications. International Conference on Intelligent Systems Applications to

Power Systems.

Li SF et al., (2011). Overlap area assisted guaranteed handover scheme for HAP communication

system. Communication pp. 131-137.

Liao M et al., (2015). Analysis of maximum traffic intensity under pre-set quality of service requirements

in low earth orbit mobile satellite system for fix channel reservation. IET Communication 9(13):1575–

Lin Y, He S, Zheng J. (2010). The Suggestionto ISL Technology Development of Global Navigation

Satellites. Spacecraft Engineering. 19(6): 1-7.

Liu Y, BW, Wang B. (2015). An Improved Satellites Routing Handover Strategy. International

Conference on Estimation, Detection and Information Fusion p. 290 – 292.

Mirette, Sadek, Sonia Aisa. (2012). Personal satellite communication: Technologies and challenges.

IEEE Wireless Communications.

Seyedi Y, Rahimi F. (2012). A trace-time framework for prediction of elevation angle over land mobile

LEO satellites networks 62: 793–804.

Shkelzen Cakaj et al. (2014). The Coverage Analysis for Low Earth Orbiting Satellites at Low Elevation.

IJACSA 5(6).

Stallings W. (2003). Wireless Communications and Networks. Pearson Prentice Hall United States.

nd Ed.

Vijayalakshmi S et al. (2014). Particle swarm optimization with aging leader and challenges for

multi-swarm optimization Technology (IJARCET).Volume 3 Issue 3, March 2014.

Contacts | Feedback
© 2002-2014 BUITEMS