Advertisement

Performance evaluation for underlay cognitive satellite-terrestrial cooperative networks

  • Yuhan Ruan
  • Yongzhao Li
  • Cheng-Xiang Wang
  • Rui Zhang
  • Hailin Zhang
Research Paper
  • 16 Downloads

Abstract

With the continuously increasing demand for broadband applications and services, underlay cognitive satellite-terrestrial networks, enabling to accommodate better wireless services within the scarce spectrum, have attracted tremendous attentions recently. In this network, satellite communications are allowed to operate in the frequency bands allocated to terrestrial networks under the interference constraints imposed by terrestrial network, which may lead to a performance degradation of the satellite network. To guarantee the performance of the primary terrestrial network as well as the secondary satellite network, we introduce the cooperation into cognitive satellite-terrestrial networks and investigate the performance of the new framework, i.e., cognitive satellite-terrestrial cooperative network (CSTCN). Specifically, by restricting the transmit power of satellite communications with interference power constraints imposed by terrestrial communications, we firstly obtain the received signal-to-interference-plus-noise ratio (SINR) of the considered network. Moreover, by employing the moment generating function (MGF) approach, closed-form expressions for symbol error rate (SER) and outage probability (OP) of the considered cognitive network are derived. The analytical results obtained in this paper can provide theoretical support for optimizing the performance of satellite-terrestrial networks.

Keywords

cognitive satellite-terrestrial cooperative network interference constraints moment generating function symbol error rate outage probability 

Notes

Acknowledgements

This work was supported by National Key R&D Program of China (Grant No. 2016YFB1200202), National Natural Science Foundation of China (Grant No. 61771365), Natural Science Foundation of Shaanxi Province (Grant No. 2017JZ022), Programme of Introducing Talents of Discipline to Universities (111 Project) (Grant No. B08038), EU H2020 RISE TESTBED Project (Grant No. 734325), and EPSRC TOUCAN Project (Grant No. EP/L020009/1).

References

  1. 1.
    Evans B, Werner M, Lutz E, et al. Integration of satellite and terrestrial systems in future multimedia communications. IEEE Wirel Commun, 2005, 12: 72–80CrossRefGoogle Scholar
  2. 2.
    Sadek M, Aissa S. Personal satellite communication: technologies and challenges. IEEE Wirel Commun, 2012, 19: 28–35CrossRefGoogle Scholar
  3. 3.
    Ruan Y H, Li Y Z, Wang C X, et al. Energy efficient adaptive transmissions in integrated satellite-terrestrial networks with SER constraints. IEEE Trans Wirel Commun, 2018, 17: 210–222CrossRefGoogle Scholar
  4. 4.
    Li H J, Yin H, Dong F H, et al. Capacity upper bound analysis of the hybrid satellite terrestrial communication systems. IEEE Commun Lett, 2016, 20: 2402–2405CrossRefGoogle Scholar
  5. 5.
    Zhang J X, Evans B, Imran M A, et al. Green hybrid satellite terrestrial networks: fundamental trade-off analysis. In: Proceedings of IEEE Vehicular Technology Conference (VTC’16), Nanjing, 2016Google Scholar
  6. 6.
    Arti M K, Jindal S K. OSTBC transmission in shadowed-Rician land mobile satellite links. IEEE Trans Veh Technol, 2016, 65: 5771–5777CrossRefGoogle Scholar
  7. 7.
    Khan A H, Imran M A, Evans B G. Semi-adaptive beamforming for OFDM based hybrid terrestrial-satellite mobile system. IEEE Trans Wirel Commun, 2012, 11: 3424–3433CrossRefGoogle Scholar
  8. 8.
    Sithamparanathan K, de Nardis L, Di Benedetto M G, et al. Cognitive satellite terrestrial radios. In: Proceedings of IEEE Global Communications Conference (GLOBECOM’10), Miami, 2010Google Scholar
  9. 9.
    Ge X H, Tu S, Mao G Q, et al. 5G ultra-dense cellular networks. IEEE Wirel Commun, 2016, 23: 72–79CrossRefGoogle Scholar
  10. 10.
    Wang C X, Wu S B, Bai L, et al. Recent advances and future challenges for massive MIMO channel measurements and models. Sci China Inf Sci, 2016, 59: 021301Google Scholar
  11. 11.
    Höyhtyä M, kyröläinen J, Hulkkonen A, et al. Application of cognitive radio techniques to satellite communication. In: Proceedings of IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN’12), Bellevue, 2012. 540–551CrossRefGoogle Scholar
  12. 12.
    Sharma S K, Chatzinotas S, Ottersten B. Satellite cognitive communications: interference modeling and techniques selection. In: Proceedings of IEEE Advanced Satellite Multimedia Systems Conference (ASMS) and Signal Processing for Space Communications Workshop (SPSC), Baiona, 2012. 111–118Google Scholar
  13. 13.
    Haider F, Wang C X, Haas H, et al. Spectral and energy efficiency analysis for cognitive radio networks. IEEE Trans Wirel Commun, 2015, 14: 2969–2980CrossRefGoogle Scholar
  14. 14.
    Icolari V, Guidotti A, Tarchi D, et al. An interference estimation technique for satellite cognitive radio systems. In: Proceedings of IEEE International Conference on Communications (ICC’15), London, 2015. 892–897Google Scholar
  15. 15.
    Kuang L L, Chen X, Jiang C X, et al. Radio resource management in future terrestrial-satellite communication networks. IEEE Wirel Commun, 2017, 24: 81–87CrossRefGoogle Scholar
  16. 16.
    Ruan Y H, Li Y Z, Wang C X, et al. Outage performance of integrated satellite-terrestrial networks with hybrid CCI. IEEE Commun Lett, 2017, 21: 1545–1548CrossRefGoogle Scholar
  17. 17.
    Lagunas E, Sharma S K, Maleki S, et al. Resource allocation for cognitive satellite communications with incumbent terrestrial networks. IEEE Trans Cogn Commun Netw, 2015, 1: 305–317CrossRefGoogle Scholar
  18. 18.
    Guidolin F, Nekovee M, Badia L, et al. A cooperative scheduling algorithm for the coexistence of fixed satellite services and 5G cellular network. In: Proceedings of IEEE International Conference on Communications (ICC’15), London, 2015. 1322–1327Google Scholar
  19. 19.
    Zhang W S, Wang C X, Chen D, et al. Energy-spectral efficiency tradeoff in cognitive radio networks. IEEE Trans Veh Technol, 2016, 65: 2208–2218CrossRefGoogle Scholar
  20. 20.
    An K, Ouyang J, Lin M, et al. Outage analysis of multi-antenna cognitive hybrid satellite-terrestrial relay networks with beamforming. IEEE Commun Lett, 2015, 19: 1157–1160CrossRefGoogle Scholar
  21. 21.
    Bhatnagar M R, Arti M K. Performance analysis of AF based hybrid satellite-terrestrial cooperative network over generalized fading channels. IEEE Commun Lett, 2013, 17: 1912–1915CrossRefGoogle Scholar
  22. 22.
    Sreng S, Escrig B, Boucheret M L. Exact symbol error probability of hybrid/integrated satellite-terrestrial cooperative network. IEEE Trans Wirel Commun, 2013, 12: 1310–1319CrossRefGoogle Scholar
  23. 23.
    Ruan Y H, Li Y Z, Zhang R, et al. Performance analysis of hybrid satellite-terrestrial cooperative networks with distributed alamouti code. In: Proceedings of IEEE Vehicular Technology Conference (VTC’16), Nanjing, 2016Google Scholar
  24. 24.
    Chun L. A statistical model for a land mobile satellite link. IEEE Trans Veh Technol, 1985, 34: 122–127CrossRefGoogle Scholar
  25. 25.
    Peppas K P. Accurate closed-form approximations to generalised-K sum distributions and applications in the performance analysis of equal-gain combining receivers. IET Commun, 2011, 5: 982–989CrossRefGoogle Scholar
  26. 26.
    Ruan Y H, Li Y Z, Wang C X, et al. Effective capacity analysis for underlay cognitive satellite-terrestrial networks. In: Proceedings of IEEE International Conference on Communications (ICC’17), Paris, 2017Google Scholar
  27. 27.
    Gradshteyn I S, Ryzhik I M. Table of Integrals, Series, and Products. 6th ed. Orlando: Academic Press, 2000zbMATHGoogle Scholar
  28. 28.
    Simon M K, Alouini M S. Digital Communication over Fading Channels. Hoboken: John Wiley & Sons, 2005Google Scholar
  29. 29.
    Simon M K, Alouini M S, Ko Y C. Outage probability of diversity systems over generalized fading channels. IEEE Trans Commun, 2000, 48: 1783–1787CrossRefGoogle Scholar
  30. 30.
    Adamchik V S, Marichev O I. The algorithm for calculating integrals of hypergeometric type functions and its realization in reduce systems. In: Proceedings of International Conference Symposium on Algebraic Computing, Tokyo, 1990. 212–224Google Scholar
  31. 31.
    Ikki S S, Aissa S. Performance analysis of two-way amplify-and-forward relaying in the presence of co-channel interferences. IEEE Trans Commun, 2012, 60: 933–939CrossRefzbMATHGoogle Scholar
  32. 32.
    Mckay M R, Zanella A, Collings I B, et al. Error probability and SINR analysis of optimum combining in rician fading. IEEE Trans Commun, 2009, 57: 676–687CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yuhan Ruan
    • 1
  • Yongzhao Li
    • 1
  • Cheng-Xiang Wang
    • 2
  • Rui Zhang
    • 1
  • Hailin Zhang
    • 1
  1. 1.State Key Laboratory of Integrated Service NetworkXidian UniversityXi’anChina
  2. 2.School of Engineering and Physical SciencesHeriot-Watt UniversityEdinburghUK

Personalised recommendations