Performance evaluation for underlay cognitive satellite-terrestrial cooperative networks

Yuhan Ruan, Yongzhao Li*, Cheng-Xiang Wang, Rui Zhang, Hailin Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Article number102306
JournalScience China Information Sciences
Issue number10
Early online date9 Aug 2018
Publication statusPublished - Oct 2018


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

ASJC Scopus subject areas

  • General Computer Science


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