Abstract
In this paper, we consider an underlay multipleinput- multiple-output (MIMO) cognitive radio network (CRN) including a pair of primary nodes, a couple of secondary nodes, and an eavesdropper, where the secondary transmitter is powered by the renewable energy harvested from the primary transmitter in order to improve both energy efficiency and spectral efficiency. Based on whether the channel state information (CSI) of wiretap links are available or not, the secrecy outage performance of the optimal antenna selection (OAS) scheme and suboptimal antenna selection (SAS) scheme for underlay MIMO CRN with energy harvesting are investigated and compared with traditional space-time transmission scheme. The closed-form expressions for exact and asymptotic secrecy outage probability are derived. Monte-Carlo simulations are conducted to testify the accuracy of the analytical results. The analysis illustrates that OAS scheme outperforms SAS scheme. Furthermore, the asymptotic result shows that no matter which scheme is considered, the OAS and SAS schemes can achieve the same secrecy diversity order.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 192-203 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Green Communications and Networking |
| Volume | 1 |
| Issue number | 2 |
| DOIs | |
| State | Published - Mar 20 2017 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledgements: This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61471076, 61401372, the Program for Changjiang Scholars and Innovative Research Team in University under Grant IRT 16R72, the special fund for Key Lab of Chongqing Municipal Education Commission, the Project of Fundamental and Frontier Research Plan of Chongqing under Grant cstc2015jcyjBX0085, and the Scientific and Technological Research Program of Chongqing Municipal Education Commission under Grant KJ1600413. Parts of this publication were made possible by PDRA (PostDoctoral Research Award) grant # PDRA1-1227-13029 from the Qatar National Research Fund (QNRF) (a member of Qatar Foundation (QF)).
