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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhaowei Zhang Jianwei Wang Hailin Zhang |
| Copyright Year | 2015 |
| Description | Author affiliation: State Key Lab. of Integrated Service Networks, Xidian Univ., Xi'an, China (Zhaowei Zhang; Jianwei Wang; Hailin Zhang) |
| Abstract | For the deep-space communications, the long distance transmission significantly deteriorates the received signal-to-noise ratio (SNR) at the receiver. Also, the accelerating movement between the high-speed transmitter-receiver pair causes the accelerating Doppler-shift. To address the Doppler-shift acquisition problem under not only the very low SNR but also the very high accelerating movement scenarios, in this paper, we develop one joint-detection (JD) scheme including two algorithms, called the single JD and the iterative JD, respectively, to significantly increase the acquisition probability. First, the single JD algorithm, which has a concise closed-form expression for the net increment of acquisition probability, uses multiple combination periods to narrow the search range for the next acquisition. Second, the iterative JD algorithm, which builds on the iterative of single JD algorithm, achieves the further increase of acquisition probability. Moreover, the simulation results show that our proposed scheme can significantly increase the acquisition probability of Doppler-shift for the deep-space communications. |
| Starting Page | 476 |
| Ending Page | 481 |
| File Size | 292729 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479984060 |
| DOI | 10.1109/WCNC.2015.7127516 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-03-09 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | acquisition probability Deep-space communications low SNR accelerating Doppler-shift joint-detection |
| Content Type | Text |
| Resource Type | Article |
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