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Asynchronous device detection for cognitive device-to-device communications
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Li, Bin, Guo, Weisi, Liang, Y., An, C. and Zhao, Chenglin (2018) Asynchronous device detection for cognitive device-to-device communications. IEEE Transactions on Wireless Communications, 17 (4). 2443 -2456. doi:10.1109/TWC.2018.2796553 ISSN 1536-1276.
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WRAP-asynchronous-device-detection-cognitive-device-device-Guo-2018.pdf - Accepted Version - Requires a PDF viewer. Download (1203Kb) | Preview |
Official URL: http://doi.org/10.1109/TWC.2018.2796553
Abstract
Dynamic spectrum sharing will facilitate the interference coordination in device-to-device (D2D) communications. In the absence of network level coordination, the timing synchronization among D2D users will be unavailable, leading to inaccurate channel state estimation and device detection, especially in time-varying fading environments. In this study, we design an asynchronous device detection/discovery framework for cognitive-D2D applications, which acquires timing drifts and dynamical fading channels when directly detecting the existence of a proximity D2D device (e.g. or primary user). To model and analyze this, a new dynamical system model is established, where the unknown timing deviation follows a random process, while the fading channel is governed by a discrete state Markov chain. To cope with the mixed estimation and detection (MED) problem, a novel sequential estimation scheme is proposed, using the conceptions of statistic Bayesian inference and random finite set. By tracking the unknown states (i.e. varying time deviations and fading gains) and suppressing the link uncertainty, the proposed scheme can effectively enhance the detection performance. The general framework, as a complimentary to a network-aided case with the coordinated signaling, provides the foundation for development of flexible D2D communications along with proximity-based spectrum sharing.
| Item Type: | Journal Article | |||||||||
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| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | |||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||
| Library of Congress Subject Headings (LCSH): | Cognitive radio networks, Bayesian statistical decision theory -- Industrial applications | |||||||||
| Journal or Publication Title: | IEEE Transactions on Wireless Communications | |||||||||
| Publisher: | IEEE | |||||||||
| ISSN: | 1536-1276 | |||||||||
| Official Date: | April 2018 | |||||||||
| Dates: |
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| Volume: | 17 | |||||||||
| Number: | 4 | |||||||||
| Page Range: | 2443 -2456 | |||||||||
| DOI: | 10.1109/TWC.2018.2796553 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| Date of first compliant deposit: | 15 January 2018 | |||||||||
| Date of first compliant Open Access: | 27 February 2018 | |||||||||
| RIOXX Funder/Project Grant: |
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