Open Access
| Issue |
ITM Web Conf.
Volume 47, 2022
2022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
|
|
|---|---|---|
| Article Number | 01006 | |
| Number of page(s) | 13 | |
| Section | Computer Science and System Design, Application | |
| DOI | https://doi.org/10.1051/itmconf/20224701006 | |
| Published online | 23 June 2022 | |
- A. Sultan, “Sensing and Transmit Energy Optimization for an Energy Harvesting Cognitive Radio,” IEEE Wireless Communication Letters, vol. 1, no. 5, pp. 500-503, 2012. [CrossRef] [Google Scholar]
- X. Gao, W. Xu, S. Li, and J. Lin, “An online energy allocation strategy for energy harvesting cognitive radio systems,” in International Conference on Wireless Communications & Signal Processing, 2013. [Google Scholar]
- S. Park, H. Kim, and D. Hong, “Cognitive Radio Networks with Energy Harvesting,” IEEE Transactions on Wireless Communications, vol. 12, no. 3, pp. 1386-1397, 2013. [CrossRef] [Google Scholar]
- S. Park and D. Hong, “Optimal Spectrum Access for Energy Harvesting Cognitive Radio Networks,” IEEE Transactions on Wireless Communications, vol. 12, no. 12, pp. 6166-6179, 2013. [CrossRef] [Google Scholar]
- D. T. Hoang, D. Niyato, P. Wang, and D. I. Kim, “Opportunistic Channel Access and RF Energy Harvesting in Cognitive Radio Networks,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 11, pp. 2039-2052, 2014. [CrossRef] [Google Scholar]
- Y. Liu, P. Ning, and H. Dai, “Authenticating primary users’ signals in cognitive radio networks via integrated cryptographic and wireless link signatures,” in 2010 IEEE symposium on security and privacy, 2010, pp. 286-301: IEEE. [Google Scholar]
- A. Zilinskas, “Simulation-based optimization: Parametric optimization techniques and reinforcement learning,” Interfaces, vol. 35, no. 6, p. 535, 2005. [Google Scholar]
- P. Marbach and J. N. Tsitsiklis, “Simulation-based optimization of Markov reward processes,” IEEE Transactions on Automatic Control, vol. 46, no. 2, pp. 191-209, 2001. [CrossRef] [MathSciNet] [Google Scholar]
- O. Buffet, A. Dutech, and F. Charpillet, “Shaping multi-agent systems with gradient reinforcement learning,” Autonomous Agents and Multi-Agent Systems, vol. 15, no. 2, pp. 197-220, 2007. [CrossRef] [Google Scholar]
- D. P. Bertsekas, “Nonlinear programming,” Journal of the Operational Research Society, vol. 48, no. 3, pp. 334-334, 1997. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.