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Research Article

Wireless Network Design Optimization for Computer Teaching with Deep Reinforcement Learning Application

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Article: 2218169 | Received 30 Apr 2023, Accepted 22 May 2023, Published online: 04 Jun 2023

References

  • Ahmad, A., S. Ahmad, M. H. Rehmani, and N. U. Hassan. 2015. A survey on radio resource allocation in cognitive radio sensor networks [J]. IEEE Communications Surveys & Tutorials 17 (2):888–1828. doi:10.1109/COMST.2015.2401597.
  • Ahmed, E., A. Gani, S. Abolfazli, L. J. Yao, and S. U. Khan. 2016. Channel assignment algorithms in cognitive radio networks: Taxonomy, open issues, and challenges[J. IEEE Communications Surveys & Tutorials 18 (1):795–823. doi:10.1109/COMST.2014.2363082.
  • Bany Salameh, H., Z. Khader, and A. Al Ajlouni. 2021. Intelligent secure networking in in-band full-duplex dynamic access networks: Spectrum management and routing protocol [J]. Journal of Network and Systems Management 29 (2):1–18. doi:10.1007/s10922-021-09588-7.
  • Bkassiny, M., Y. Li, and S. K. Jayaweera. 2013. A survey on machine-learning techniques in cognitive radios [J]. IEEE Communications Surveys & Tutorials 15 (3):1136–59. doi:10.1109/SURV.2012.100412.00017.
  • Du, J., C. Jiang, Z. Han, H. Zhang, S. Mumtaz, and Y. Ren. 2017. Contract mechanism and performance analysis for data transaction in mobile social networks [J]. IEEE Transactions on Network Science and Engineering 6 (2):103–15. doi:10.1109/TNSE.2017.2787746.
  • Erbas, İ., R. Çipuri, and A. Joni. 2021. The impact of technology on teaching and teaching English to elementary school students [J]. Linguistics & Culture Review 5 (S3):1316–36. doi:10.21744/lingcure.v5nS3.1815.
  • Fan, X., and Y. Huo. 2020. Blockchain based dynamic spectrum access of non-real-time data in cyber-physical-social systems [J]. IEEE Access 8:64486–98. doi:10.1109/ACCESS.2020.2985580.
  • Gao, Z., H. Zhu, S. Li, S. Du, and X. Li. 2012. Security and privacy of collaborative spectrum sensing in cognitive radio networks [J]. IEEE Wireless Communications 19 (6):106–12. doi:10.1109/MWC.2012.6393525.
  • Hbaci, I., H. Y. Ku, and R. Abdunabi. 2021. evaluating higher education educators’ computer technology competencies in Libya [J]. Journal of Computing in Higher Education 33 (1):188–205. doi:10.1007/s12528-020-09261-z.
  • Hlophe, M. C., and B. T. Maharaj. 2021. AI meets CRNs: A prospective review on the application of deep architectures in spectrum management [J]. IEEE Access 9:113954–96. doi:10.1109/ACCESS.2021.3104099.
  • Huang, Q., Y. Gui, F. Wu, G. Chen, and Q. Zhang. 2015. A general privacy-preserving auction mechanism for secondary spectrum markets [J]. IEEE/ACM Transactions on Networking 24 (3):1881–93. doi:10.1109/TNET.2015.2434217.
  • Kaur, A., and K. Kumar. 2022. A comprehensive survey on machine learning approaches for dynamic spectrum access in cognitive radio networks [J]. Journal of Experimental & Theoretical Artificial Intelligence 34 (1):1–40. doi:10.1080/0952813X.2020.1818291.
  • Kotobi, K., and S. G. Bilen. 2018. Secure blockchains for dynamic spectrum access: A decentralized database in moving cognitive radio networks enhances security and user access [J]. IEEE Vehicular Technology Magazine 13 (1):32–39. doi:10.1109/MVT.2017.2740458.
  • Li, X., and Q. Zhu. 2018. Social incentive mechanism based multi-user sensing time optimization in cooperative spectrum sensing with mobile crowd sensing [J]. Sensors 18 (1):250. doi:10.3390/s18010250.
  • Liang, Y. C., J. Tan, H. Jia, J. Zhang, and L. Zhao. 2021. Realizing intelligent spectrum management for integrated satellite and terrestrial networks [J]. Journal of Communications and Information Networks 6 (1):32–43. doi:10.23919/JCIN.2021.9387703.
  • Liang, Y. C., Y. Zeng, E. C. Y. Peh, and A. Tuan Hoang. 2008. Sensing-throughput tradeoff for cognitive radio networks [J]. IEEE Transactions on Wireless Communications 7 (4):1326–37. doi:10.1109/TWC.2008.060869.
  • Marinho, J., and E. Monteiro. 2012. Cognitive radio: Survey on communication protocols, spectrum decision issues, and future research directions [J]. Wireless Networks 18 (2):147–64. doi:10.1007/s11276-011-0392-1.
  • Mihovska, A., and R. Prasad. 2021. Spectrum sharing and dynamic spectrum management techniques in 5G and beyond networks: A survey [J]. Journal of Mobile Multimedia 1:65–78.
  • Nguyen, H., and R. Santagata. 2021. Impact of computer modeling on learning and teaching systems thinking [J]. Journal of Research in Science Teaching 58 (5):661–88. doi:10.1002/tea.21674.
  • Peh, Y., E. C. Liang, Y. C, and Y. L. Guan. 2009. Optimization of cooperative sensing in cognitive radio networks: A sensing-throughput tradeoff view [J]. IEEE Transactions on Vehicular Technology 58 (9):5294–99. doi:10.1109/TVT.2009.2028030.
  • Ran, H., M. Kasli, and W. G. Secada. 2021. A meta-analysis on computer technology intervention effects on mathematics achievement for low-performing students in K-12 classrooms [J]. Journal of Educational Computing Research 59 (1):119–53. doi:10.1177/0735633120952063.
  • Randhava, K. S., M. Roslee, and Z. Yusoff. 2021. Dynamic spectrum management using frequency selection at licensed and unlicensed bands for efficient vehicle-to-vehicle communication[J. F1000Research 10 (10):1309. doi:10.12688/f1000research.73481.1.
  • Sekaran, R., S. N. Goddumarri, S. Kallam, M. Ramachandran, R. Patan, and D. Gupta. 2021. 5G integrated spectrum selection and spectrum access using AI-Based frame work for IoT based sensor networks [J]. Computer Networks 186:107649. doi:10.1016/j.comnet.2020.107649.
  • Shah-Mohammadi, F., H. H. Enaami, and A. Kwasinski. 2021. Neural network cognitive engine for autonomous and distributed underlay dynamic spectrum access [J]. IEEE Open Journal of the Communications Society 2:719–37. doi:10.1109/OJCOMS.2021.3069801.
  • Tragos, E. Z., S. Zeadally, A. G. Fragkiadakis, and V. A. Siris. 2013. Spectrum assignment in cognitive radio networks: A comprehensive survey [J]. IEEE Communications Surveys & Tutorials 15 (3):1108–35. doi:10.1109/SURV.2012.121112.00047.
  • Wang, W., L. Chen, K. G. Shin, and L. Duan. 2015. Thwarting intelligent malicious behaviors in cooperative spectrum sensing [J]. IEEE Transactions on Mobile Computing 14 (11):2392–405. doi:10.1109/TMC.2015.2398446.
  • Wang, W., H. Li, Y. Sun, and Z. Han. 2009. Securing collaborative spectrum sensing against untrustworthy secondary users in cognitive radio networks [J]. EURASIP Journal on Advances in Signal Processing 2010 (1):1–15. doi:10.1155/2010/695750.
  • Wang, Y., Z. Ye, P. Wan, and J. Zhao. 2019. A survey of dynamic spectrum allocation based on reinforcement learning algorithms in cognitive radio networks [J]. Artificial Intelligence Review 51 (3):493–506. doi:10.1007/s10462-018-9639-x.
  • Wu, H., S. Jin, and W. Yue. 2022. Pricing policy for a dynamic spectrum allocation scheme with batch requests and impatient packets in cognitive radio networks [J]. Journal of Systems Science and Systems Engineering 31 (2):133–49. doi:10.1007/s11518-022-5521-0.
  • Yi, C., J. Cai, and G. Zhang. 2016. Spectrum auction for differential secondary wireless service provisioning with time-dependent valuation information [J]. IEEE Transactions on Wireless Communications 16 (1):206–20. doi:10.1109/TWC.2016.2621765.
  • Zhang, Y., G. Hu, and Y. Cai. 2021. Proactive spectrum monitoring for suspicious wireless powered communications in dynamic spectrum sharing networks [J]. China Communications 18 (12):119–38. doi:10.23919/JCC.2021.12.008.
  • Zhang, Y., C. Lee, D. Niyato, and P. Wang. 2013. Auction approaches for resource allocation in wireless systems: A survey [J]. IEEE Communications Surveys & Tutorials 15 (3):1020–41. doi:10.1109/SURV.2012.110112.00125.