Journal of Information Systems Engineering and Management

Incorporating Morris' Design Thoughts for AI and Big Data-Enabled Coverage Optimization in China's Wireless Communication Network
Feifei Song 1 *
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1 Ph.D candidate, Department of Fine Art, International College, Krirk University, Bangkok, Thailand
* Corresponding Author
Research Article

Journal of Information Systems Engineering and Management, 2024 - Volume 9 Issue 1, Article No: 23622
https://doi.org/10.55267/iadt.07.14076

Published Online: 25 Jan 2024

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APA 6th edition
In-text citation: (Song, 2024)
Reference: Song, F. (2024). Incorporating Morris' Design Thoughts for AI and Big Data-Enabled Coverage Optimization in China's Wireless Communication Network. Journal of Information Systems Engineering and Management, 9(1), 23622. https://doi.org/10.55267/iadt.07.14076
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Song F. Incorporating Morris' Design Thoughts for AI and Big Data-Enabled Coverage Optimization in China's Wireless Communication Network. J INFORM SYSTEMS ENG. 2024;9(1):23622. https://doi.org/10.55267/iadt.07.14076
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Song F. Incorporating Morris' Design Thoughts for AI and Big Data-Enabled Coverage Optimization in China's Wireless Communication Network. J INFORM SYSTEMS ENG. 2024;9(1), 23622. https://doi.org/10.55267/iadt.07.14076
Chicago
In-text citation: (Song, 2024)
Reference: Song, Feifei. "Incorporating Morris' Design Thoughts for AI and Big Data-Enabled Coverage Optimization in China's Wireless Communication Network". Journal of Information Systems Engineering and Management 2024 9 no. 1 (2024): 23622. https://doi.org/10.55267/iadt.07.14076
Harvard
In-text citation: (Song, 2024)
Reference: Song, F. (2024). Incorporating Morris' Design Thoughts for AI and Big Data-Enabled Coverage Optimization in China's Wireless Communication Network. Journal of Information Systems Engineering and Management, 9(1), 23622. https://doi.org/10.55267/iadt.07.14076
MLA
In-text citation: (Song, 2024)
Reference: Song, Feifei "Incorporating Morris' Design Thoughts for AI and Big Data-Enabled Coverage Optimization in China's Wireless Communication Network". Journal of Information Systems Engineering and Management, vol. 9, no. 1, 2024, 23622. https://doi.org/10.55267/iadt.07.14076
ABSTRACT
Morris changes this study's China cellular network AI and Big Data Analytics. Scalability, regulatory compliance, and resource allocation efficiency are checked. Numerous methods seamlessly combine qualitative interview, document, and case study findings with quantitative network performance statistics. Qualitative study highlights industrial resource allocation, efficiency, and user-centric design issues. Innovative problem-solving emphasizes tech and regs. Researchers think Morris' designs improve China's wireless network. Explain and apply Morris' design concepts to problems. This comprehensive theoretical and practice study optimizes networks using Morris' design theories. Interdisciplinary research improves Morris' digital ideas. This research ingeniously integrates theory and practice to create network theory. Research employing mixed methods. Interviews, document analysis, and case studies increase efficiency, resource allocation, and user-centric design. Data quality and processing speed are investigated in quantitative network performance studies. Quantifying complex relationships with correlation and regression analysis strengthens the study's powerful method. Innovative regulatory compliance and scalability solutions demonstrate the study's cutting-edge approach. The paper then examines key findings and implications. Network optimization requires high-quality data, feature engineering, and user-centered design, according to research. Executives get proper network optimizing guidance. The essay emphasizes industry regulatory and technical improvements. Morris optimized networks theoretically. This integrated strategy boosts theory and digital relevance. Wireless network enhancements in China. Effectiveness, user experience, and data-driven accuracy help researchers optimize networks. This study addresses specific challenges and extends network theory to create future-ready networks utilizing Morris' design methods. Chinese wireless communication network optimization demonstrates this research's practical and theoretical benefits.
KEYWORDS
REFERENCES
  • Abbasi, A., Sarker, S., & Chiang, R. H. L. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), 1-32.
  • Ali, L., Nawaz, A., Bai, Y., Raza, A., Anwar, M. K., Raheel Shah, S. A., & Raza, S. S. (2020). Numerical simulations of GFRP-Reinforced columns having polypropylene and polyvinyl alcohol fibers. Complexity, 2020, 1-14.
  • Bahlke, F., Ramos-Cantor, O. D., Henneberger, S., & Pesavento, M. (2018). Optimized cell planning for network slicing in heterogeneous wireless communication networks. IEEE Communications Letters, 22(8), 1676-1679.
  • Bi, S., Zeng, Y., & Zhang, R. (2016). Wireless powered communication networks: An overview. IEEE Wireless Communications, 23(2), 10-18.
  • Born, G., Morris, J., Diaz, F., & Anderson, A. (2021). Artifical Intelligence, Music Recommendation, and the Curation of Culture. Retrieved from https://tspace.library.utoronto.ca/handle/1807/129105
  • Cao, L. (2017). Data science: a comprehensive overview. ACM Computing Surveys (CSUR), 50(3), 1-42.
  • Chen, M., Poor, H. V., Saad, W., & Cui, S. (2021). Convergence Time Optimization for Federated Learning over Wireless Networks. IEEE Transactions on Wireless Communications, 20(4), 2457-2471.
  • Chen, Z., Wu, J., Gan, W., & Qi, Z. (2022). Metaverse Security and Privacy: An Overview. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, 2950-2959.
  • Dai, Y., Liu, A., Qin, J., Guo, Y., Jong, M. S. Y., Chai, C. S., & Lin, Z. (2023). Collaborative construction of artificial intelligence curriculum in primary schools. Journal of Engineering Education, 112(1), 23-42.
  • Englhardt, Z., Ma, C., Morris, M. E., Xu, X., Chang, C. C., Qin, L., ... Iyer, V. (2023). From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models. https://doi.org/10.48550/arXiv.2311.13063
  • Erpek, T., O'Shea, T. J., Sagduyu, Y. E., Shi, Y., & Clancy, T. C. (2020). Deep Learning for Wireless Communications. Studies in Computational Intelligence, 867, 223-266.
  • Feng, D., Jiang, C., Lim, G., Cimini, L. J., Feng, G., & Li, G. Y. (2013). A survey of energy-efficient wireless communications. IEEE Communications Surveys and Tutorials, 15(1), 167-178.
  • Fletcher, S., & Telecom, N. E. C. (2014). Cellular Architecture for 5g. IEEE Communications Magazine, February, 122-130.
  • Gong, S., Lu, X., Hoang, D. T., Niyato, D., Shu, L., Kim, D. I., & Liang, Y. C. (2020). Toward Smart Wireless Communications via Intelligent Reflecting Surfaces: A Contemporary Survey. IEEE Communications Surveys and Tutorials, 22(4), 2283-2314.
  • Goodman, S. M., Buehler, E., Clary, P., Coenen, A., Donsbach, A., Horne, T. N., ... Morris, M. R. (2022). Lampost: Design and evaluation of an ai-assisted email writing prototype for adults with dyslexia. In Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility (pp. 1-18). https://doi.org/10.48550/arXiv.2207.02308
  • Hasani, N., Paravastu, S. S., Farhadi, F., Yousefirizi, F., Morris, M. A., Rahmim, A., ... Saboury, B. (2022). Artificial intelligence in lymphoma PET imaging: a scoping review (current trends and future directions). PET clinics, 17(1), 145-174.
  • Hu, J., & Vasilakos, A. V. (2016). Energy Big Data Analytics and Security: Challenges and Opportunities. IEEE Transactions on Smart Grid, 7(5), 2423-2436.
  • Hu, S., Chen, X., Ni, W., Hossain, E., & Wang, X. (2021). Distributed machine learning for wireless communication networks: Techniques, architectures, and applications. IEEE Communications Surveys and Tutorials, 23(3), 1458-1493.
  • Hu, S., Chen, X., Ni, W., Wang, X., & Hossain, E. (2020). Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey. IEEE Transactions on Green Communications and Networking, 4(2), 461-496.
  • Huang, H., Guo, S., Gui, G., Yang, Z., Zhang, J., Sari, H., & Adachi, F. (2020). Deep learning for physical-layer 5g wireless techniques: Opportunities, challenges and solutions. IEEE Wireless Communications, 27(1), 214-222.
  • Huber, R., D'Onofrio, C., Devaraju, A., Klump, J., Loescher, H. W., Kindermann, S., ... Stocker, M. (2021). Integrating data and analysis technologies within leading environmental research infrastructures: Challenges and approaches. Ecological Informatics, 61, 101245.
  • Jia, S., Yuan, X., & Liang, Y. C. (2021). Reconfigurable Intelligent Surfaces for Energy Efficiency in D2D Communication Network. IEEE Wireless Communications Letters, 10(3), 683-687.
  • Lee, E. E., Torous, J., De Choudhury, M., Depp, C. A., Graham, S. A., Kim, H. C., ... Jeste, D. V. (2021). Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(9), 856-864.
  • Li, Z., Chen, W., Wu, Q., Cao, H., Wang, K., & Li, J. (2022). Robust Beamforming Design and Time Allocation for IRS-Assisted Wireless Powered Communication Networks. IEEE Transactions on Communications, 70(4), 2838-2852.
  • Liaskos, C., Nie, S., Tsioliaridou, A., Pitsillides, A., Ioannidis, S., & Akyildiz, I. (2018). A New Wireless Communication Paradigm through Software-Controlled Metasurfaces. IEEE Communications Magazine, 56(9), 162-169.
  • Liu, D., Xu, Y., Wang, J., Xu, Y., Anpalagan, A., Wu, Q., ... Shen, L. (2019). Self-Organizing Relay Selection in UAV Communication Networks: A Matching Game Perspective. IEEE Wireless Communications, 26(6), 102-110.
  • Ma, G., Ma, J., Li, H., Wang, Y., Wang, Z., & Zhang, B. (2022). Customer behavior in purchasing energy-saving products: Big data analytics from online reviews of e-commerce. Energy Policy, 165, 112960.
  • Morfidis, K., & Kostinakis, K. (2018). Approaches to the rapid seismic damage prediction of r/c buildings using artificial neural networks. Engineering Structures, 165, 120-141.
  • Morris, A., Guan, J., & Azhar, A. (2021). An XRI Mixed-Reality Internet-of-Things Architectural Framework Toward Immersive and Adaptive Smart Environments. Proceedings - 2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2021, 68-74.
  • Morris, G. R., Sheridan, S. M., Xu, J., Liang, W., Lv, F., Foster, R., ... Perrin, S. (2021). Educational symbiosis: Designing English language learning courses to support syntegrative education. In Trends and developments for the future of language education in higher education (pp. 187-207). IGI Global.
  • Morris, R. R., Kouddous, K., Kshirsagar, R., & Schueller, S. M. (2018). Towards an artificially empathic conversational agent for mental health applications: system design and user perceptions. Journal of medical Internet research, 20(6), e10148.
  • Morris, R. R., & Picard, R. (2014). Crowd-powered positive psychological interventions. Journal of Positive Psychology, 9(6), 509-516.
  • Nawaz, A., Su, X., & Nasir, I. M. (2021). BIM Adoption and its impact on planning and scheduling influencing mega plan projects-(CPEC-) quantitative approach. Complexity, 2021, 1-9.
  • Peters, J. E. J. (2021). Hypervolition: Our Sacrifice of Choice (Doctoral dissertation, OCAD University, Toronto, Canada). Retrieved from https://openresearch.ocadu.ca/id/eprint/3393/
  • Ji, C., Su, X., Qin, Z., & Nawaz, A. (2022). Probability analysis of construction risk based on noisy-or gate bayesian networks. Reliability Engineering & System Safety, 217, 107974.
  • Sodhro, A. H., Obaidat, M. S., Abbasi, Q. H., Pace, P., Pirbhulal, S., Fortino, G., ... Qaraqe, M. (2019). Quality of service optimization in an IoT-driven intelligent transportation system. IEEE Wireless Communications, 26(6), 10-17.
  • Quartagno, M., Ghorani, E., Morris, T. P., Seckl, M. J., & Parmar, M. K. (2023). How to design a MAMS-ROCI (aka DURATIONS) randomised trial: the REFINE-Lung case study. https://doi.org/10.48550/arXiv.2304.09521
  • Sadi, Y., & Ergen, S. C. (2015). Joint optimization of communication and controller components of wireless networked control systems. In 2015 IEEE International Conference on Communications (ICC) (pp. 6487-6493). IEEE.
  • Scholz, T. M. (2017). Big Data in Organizations and the Role of Human Resource Management. Peter Lang International Academic Publishers.
  • Song, G., & Li, Y. (2005). Cross-layer optimization for OFDM wireless networks - Part I: Theoretical framework. IEEE Transactions on Wireless Communications, 4(2), 614-624.
  • Sun, H., Chen, X., Shi, Q., Hong, M., Fu, X., & Sidiropoulos, N. D. (2018). Learning to Optimize: Training Deep Neural Networks for Interference Management. IEEE Transactions on Signal Processing, 66(20), 5438-5453.
  • Vadivel, S., Konda, S., Balmuri, K. R., Stateczny, A., & Parameshachari, B. D. (2021). Dynamic route discovery using modified grasshopper optimization algorithm in wireless ad-hoc visible light communication network. Electronics (Switzerland), 10(10), 1176.
  • Van Huynh, N., Hoang, D. T., Niyato, D., Wang, P., & Kim, D. I. (2018). Optimal Time Scheduling for Wireless-Powered Backscatter Communication Networks. IEEE Wireless Communications Letters, 7(5), 820-823.
  • Venkatesh, V. (2022). Viswanath Venkatesh Pamplin College of Business Virginia Tech, Blacksburg VA 24061, USA. Annals of Operations Research, 308, 641-652.
  • Wang, C. X., Renzo, M. Di, Stańczak, S., Wang, S., & Larsson, E. G. (2020). Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges. IEEE Wireless Communications, 27(1), 16-23.
  • Wang, S., & Nie, J. (2010). Energy efficiency optimization of cooperative communication in wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2010, 1-8.
  • Wang, Z., Duan, L., & Zhang, R. (2019). Adaptive Deployment for UAV-Aided Communication Networks. IEEE Transactions on Wireless Communications, 18(9), 4531-4543.
  • Yang, H., Xiong, Z., Zhao, J., Niyato, D., Xiao, L., & Wu, Q. (2021). Deep Reinforcement Learning-Based Intelligent Reflecting Surface for Secure Wireless Communications. IEEE Transactions on Wireless Communications, 20(1), 375-388.
  • Yang, Z., Chen, M., Saad, W., Hong, C. S., Shikh-Bahaei, M., Poor, H. V., & Cui, S. (2020). Delay minimization for federated learning over wireless communication networks. https://doi.org/10.48550/arXiv.2007.03462
  • Yao, Q., Huang, A., Shan, H., Quek, T. Q. S., & Wang, W. (2016). Delay-Aware Wireless Powered Communication Networks - Energy Balancing and Optimization. IEEE Transactions on Wireless Communications, 15(8), 5272-5286.
  • Yang, Y., Zhang, M., Lin, Z., Bae, K.-H. Avotra, A. A. R. N., & Nawaz, A. (2021). Green logistics performance and infrastructure on service trade and environment-measuring firm’s performance and service quality. Journal of King Saud University-Science, 34(1), 101683.
  • Yu, X., Xu, D., & Schober, R. (2019). MISO wireless communication systems via intelligent reflecting surfaces: (Invited paper). 2019 IEEE/CIC International Conference on Communications in China, ICCC 2019, 735-740.
  • Zappone, A., Di Renzo, M., Debbah, M., Lam, T. T., & Qian, X. (2019). Model-Aided Wireless Artificial Intelligence: Embedding Expert Knowledge in Deep Neural Networks for Wireless System Optimization. IEEE Vehicular Technology Magazine, 14(3), 60-69.
  • Zhang, T., Wang, Y., Liu, Y., Xu, W., & Nallanathan, A. (2020). Cache-Enabling UAV Communications: Network Deployment and Resource Allocation. IEEE Transactions on Wireless Communications, 19(11), 7470-7483.
  • Zhao, J. (2019). A survey of intelligent reflecting surfaces (IRSs): Towards 6G wireless communication networks. https://doi.org/10.48550/arXiv.1907.04789
  • Zhu, Z., Lambotharan, S., Chin, W. H., & Fan, Z. (2012). Overview of demand management in smart grid and enabling wireless communication technologies. IEEE Wireless Communications, 19(3), 48-56.
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