Machine Learning Approaches for Next-Generation Wireless Networks
Keywords:
Wireless Communication Evolution, High-Speed Data Transfer, Fading And Interference Mitigation, Large-Scale Wireless Data, Machine Learning In Communications, Artificial Intelligence Integration, Deep Neural Networks, Computer Vision Inspiration, Experience-Based Learning, Data-Driven Decision Making, High-Dimensional Optimization, Coupled System Modeling, AI-Driven Communication Design, Limitations Of Control Theory, Low-Dimensional Policy Learning, Physical Layer Intelligence, Machine Learning At Physical Layer, Latency And Overhead Reduction, Predictive Gain In Wireless Systems, Intelligent Communication Architectures.Abstract
Over the last few decades, the world has witnessed a tremendous growth in wireless communication. Wireless devices used to connect people around the globe are increasing in number, requiring faster data transfer with better tolerance to fading and interference. Simultaneously, the data generated by these devices is becoming larger and more complex. Learning from prior observations, decisions, and actions has been widely recognized as an effective way of tackling complex problems. For instance, human learning is often accomplished using visual information, and the fact that trained humans can classify tens of thousands of objects makes it natural to use deep neural networks, which take advantage of large labeled datasets, for computer vision problems.
Inspired by the capability of humans to learn from experiences or by leveraging the experiences of others, machine learning finds its place within Artificial Intelligence (AI). AI resides at the higher levels of the communication chain, bringing together advanced optimization and machine learning algorithms to tackle increasingly complex problems, and proposes entirely new designs. Control Theory has been dismissed due to the complexities introduced by the massive data sets and high-dimensional or coupled nature of the problems it has been addressing in practice. High-dimensional and coupled problems are tackled using AI tools that learn lower dimensional descriptions or policies of the original problems. The physical layer, offering advantages of reduced latency, overhead, processing, and predictive gain, has been applying Machine Learning (ML) to specific problems with promising results.
References
1. Kulin, M., Kazaz, T., De Poorter, E., & Moerman, I. (2021). A survey on machine learning-based performance improvement of wireless networks: PHY, MAC and network layer. Electronics, 10(3), 318.
2. Frikha, M. S., Gammar, S. M., Lahmadi, A., & Andrey, L. (2021). Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey. Computer Communications, 178, 98–113.
3. Krishna AzithTejaGanti, V., Senthilkumar, K. P., Robinson L, T., Karunakaran, S., Pandugula, C., & Khatana, K. (2024, November). Energy-Efficient Real-Time Hybrid Deep Learning Framework for Adaptive Iot Intrusion Detection with Scalable and Dynamic Threat Mitigation. In Proceedings of the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024).
4. Alwarafy, A., Abdallah, M., Ciftler, B. S., Al-Fuqaha, A., & Hamdi, M. (2021). Deep reinforcement learning for radio resource allocation and management in next generation heterogeneous wireless networks: A survey. IEEE Communications Surveys & Tutorials, 23(4), 2166–2196.
5. Letaief, K. B., Shi, Y., Lu, J., & Lu, J. (2021). The roadmap to 6G: AI empowered wireless networks. IEEE Communications Magazine, 59(8), 84–90.
6. Pandiri, L., Paleti, S., Kaulwar, P. K., Malempati, M., & Singireddy, J. (2023). Transforming financial and insurance ecosystems through intelligent automation, secure digital infrastructure, and advanced risk management strategies. Educational Administration: Theory and Practice, 29(4), 4777-4793.
7. Chen, M., Saad, W., Yin, C., & Debbah, M. (2021). Data-driven resource allocation for wireless networks: A machine learning perspective. IEEE Communications Surveys & Tutorials, 23(3), 1689–1717.
8. Annapareddy, V. N. (2024). Leveraging Artificial Intelligence, Machine Learning, and Cloud-Based IT Integrations to Optimize Solar Power Systems and Renewable Energy Management. Machine Learning, and Cloud-Based IT Integrations to Optimize Solar Power Systems and Renewable Energy Management (December 06, 2024).
9. Park, J., Samarakoon, S., Bennis, M., & Debbah, M. (2021). Wireless network intelligence at the edge: A distributed learning perspective. IEEE Network, 35(1), 74–81.
10. Singireddy, S. (2024). The Integration of AI and Machine Learning in Transforming Underwriting and Risk Assessment Across Personal and Commercial Insurance Lines. Journal of Computational Analy-sis and Applications (JoCAAA), 33(08), 3966-3991.
11. Mao, B., Fadlullah, Z. M., Kato, N., & Li, J. (2021). Machine learning for intelligent wireless networks: A survey. IEEE Communications Surveys & Tutorials, 23(2), 1157–1187.
12. Jiang, W., Schotten, H. D., & Zhang, H. (2021). Deep learning for wireless communications: A comprehensive survey. IEEE Communications Surveys & Tutorials, 23(3), 1949–1982.
13. Davuluri, P. S. L. (2023). AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems (December 15, 2023).
14. Boulogeorgos, A. A. A., Alexiou, A., & Karagiannidis, G. K. (2021). Machine learning for wireless communications: Recent advances and future directions. IEEE Access, 9, 123456–123480.
15. Chowdhury, M. Z., Shahjalal, M., Ahmed, S., & Jang, Y. M. (2021). 6G wireless communication systems: Applications, requirements, technologies, challenges, and research directions. IEEE Open Journal of the Communications Society, 1, 957–975.
16. Sheelam, G. K. (2024). AI-driven spectrum management: Using machine learning and agentic intelligence for dynamic wireless optimization. European Advanced Journal for Emerging Technologies (EAJET), 2(1), 3050-9742.
17. Saad, W., Bennis, M., & Chen, M. (2021). A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE Network, 35(3), 134–142.
18. Shome, D., Waqar, O., & Khan, W. U. (2022). Federated learning and next generation wireless communications: A survey on bidirectional relationship. Transactions on Emerging Telecommunications Technologies, 33(7), e4458.
19. Sriram, H. K., Challa, S. R., Challa, K., & ADUSUPALLI, B. (2024). Strategic Financial Growth: Strengthening Investment Management. Secure Transactions, and Risk Protection in the Digital Era. Secure Transactions, and Risk Protection in the Digital Era (November 10, 2024).
20. Liu, J., Huang, J., Zhou, Y., Li, X., Ji, S., Xiong, H., & Dou, D. (2022). From distributed machine learning to federated learning: A survey. Knowledge and Information Systems, 64, 885–917.
21. Kolla, S., Meda, R., Balleda, L., & Thimmapuram, C. R. (2024). The utility value of ROX index and modified ROX index in determining the efficiency of HFNC in children admitted with respiratory distress. International Journal of Contemporary Pediatrics, 11(6), 775.
22. Iliadis, L. A., Zaharis, Z. D., Sotiroudis, S., Sarigiannidis, P., Karagiannidis, G. K., & Goudos, S. K. (2022). The road to 6G: A comprehensive survey of deep learning applications in cell-free massive MIMO communications systems. Journal on Wireless Communications and Networking, 2022, 68.
23. Ohtsuki, T. (2023). Machine learning in 6G wireless communications. IEICE Transactions on Communications, E106.B(2), 75–83.
24. Yadav, V. (2024). Predictive Analytics for Preventive Medicine: Analyzing how Predictive Analytics is Utilized for Forecasting Patient Health Trends and Preventive Disease. Progress in Medical Sciences. PMS-1126. Prog Med Sci, 8(4).
25. Khan, M. A., & Kaushik, A. (2022). Deep learning-based resource allocation for 5G and beyond wireless networks. Wireless Networks, 28, 1–18.
26. Sangeetha, S. K. B., & Dhaya, R. (2022). Deep learning era for future 6G wireless communications—Theory, applications, and challenges. In Artificial intelligent techniques for wireless communication and networking. Wiley.
27. Singireddy, J. (2024). AI-driven payroll systems: Ensuring compliance and reducing human error. American Data Science Journal for Advanced Computations (ADSJAC) ISSN, 3067-4166.
28. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2022). Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 24(1), 1–36.
29. Khan, L. U., Saad, W., Han, Z., Hossain, E., & Hong, C. S. (2022). Federated learning for wireless communications: Motivation, opportunities, and challenges. IEEE Communications Surveys & Tutorials, 24(2), 1022–1052.
30. Afolabi, I., Taleb, T., Samdanis, K., Ksentini, A., & Flinck, H. (2022). Network slicing and softwarization: A survey on principles, enabling technologies, and solutions. IEEE Communications Surveys & Tutorials, 24(1), 242–281.
31. Srinivas Kalisetty, D. A. S. (2024). Leveraging Artificial Intelligence and Machine Learning for Predictive Bid Analysis in Supply Chain Management: A Data-Driven Approach to Optimize Procurement Strategies.
32. Gacanin, H., & Wagner, S. (2022). Artificial intelligence and machine learning for future wireless networks. IEEE Communications Magazine, 60(6), 22–28.
33. Zhang, H., Niyato, D., Wang, J., Zhang, Y., & Poor, H. V. (2022). Edge intelligence for 6G wireless networks: A comprehensive survey. IEEE Wireless Communications, 29(4), 96–103.
34. Chaccour, C., Soorki, M. N., Saad, W., Bennis, M., Popovski, P., & Debbah, M. (2022). Seven defining features of terahertz (THz) wireless systems: A fellowship of communication and sensing. IEEE Communications Surveys & Tutorials, 24(2), 867–930.
35. Koppolu, H. K. R. (2024). The impact of data engineering on service quality in 5G-enabled cable and media networks. European Advanced Journal for Science & Engineering (EAJSE), 1(1).
36. Alnoman, A., & Anpalagan, A. (2022). Intelligent resource allocation in beyond-5G wireless networks using deep reinforcement learning. IEEE Access, 10, 12345–12360.
37. Ye, H., Li, G. Y., & Juang, B.-H. F. (2022). Deep learning based end-to-end wireless communication systems without pilots. IEEE Transactions on Cognitive Communications and Networking, 8(2), 744–758.
38. Khan, M. U., Testi, E., Chiani, M., & Paolini, E. (2024). Joint power control and pilot assignment in cell-free massive MIMO using deep learning. IEEE Open Journal of the Communications Society, 5, 5260–5275.
39. Ikudabo, A. O., & Kumar, P. (2024). AI-driven risk assessment and management in banking: balancing innovation and security. International Journal of Research Publication and Reviews, 5(10), 3573-88.
40. Garey, W., Rouil, R., Black, E., Ropitault, T., & Gao, W. (2023). O-RAN with machine learning in ns-3. In Proceedings of the 2023 Workshop on ns-3 (WNS3 2023).
41. Khan, M. A., & Kaushik, A. (2023). Machine learning empowered emerging wireless networks in 6G: Recent advancements, challenges and future trends. IEEE Access, 11, 83017–83051.
42. Khan, M. A., & Kaushik, A. (2023). A comprehensive survey of machine learning techniques in next-generation wireless networks and the Internet of Things. Information Sciences, 28(4), 959–967.
43. Ramanakar Reddy Danda, Z. Y., Mandala, G., & Maguluri, K. K. (2024). Smart Medicine: The Role of Artificial Intelligence and Machine Learning in Next-Generation Healthcare Innovation.
44. Elgendy, I. A., Zhang, W., Liu, C., & Li, Q. (2023). Machine learning techniques for resource management in 5G and beyond wireless networks. IEEE Access, 11, 1–20.
45. Mao, C., Pan, C., & others. (2023). Deep learning in physical layer communications: Evolution and prospects in 5G and 6G networks. IET Communications, 17(13), 1605–1624.
46. Hossain, E., Khandaker, M. R. A., & others. (2023). Machine learning for wireless communications: An overview of recent developments and future research directions. IEEE Access, 11, 1–25.
47. Al-Turjman, F., Debbah, M., & others. (2023). Artificial intelligence for 6G wireless systems: Opportunities, challenges, and future directions. IEEE Network, 37(2), 12–19.
48. Zappone, A., Di Renzo, M., Debbah, M., & others. (2023). Wireless communications and machine learning: A joint perspective on intelligent network optimization. IEEE Transactions on Wireless Communications, 22(8), 5120–5138.
49. Alwarafy, A., Abdallah, M., Ciftler, B. S., Al-Fuqaha, A., & Hamdi, M. (2023). Deep reinforcement learning for wireless network optimization: Recent advances and future directions. IEEE Access, 11, 1–22.
50. Abd Elaziz, M. A., Al-qaness, M. A. A., Ewees, A. A., & others. (2024). Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology. WIREs Data Mining and Knowledge Discovery, 14, e1521.
51. Alhammadi, A., & others. (2024). Artificial intelligence in 6G wireless networks: Opportunities, applications, and challenges. International Journal of Intelligent Systems, 2024, 8845070.
52. Kummari, D. N. (2022). IoT-enabled additive manufacturing: Improving prototyping speed and customization in the automotive sector. Migration Letters, 19(S8), 2084-2104.
53. Zhao, J., Liu, C., Liao, J., & Wang, D. (2024). Deep learning in wireless communications for physical layer. Physical Communication, 67, 102503.
54. Akhtarshenas, A., Vahedifar, M. A., Ayoobi, N., Maham, B., Alizadeh, T., Ebrahimi, S., & López-Pérez, D. (2024). Federated learning: A cutting-edge survey of the latest advancements and applications. Computer Communications, 225, 1–24.
55. Guo, W., Zhuang, F., Zhang, X., Tong, Y., & Dong, J. (2024). A comprehensive survey of federated transfer learning: Challenges, methods and applications. Frontiers of Computer Science, 18, 186356.
56. Khan, M. A., & Kaushik, A. (2024). On challenges of sixth-generation (6G) wireless networks: A comprehensive survey of requirements, applications, and security issues. Journal of Network and Computer Applications, 232, 104040.
57. Ibrahim, A., Hassan, M., & others. (2024). Machine learning-enabled intelligent resource management for 6G wireless networks. IEEE Access, 12, 1–20.
58. Abdel Hadi, A., & others. (2024). Intelligent spectrum management for next-generation wireless networks using machine learning. IEEE Transactions on Cognitive Communications and Networking, 10(2), 500–515.
59. Khan, M. U., Testi, E., Chiani, M., & Paolini, E. (2024). User clustering in cell-free massive MIMO NOMA system: A learning based and user centric approach. Alexandria Engineering Journal, 90, 183–196.
60. Khan, M. U., Testi, E., Chiani, M., & Paolini, E. (2024). Cell-free massive MIMO challenges and opportunities: A survey. ICT Express, 10(1), 1–15.
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