A Pragmatic Investigation of Artificial Intelligence Algorithms Implementation to Signal Processing for Cellular Networks

S. Orike; S. M. Ekolama; J. C. Adinnu

S. Orike
S. M. Ekolama
J. C. Adinnu

Keywords: Mobile, Cellular Network, Algorithms, Artificial Intelligence, Communication, Signal Processing, Artificial Neural Network, Network boosting

Abstract

The paper review investigates the revolutionary capacity of artificial intelligence (AI) algorithms within the domain of signal processing for cellular networks. AI has become an indispensable instrument in the digital age for augmenting the intelligence and adaptability of networks. This research examines the use of AI-powered methods, such as neural networks and reinforcement learning, to optimise the distribution of resources in accordance with user demand and network capacity. The use of artificial intelligence (AI) in signal processing has the potential to decrease interference, improve signal quality, and proactively resolve prospective problems by employing predictive maintenance that leverages historical data. The notion of self-healing networks is presented, placing emphasis on customised services, latency, and data rates, with the aim of automating the process of ensuring network resilience. The use of artificial intelligence is considered essential for optimising energy consumption and ensuring the security of cellular communication systems. Particularly as cellular networks transit to 6G, sophisticated techniques such as beamforming and MIMO (multiple input, multiple output) are recognised as indispensable for attaining increased data rates and spectral efficiency. By incorporating artificial intelligence (AI) into the optimisation of these processes, the complete potential of next-generation cellular networks could be unlocked, leading to enhancements in both data speed and communication reliability.

References

Adeniyi, O., Sadiq, A. S., Pillai, P., Taheir, M. A., & Kaiwartya, O. (2023). Proactive Self-Healing Approaches in Mobile Edge Computing: A Systematic Literature Review. Computers, 12(3), 63.
Ahmad, W. S. H. M. W., Radzi, N. A. M., Samidi, F. S., Ismail, A., Abdullah, F., Jamaludin, M. Z., & Zakaria, M. (2020a). 5G technology: Towards dynamic spectrum sharing using cognitive radio networks. IEEE access, 8, 14460-14488.
Ahokangas, P., Gisca, O., Matinmikko-Blue, M., Yrjölä, S., & Gordon, J. (2023). Toward an integrated framework for developing European 6G innovation. Telecommunications Policy, 47(9), 102641.
Ali, E., Ismail, M., Nordin, R., & Abdulah, N. F. (2017). Beamforming techniques for massive MIMO systems in 5G: overview, classification, and trends for future research. Frontiers of Information Technology & Electronic Engineering, 18, 753-772.
Aliu, O. G., Imran, A., Imran, M. A., & Evans, B. (2012). A survey of self organisation in future cellular networks. IEEE Communications Surveys & Tutorials, 15(1), 336-361.
Alkurd, R., Abualhaol, I., & Yanikomeroglu, H. (2020). Big-data-driven and AI-based framework to enable personalization in wireless networks. IEEE Communications Magazine, 58(3), 18-24.
Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., ... & Wattam, S. (2020). Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. Renewable and Sustainable Energy Reviews, 130, 109899.
Balmer, R. E., Levin, S. L., & Schmidt, S. (2020). Artificial Intelligence Applications in Telecommunications and other network industries. Telecommunications Policy, 44(6), 101977.
Bocken, N. M. P., Allwood, J. M., Willey, A. R., & King, J. M. H. (2011). Development of an eco-ideation tool to identify stepwise greenhouse gas emissions reduction options for consumer goods. Journal of Cleaner Production, 19(12), 1279-1287.
Bousdekis, A., & Mentzas, G. (2017). Condition-based predictive maintenance in the frame of industry 4.0. In Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing: IFIP WG 5.7 International Conference, APMS 2017, Hamburg, Germany, September 3-7, 2017, Proceedings, Part I (pp. 399-406). Springer International Publishing.
Cheng, X., Fang, L., Hong, X., & Yang, L. (2017). Exploiting mobile big data: Sources, features, and applications. IEEE Network, 31(1), 72-79.
Coronado, E., Behravesh, R., Subramanya, T., Fernández-Fernández, A., Siddiqui, S., Costa-Pérez, X., & Riggio, R. (2022). Zero touch management: A survey of network automation solutions for 5G and 6G networks. IEEE Communications Surveys & Tutorials.
Dai, B., Cao, Y., Wu, Z., Dai, Z., Yao, R., & Xu, Y. (2021). Routing optimization meets Machine Intelligence: A perspective for the future network. Neurocomputing, 459, 44-58.
Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J., & Barbosa, J. (2020). Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Computers in Industry, 123, 103298.
Danish, M. S. S., & Senjyu, T. (2023). AI-Enabled Energy Policy for a Sustainable Future. Sustainability, 15(9), 7643.
Ding, Y., Huang, Y., Tang, L., Qin, X., & Jia, Z. (2022). Resource allocation in V2X communications based on multi-agent reinforcement learning with attention mechanism. Mathematics, 10(19), 3415.
Froehlich, A. (2023). Self-healing networks goals, benefits and how they work. Retrieved from https://www.techtarget.com/searchnetworking/tip/Self-healing-networks-goals-benefits-and-how-they-work.
Gill, S. S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., ... & Uhlig, S. (2022). AI for next generation computing: Emerging trends and future directions. Internet of Things, 19, 100514.
Gür, G. (2020). Expansive networks: Exploiting spectrum sharing for capacity boost and 6G vision. Journal of Communications and Networks, 22(6), 444-454.
Gutierrez, C. A., Caicedo, O., & Campos-Delgado, D. U. (2021). 5G and beyond: past, present and future of the mobile communications. IEEE Latin America Transactions, 19(10), 1702-1736.
Han, B., Gopalakrishnan, V., Ji, L., & Lee, S. (2015). Network function virtualization: Challenges and opportunities for innovations. IEEE communications magazine, 53(2), 90-97.
Hlophe, M. C. (2020). A model-based deep learning approach to spectrum management in distributed cognitive radio networks (Doctoral dissertation, University of Pretoria).
Hossain, E., Niyato, D., & Han, Z. (2009). Dynamic spectrum access and management in cognitive radio networks. Cambridge university press.
Huang, C., He, R., Ai, B., Molisch, A. F., Lau, B. K., Haneda, K., ... & Zhong, Z. (2022). Artificial intelligence enabled radio propagation for communications—Part I: Channel characterization and antenna-channel optimization. IEEE Transactions on Antennas and Propagation, 70(6), 3939-3954.
Ibrahim, A., Thiruvady, D., Schneider, J. G., & Abdelrazek, M. (2020). The challenges of leveraging threat intelligence to stop data breaches. Frontiers in Computer Science, 2, 36.
Li, B., Zou, Y., Zhu, J., & Cao, W. (2021). Impact of hardware impairment and co-channel interference on security-reliability trade-off for wireless sensor networks. IEEE Transactions on Wireless Communications, 20(11), 7011-7025.
Li, R., Zhao, Z., Zhou, X., Ding, G., Chen, Y., Wang, Z., & Zhang, H. (2017). Intelligent 5G: When cellular networks meet artificial intelligence. IEEE Wireless communications, 24(5), 175-183.
Liu, Q., Hagenmeyer, V., & Keller, H. B. (2021). A review of rule learning-based intrusion detection systems and their prospects in smart grids. IEEE Access, 9, 57542-57564.
Mao, Q., Hu, F., & Hao, Q. (2018). Deep learning for intelligent wireless networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 20(4), 2595-2621.
Mata, J., de Miguel, I., Duran, R. J., Merayo, N., Singh, S. K., Jukan, A., & Chamania, M. (2018). Artificial intelligence (AI) methods in optical networks: A comprehensive survey. Optical switching and networking, 28, 43-57.
Nguyen, D. C., Cheng, P., Ding, M., Lopez-Perez, D., Pathirana, P. N., Li, J., ... & Poor, H. V. (2020). Enabling AI in future wireless networks: A data life cycle perspective. IEEE Communications Surveys & Tutorials, 23(1), 553-595.
Qamar, F., Hindia, M. N., Dimyati, K., Noordin, K. A., & Amiri, I. S. (2019). Interference management issues for the future 5G network: a review. Telecommunication Systems, 71, 627-643.
Qamar, F., Siddiqui, M. U. A., Hindia, M. N., Hassan, R., & Nguyen, Q. N. (2020). Issues, challenges, and research trends in spectrum management: A comprehensive overview and new vision for designing 6G networks. Electronics, 9(9), 1416.
Randhava, K. S., Roslee, M., & Yusoff, Z. (2021). Dynamic spectrum management using frequency selection at licensed and unlicensed bands for efficient vehicle-to-vehicle communication. F1000Research, 10, 1309.
Salh, A., Audah, L., Abdullah, Q., Noorsaliza, A., Shah, N. S. M., Mukred, J., & Hamzah, S. (2021). Development of a fully data-driven artificial intelligence and deep learning for URLLC application in 6g wireless systems: a survey. arXiv preprint arXiv:2108.10076.
Shen, S., Yu, C., Zhang, K., Ni, J., & Ci, S. (2021). Adaptive and dynamic security in AI-empowered 6G: From an energy efficiency perspective. IEEE Communications Standards Magazine, 5(3), 80-88.
Shen, X., Gao, J., Wu, W., Lyu, K., Li, M., Zhuang, W., ... & Rao, J. (2020). AI-assisted network-slicing based next-generation wireless networks. IEEE Open Journal of Vehicular Technology, 1, 45-66.
Sheth, K., Patel, K., Shah, H., Tanwar, S., Gupta, R., & Kumar, N. (2020). A taxonomy of AI techniques for 6G communication networks. Computer communications, 161, 279-303.
Siddiqui, M. U. A., Qamar, F., Ahmed, F., Nguyen, Q. N., & Hassan, R. (2021). Interference management in 5G and beyond network: Requirements, challenges and future directions. IEEE Access, 9, 68932-68965.
Singh, S., Karimipour, H., HaddadPajouh, H., & Dehghantanha, A. (2020). Artificial intelligence and security of industrial control systems. Handbook of Big Data Privacy, 121-164.
Sookhak, M., Tang, H., He, Y., & Yu, F. R. (2018). Security and privacy of smart cities: a survey, research issues and challenges. IEEE Communications Surveys & Tutorials, 21(2), 1718-1743.
Tavakoli, R., Nabi, M., Basten, T., & Goossens, K. (2018). Dependable interference-aware time-slotted channel hopping for wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 14(1), 1-35.
Tyagi, S. K. S., Mukherjee, A., Pokhrel, S. R., & Hiran, K. K. (2020). An intelligent and optimal resource allocation approach in sensor networks for smart agri-IoT. IEEE Sensors Journal, 21(16), 17439-17446.
Wang, C. X., Di Renzo, M., Stanczak, 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, J., & Gao, R. X. (2022). Innovative smart scheduling and predictive maintenance techniques. In Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology (pp. 181-207). Elsevier.
Wang, X., Kong, L., Kong, F., Qiu, F., Xia, M., Arnon, S., & Chen, G. (2018). Millimeter wave communication: A comprehensive survey. IEEE Communications Surveys & Tutorials, 20(3), 1616-1653.
Waqas, M., Tu, S., Halim, Z., Rehman, S. U., Abbas, G., & Abbas, Z. H. (2022). The role of artificial intelligence and machine learning in wireless networks security: Principle, practice and challenges. Artificial Intelligence Review, 55(7), 5215-5261.
Wilkinson, P., Smith, K. R., Davies, M., Adair, H., Armstrong, B. G., Barrett, M., ... & Chalabi, Z. (2009). Public health benefits of strategies to reduce greenhouse-gas emissions: household energy. The Lancet, 374(9705), 1917-1929.
Yang, H., Alphones, A., Xiong, Z., Niyato, D., Zhao, J., & Wu, K. (2020). Artificial-intelligence-enabled intelligent 6G networks. IEEE Network, 34(6), 272-280.
Zhang, C., Ueng, Y. L., Studer, C., & Burg, A. (2020). Artificial intelligence for 5G and beyond 5G: Implementations, algorithms, and optimizations. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 10(2), 149-163.