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1707334 Vol 8 · Issue 10 Download Paper

Leveraging Generative AI for Dynamic Network Slicing in RedCap 5G Networks

Asrar Ahmad Ansari V. Suresh Kumar

Subject area: Science,Engineering and Technology  ·  Area of research: Medical Education

Abstract

The advent of Reduced Capability (RedCap) devices in 5G networks introduces unique challenges in network slicing due to their constrained computational and communication capabilities. Adaptive network slicing is crucial to optimize resource allocation and maintain quality of service (QoS) for diverse RedCap use cases. Generative Artificial Intelligence (GenAI) presents a promising approach to enhance adaptability by predicting network conditions, automating slice reconfiguration, and optimizing resource distribution dynamically. This paper explores the integration of GenAI models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), to facilitate efficient network slicing for RedCap-enabled 5G networks. GenAI can synthesize realistic network traffic patterns, anticipate congestion, and proactively adjust slice parameters based on learned insights. By leveraging reinforcement learning-driven GenAI models, real-time decision-making can be enhanced, leading to improved spectral efficiency, reduced latency, and optimized power consumption. It present a framework that utilizes GenAI for slice elasticity, ensuring seamless adaptation to changing network conditions while minimizing service disruptions. The proposed approach is evaluated through simulations, demonstrating its effectiveness in dynamically balancing network loads and maintaining QoS in RedCap scenarios. Our findings highlight that GenAI-driven adaptive slicing significantly enhances network efficiency, making 5G RedCap deployments more robust and scalable.

Keywords

Generative AI, Adaptive Network Slicing, RedCap 5G, AI-driven Optimization, and Resource Allocation

References

[1] H. Ullah, N. G. Nair, A. Moore, C. Nugent, P. Muschamp, and M. Cuevas, “5G Communication: An Overview of Vehicle-To-Everything, Drones, and Healthcare Use-Cases,” IEEE Access 7 (2019): 37251–37268.

[2] L. Zeng and X. Dong, “Artistic Style Conversion Based on 5G Virtual Reality and Virtual Reality Visual Space,” Mobile Information Systems 2021 (2021): 1–8.

[3] C. X. Wang, M. Di Renzo, S. Stanczak, S. Wang, and E. G. Larsson, “Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges,” IEEE Wireless Communications 27, no. 1 (2020): 16–23.

[4] A. Dash, D. Pradhan, H. M. Tun, and Z. M. Naing, “m-MTC for Optimized Communication in 5G,” Journal of Network Security Computer Networks 8, no. 3 (2022): 1–8.

[5] K. Shafique, B. A. Khawaja, F. Sabir, S. Qazi, and M. Mustaqim, “Internet of Things (IoT) for Next-Generation Smart Systems: A Review of Current Challenges, Future Trends and Prospects for Emerging 5G-IoT Scenarios,” IEEE Access 8 (2020): 23022–23040.

[6] X. Kong, Y. Wu, H. Wang, and F. Xia, “Edge Computing for Internet of Everything: A Survey,” IEEE Internet of Things Journal 9, no. 23 (2022): 23472–23485.

[7] V. P. Rekkas, S. Sotiroudis, P. Sarigiannidis, S. Wan, G. K. Karagiannidis, and S. K. Goudos, “Machine Learning in Beyond 5G/6G Networks—State-Of-The-Art and Future Trends,” Electronics 10, no. 22 (2021): 2786.

[8] R. Vadisetty, "Multi Layered Cloud Technologies to achieve Interoperability in AI," 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC), Guntur, India, 2024, pp. 1-5.

[9] S. Mumtaz, J. Rodriguez, and L. Dai, “Introduction to mmWave Massive MIMO,” in mmWave Massive MIMO (Cambridge, MA: Elsevier, 2017), 1–18.

[10] M. Z. Chowdhury, M. Shahjalal, S. Ahmed, and Y. M. Jang, “6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions,” IEEE Open Journal of the Communications Society 1 (2020): 957–975.

[11] C. De Alwis, A. Kalla, Q. V. Pham, et al., “Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research,” IEEE Open Journal of the Communications Society 2 (2021): 836–886.

[12] A. Slalmi, H. Chaibi, A. Chehri, R. Saadane, and G. Jeon, “Toward 6G: Understanding Network Requirements and Key Performance Indicators,” Transactions on Emerging Telecommunications Technologies 32, no. 3 (2021): e4201.

[13] A. K. Singh, S. Maurya, N. Kumar, and S. Srivastava, “Heuristic Approaches for the Reliable SDN Controller Placement Problem,” Transactions on Emerging Telecommunications Technologies 31, no. 2 (2020): e3761.

[14] A. K. Singh, S. Maurya, and S. Srivastava, “Varna-Based Optimization: A Novel Method for Capacitated Controller Placement Problem in SDN,” Frontiers of Computer Science 14 (2020): 1–26.

[15] W. Wu, C. Zhou, M. Li, et al., “AI-Native Network Slicing for 6G Networks,” IEEE Wireless Communications 29, no. 1 (2022): 96–103.

[16] D. Lake, N. Wang, R. Tafazolli, and L. Samuel, “Softwarization of 5G Networks– Implications to Open Platforms and Standardizations,” IEEE Access 9 (2021): 88902–88930.

[17] R. P. Goldberg, “Survey of Virtual Machine Research,” Computer 7, no. 6 (1974): 34–45.

[18] J. Rachid, “NGMN 5G Initiative White Paper,” 2015.

[19] X. Li, R. Ni, J. Chen, Y. Lyu, Z. Rong, and R. Du, “End-To-End Network Slicing in Radio Access Network, Transport Network and Core Network Domains,” IEEE Access 8 (2020): 29525–29537.

[20] G. Brown, Service-Oriented 5G Core Networks (Istanbul, Türkiye: Huawei Technologies Co, 2017).

[21] Shashi Kant Gupta et al 2022 ECS Trans. 107 2927 https://doi.org/10.1149/10701.2927ecst

[22] S. Saxena, D. Yagyasen, C. N. Saranya, R. S. K. Boddu, A. K. Sharma and S. K. Gupta, "Hybrid Cloud Computing for Data Security System," 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 2021, pp. 1-8, doi: 10.1109/ICAECA52838.2021.9675493.

[23] S. K. Gupta, B. Pattnaik, V. Agrawal, R. S. K. Boddu, A. Srivastava and B. Hazela, "Malware Detection Using Genetic Cascaded Support Vector Machine Classifier in Internet of Things," 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA), 2022, pp. 1-6, doi: 10.1109/ICCSEA54677.2022.9936404.

[24] Natarajan, R.; Lokesh, G.H.; Flammini, F.; Premkumar, A.; Venkatesan, V.K.; Gupta, S.K. A Novel Framework on Security and Energy Enhancement Based on Internet of Medical Things for Healthcare 5.0. Infrastructures 2023, 8, 22. https://doi.org/10.3390/infrastructures8020022

[25] V. S. Kumar, A. Alemran, D. A. Karras, S. Kant Gupta, C. Kumar Dixit and B. Haralayya, "Natural Language Processing using Graph Neural Network for Text Classification," 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), Chickballapur, India, 2022, pp. 1-5, doi: 10.1109/ICKECS56523.2022.10060655.

[26] M. Sakthivel, S. Kant Gupta, D. A. Karras, A. Khang, C. Kumar Dixit and B. Haralayya, "Solving Vehicle Routing Problem for Intelligent Systems using Delaunay Triangulation," 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), Chickballapur, India, 2022, pp. 1-5, doi: 10.1109/ICKECS56523.2022.10060807.

[27] S. Tahilyani, S. Saxena, D. A. Karras, S. Kant Gupta, C. Kumar Dixit and B. Haralayya, "Deployment of Autonomous Vehicles in Agricultural and using Voronoi Partitioning," 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), Chickballapur, India, 2022, pp. 1-5, doi: 10.1109/ICKECS56523.2022.10060773.

[28] V. S. Kumar, A. Alemran, S. K. Gupta, B. Hazela, C. K. Dixit and B. Haralayya, "Extraction of SIFT Features for Identifying Disaster Hit areas using Machine Learning Techniques," 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), Chickballapur, India, 2022, pp. 1-5, doi: 10.1109/ICKECS56523.2022.10060037.

[29] Sharma, Chirag, et al. "Lightweight Security for IoT." Journal of Intelligent & Fuzzy Systems Preprint (2023): 1-17.

[30] Akram, Abeeda, et al. "On Layout Optimization of Wireless Sensor Network Using Meta-Heuristic Approach." Comput. Syst. Sci. Eng. 46.3 (2023): 3685-3701.

[31] Shakir, Khan, and Alotaibi Reemiah Muneer. "A novel thresholding for prediction analytics with machine learning techniques." International Journal of Computer Science & Network Security 23.1 (2023): 33-40.

[32] Tayyab, Moeen, et al. "Recognition of Visual Arabic Scripting News Ticker From Broadcast Stream." IEEE Access 10 (2022): 59189-59204.

[33] Khan, Shakir. "Business Intelligence Aspect for Emotions and Sentiments Analysis." 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT). IEEE, 2022.

[34] AlSuwaidan, Lulwah, et al. "Swarm Intelligence Algorithms for Optimal Scheduling for Cloud‐Based Fuzzy Systems." Mathematical Problems in Engineering 2022.1 (2022): 4255835.

[35] Sultan Ahmad, Sudan Jha, Abubaker E. M. Eljialy and Shakir Khan, “A Systematic Review on e-Wastage Frameworks” International Journal of Advanced Computer Science and Applications(IJACSA), 12(12), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121287

[36] Khan, Shakir, and Mohammed Ali Alshara. "Adopting Open Source Software for Integrated Library System and Digital Library Automation." International Journal of Computer Science and Network Security 20.9 (2020): 158-165.

[37] Khan, Shakir, and Amani Alfaifi. "Modeling of coronavirus behavior to predict it’s spread." International Journal of Advanced Computer Science and Applications 11.5 (2020): 394-399.

[38] Khan, Shakir. "Modern Internet of Things as a challenge for higher education." International Journal of Computer Science and Network Security 18.12 (2018): 34-41.

[39] Khan, Shakir, and M. Alajmi. "The Role Of Open Source Technology In Development Of E-Learning Education." Edulearn17 Proceedings. IATED, 2017.

[40] AlAjmi, M., and Shakir Khan. "Part of Ajax And Openajax In Cutting Edge Rich Application Advancement For E-Learning." INTED2015 Proceedings. IATED, 2015.

[41] Sattar, Kamran, et al. "Social networking in medical schools: Medical student’s viewpoint." Biomed Res 27.4 (2016): 1378-84.

[42] AlAjmi, Mohamed F., Shakir Khan, and Abdulkadir Alaydarous. "Data Protection Control and Learning Conducted Via Electronic Media IE Internet." International Journal of Advanced Computer Science and Applications 5.11 (2014).

[43] Khan, Shakir, et al. "Keeping Data on Clouds: Cloud Computing Significance." International Journal of Engineering & Science Research 3.2 (2013): 2321-2327.

[44] AlAjmi, Mohammed, and Shakir Khan. "Data Mining–Based, Service Oriented Architecture (SOA) In E-Learning." Iceri2012 Proceedings. IATED, 2012.

[45] AlAjmi, M., and Shakir Khan. "The Utility of New Technologies in Enhancing Learning Vigilance in Educationally Poor Populations." EDULEARN12 Proceedings. IATED, 2012.

[46] AlAjmi, Mohamed F., and Shakir Khan. "Effective Use of Web 2.0 Tools Complex Pharmatical Skills Teaching And Learning." ICERI2011, 3rd International Conference on Education and New Learning Technologies, Spain. 2011.

[47] Alajmi, M., and S. Khan. "EFFECTIVE USE OF WEB 2.0 TOOLS IN PHARMACY STUDENTS'CLINICAL SKILLS PRACTICE DURING FIELD TRAINING." iceri2011 proceedings. IATED, 2011.

[48] Khan, Shakir, Mohammed AlAjmi, and Arun Sharma. "Safety Measures Investigation in Moodle LMS." Special Issue of International Journal of Computer Applications (2012).

[49] Khan, Shakir, and Arun Sharma. "Moodle Based LMS and Open Source Software (OSS) Efficiency in E-Learning." International Journal of Computer Science & Engineering Technology 3.4 (2012): 50-60.

[50] AlAjmi, Mohamed F., Arun Sharma Head, and Shakir Khan. "Growing cloud computing efficiency." International Journal of Advanced Computer Science and Applications (IJACSA) 3.5 (2012).

[51] AlAjmi, Mohamed F., Shakir Khan, and Arun Sharma. "Studying data mining and data warehousing with different e-learning system." International Journal of Advanced Computer Science and Applications 4.1 (2013).

[52] Xiang Li, Wang Zhou, Amin Ul Haq, Shakir Khan, LDPMF: Local differential privacy enhanced matrix factorization for advanced recommendation, Knowledge-Based Systems, Volume 309, 2025, 112892, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2024.112892.

[53] Khan, S., Alghayadh, F.Y., Ahanger, T.A. et al. Deep learning model for efficient traffic forecasting in intelligent transportation systems. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-10537-z

[54] Saif, Sohail, et al. "A secure data transmission framework for IoT enabled healthcare." Heliyon 10.16 (2024).

[55] Veluri, Rahul Chiranjeevi, et al. "Modified M‐RCNN approach for abandoned object detection in public places." Expert Systems 42.2 (2025): e13648.

[56] Jian, Wang, et al. "Feature elimination and stacking framework for accurate heart disease detection in IoT healthcare systems using clinical data." Frontiers in Medicine 11 (2024): 1362397.

[57] Jian, Wang, et al. "SA-Bi-LSTM: Self Attention With Bi-Directional LSTM based Intelligent Model for Accurate Fake News Detection to ensured information integrity on social media platforms." IEEE Access (2024).

[58] S. Khan and S. Alqahtani, "Hybrid machine learning models to detect signs of depression," Multimedia Tools and Applications, pp. 1-19, 2023.

[59] Eldosoky, Mahmoud A., Jian Ping Li, Amin Ul Haq, Fanyu Zeng, Mao Xu, Shakir Khan, and Inayat Khan. "WallNet: Hierarchical Visual Attention-Based Model for Putty Bulge Terminal Points Detection." The Visual Computer (2024): 1-16.

[60] Saboor, Abdus, et al. "DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system." Scientific Reports 14.1 (2024): 6425.

[61] M. Azrour, J. Mabrouki, A. Guezzaz, S. Ahmad, S. Khan, and S. Benkirane, "IoT, Machine Learning and Data Analytics for Smart Healthcare," ed: CRC Press, 2024.

[62] Sreekumar, Das, S., Debata, B.R., Gopalan, R., Khan, S. (2024). Diabetes Prediction: A Comparison Between Generalized Linear Model and Machine Learning. In: Acharjya, D.P., Ma, K. (eds) Computational Intelligence in Healthcare Informatics. Studies in Computational Intelligence, vol 1132. Springer, Singapore. https://doi.org/10.1007/978-981-99-8853-2_4

[63] Khan, S., Serajuddin, M., Hasan, Z., Alvi, S.A.M., Ayub, R., Sharma, A. (2025). Natural Language Generation (NLG) with Reinforcement Learning (RL). In: Dev, A., Sharma, A., Agrawal, S.S., Rani, R. (eds) Artificial Intelligence and Speech Technology. AIST 2023. Communications in Computer and Information Science, vol 2268. Springer, Cham. https://doi.org/10.1007/978-3-031-75167-7_25

[64] I. Keshta et al., "Energy efficient indoor localisation for narrowband internet of things," CAAI Transactions on Intelligence Technology, 2023.

[65] Khan, S., Khari, M. & Azrour, M. IoT in retail and e-commerce. Electron Commer Res (2023). https://doi.org/10.1007/s10660-023-09785-3

[66] Halder, P., Hassan, M.M., Rahman, A.K.Z.R., Akter, L., Ahmed, A.S., Khan, S., Chatterjee, S., Raihan, M.: Prospects and setbacks for migrating towards 5G wireless access in developing Bangladesh: A comparative study. J. Eng. 2023, e12319 (2023). https://doi.org/10.1049/tje2.12319

[67] S. Khan et al., "Manufacturing industry based on dynamic soft sensors in integrated with feature representation and classification using fuzzy logic and deep learning architecture," The International Journal of Advanced Manufacturing Technology, vol. 128, pp. 2885–2897, 2023.

[68] Alotaibi, Reemiah Muneer, and Shakir Khan. "Big Data and Predictive Data Analytics in the Smes Industry Using Machine Learning Approach." 2023 6th International Conference on Contemporary Computing and Informatics (IC3I). Vol. 6. IEEE, 2023.

[69] M. J. Antony, B. P. Sankaralingam, S. Khan, A. Almjally, N. A. Almujally, and R. K. Mahendran, "Brain–Computer Interface: The HOL–SSA Decomposition and Two-Phase Classification on the HGD EEG Data," Diagnostics, vol. 13, no. 17, p. 2852, 2023.

[70] Yousef, Rammah, et al. "Bridged-U-Net-ASPP-EVO and deep learning optimization for brain tumor segmentation." Diagnostics 13.16 (2023): 2633.

[71] Saurabh, et al. ‘Lightweight Security for IoT’. 1 Jan. 2023: 5423 – 5439.

[72] Khan, Shakir, et al. "Transformer Architecture-Based Transfer Learning for Politeness Prediction in Conversation." Sustainability 15.14 (2023): 10828.

[73] M. S. Rao, S. Modi, R. Singh, K. L. Prasanna, S. Khan, and C. Ushapriya, "Integration of Cloud Computing, IoT, and Big Data for the Development of a Novel Smart Agriculture Model," in 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2023, pp. 2779-2783: IEEE.

[74] Akram, Abeeda, et al. "On Layout Optimization of Wireless Sensor Network Using Meta-Heuristic Approach." Comput. Syst. Sci. Eng. 46.3 (2023): 3685-3701.

[75] S. Khan, V. Ch, K. Sekaran, K. Joshi, C. K. Roy, and M. Tiwari, "Incorporating Deep Learning Methodologies into the Creation of Healthcare Systems," in 2023 International Conference on Artificial Intelligence and Smart Communication (AISC), 2023, pp. 994-998: IEEE.

[76] S. Khan, G. K. Moorthy, T. Vijayaraj, L. H. Alzubaidi, A. Barno, and V. Vijayan, "Computational Intelligence for Solving Complex Optimization Problems," in E3S Web of Conferences, 2023, vol. 399, p. 04038: EDP Sciences.

[77] Shakir, Khan, and Alotaibi Reemiah Muneer. "A novel thresholding for prediction analytics with machine learning techniques." International Journal of Computer Science & Network Security 23.1 (2023): 33-40.

[78] Alfaifi, Asma Abdulsalam, and Shakir Gayour Khan. "Utilizing data from Twitter to explore the UX of “Madrasati” as a Saudi e-learning platform compelled by the pandemic." Arab Gulf Journal of Scientific Research 39.3 (2021).

[79] AlSuwaidan, Lulwah, et al. "Swarm Intelligence Algorithms for Optimal Scheduling for Cloud‐Based Fuzzy Systems." Mathematical Problems in Engineering 2022.1 (2022): 4255835.

[80] Sultan Ahmad, Sudan Jha, Abubaker E. M. Eljialy and Shakir Khan, “A Systematic Review on e-Wastage Frameworks” International Journal of Advanced Computer Science and Applications (IJACSA), 12(12), 2021.

[81] Khan, Shakir. "Visual Data Analysis and Simulation Prediction for COVID-19 in Saudi Arabia Using SEIR Prediction Model." International Journal of Online & Biomedical Engineering 17.8 (2021).

[82] Khan, Shakir, and Mohammed Altayar. "Industrial internet of things: Investigation of the applications, issues, and challenges." Int. J. Adv. Appl. Sci 8.1 (2021): 104-113.

[83] S. Khan, "Study Factors for Student Performance Applying Data Mining Regression Model Approach," International Journal of Computer Science Network Security, vol. 21, no. 2, pp. 188-192, 2021.

[84] Khan, Shakir, and Amani Alfaifi. "Modeling of coronavirus behavior to predict it’s spread." International Journal of Advanced Computer Science and Applications 11.5 (2020): 394-399.

[85] S. Khan and M. Alshara, "Development of Arabic evaluations in information retrieval," International Journal of Advanced Applied Sciences, vol. 6, no. 12, pp. 92-98, 2019.

[86] S. Khan and M. Alshara, "Fuzzy Data Mining Utilization to Classify Kids with Autism," International Journal of Computer Science Network Security, vol. 19, no. 2, pp. 147-154, 2019.

[87] S. Khan and M. F. AlAjmi, "A Review on Security Concerns in Cloud Computing and their Solutions," International Journal of Computer Science Network Security, vol. 19, no. 2, p. 10, 2019.

[88] Khan, Shakir. "Modern Internet of Things as a challenge for higher education." International Journal of Computer Science and Network Security 18.12 (2018): 34-41.

[89] S. Khan, A. S. Al-Mogren, and M. F. AlAjmi, "Using cloud computing to improve network operations and management," presented at the 5th National Symposium on Information Technology: Towards New Smart World (NSITNSW), 2015.

[90] AlAjmi, Mohamed F., and Shakir Khan. "Effective Use of Web 2.0 Tools Complex Pharmatical Skills Teaching And Learning." ICERI2011, 3rd International Conference on Education and New Learning Technologies, Spain. 2011.

[91] M. F. AlAjmi, S. Khan, and A. Sharma, "Collaborative learning outline for mobile environment," in 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), 2014, pp. 429-434: IEEE.

[92] S. Khan, P. Sharma, K. R. Prasad, S. D, M. Serajuddin and R. Ayub, "The Implementation of Machine Learning in the Development of Sustainable Supply Chains," 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Gautam Buddha Nagar, India, 2023, pp. 292-296, doi: 10.1109/UPCON59197.2023.10434528.

[93] Tayyab, Moeen, et al. "Recognition of Visual Arabic Scripting News Ticker From Broadcast Stream." IEEE Access 10 (2022): 59189-59204.

[94] Khan, Shakir. "Business Intelligence Aspect for Emotions and Sentiments Analysis." 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT). IEEE, 2022.

[95] Khan, Shakir, and Mohammed Ali Alshara. "Adopting Open Source Software for Integrated Library System and Digital Library Automation." International Journal of Computer Science and Network Security 20.9 (2020): 158-165.

[96] Khan, Shakir, and M. Alajmi. "The Role Of Open Source Technology In Development Of E-Learning Education." Edulearn17 Proceedings. IATED, 2017.

[97] AlAjmi, M., and Shakir Khan. "Part of Ajax And Openajax In Cutting Edge Rich Application Advancement For E-Learning." INTED2015 Proceedings. IATED, 2015.

[98] Sattar, Kamran, et al. "Social networking in medical schools: Medical student’s viewpoint." Biomed Res 27.4 (2016): 1378-84.

[99] AlAjmi, Mohamed F., Shakir Khan, and Abdulkadir Alaydarous. "Data Protection Control and Learning Conducted Via Electronic Media IE Internet." International Journal of Advanced Computer Science and Applications 5.11 (2014).

[100] Khan, Shakir, et al. "Keeping Data on Clouds: Cloud Computing Significance." International Journal of Engineering & Science Research 3.2 (2013): 2321-2327.

[101] AlAjmi, Mohammed, and Shakir Khan. "Data Mining–Based, Service Oriented Architecture (SOA) In E-Learning." Iceri2012 Proceedings. IATED, 2012.

[102] AlAjmi, M., and Shakir Khan. "The Utility of New Technologies in Enhancing Learning Vigilance in Educationally Poor Populations." EDULEARN12 Proceedings. IATED, 2012.

[103] Alajmi, M., and S. Khan. "EFFECTIVE USE OF WEB 2.0 TOOLS IN PHARMACY STUDENTS'CLINICAL SKILLS PRACTICE DURING FIELD TRAINING." iceri2011 proceedings. IATED, 2011.

[104] Khan, Shakir, Mohammed AlAjmi, and Arun Sharma. "Safety Measures Investigation in Moodle LMS." Special Issue of International Journal of Computer Applications (2012).

[105] Khan, Shakir, and Arun Sharma. "Moodle Based LMS and Open Source Software (OSS) Efficiency in E-Learning." International Journal of Computer Science & Engineering Technology 3.4 (2012): 50-60.

[106] AlAjmi, Mohamed F., Arun Sharma Head, and Shakir Khan. "Growing cloud computing efficiency." International Journal of Advanced Computer Science and Applications (IJACSA) 3.5 (2012).

[107] AlAjmi, Mohamed F., Shakir Khan, and Arun Sharma. "Studying data mining and data warehousing with different e-learning system." International Journal of Advanced Computer Science and Applications 4.1 (2013).

[108] Khan, Shakir. "Data visualization to explore the countries dataset for pattern creation." International Journal of Online & Biomedical Engineering 17.13 (2021).

[109] AlAjmi, Mohamed Fahad, Shakir Khan, and Abu Sarwar Zamani. "Using instructive data mining methods to revise the impact of virtual classroom in e-learning." International Journal of Advanced Science and Technology 45.9 (2012): 125-134.

[110] Khan, Shakir. "Artificial intelligence virtual assistants (Chatbots) are innovative investigators." IJCSNS 20.2 (2020).

[111] Parisa, S.K., Banerjee, S. and Whig, P. 2023. AI-Driven Zero Trust Security Models for Retail Cloud Infrastructure: A Next-Generation Approach. International Journal of Sustainable Devlopment in field of IT. 15, 15 (Sep. 2023).

[112] Banerjee, S. and Parisa, S.K. 2023. AI-Powered Blockchain for Securing Retail Supply Chains in Multi-Cloud Environments. International Journal of Sustainable Development in computer Science Engineering. 9, 9 (Feb. 2023).

[113] Somnath Banerjee. Exploring Cryptographic Algorithms: Techniques, Applications, and Innovations. International Journal of Advanced Research in Science, Communication and Technology, 2024, pp.607 - 620. ⟨10.48175/ijarsct-18097⟩. ⟨hal-04901389⟩

[114] Somnath Banerjee. Advanced Data Management: A Comparative Study of Legacy ETL Systems and Unified Platforms. International Research Journal of Modernization in Engineering Technology and Science, 2024, 6 (11), pp.5677-5688. ⟨10.56726/IRJMETS64743⟩. ⟨hal-04887441⟩

[115] Parisa, S.K. and Banerjee, S. 2024. AI-Enabled Cloud Security Solutions: A Comparative Review of Traditional vs. Next-Generation Approaches. International Journal of Statistical Computation and Simulation. 16, 1 (Jan. 2024).

[116] Somnath Banerjee. Intelligent Cloud Systems: AI-Driven Enhancements in Scalability and Predictive Resource Management. International Journal of Advanced Research in Science, Communication and Technology, 2024, pp.266 - 276. ⟨10.48175/ijarsct-22840⟩. ⟨hal-04901380⟩

[117] Banerjee, S., Whig, P. and Parisa, S.K. 2024. Cybersecurity in Multi-Cloud Environments for Retail: An AI-Based Threat Detection and Response Framework. Transaction on Recent Developments in Industrial IoT. 16, 16 (Oct. 2024).

[118] Banerjee, S., Whig, P. and Parisa, S.K. 2024. Leveraging AI for Personalization and Cybersecurity in Retail Chains: Balancing Customer Experience and Data Protection. Transactions on Recent Developments in Artificial Intelligence and Machine Learning. 16, 16 (Aug. 2024).

[119] Somnath Banerjee. Neural Architecture Search Based Deepfake Detection Model using YOLO. International Journal of Advanced Research in Science, Communication and Technology, 2025, 5 (1), pp.375 - 383. ⟨10.48175/ijarsct-22938⟩. ⟨hal-04901372⟩

How to cite this paper

Asrar Ahmad Ansari, V. Suresh Kumar "Leveraging Generative AI for Dynamic Network Slicing in RedCap 5G Networks" Iconic Research And Engineering Journals Volume 8 Issue 10 2025 Page 460-471
Asrar Ahmad Ansari, V. Suresh Kumar "Leveraging Generative AI for Dynamic Network Slicing in RedCap 5G Networks" Iconic Research And Engineering Journals, vol. 8, no. 10, Apr. 2025
Asrar Ahmad Ansari, V. Suresh Kumar (2025). Leveraging Generative AI for Dynamic Network Slicing in RedCap 5G Networks. Iconic Research And Engineering Journals, 8(10).
Asrar Ahmad Ansari, V. Suresh Kumar "Leveraging Generative AI for Dynamic Network Slicing in RedCap 5G Networks" Iconic Research And Engineering Journals, vol. 8, no. 10, Apr. 2025. 
@article{1707334,
      author = {Asrar Ahmad Ansari, V. Suresh Kumar},
      title = {Leveraging Generative AI for Dynamic Network Slicing in RedCap 5G Networks},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {10},
      pages = {460-471},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707334.pdf},
      abstract = {The advent of Reduced Capability (RedCap) devices in 5G networks introduces unique challenges in network slicing due to their constrained computational and communication capabilities. Adaptive network slicing is crucial to optimize resource allocation and maintain quality of service (QoS) for diverse RedCap use cases. Generative Artificial Intelligence (GenAI) presents a promising approach to enhance adaptability by predicting network conditions, automating slice reconfiguration, and optimizing resource distribution dynamically. This paper explores the integration of GenAI models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), to facilitate efficient network slicing for RedCap-enabled 5G networks. GenAI can synthesize realistic network traffic patterns, anticipate congestion, and proactively adjust slice parameters based on learned insights. By leveraging reinforcement learning-driven GenAI models, real-time decision-making can be enhanced, leading to improved spectral efficiency, reduced latency, and optimized power consumption. It present a framework that utilizes GenAI for slice elasticity, ensuring seamless adaptation to changing network conditions while minimizing service disruptions. The proposed approach is evaluated through simulations, demonstrating its effectiveness in dynamically balancing network loads and maintaining QoS in RedCap scenarios. Our findings highlight that GenAI-driven adaptive slicing significantly enhances network efficiency, making 5G RedCap deployments more robust and scalable.},
      keywords = {Generative AI, Adaptive Network Slicing, RedCap 5G, AI-driven Optimization, and Resource Allocation},
      month = {April},
  }