Channel Allocation in mobile multimedia network using artificial neural networks
DOI:
https://doi.org/10.29070/xv694s36Keywords:
Channel Allocation, Mobile Multimedia Networks, Artificial Neural Network, quality of service, network management solutionsAbstract
Capital asset control systems may enhance network performance, reduce congestion, and optimise resource allocation with the use of artificial intelligence. Using machine learning models like decision trees and neural networks allows for more informed and adaptive admission control options. These models can precisely predict how the network's quality of service (QoS) will be affected by accepting a new call. Exploring AI-enhanced CAC schemes requires a thorough analysis of various machine learning methods and their potential applications to real-time network management. To ensure that AI-based CAC implementations are actually possible in resource-limited mobile situations, it is crucial to consider their computational complexity and resource requirements. Accuracy and computational efficiency are two competing goals, and this study examines both. Finding a happy medium that can meet the stringent requirements of mobile multimedia networks is the driving force behind this research. Also covered in this study is the possibility of mixing AI-powered CAC with cutting-edge tech like 5G networks and cloud computing. Collectively, these technology and artificial intelligence (AI) have to open up new opportunities for situationally-conscious, dynamic admission manipulate. As a result, mobile multimedia networks might be a lot more efficient and adaptable. An exploration of CAC schemes is provided in this article, which draws attention to the potential game-changing impact of AI-based methods for mobile multimedia network optimisation. The increasing demand for multimedia services is creating new challenges, yet these challenges may be amenable to artificial intelligence integration into CAC systems. More adaptive and intelligent network management solutions would be possible as a result.
References
Ayodele, T. M., Adewumi, A. O., & Charles, M. (2018). Artificial neural network models for prediction of energy consumption in buildings: A review. Renewable and Sustainable Energy Reviews, 81(1), 1881-1892.
Bi, S., Zhang, Z., Li, X., & Gulliver, T. A. (2019). An energy-efficient artificial neural network-based cognitive radio network with spectrum sensing and sharing. IEEE Transactions on Industrial Informatics, 16(3), 2018-2028.
Chen, J., Wang, X., & Xu, B. (2018). An efficient wireless spectrum management scheme based on deep reinforcement learning. IEEE Access, 6, 67051-67063.
Cheng, Z., Zhang, H., Lin, C., & Wang, X. (2018). Dynamic channel allocation for device-to-device communication networks: A deep reinforcement learning approach. IEEE Transactions on Vehicular Technology, 67(5), 4263-4274.
Dashtinezhad, S., Nasiri-Kenari, M., & Xiao, P. (2017). Energy-efficient dynamic spectrum management using artificial intelligence in cognitive radio sensor networks. IEEE Transactions on Vehicular Technology, 66(10), 8896-8911.
El-Sayed, H., El-Dolil, S., & Mohamed, S. (2020). Energy efficient and interference-aware cognitive radio networks using deep learning for 5G. In 2020 IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 1-6).
Ferrag, M. A., & Maglaras, L. A. (2021). Smart edge computing and IoT-enabled artificial intelligence in 5G networks: Opportunities, challenges, and solutions. Journal of Network and Computer Applications, 177, 102893.
Huang, Y., Tang, M., Yang, K., & Shao, H. (2020). Dynamic spectrum management in cognitive radio networks: A machine learning perspective. IEEE Transactions on Cognitive Communications and Networking, 6(1), 2-13.
Jia, Y., Xu, W., Yang, Y., & Xiao, X. (2017). Spectrum auction based dynamic channel allocation for cognitive radio networks. IEEE Access, 5, 24613-24623.
Kim, H., Lee, J., & Kang, M. (2018). Dynamic channel allocation in cognitive radio networks based on deep reinforcement learning. IEEE Access, 6, 3241-3250.
Kumar, P., & Tripathi, A. (2017). A survey on resource allocation techniques using machine learning for 5G networks. In 2017 2nd International Conference for Convergence in Technology (I2CT) (pp. 700-705).
Lee, S., Kim, J., & Lim, J. (2019). Deep reinforcement learning-based dynamic spectrum access in cognitive radio networks. IEEE Access, 7, 12736-12748.
Li, Y., Wang, J., Zhao, Z., & Wang, X. (2021). Resource allocation in NOMA-based cognitive radio networks: A deep reinforcement learning approach. IEEE Transactions on Cognitive Communications and Networking, 7(1), 2-20.
Liu, B., Zhang, Y., Liu, Y., & Zhang, X. (2020). Dynamic channel allocation for device-to-device communications using deep reinforcement learning. IEEE Transactions on Vehicular Technology, 69(5), 5515-5528.
Lu, L., Zhao, J., & Zheng, J. (2018). A survey on artificial intelligence for cognitive radio networks. IEEE Access, 6, 16517-16534.
Ma, X., & Han, Z. (2016). Cooperative spectrum sensing and channel allocation in cognitive radio networks: A deep learning perspective. IEEE Transactions on Vehicular Technology, 65(10), 8355-8366.
Miao, Y., Li, C., & Lei, X. (2018). Deep reinforcement learning for dynamic channel allocation in D2D communications underlaying LTE-A networks. IEEE Access, 6, 28436-28445.
Mostafa, A. (2017). Channel assignment for cognitive radio networks using a competitive learning approach. In 2017 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6).
Nguyen, H. X., Nguyen, H. T., & Van Pham, T. (2018). Deep reinforcement learning for cognitive radio networks: A review. IEEE Access, 6, 29114-29127.
Qian, Q., Zhang, Y., Zhang, Y., & Zhao, H. (2020). Spectrum management in 5G cognitive radio networks based on deep reinforcement learning. IEEE Access, 8, 6724-6735.
Rajput, S. S., & Prasad, R. (2021). Machine learning-based channel allocation in 5G cognitive radio networks: A comprehensive review. IEEE Transactions on Vehicular Technology, 70(1), 453-464.
Sharma, M., & Kumar, M. (2020). Deep reinforcement learning for dynamic spectrum management in 5G cognitive radio networks. In 2020 IEEE 17th India Council International Conference (INDICON) (pp. 1-6).
Shen, Z., Jiang, C., & Yu, S. (2017). An energy-efficient dynamic spectrum allocation scheme in cognitive radio networks. IEEE Internet of Things Journal, 4(3), 628-636.
Sun, Y., Zhang, R., & Zhang, H. (2017). Deep reinforcement learning for dynamic spectrum access in cognitive radio networks. IEEE Access, 5, 1693-1702.
Trang, H. T. T., Hieu, N. T., & Trang, T. T. T. (2018). A deep learning approach for dynamic spectrum management in cognitive radio networks. In 2018 International Conference on Advanced Technologies for Communications (ATC) (pp. 147-152).
Wang, G., Li, Z., & Song, L. (2020). Deep reinforcement learning based dynamic spectrum management for cognitive radio networks in 5G. IEEE Access, 8, 9850-9862.
Wang, H., Gao, S., & Han, Z. (2021). A survey on deep learning for intelligent radio resource management in 5G. IEEE Transactions on Cognitive Communications and Networking, 7(1), 2-20.
Wu, D., Xiao, L., & Lu, S. (2019). Cognitive radio networks for vehicular communications: A deep reinforcement learning approach.
This work is licensed under a 