An Optimal Resource Allocation in 5G Environment Using Novel Deep Learning Approach

Authors

  • Raja Varma Pamba LBS Institute of Technology for Women, Poojapura, Trivandrum, India
  • Rahul Bhandari Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab, India
  • A. Asha Rajalakshmi Engineering College, Rajalakshmi Nagar, Thandalam, Chennai, India
  • Ankur Singh Bist Graphic Era Hill University Bhimtal Campus, Uttarakhand, India

DOI:

https://doi.org/10.13052/jmm1550-4646.1959

Keywords:

Resource allocation, 5G Networks, QoS, web traffic, Deep Learning

Abstract

In recent times, the advancement in network devices has focused entirely on the miniaturization of services that should ensure better connectivity between them via fifth generation (5G) technology. The 5G network communication aims to improve Quality of Service (QoS). However, the allocation of resources is a core problem that increases the complexity of packet scheduling. In this paper, a resource allocation model is developed using a novel deep learning algorithm for optimal resource allocation. The novel deep learning is formulated using the constraints associated with optimal radio resource allocation. The objective function design aims at reducing the system delay. The study predicts the traffic in a complex environment and allocates resources accordingly. The simulation was conducted to test the scheduling efficacy and the results showed an improved rate of allocation than the other methods.

Downloads

Download data is not yet available.

Author Biographies

Raja Varma Pamba, LBS Institute of Technology for Women, Poojapura, Trivandrum, India

Raja Varma Pamba is an Assistant Professor working in LBS Institute of Technology for Women, Poojapura, Trivandrum, Kerala, India. She has over 13 years of experience in teaching and 3 years of industrial experience. She completed her B. Tech from Govt Rajiv Gandhi Institute of Technology, Kottayam and M. Tech from Visveswaraya National Institute of Technology, Nagpur, Maharashtra. She received her PhD in CSE from Mahatma Gandhi University, Kottayam, Kerala with a specialization in Machine Learning. She has over 10 paper publications to her credit and participated in many national and international conferences.

Rahul Bhandari, Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab, India

Rahul Bhandari is working in Chandigarh University, Punjab as an Associate Professor. He has over 12 years of experience in teaching. He has completed their B.E. and M.Tech. from Jodhpur Institute of Engineering Technology, Jodhpur. He received Ph.D. degree from Career Point University, Kota. There are 9 papers published in international journal and actively participate in FDP as well as STTP.

A. Asha, Rajalakshmi Engineering College, Rajalakshmi Nagar, Thandalam, Chennai, India

A. Asha is currently working as an Associate Professor in the Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai. She received her B. E. degree from M.S. University in 2000 and Master’s Degree in Communication Systems from SASTRA University in 2002 and PhD in Information and communication engineering from Anna University Chennai, in 2019. She has a teaching experience of 18 years and her research interests are Wireless Sensor Networks, Cloud computing and Internet of Things.

Ankur Singh Bist, Graphic Era Hill University Bhimtal Campus, Uttarakhand, India

Ankur Singh Bist is currently working as Associate Professor at Graphic Era Hill University Bhimtal, INDIA. His area of research is computer virology and Machine Learning. He has written more than 100 research papers. He has worked as reviewer/editor in more than 350 conferences/journals. He is active member of various research societies and educational group which covers more than 50 countries of world. He has received 5 international awards and medals of honors by renowned societies.

References

Pereira, R. S., Lieira, D. D., da Silva, M. A., Pimenta, A. H., da Costa, J. B., Rosário, D., … and Meneguette, R. I. (2020). RELIABLE: Resource allocation mechanism for 5G network using mobile edge computing. Sensors, 20(19), 5449.

Huang, J., Xing, C. C., Qian, Y., and Haas, Z. J. (2017). Resource allocation for multicell device-to-device communications underlaying 5G networks: A game-theoretic mechanism with incomplete information. IEEE Transactions on Vehicular Technology, 67(3), 2557–2570.

Wang, D., Song, B., Chen, D., and Du, X. (2019). Intelligent cognitive radio in 5G: AI-based hierarchical cognitive cellular networks. IEEE Wireless Communications, 26(3), 54-61.

Haryadi, S., and Aryanti, D. R. (2017, October). The fairness of resource allocation and its impact on the 5G ultra-dense cellular network performance. In 2017 11th International Conference on Telecommunication Systems Services and Applications (TSSA) (pp. 1–4). IEEE.

Bashir, A. K., Arul, R., Basheer, S., Raja, G., Jayaraman, R., and Qureshi, N. M. F. (2019). An optimal multitier resource allocation of cloud RAN in 5G using machine learning. Transactions on emerging telecommunications technologies, 30(8), e3627.

Chen, M., Miao, Y., Gharavi, H., Hu, L., and Humar, I. (2019). Intelligent traffic adaptive resource allocation for edge computing-based 5G networks. IEEE transactions on cognitive communications and networking, 6(2), 499–508.

Richart, M., Baliosian, J., Serrati, J., Gorricho, J. L., Agüero, R., and Agoulmine, N. (2017, November). Resource allocation for network slicing in WiFi access points. In 2017 13th International conference on network and service management (CNSM) (pp. 1–4). IEEE.

Wu, D., Zhang, Z., Wu, S., Yang, J., and Wang, R. (2018). Biologically inspired resource allocation for network slices in 5G-enabled Internet of Things. IEEE Internet of Things Journal, 6(6), 9266–9279.

Yu, P., Zhou, F., Zhang, X., Qiu, X., Kadoch, M., and Cheriet, M. (2020). Deep learning-based resource allocation for 5G broadband TV service. IEEE Transactions on Broadcasting, 66(4), 800–813.

Shahzadi, R., Niaz, A., Ali, M., Naeem, M., Rodrigues, J. J., Qamar, F., and Anwar, S. M. (2019). Three tier fog networks: Enabling IoT/5G for latency sensitive applications. China Communications, 16(3), 1–11.

Ari, A. A. A., Gueroui, A., Titouna, C., Thiare, O., and Aliouat, Z. (2019). Resource allocation scheme for 5G C-RAN: a Swarm Intelligence based approach. Computer Networks, 165, 106957.

Tadayon, N., and Aissa, S. (2018). Radio resource allocation and pricing: Auction-based design and applications. IEEE Transactions on Signal Processing, 66(20), 5240–5254.

Othman, A., and Nayan, N. A. (2019). Efficient admission control and resource allocation mechanisms for public safety communications over 5G network slice. Telecommunication Systems, 72(4), 595–607.

Korrai, P. K., Lagunas, E., Sharma, S. K., Chatzinotas, S., and Ottersten, B. (2019, September). Slicing based resource allocation for multiplexing of eMBB and URLLC services in 5G wireless networks. In 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) (pp. 1–5). IEEE.

Korrai, P., Lagunas, E., Sharma, S. K., Chatzinotas, S., Bandi, A., and Ottersten, B. (2020). A RAN resource slicing mechanism for multiplexing of eMBB and URLLC services in OFDMA based 5G wireless networks. IEEE Access, 8, 45674–45688.

Zhang, H., Liu, N., Chu, X., Long, K., Aghvami, A. H., and Leung, V. C. (2017). Network slicing based 5G and future mobile networks: mobility, resource management, and challenges. IEEE communications magazine, 55(8), 138–145.

Ma, Z., Li, B., Yan, Z., and Yang, M. (2020). QoS-Oriented joint optimization of resource allocation and concurrent scheduling in 5G millimeter-wave network. Computer Networks, 166, 106979.

Yan, M., Feng, G., Zhou, J., Sun, Y., and Liang, Y. C. (2019). Intelligent resource scheduling for 5G radio access network slicing. IEEE Transactions on Vehicular Technology, 68(8), 7691–7703.

Gutterman, C., Grinshpun, E., Sharma, S., and Zussman, G. (2019, July). RAN resource usage prediction for a 5G slice broker. In Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing (pp. 231–240).

Toscano, M., Grunwald, F., Richart, M., Baliosian, J., Grampín, E., and Castro, A. (2019, July). Machine learning aided network slicing. In 2019 21st International Conference on Transparent Optical Networks (ICTON) (pp. 1–4). IEEE.

Lei, L., You, L., He, Q., Vu, T. X., Chatzinotas, S., Yuan, D., and Ottersten, B. (2019). Learning-assisted optimization for energy-efficient scheduling in deadline-aware NOMA systems. IEEE Transactions on Green Communications and Networking, 3(3), 615–627.

Luo, J., Tang, J., So, D. K., Chen, G., Cumanan, K., and Chambers, J. A. (2019). A deep learning-based approach to power minimization in multi-carrier NOMA with SWIPT. IEEE Access, 7, 17450–17460.

Dong, R., She, C., Hardjawana, W., Li, Y., and Vucetic, B. (2019). Deep learning for hybrid 5G services in mobile edge computing systems: Learn from a digital twin. IEEE Transactions on Wireless Communications, 18(10), 4692–4707.

Zhang, S., Xiang, C., Cao, S., Xu, S., and Zhu, J. (2019). Dynamic carrier to MCPA allocation for energy efficient communication: Convex relaxation versus deep learning. IEEE Transactions on Green Communications and Networking, 3(3), 628–640.

Mismar, F. B., and Evans, B. L. (2019). Deep learning in downlink coordinated multipoint in new radio heterogeneous networks. IEEE Wireless Communications Letters, 8(4), 1040–1043.

Abdelreheem, A., Omer, O. A., Esmaiel, H., and Mohamed, U. S. (2019, April). Deep learning-based relay selection in D2D millimeter wave communications. In 2019 International Conference on Computer and Information Sciences (ICCIS) (pp. 1–5). IEEE.

Ahmed, K. I., Tabassum, H., and Hossain, E. (2019). Deep learning for radio resource allocation in multi-cell networks. IEEE Network, 33(6), 188–195.

Ma, Z., Li, B., Yan, Z., and Yang, M. (2020). QoS-Oriented joint optimization of resource allocation and concurrent scheduling in 5G millimeter-wave network. Computer Networks, 166, 106979.

Tayyaba, S. K., and Shah, M. A. (2019). Resource allocation in SDN based 5G cellular networks. Peer-to-Peer Networking and Applications, 12(2), 514–538.

Chien, H. T., Lin, Y. D., Lai, C. L., and Wang, C. T. (2019). End-to-end slicing with optimized communication and computing resource allocation in multi-tenant 5G systems. IEEE Transactions on Vehicular Technology, 69(2), 2079–2091.

Tun, Y. K., Tran, N. H., Ngo, D. T., Pandey, S. R., Han, Z., and Hong, C. S. (2019). Wireless network slicing: Generalized kelly mechanism-based resource allocation. IEEE Journal on Selected Areas in Communications, 37(8), 1794–1807.

Liu, Y., Tang, A., and Wang, X. (2019). Joint incentive and resource allocation design for user provided network under 5G integrated access and backhaul networks. IEEE Transactions on Network Science and Engineering, 7(2), 673–685.

Roostaei, R., Dabiri, Z., and Movahedi, Z. (2021). A game-theoretic joint optimal pricing and resource allocation for Mobile Edge Computing in NOMA-based 5G networks and beyond. Computer Networks, 198, 108352.

Lien, S. Y., Deng, D. J., Lin, C. C., Tsai, H. L., Chen, T., Guo, C., and Cheng, S. M. (2020). 3GPP NR sidelink transmissions toward 5G V2X. IEEE Access, 8, 35368–35382.

Tang, F., Zhou, Y., and Kato, N. (2020). Deep reinforcement learning for dynamic uplink/downlink resource allocation in high mobility 5G HetNet. IEEE Journal on Selected Areas in Communications, 38(12), 2773–2782.

Ali, R., Zikria, Y. B., Garg, S., Bashir, A. K., Obaidat, M. S., and Kim, H. S. (2021). A Federated Reinforcement Learning Framework for Incumbent Technologies in Beyond 5G Networks. IEEE Network, 35(4), 152–159.

Zhiyong Du, Bin Jiang, Qihui Wu, Yuhua Xu, Kun Xu, Towards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks, A Reinforcement Learning Perspective-Springer Singapore.

Downloads

Published

2023-08-14

How to Cite

Pamba, R. V. ., Bhandari, R. ., Asha, A. ., & Bist, A. S. . (2023). An Optimal Resource Allocation in 5G Environment Using Novel Deep Learning Approach. Journal of Mobile Multimedia, 19(05), 1331–1356. https://doi.org/10.13052/jmm1550-4646.1959

Issue

2023: Vol 19 Iss 5

Section

Articles

Most read articles by the same author(s)