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Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

IS3CQL : Intelligent sleep scheduling of small cells in high density 5G MIMO networks via incremental Deep Q learning optimizations

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pp. 795–804Vol. 45Issue 3April 2024DOI: 10.47974/JIOS-1577XML
Received:
03 Apr 2024
Published Online:
27 Apr 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1577
Pages:
795–804

Abstract

Effective management of small cell networks is crucial as the demand for fast, low-latency communication grows as a result of the development of 5G and Massive Multiple Input Multiple Output (M-MIMO) technology. Small cell sleep cycle scheduling in high-density networks frequently encounter issues as it tries to strike a compromise between energy efficiency, quality of service (QoS), and maintaining a high throughput. Additionally, these traditional methods ignore the complex spatial and temporal communication characteristics, resulting in subpar network performance. In order to overcome these difficulties, we suggest a brand-new intelligent sleep scheduling approach that makes use of both fundamental green computing ideas and incremental deep learning. In order to determine the best sleep schedules for tiny cells, our model takes into account both temporal and spatial communication factors, such as end-to-end delay, packet delivery ratio, throughput, and energy consumption of the nodes. The model adjusts the small cells’ sleep scheduling based on the ongoing spatial-temporal fluctuations in the network, providing the best possible use of resources. Additionally, its focus on green computing helps to lower energy consumption during extensive connections, making it an environmentally beneficial alternative for different scenarios. Under enormous MIMO situations, the results from our model show a significant improvement over the current approaches. These improvements include a notable improvement in communication consistency, a notable increase in throughput, a noticeable increase in packet delivery ratio, and a noticeable decrease in energy consumption and delay. In summary, our concept is a major step forward in the continuous development of intelligent, resilient, and effective 5G MIMO networks.

Keywords

Intelligent sleep schedulingHigh density 5G networksMassive MIMOIncremental deep Q learningGreen computingProcess

Subject Classifications

68T05

References

[1] H. Çelebi, Y. Yapıcı, İ. Güvenç and H. Schulzrinne, “Load-Based On/Off Scheduling for Energy-Efficient Delay-Tolerant 5G Networks,” in IEEE Transactions on Green Communications and Networking, vol. 3, no. 4, pp. 955-970, Dec. (2019), doi: 10.1109/TGCN.2019.2931700.
[2] J. -M. Liang, C. -K. Hsu, J. -J. Chen, K. -R. Wu and Y. -C. Tseng, “Three-Stage DRX Scheduling for Joint Downlink Transmission in C-RAN,” in IEEE Wireless Communications Letters, vol. 9, no. 2, pp. 129-133, Feb. (2020), doi: 10.1109/LWC.2019.2943471.
[3] S. Ali, A. Ferdowsi, W. Saad, N. Rajatheva and J. Haapola, “Sleeping Multi-Armed Bandit Learning for Fast Uplink Grant Allocation in Machine Type Communications,” in IEEE Transactions on Communications, vol. 68, no. 8, pp. 5072-5086, Aug. (2020), doi: 10.1109/TCOMM.2020.2989338.
[4] Z. Fang, J. Wang, Y. Ren, Z. Han, H. V. Poor and L. Hanzo, “Age of Information in Energy Harvesting Aided Massive Multiple Access Networks,” in IEEE Journal on Selected Areas in Communications, vol. 40, no. 5, pp. 1441-1456, May (2022), doi: 10.1109/JSAC.2022.3143252.
[5] Kim, YH., Bae, SH., Kwon, CH. et al. Analysis of Application of Small-Cell and D2D Communication Technology to Improve Performance in 5G Communication Environment. J. Electr. Eng. Technol. 17, 1347–1356 (2022). https://doi.org/10.1007/s42835-021-00935-x
[6] Deepa, V., Haridass, M., Selvamuthu, D. et al. Analysis of energy efficiency of small cell base station in 4G/5G networks. Telecommun Syst 82, 381–401 (2023). https://doi.org/10.1007/s11235-022-00987-y
[7] Natarajan, J., Rebekka, B. Machine Learning Based Small Cell ON/OFF for Energy Efficiency in 5G Heterogeneous Networks. Wireless Pers Commun 130, 2367–2383 (2023). https://doi.org/10.1007/s11277-023-10383-7
[8] Liu, Z., Wu, J., Yuan, Y. et al. Robust Power Control for 5G Small Cell Networks with Sleep Strategy. Wireless Pers Commun 116, 2205–2222 (2021). https://doi.org/10.1007/s11277-020-07787-0
[9] Venkateswararao, K., Swain, P. Binary-PSO-based energy-efficient small cell deployment in 5G ultra-dense network. J Supercomput 78, 1071–1092 (2022). https://doi.org/10.1007/s11227-021-03910-5
[10] Rashidi, Z., Hakami, V., Geranmayeh, P. et al. Multi-agent learning algorithms for content placement in cache-enabled small cell networks: 4G and 5G use cases. Neural Comput & Applic 34, 11641–11668 (2022). https://doi.org/10.1007/s00521-022-07051-5
[11] Ghosh, S., De, D. E2M3E2M3: energy-efficient massive MIMO–MISO 5G HetNet using Stackelberg game. J Supercomput 77, 13549–13583 (2021). https://doi.org/10.1007/s11227-021-03809-1
[12] Obakhena, H.I., Imoize, A.L., Anyasi, F.I. et al. Application of cell-free massive MIMO in 5G and beyond 5G wireless networks: a survey. J. Eng. Appl. Sci. 68, 13 (2021). https://doi.org/10.1186/s44147-021-00014-y
[13] Limkar, S., Ashok, W. V., Singh, S., Singh, A., Wagh, S. K., & Ajani, S. N. A mechanism to ensure identity-based anonymity and authentication for IoT infrastructure using cryptography. Journal of Discrete Mathematical Sciences and Cryptography, 26(5), 1597-1611 (2023).
[14] Kumar, Anil, and Sandeep Kumar Sharma. “Information cryptography using cellular automata and digital image processing.” Journal of Discrete Mathematical Sciences and Cryptography 25.4 : 1105-1111 (2022).
[15] Sapkal, Vinod, Gupta, Praveen & Khan, Aboo Bakar. Deep learning based phishing website identification system using CNN-LSTM classifier, Journal of Information and Optimization Sciences, 44:3, 315–330 (2023), DOI: 10.47974/JIOS-1343.

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