International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 18, No. 5, 8 Oct. 2026
Cover page and Table of Contents: PDF (size: 1791KB)
Implementation of Adaptive Sleep Modes for Enhancing Energy Efficiency of Ultra Dense Networks Using Traffic-Aware Grasshopper Optimization Algorithm
PDF (1791KB), PP.161-181
Views: 0 Downloads: 0
Author(s)
Nilakshee Rajule
1,*
Mithra Venkatesan
1
Radhika Menon
2
Anju Kulkarni
3
Index Terms
Base Station Sleeping, Traffic Prediction, Grasshopper Optimization, Energy Efficiency, Ultra Dense Networks.
Abstract
Energy consumption has emerged as a critical concern in next-generation wireless communication networks due to the increasing demand for high data rates and seamless connectivity. Ultra-Dense Networks (UDNs) in fifth-generation (5G) systems have been identified as a promising solution to support this demand by deploying a large number of small cell base stations (SBSs) alongside macro base stations (MBSs). However, the dense deployment significantly increases overall power consumption, especially when SBSs remain active under low traffic conditions caused by user mobility.
To address this issue, this paper proposes a novel adaptive sleep mode optimization framework that integrates traffic prediction with the Grasshopper Optimization Algorithm (GOA). Specifically, historical traffic patterns are analyzed to predict future traffic loads at each base station, and these predicted loads are used as input to the GOA to optimally determine the operational mode (active, light sleep, deep sleep, or off) of SBSs under QoS and coverage constraints. This predictive optimization enables dynamic and energy-efficient network adaptation.
The proposed approach enhances the overall energy efficiency (EE) and spectral efficiency (SE) of a two-tier heterogeneous network. Simulation results demonstrate that the proposed method achieves up to 29% improvement in energy efficiency and 21% improvement in spectral efficiency compared to existing approaches.
Cite This Paper
Nilakshee Rajule, Mithra Venkatesan, Radhika Menon, Anju Kulkarni, "Implementation of Adaptive Sleep Modes for Enhancing Energy Efficiency of Ultra Dense Networks Using Traffic-Aware Grasshopper Optimization Algorithm", International Journal of Computer Network and Information Security(IJCNIS), Vol.18, No.5, pp. 161-181, 2026. DOI:10.5815/ijcnis.2026.05.10
Reference
[1]Md. Noor-A-Rahim, Zilong Liu, Haeyoung Lee, G. G. Md. Nawaz Ali, Dirk Pesch, and Pei Xiao. 2022. A Survey on Resource Allocation in Vehicular Networks. Trans. Intell. Transport. Sys. 23, 2 (Feb. 2022), 701–721. https://doi.org/10.1109/TITS.2020.3019322
[2]Stoynov, Viktor, Vladimir Poulkov, Zlatka Valkova-Jarvis, Georgi Iliev, and Pavlina Koleva. 2023. "Ultra-Dense Networks: Taxonomy and Key Performance Indicators" Symmetry 15, no. 1: 2. https://doi.org/10.3390/sym15010002
[3]H. Zhang, H. Liu, J. Cheng and V. C. M. Leung, "Downlink Energy Efficiency of Power Allocation and Wireless Backhaul Bandwidth Allocation in Heterogeneous Small Cell Networks," in IEEE Transactions on Communications, vol. 66, no. 4, pp. 1705-1716, April 2018, doi: 10.1109/TCOMM.2017.2763623
[4]Jun Xu, Dejun Yang, Energy-efficient resource allocation for D2D communication underlaying cellular networks with incomplete CSI, Computer Networks, Volume 251, 2024, 110664, ISSN 1389-1286, https://doi.org/10.1016/j.comnet.2024.110664.
[5]Kang, M.W.; Chung, Y.W. An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization. Energies 2017, 10, 1417. https://doi.org/10.3390/en10091417
[6]Alam, M.J., Chugh, R., Azad, S. et al. Ant colony optimization-based solution to optimize load balancing and throughput for 5G and beyond heterogeneous networks. J Wireless Com Network 2024, 44 (2024). https://doi.org/10.1186/s13638-024-02376-2
[7]Aanchal Agrawal, A.K. Pal, Adaptive Hybrid Genetic-Ant Colony Optimization for Dynamic Self-Healing and Network Performance Optimization in 5G/6G Networks, Procedia Computer Science, Volume 252, 2025, Pages 404-413, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2024.12.041.
[8]Zhang, H., Jiang, C., Bennis, M., Debbah, M., Han, Z., & Leung, V. C. M. (2018). Heterogeneous Ultra Dense Networks: Part 2. IEEE Communications Magazine, 56(6), 12-13. https://doi.org/10.1109/MCOM.2018.8387196
[9]N. Al-Falahy and O. Y. K. Alani, "Network Capacity Optimisation in Millimetre Wave Band Using Fractional Frequency Reuse," in IEEE Access, vol. 6, pp. 10924-10932, 2018, doi: 10.1109/ACCESS.2017.2762338.
[10]Sherif, A.; Haci, H. A Novel Bio-Inspired Energy Optimization for Two-Tier Wireless Communication Networks: A Grasshopper Optimization Algorithm (GOA)-Based Approach. Electronics 2023, 12, 1216. https://doi.org/10.3390/electronics12051216
[11]E. P. Lens Shiang, W. -C. Chien, C. -F. Lai and H. -C. Chao, "Gated Recurrent Unit Network-based Cellular Trafile Prediction," 2020 International Conference on Information Networking (ICOIN), Barcelona, Spain, 2020, pp. 471-476, doi: 10.1109/ICOIN48656.2020.9016439.
[12]Wu, J., Wong, E.W., Chan, Y., & Zukerman, M. (2017). Energy Efficiency-QoS Tradeoff in Cellular Networks with Base-Station Sleeping. GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 1-7.
[13]Huang, Chih-Wei & Chen, Po. (2020). Joint Demand Forecasting and DQN-Based Control for Energy-Aware Mobile Traffic Offloading. IEEE Access. PP. 1-1. 10.1109/ACCESS.2020.2985679.
[14]G. Jang, N. Kim, T. Ha, C. Lee and S. Cho, "Base Station Switching and Sleep Mode Optimization With LSTM-Based User Prediction," in IEEE Access, vol. 8, pp. 222711-222723, 2020, doi: 10.1109/ACCESS.2020.3044242.
[15]Y. Chang, W. Chen, J. Li, J. Liu, H. Wei, Z. Wang, and N. Al-Dhahir, “Collaborative multi-BS power management for dense radio access network using deep reinforcement learning,” IEEE Transactions on Green Communications and Networking, vol. 7, no. 4, pp. 2104–2116, 2023.
[16]Y. Zhu and S. Wang, "Joint Traffic Prediction and Base Station Sleeping for Energy Saving in Cellular Networks," ICC 2021 - IEEE International Conference on Communications, Montreal, QC, Canada, 2021, pp. 1-6, doi: 10.1109/ICC42927.2021.9500442.
[17]Wu, Qiong & Chen, Xu & Zhou, Zhi & Chen, Liang & Zhang, Junshan. (2021). Deep Reinforcement Learning With Spatio-Temporal Traffic Forecasting for Data-Driven Base Station Sleep Control. IEEE/ACM Transactions on Networking. PP. 1-14. 10.1109/TNET.2021.3053771.
[18]Shinkuma, Ryoichi & Kishi, Naoki & Ota, Kaoru & Dong, Mianxiong & Sato, Takehiro & Oki, Eiji. (2021). Smarter Base Station Sleeping for Greener Cellular Networks. IEEE Network. 35. 98-103. 10.1109/MNET.110.2100224.
[19]Vasileios Perifanis, Nikolaos Pavlidis, Remous-Aris Koutsiamanis, Pavlos S. Efraimidis, Federated learning for 5G base station traffic forecasting, Computer Networks, Volume 235, 2023, 109950, ISSN 1389-1286, https://doi.org/10.1016/j.comnet.2023.109950.
[20]J. Lee, F. Solat, T. Y. Kim and H. V. Poor, "Federated Learning-Empowered Mobile Network Management for 5G and Beyond Networks: From Access to Core," in IEEE Communications Surveys & Tutorials, vol. 26, no. 3, pp. 2176-2212, thirdquarter 2024, doi: 10.1109/COMST.2024.3352910.
[21]Jane-Hwa Huang and Sz-Yan Hsu. 2020. QoS provisioning in energy-efficient cooperative networks with power assignment and relay deployment planning. Wirel. Netw. 26, 7 (Oct 2020), 5207–5222. https://doi.org/10.1007/s11276-020-02375-3
[22]T. S. Syed and G. A. Safdar, "Energy-efficient GCSA medium access protocol for infrastructure-based cognitive radio networks," IEEE Systems Journal, vol. 14, no. 2, pp. 2070-2079, 2020. doi: 10.1109/JSYST.2020.2964989
[23]M. Rajkumar, R. M. Suresh, and R. Sasikumar, "An effective cluster based data dissemination in a hybrid cellular ad hoc network," Concurrency and Computation: Practice and Experience, vol. 32, no. 4, Art. no. e5125, 2018. doi: 10.1002/cpe.5125
[24]S. Bhattacharjee, T. Acharya, and U. Bhattacharya, "Energy-efficient multicasting in hybrid cognitive small cell networks: A cross-layer approach," IEEE/ACM Transactions on Networking, vol. 28, no. 4, pp. 1542-1555, 2020. doi: 10.1109/TNET.2020.2971064
[25]Y. Xu, W. Jiao, and M. Tian, "An energy-efficient routing protocol for 3D wireless sensor networks," IEEE Sensors Journal, vol. 21, no. 3, pp. 2971-2980, 2021. doi: 10.1109/JSEN.2020.3046520
[26]D. Sharma, S. Singhal, A. Rai, and A. Singh, "Analysis of power consumption in standalone 5G network and enhancement in energy efficiency using a novel routing protocol," Sustainable Energy, Grids and Networks, vol. 25, pp. 100474, 2021. doi: 10.1016/j.segan.2020.100474
[27]S. Habibi, V. Solouk, and H. Kalbkhani, "Adaptive energy-efficient small cell sleeping and zooming in heterogeneous cellular networks," Telecommunication Systems, vol. 77, no. 1, pp. 37-49, 2021. doi: 10.1007/s11235-021-00755-7
[28]M. Beitollahi and N. Lu, "Multi-frame scheduling for federated learning over energy-efficient 6G wireless networks," in Proc. IEEE INFOCOM 2022 - IEEE Conference on Computer Communications, pp. 1-6, 2022. doi: 10.1109/INFOCOMWKSHPS54753.2022.9798090
[29]S. Malta, P. Pinto, and M. Fernández-Veiga, "Using reinforcement learning to reduce energy consumption of ultra-dense networks with 5G use cases requirements," IEEE Access, vol. 11, pp. 34608-34618, 2023. doi: 10.1109/ACCESS.2023.3289401
[30]T. Kim, S. Lee, H. Choi, H.-S. Park, and J. Choi, "An energy-efficient multi-level sleep strategy for periodic uplink transmission in industrial private 5G networks," Sensors, vol. 23, no. 22, Art. no. 9070, 2023. doi: 10.3390/s23229070
[31]J. Natarajan, "Cell throughput contribution rate based sleep control algorithm for energy efficiency in 5G heterogeneous networks," International Journal of Communication Systems, vol. 35, e5235, 2022. DOI: 10.1002/dac.5235.
[32]Z. M. P. B. A. Baidowi and X. Chu, "Nature Inspired Energy Optimisation of a Two-tier Network using Bias Factor," in Proceedings of the 2021 IEEE Symposium on Wireless Technology & Applications (ISWTA), Shah Alam, Malaysia, 2021, pp. 37–42. DOI: 10.1109/ISWTA51927.2021.9409421.
[33]Z. M. P. A. Baidowi and X. Chu, "An Optimal Energy Efficiency of a Two-tier Network in Control-Data Separation Architecture," Journal of Communications, vol. 15, pp. 545–550, 2020. DOI: 10.12720/jcm.15.7.545-550.