Optimised Sleep Mode Activation in Cellular Networks: A Practical Case Study

Abstract

This paper explores the feasibility of applying sleep-mode strategies in a realistic RAN network, using a deployment scenario from a local MNO as a case study. By modelling spatially correlated shadowing and 3GPP-compliant path loss, we evaluate individual and combined eNB coverage under both optimized and sequential activation schemes. Results show that optimisation significantly improves coverage at low activation levels, achieving near-complete service with fewer active sites. A fitted similarity model characterizes convergence between the two approaches, introducing a deployment-specific parameter ? that helps guide practical energy-saving decisions based on traffic load. The proposed method offers both performance gains and planning insights for dynamic, energy-aware RAN control.

Introduction

Despite advancements in 5G, LTE (4G) continues to be the dominant mobile communication technology globally, with over 85% global population coverage (excluding China) expected by end of 2024, and 95% by 2030. In contrast, 5G mid-band coverage will reach only 40% by the end of 2024, emphasizing the sustained reliance on LTE, particularly in regions facing economic and infrastructure limitations.

Problem Context

• Energy consumption in Radio Access Networks (RANs), especially from base stations (eNBs), represents 60–80% of mobile operator energy use.

• While sleep-mode mechanisms can reduce energy usage, existing approaches (heuristics, reactive thresholds, or ML-based models) face issues like:

  • Poor scalability and generalization

  • Sensitivity to network topology and traffic

  • High data/training requirements

  • Incompatibility with legacy LTE infrastructure

Proposed Solution

The study presents a mathematically grounded, closed-form optimization approach for eNB activation during low-traffic periods, avoiding the drawbacks of ML-based or heuristic methods. The approach uses:

• Realistic spatial RSRP maps based on:

  • 3GPP-compliant Urban Macro (UMa) path loss models

  • Log-normal shadowing with spatial correlation

• Two strategies are compared:

  1. Sequential Activation: eNBs are activated in order of their average individual RSRP contribution.

  2. Optimized Activation: A combinatorial method selects the best subset of eNBs that maximizes total coverage.

Optimization Framework

• The objective is to minimize the number of active eNBs while keeping the outage ratio (percentage of area with RSRP < threshold) below a limit.

• Outage ratio and QoS thresholds (e.g., −130 dBm RSRP) are used to model coverage quality.

• A Jaccard similarity index and exponential convergence model (parameter α) are introduced to measure and characterize how fast the sequential strategy converges toward the optimized solution.

Simulation Setup & Results

• A 4.5 km × 4.5 km area was modeled with 13 eNBs deployed using Poisson-disc sampling.

• Coverage maps reveal that no single eNB can fully cover the area, justifying multi-eNB coordination.

• Key findings:

  • To achieve 99% area coverage (above −130 dBm):

    • Optimized strategy needs only 3 eNBs

    • Sequential strategy needs 6 eNBs

  • The optimized approach offers a 3× efficiency gain at low traffic loads.

  • Contour maps show more uniform and reliable coverage under the optimized strategy.

Practical Implications

• Operators in developing regions (e.g., Africa, Middle East) where LTE still dominates can benefit from this lightweight, transparent method.

• The model supports gradual 5G rollout without disrupting existing LTE infrastructure.

• It provides a framework to reassess energy strategies, especially in energy-constrained environments (e.g., unreliable power grids).

Conclusion

This study investigated the feasibility of eNB sleep-mode activation in a realistic LTE deployment modelled on a local operator’s macro-cellular network. Leveraging 3GPP-compliant path loss with spatially correlated shadowing and real-world inter-site spacing, we evaluated optimized versus sequential activation schemes across varying traffic conditions. Results showed that optimized activation significantly improves coverage and efficiency in low-load scenarios. At the ?140 dBm worst-case coverage threshold, 99.9% area coverage was achieved using only 3 eNBs, compared to 5 required in the sequential method. Average RSRP and CDF analyses further confirmed that optimized schemes yield stronger service quality for a given number of active eNBs, particularly when k is small. Importantly, the derived convergence parameter ?, obtained through Jaccard similarity modeling, provides a compact yet informative descriptor of how rapidly sequential and optimized strategies align as load increases. This makes ? a practical planning metric, enabling operators to estimate when optimisation yields meaningful gains versus when simple sequential reactivation suffices, thereby guiding hybrid strategies that dynamically balance energy savings, performance robustness, and computational complexity. This provides a practical framework that can be directly integrated into operator planning tools for energy-aware RAN control.

References

[1] Ericsson, \"5G network coverage outlook,\" Ericsson Mobility Report, Nov. 2024. [Online]. Available:https://www.ericsson.com/en/reports-and-papers/mobility-report/dataforecasts/network-coverageericsson.com+1ericsson.com+1 [2] Ericsson, \"Ericsson Mobility Report: November 2023,\" Ericsson, 2023. [Online]. Available: https://www.ericsson.com/en/reports-and-papers/mobility-report/dataforecasts/mobile-subscriptions [3] F. Richter, A. Fehske, and G. Fettweis, \"Energy Efficiency Aspects of Base Station Deployment Strategies for Cellular Networks,\" Proc. IEEE VTC, 2009. [4] G. Auer et al., \"How much energy is needed to run a wireless network?,\" IEEE Wireless Communications, vol. 18, no. 5, pp. 40–49, Oct. 2011. [5] Huawei, \"Energy Efficiency White Paper,\" Huawei Technologies, 2022. [6] Nokia, \"Sustainable Networks: Powering Energy Efficiency,\" White Paper, 2023. [7] Samsung, \"5G and Green ICT: Samsung Sustainability Report,\" 2023. [8] L. M. P. Larsen et al., \"Toward Greener 5G and Beyond Radio Access Networks—A Survey,\" IEEE Open Journal of the Communications Society, vol. 4, pp. 768–786, Mar. 2023. doi: 10.1109/OJCOMS.2023.3257889 [9] L. Kundu, X. Lin, and R. Gadiyar, \"Toward Energy Efficient RAN: From Industry Standards to Trending Practice,\" IEEE Wireless Communications, vol. 32, no. 1, pp. 36–45, Feb. 2025. [10] H. Alshaer et al., \"Energy Saving in 6G O-RAN Using DQN-Based xApp,\" Proc. IEEE International Conference on Communications Workshops (ICC Workshops), 2024. [11] A. Chatzopoulos et al., \"Energy-Efficient Power Management for O-RAN Base Stations Utilizing Pedestrian Flow Analytics and Non-Terrestrial Networks,\" Proc. IEEE ICC, 2024. [12] Siemens, \"Private 5G & Advanced connectivity for industries,\" Siemens AG, 2024. [Online]. Available: https://assets.new.siemens.com/siemens/assets/api/uuid%3Aafe5e219-9669-4210-aab0-6ffb55670e3b/5-Flexible-Production-for-Industry-5G.pdfPress Release Distribution Services+2Siemens Assets+2Siemens Assets+2 [13] Nokia, \"Nokia MEA Mobile Broadband Index 2024: 5G driving rapid digital transformation,\" Nokia Corporation, Oct. 2024. [Online]. Available: https://www.nokia.com/about-us/news/releases/2024/10/15/nokia-mea-mobile-broadband-index-2024-5g-driving-rapid-digital-transformation/Nokia.com [14] 3GPP TR 36.814 V9.0.0, “Further advancements for E-UTRA physical layer aspects (Release 9),” Mar. 2010. [15] 3GPP TR 36.873 V12.2.0, “Study on 3D channel model for LTE,” Jun. 2015. [16] T. S. Rappaport, R. W. Heath, R. C. Daniels, and J. N. Murdock, Millimeter Wave Wireless Communications, Pearson Education, 2015. [17] M. S. Islam, M. M. Billah, M. M. Rahman and S. M. Ahsan, “Performance evaluation of different similarity indices for link prediction in social networks,” in Proc. IEEE International Conference on Informatics, Electronics & Vision (ICIEV), Dhaka, Bangladesh, 2013, pp. 1–6. doi: 10.1109/ICIEV.2013.6572655 [18] 3GPP TR 38.901 V16.1.0, “Study on channel model for frequencies from 0.5 to 100 GHz (Release 16),”. [19] 3GPP TS 36.133 V15.3.0, “Evolved Universal Terrestrial Radio Access (E-UTRA); Requirements for support of radio resource management,”.

Copyright

Copyright © 2025 Salahedin Rehan, Abderaof Elmrabet, Osama Hdida, Mohammed Elghadi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.