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