|
Optimization of Base Station Sleep Strategy and Resource Allocation in Cellular Wireless Networks Using the Artificial Single‑Cell Optimization Algorithm
|
Hamidreza Firouzianfar1   , Mohammad Tolou Askari *2 , Javad Safaei Koochaksaraei1 , Mahmoud Samiei Moghaddam3 , Vahid Ghods1 |
1- Department of Electrical Engineering, Se.C., Islamic Azad University, Semnan, Iran, Department of Electrical Engineering, Se.C., Islamic Azad University, Semnan, Iran.
2- Department of Electrical Engineering, Se.C., Islamic Azad University, Semnan, Iran , m.askari1980@iau.ac.ir
3- Department of Electrical Engineering, Da.C., Islamic Azad University, Damghan, Iran., Department of Electrical Engineering, Da.C., Islamic Azad University, Damghan, Iran. |
|
|
Abstract: (11 Views) |
The increasing densification of small base stations in heterogeneous cellular networks, while improving coverage and capacity, simultaneously leads to a significant rise in overall network energy consumption. In this paper, a bi‑level optimization framework for energy management in cellular networks is proposed, in which decisions on small base station sleep strategies and radio resource allocation are jointly optimized while explicitly considering users’ quality of service (QoS). At the upper level of the proposed framework, the active, light‑sleep, and deep‑sleep states of small base stations are modeled by accounting for switching costs and wake‑up delays, with the objective of minimizing total network energy consumption subject to QoS constraints. At the lower level, the power and subchannel allocation problem is solved to strike a balance among data rate, energy consumption, and user fairness. The overall problem is formulated as a mixed‑integer nonlinear programming (MINLP) problem and is solved using the Artificial Single‑Cell Optimization Algorithm (ACA), which is capable of jointly optimizing discrete and continuous variables with satisfactory convergence behavior. Simulation results based on standard 3GPP scenarios in MATLAB software demonstrate that, compared with benchmark algorithms such as PSO, GA, and DQN, the proposed method achieves a substantial reduction in energy consumption without noticeable degradation in QoS or user fairness. In particular, by appropriately tuning the trade‑off parameters, the proposed framework is able to achieve more than 40% energy savings while maintaining a high user satisfaction rate, and it exhibits effective adaptive behavior in response to dynamic variations in network traffic. |
|
| Keywords: Heterogeneous cellular networks, base station sleep strategy, small base stations, radio resource allocation, power and subchannel allocation |
|
|
Full-Text [PDF 841 kb]
(9 Downloads)
|
Type of Study: Research |
Subject:
Special
Received: 2025/08/16 | Accepted: 2025/10/25 | Published: 2025/12/1
|
| * Corresponding Author Address: Department of Electrical Engineering, Se.C., Islamic Azad University, Semnan, Iran. |
|
|
|
|
|
|
| Send email to the article author |
|
|
|
| Add your comments about this article |
|
|
|
