An Energy-Efficient Small-Cell Operation Algorithm for Ultra-Dense Cellular Networks

作     者：Hasan, MD. Mehedi Haq, MD. Shayanul Hossain, MD. Maruf Tiash, Junaed Kiron Kwon, Sungoh 

作者机构：Amer Int Univ Bangladesh Dept Comp Sci Dhaka 1229 Bangladesh Univ Ulsan Sch Elect Engn Ulsan 44610 South Korea 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2024年第12卷

页      面：191650-191660页

核心收录：

基　　金：National Research Foundation of Korea (NRF), Ministry of Education [NRF-2021R1I1A3A04037415] American International University-Bangladesh (AIUB) 

主　　题：Switches Energy efficiency Base stations Quality of service Clustering algorithms Heuristic algorithms Power demand Costs 5G mobile communication Ultra-dense networks Ultra-dense cellular network energy efficiency user equipment physical resource block quality of service throughput 

摘      要：The ultra-dense network (UDN) incorporates a high densification of small cells, resulting in improved capacity and coverage for a fifth-generation (5G) cellular network. However, the proliferation of small cells within the 5G network has led to a notable surge in energy consumption. To improve energy efficiency in a UDN, this paper proposes a small-cell operation algorithm considering sleep mode and active mode switching for small cells. The algorithm periodically monitors the radio resource status of the macro cells and small cells for the mode-switching process. If a macro cell with a lower radio load is detected, the algorithm triggers a small cell sleep mode switching. To that end, the algorithm finds a small cell with the lowest radio load within the coverage of the macro cell. Therefore, the algorithm switches the small cell to sleep mode estimating the accumulated load in the macro. An active mode switching is triggered by the algorithm when a macro cell with a high radio load is detected. The algorithm then generates a location dataset consisting of the location information of inactive small cells and static UEs. A clustering technique is adopted on the dataset to find clusters containing at least one small cell. Considering the average reported reference signal received power (RSRP) of the UEs, a cluster is selected, and the small cells in the cluster are switched to active mode. Through system-level simulations, the performance of the proposed algorithm is evaluated. The simulation results showed that the proposed approach can ensure a higher energy efficiency than previous algorithms.