Citation

Al-Hasan, Md. and Aktar, Mst. Rubina and Farid, Fahmid Al and Anower, Md. Shamim and Miah, Abu Saleh Musa and Abdul Karim, Md. Hezerul (2026) Performance Analysis of Bandwidth Aware Hybrid Powered 5G Cloud Radio Access Network. Computers, Materials & Continua, 87 (1). pp. 1-10. ISSN 1546-2226

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Abstract

The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic, creating significant challenges for network energy consumption. Also, with the extraordinary growth of mobile communications, the data traffic has dramatically expanded, which has led to massive grid power consumption and incurred high operating expenditure (OPEX). However, the majority of current network designs struggle to efficiently manage a massive amount of data using little power, which degrades energy efficiency performance. Thereby, it is necessary to have an efficient mechanism to reduce power consumption when processing large amounts of data in network data centers. Utilizing renewable energy sources to power the Cloud Radio Access Network (C-RAN) greatly reduces the need to purchase energy from the utility grid. In this paper, we propose a bandwidth-aware hybrid energypowered C-RAN that focuses on throughput and energy efficiency (EE) by lowering grid usage, aiming to enhance the EE. This paper examines the energy efficiency, spectral efficiency (SE), and average on-grid energy consumption, dealing with the major challenges of the temporal and spatial nature of traffic and renewable energy generation across various network setups. To assess the effectiveness of the suggested network by changing the transmission bandwidth, a comprehensive simulation has been conducted. The numerical findings support the efficacy of the suggested approach.

Item Type:	Article
Uncontrolled Keywords:	5G, bandwidth
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	03 Mar 2026 04:06
Last Modified:	03 Mar 2026 04:06
URII:	http://shdl.mmu.edu.my/id/eprint/15444

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