Capacity and frequency optimization of wireless backhaul network using traffic forecasting

Mahmood, Atif and Mat Kiah, Miss Laiha and Z'aba, Muhammad Reza and Qureshi, Adnan N. and Kassim, Muhammad Shahreeza Safiruz and Hasan, Zati Hakim Azizul and Kakarla, Jagadeesh and Amiri, Iraj Sadegh and Azzuhri, Saaidal Razalli (2020) Capacity and frequency optimization of wireless backhaul network using traffic forecasting. IEEE Access, 8. pp. 23264-23276. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2020.2970224.

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Abstract

Telecom operators are aiming to provide high-grade data, multimedia applications and low latency videos for smart devices. As today's mobile data is experiencing rapid growth and the usage of smart devices are fabricating unparalleled challenges for telecom operators to meet the global bandwidth requirement. From the first generation to fourth generation, the technology evolution is predominantly governed by the hardware side but now it is moving towards the concept of cognitive network management, resource orchestration and machine learning-based solutions. In this paper, we propose the adaptive capacity and frequency optimization (ACFO) method for adaptive optimization based on time series forecasting approach. The daily capacity utilization of microwave (MW) links is analyzed to use forecasted demand. Based on the projected demand, the capacity and frequency optimization will be executed. The two main forecasting models 1) SARIMA and 2) MLP are used and for performance evaluation, we used RMSE and MAPE criterion. The analytic outcomes show that MLP with two layers and six hidden nodes (6/6) are good enough to achieve the desired results. In some cases, we need to exceed the hidden nodes up to fifteen (15/15). By using the forecasting approach, the reactive optimization will successfully shift to the predicted/proactive optimization, will balance the resource distribution and can condense the wastage of resources. The outcome of the study will be a contribution to the dynamic resource optimization in wireless backhaul network.

Item Type: Article
Funders: Universiti Malaya (BK032-2018)
Uncontrolled Keywords: Time series forecasting; Capacity planning; Dynamic resource optimization; Wireless backhaul optimization
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Computer Science & Information Technology > Department of Artificial Intelligence
Faculty of Computer Science & Information Technology > Department of Computer System & Technology
Depositing User: Ms Zaharah Ramly
Date Deposited: 31 May 2023 03:26
Last Modified: 31 May 2023 03:26
URI: http://eprints.um.edu.my/id/eprint/37097

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