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Wireless backhaul network’s capacity optimization using time series forecasting approach

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Abstract

The extensive technological participation in our daily life and business world has transformed the technology model predominantly from last decade and it has created many directions for further research and development. In previous generations of technology, the major development was hardware side but now it is shifting to the smart and intelligent solutions based on AI. The new challenges have provoked and complexity is increasing for resource orchestration because of the nonstop increasing network and its applications. In cellular architecture, the backhaul network (intermediate network) is used to connect the radio and core network. The key technology is the point to point fixed links that is used in the wireless backhaul network. More than 60% radio base station are connected using this technology. Currently, the static approach is used for planning and optimization of the network which is now becoming very difficult as the network is increasing continuously and becoming more complex. The dynamic resource allocation method is proposed for the future capacity forecasting system which is founded on the factual employment of point to point links. Capacity is a crucial factor in wireless backhaul network so best capacity optimization can lead to a good frequency reuse and optimal use of other network resources. The development is based on three different models namely (1) Autoregressive (AR), (2) Seasonal Autoregressive Integrated Moving Average (SARIMA) and (3) Multi-Layer Perceptron (MLP) neural network. Root Means Squared Error (RMSE) and Means Absolute Percentage Error (MAPE) are performance criterion that are used to evaluate the models. When we compare the models' performance, the MLP results are most credible but it takes more time to converge than AR and SARIMA. By using the proposed estimation method, the static optimization will positively move to the dynamic (forecasted) optimization and the distribution of capacity utilization will be right skewed. Hence, the proposed system is efficient and has the ability to optimize the network according to the actual network’s capacity utilization. It will assist the network planner to perform more efficiently, resource distribution will be more balanced and the wastage of resources can be reduced.

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Source: Cisco VNI Global IP Traffic Forecast, 2017–2022)

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Acknowledgement

This work has been done under the Grant BK032-2018 by University of Malaya.

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Authors and Affiliations

  1. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Atif Mahmood, Miss Laiha Mat Kiah, Saaidal Razalli Azzuhri & Muhammad Reza Z’aba

  2. Operations and Supply Chain Management, Director of Undergraduate Studies, Brunel Business School, Brunel University London, London, UB8 3PH, UK

    Muhammad Mustafa Kamal

  3. Management Sciences, Brunel Business School, Brunel University London, London, UB8 3PH, UK

    Tillal Eldabi

  4. Faculty of Information Technology, University of Central Punjab, Lahore, Pakistan

    Adnan N. Qureshi

  5. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Zati Hakim Azizul

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  1. Atif Mahmood
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Correspondence to Saaidal Razalli Azzuhri.

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Mahmood, A., Kiah, M.L.M., Azzuhri, S.R. et al. Wireless backhaul network’s capacity optimization using time series forecasting approach. J Ambient Intell Human Comput 12, 1407–1418 (2021). https://doi.org/10.1007/s12652-020-02209-2

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