Capacity and Frequency Optimization of Wireless Backhaul Network Using Traffic Forecasting

Atif Mahmood,Miss Laiha Mat Kiah,Saaidal Razalli Azzuhri,Iraj Sadegh Amiri,Jagadeesh Kakarla,Muhammad Shahreeza Safiruz Kassim,Muhammad Reza Z'Aba,Adnan N Qureshi,Zati Hakim Azizul Hasan

doi:10.1109/access.2020.2970224

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.

Highlights

It has been observed that the cellular network is growing speedily for the last two decades and undergone a succeeding evolution
PROPOSED METHOD FOR CAPACITY AND FREQUENCY OPTIMIZATION Mobile network operators usually collect a vast number of key performance indicators (KPIs), that are differ from operator to operator
The training was done with 2 hidden layers with three preferences 1) with each hidden layer 6 nodes, 2) with each layer 8 nodes and 3) maximum hidden nodes used in each hidden layer are 15

Summary

Introduction

It has been observed that the cellular network is growing speedily for the last two decades and undergone a succeeding evolution. This evolution makes life easy for communication and survival in both social and business worlds. The demand of wireless devices and technology is increasing rapidly and the requirement of high data rates and mobility has increased. There are still number of wireless devices connects to broadband network on daily basis. The mobile traffic will be experiencing the exponential growth in the decade as shown by the predictions (Shown in Figure 1) [2], [3]

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