Adaptive Fuzzy-Based Models for Attenuation Time Series Forecasting

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This work proposes an Adaptive Fuzzy Prediction (AFP) method for the attenuation time series in Commercial Microwave links (CMLs). Time-series forecasting models regularly rely on the assumption that the entire data set follows the same Data Generating Process (DGP). However, the signals in wireless microwave links are severely affected by the varying weather conditions in the channel. Consequently, the attenuation time series might change its characteristics significantly at different periods. We suggest an adaptive framework to better employ the training data by grouping sequences with related temporal patterns to consider the non-stationary nature of the signals. The focus in this work is two-folded. The first is to explore the integration of static data of the CMLs as exogenous variables for the attenuation time series models to adopt diverse link characteristics. This extension allows to include various attenuation datasets obtained from additional CMLs in the training process and dramatically increasing available training data. The second is to develop an adaptive framework for short-term attenuation forecasting by employing an unsupervised fuzzy clustering procedure and supervised learning models. We empirically analyzed our framework for model and data-driven approaches with Recurrent Neural Network (RNN) and Autoregressive Integrated Moving Average (ARIMA) variations. We evaluate the proposed extensions on real-world measurements collected from 4G backhaul networks, considering dataset availability and the accuracy for 60 seconds prediction. We show that our framework can significantly improve conventional models' accuracy and that incorporating data from various CMLs is essential to the AFP framework. The proposed methods have been shown to enhance the forecasting model's performance by 30 - 40%, depending on the specific model and the data availability.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages522-528
Number of pages7
ISBN (Electronic)9780738146720
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021 - Tel Aviv, Israel
Duration: 1 Nov 20213 Nov 2021

Publication series

Name2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021

Conference

Conference2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021
Country/TerritoryIsrael
CityTel Aviv
Period1/11/213/11/21

Keywords

  • ARIMAX
  • Machine Learning
  • Microwave Links
  • RNN
  • RNN Attenuation Forecasting
  • Time Series Forecasting

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