A Low-Complexity High-Accuracy AR Based Channel Prediction Method for Interference Alignment

Masayoshi Ozawa, Tomoaki Ohtsuki, Fereidoun H. Panahi, Wenjie Jiang, Yasushi Takatori, Tadao Nakagawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Interference alignment (IA) is a technique that can suppress interference with a small number of antennas by aligning interference signals using transmit weights. These weights are designed based on the channel state information (CSI) fed back from each receiver, however, under the timevarying channel, the estimated CSI can be delayed/outdated, which will result in an imperfect IA. Therefore, IA with channel prediction has attracted much attention. The auto regressive (AR) model is known as a prediction method that predicts a future state based on only the past states. In the conventional channel prediction based IA method, the past channels are used directly for prediction. Therefore, the number of calculations for prediction can be too large. In this paper, based on the AR model, we describe a low complexity and high accuracy channel prediction method for IA. To predict the future channel, we only use the differences of channels between adjacent times instead of using the past channels directly. This will lead to a very low channel prediction error. Simulations show that the proposed method improves prediction accuracy and requires less calculation than the conventional one. Moreover, the IA with the proposed channel prediction method will achieve a higher transmission rate.

Original languageEnglish
Title of host publication2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538647271
DOIs
Publication statusPublished - 2019 Feb 20
Event2018 IEEE Global Communications Conference, GLOBECOM 2018 - Abu Dhabi, United Arab Emirates
Duration: 2018 Dec 92018 Dec 13

Publication series

Name2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings

Conference

Conference2018 IEEE Global Communications Conference, GLOBECOM 2018
CountryUnited Arab Emirates
CityAbu Dhabi
Period18/12/918/12/13

ASJC Scopus subject areas

  • Information Systems and Management
  • Renewable Energy, Sustainability and the Environment
  • Safety, Risk, Reliability and Quality
  • Signal Processing
  • Modelling and Simulation
  • Instrumentation
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'A Low-Complexity High-Accuracy AR Based Channel Prediction Method for Interference Alignment'. Together they form a unique fingerprint.

  • Cite this

    Ozawa, M., Ohtsuki, T., Panahi, F. H., Jiang, W., Takatori, Y., & Nakagawa, T. (2019). A Low-Complexity High-Accuracy AR Based Channel Prediction Method for Interference Alignment. In 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings [8647292] (2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GLOCOM.2018.8647292