Paper Info
Title
A Low-Complexity High-Accuracy AR Based Channel Prediction Method for Interference Alignment
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information (CSI) fed back from each receiver, however, under the timevarying channel, the estimated CSI can be delayed/outdated, which will result in an imperfect IA.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.
Year
DOI
Venue
2018
10.1109/GLOCOM.2018.8647292
2018 IEEE Global Communications Conference (GLOBECOM)
Field
DocType
ISSN
Small number,Autoregressive model,Mean squared prediction error,Imperfect,Computer science,Communication channel,Algorithm,Real-time computing,Interference (wave propagation),Interference alignment,Channel state information
Conference
2334-0983
ISBN
Citations 
PageRank 
978-1-5386-4727-1
1
0.37
References 
Authors
0
6
Name
Order
Citations
PageRank
Masayoshi Ozawa110.37
Tomoaki Ohtsuki25110.95
Fereidoun H. Panahi3225.12
Wenjie Jiang425626.60
Yasushi Takatori59224.89
Tadao Nakagawa62211.33