Adaptive modelling and long-range prediction of mobile fading channels

Abstract

A key element for many fading-compensation techniques is a (long-range) prediction tool for the fading channel. A linear approach, usually used to model the time evolution of the fading process, does not perform well for long-range prediction applications. In this article, we propose an adaptive fading channel prediction algorithm using a sum-sinusoidal-based state-space approach. This algorithm utilizes an improved adaptive Kalman estimator, comprising an acquisition mode and a tracking algorithm. Furthermore, for the sake of a lower computational complexity, we propose an enhanced linear predictor for channel fading, including a multi-step linear predictor and the respective tracking algorithm. Comparing the two methods in our simulations show that the proposed Kalman-based algorithm significantly outperforms the linear method, for both stationary and non-stationary fading processes, and especially for long-range predictions. The performance and the self-recovering structure, as well as the reasonable computational complexity, makes the algorithm appealing for practical applications 1 .

CQI Prediction for Shadow Fading in LTE-compatible GEO Mobile Satellite Communications System

“…3) Kalman filter: Kalman filter is often used to do channel filtering, but actually, it is also an autoregressive prediction method [6]. Kalman filter is based on linear algebra and hidden Markov model.…”

“…Many existing prediction models are feasible for short-range prediction in terrestrial system, such as ARIMA (Autoregressive Integrated Moving Average) [4], long-range prediction proposed by Hallen [5], Kalman filter [6], etc. The prediction parameter includes CQI [4], Doppler shift [6], and channel matrix.…”

“…The prediction parameter includes CQI [4], Doppler shift [6], and channel matrix. All these models do prediction according to the input data sequence's autocorrelation.…”

Low-complexity OFDM channel predictor design with varying speed receivers in time-varying wireless channels

“…In [3], Lee suggested different pilot arrangements for different receiver speed to increase the prediction accuracy and Heidari [4] came up with a channel tracking algorithm based on the prediction error threshold. In this paper, we propose a low-complexity channel predictor design to track Doppler shift changes and update the channel parameters when there is a significant change in Doppler shift.…”

On the Channel Predictability in GEO Mobile Satellite Communications

“…Therefore, channel variation is critical for the application of adaptive transmission techniques, especially in the mobile satellite communications with large propagation delay. [6][7][8] While there has been much work studying the static geostationary orbit (GEO) satellite channel, work on the time evolution of the GEO satellite channel is rare. Two soureces of time variation should be differentiated: 1) the movement of the transmitter or the receiver and 2) the movement of scatterers.…”