Interactive Multiple Model Estimation of Doubly-Selective Channels for OFDM systems

TL;DR

This paper introduces an adaptive channel estimation method for high-mobility OFDM systems using multiple models and Kalman filtering to accurately track rapid channel variations.

Contribution

It presents an innovative interactive multiple model approach combining two Kalman filters with different basis expansion models for improved channel tracking in vehicular OFDM communications.

Findings

Enhanced channel estimation accuracy in high-mobility scenarios

Significant performance gains demonstrated through simulations

Effective dynamic mixing of models improves tracking of rapid channel changes

Abstract

In this paper, we propose an algorithm for channel estimation, acquisition and tracking, for orthogonal frequency division multiplexing (OFDM) systems. The proposed algorithm is suitable for vehicular communications that encounter very high mobility. A preamble sequence is used to derive an initial estimate of the channel using least squares (LS). The temporal variation of the channel within one OFDM symbol is approximated by two complex exponential basis expansion models (CE-BEM). One of the Fourier-based BEMs is intended to capture the low frequencies in the channel (slow variations corresponding to low Doppler), while the other is destined to capture high frequencies (fast variations corresponding to high Doppler). Kalman filtering is employed to track the BEM coefficients iteratively on an OFDM symbol-by-symbol basis. An interactive multiple model (IMM) estimator is implemented to dynamically mix the estimates obtained by the two Kalman filters, each of which matched to one of the BEMs. Extensive numerical simulations are conducted to signify the gain obtained by the proposed combining technique.