Time-Varying Channel Estimation for MIMO/OFDM Systems Using Superimposed Training

Abstract

This paper deals with the problem of time-varying (TV) channel estimation for multiple-input multiple-output/orthogonal frequency-division multiplexing (MIMO/OFDM) systems based on superimposed training (ST). The time-varying coefficients of the TV channel are firstly modeled by truncated discrete Fourier bases, and then optimally estimated both in one OFDM symbols and over multiple OFDM symbols by judiciously designing the superimposed pilots. In addition, an iterative symbol detection based channel estimation scheme with analytical performance analysis, is provided to mitigate the interference due to the unknown information sequences (thus to further improve channel estimation performance as well as SER levels). Simulations confirm that the proposed estimator achieves a considerable gain in estimating TV channels, and exhibits a nearly indistinguishable symbol error rate performance from the OFDM systems of frequency-division multiplexed trainings. 1 .

Superimposed Training-Aided Channel Estimation for Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing Systems over High-Mobility Environment

“…Recently, the basis expansion models, i.e. the truncated discrete Fourier basis (DFT) models, polynomial models and discrete prolate Spheroidal sequence models, have gained special attentions, especially for the situation that channel is caused by a few strong reflectors and path delays exhibit variations due to the kinematics of the mobiles [1]- [2] [5]- [6] [16] [25]- [28].…”

“…Although the channel estimates are in general reliable, extra bandwidth or time slot is required for transmitting known pilots. In recent years, an alternative approach, referred to as superimposed training (ST), has been extensively studied in [8]- [19] [26]- [28]. In the idea of ST, additional periodic training sequences are arithmetically added to information sequence in time-or frequency-domain.…”