Linearly Time-Varying Channel Estimation and Symbol Detection for OFDMA Uplink Using Superimposed Training

The authors address the problem of Superimposed Trainings- (STs-) based Linearly Time-Varying (LTV) channel estimation and symbol detection for Orthogonal Frequency-Division Multiplexing Access (OFDMA) systems at the uplink receiver. The LTV channel coefficients are modeled by truncated Discrete Fourier Bases (DFBs). By judiciously designing the superimposed pilot symbols, they estimate the LTV channel transfer functions over the whole frequency band by using a weighted average procedure, thereby providing validity for adaptive resource allocation. They also present a performance analysis of the channel estimation approach to derive a closed-form expression for the channel estimation variances. In addition, an iterative symbol detector is presented to mitigate the superimposed training effects on information sequence recovery.