Data-Aided Noise Reduction for Long-Range Fading Prediction in Adaptive Modulation Systems

The long-range prediction (LRP) of fading signals enables adaptive transmission methods for rapidly varying mobile radio channels encountered in vehicular communications, but its performance is severely degraded by the additive noise and interference. A data-aided noise reduction (DANR) method is proposed to enhance the accuracy of fading prediction and to improve the spectral efficiency of adaptive modulation systems enabled by the LRP. The DANR includes an adaptive pilot transmission mechanism, robust noise reduction (NR), and decision-directed channel estimation. Due to improved prediction accuracy and low pilot rates, the DANR results in higher spectral efficiency than previously proposed NR techniques, which rely on oversampled pilots. It is also demonstrated that DANR-aided LRP increases the coding gain of adaptive trellis-coded modulation (ATCM). Finally, for low-to-medium signal-to-noise ratio (SNR) values, we show that LRP-enabled adaptive modulation performs better for realistic reflector configurations than for the conventional Jakes model (JM) data set.