In this paper, a performance analysis on the estimation of the so-called observation filter for the Virtual Microphone Technique (VMT) in a realistic automotive environment is presented. A performance comparison between adaptive and fixed observation filter estimation methods, namely Least Mean Square (LMS) and Minimum Mean Square Error (MMSE), respectively, was carried on. Two different experimental setups were implemented on a popular B-segment car. Eight microphones were placed at the monitoring and virtual positions in order to sense environmental acoustic noise propagating within the cabin of the car running at variable speed on a smooth asphalt. Our experimental results show that a large spectral coherence between monitoring and virtual microphone signals indicates a potentially effective and relatively wide-band virtual microphone signal reconstruction. The fixed observation filter estimation method achieves better performance than the adaptive one, guaranteeing remarkable broadband estimation accuracy. Moreover, for each considered setup, design guidelines are proposed to obtain a good trade-off between estimation accuracy and material costs.

We consider downlink communications between a Base Station (BS) and various mobile stations, equipped with multiple antennas, based on Orthogonal Frequency Division Multiplexing (OFDM). Transmission is compliant with the Long Term Evolution (LTE) standard operating in Frequency Division Duplex (FDD) mode. Since ideal feedback of channel state information to the BS may be cumbersome, we consider two suboptimal channel estimation algorithms, denoted as Resource Block (RB) and Resource Block Group (RBG). Both approaches approximate the channel as constant over multiples of the fundamental LTE block, known as Physical Resource Block (PRB). Our results show that RB and RBG incur a limited performance loss, yet guaranteeing significant saving in the amount of feedback information.