Multiple Input Multiple Output (MIMO) wireless communication link has been theoretically proven to be reliable and capable of achieving high capacity. However, these two advantageous characteristics tend to be addressed separately in many major resea
rches. Researches on various approaches to attain both characteristics in a single MIMO system are still on-going and an established approach is yet to be concluded. To address this problem, in this paper a Vertical Bell Laboratories Layered Space-Time (V-BLAST) MIMO enhanced with Rate-Compatible Convolutional (RCPC) codes with Zero Forcing (ZF) and Minimum Mean Squared Error (MMSE)-based detection is proposed. The analytical BER of the system is presented and numerically analyzed. The system performance is analyzed in Nakagami-m fading channel, which provides accuracy and flexibility in matching the signals statistics compared to other fading models. The complexity which arises in the calculations of the RCPC codes parameters is significantly reduced by using equivalent convolutional codes. Results show that the use of high-rate code allows for bandwidth efficiency and at the same time does not severely degrades the system performance. It is also shown that the MMSE-based system outperforms the conventional ZF-based system especially in the low Eb/N0 region and in severe fading conditions.
An important problem to be solved in modeling head-related impulse responses (HRIRs) is how to individualize HRIRs so that they are suitable for a listener. We modeled the entire magnitude head-related transfer functions (HRTFs), in frequency domain,
for sound sources on horizontal plane of 37 subjects using principal components analysis (PCA). The individual magnitude HRTFs could be modeled adequately well by a linear combination of only ten orthonormal basis functions. The goal of this research was to establish multiple linear regression (MLR) between weights of basis functions obtained from PCA and fewer anthropometric measurements in order to individualize a given listeners HRTFs with his or her own anthropomety. We proposed here an improved individualization method based on MLR of weights of basis functions by utilizing 8 chosen out of 27 anthropometric measurements. Our objective experiments results show a superior performance than that of our previous work on individualizing minimum phase HRIRs and also better than similar research. The proposed individualization method shows that the individualized magnitude HRTFs could approximated well the the original ones with small error. Moving sound employing the reconstructed HRIRs could be perceived as if it was moving around the horizontal plane.
