Mitigating channel fading and transceiver impairments are desirable for high-speed terahertz (THz) wireless links. This paper analyzes the performance of a multi-antenna THz wireless system by considering the combined effect of pointing errors and fluctuating two-ray (FTR) fading model. We provide a statistical characterization of the maximal ratio combining (MRC) receiver over independent and nonidentical (i.ni.d.) channel conditions in terms of multi-variate Fox’s H by deriving density and distribution functions of the signal-to-noise ratio (SNR) of a single-link THz link using incomplete Gamma function. We develop exact analytical expressions of outage probability, average bit-error-rate (BER), and ergodic capacity for both single-antenna and MRC receivers. We also present the diversity order of the system by deriving asymptotic expressions for outage probability and average BER at high SNR to obtain insights into the system performance. We validate our derived analytical expressions with Monte-Carlo simulations and demonstrate the effect of various system and channel parameters on the performance of single and multi-antenna THz wireless communications.

The difference in fundamental frequency (F0) between vowels is a segregation cue for identifying concurrent vowels. Younger listeners with normal hearing (YNH) showed that the percent identification score of both vowels improved with increasing F0 difference and asymptoted at higher F0 difference. The current study developed a deep-neural-network model consisting of a time-delay neural network and multitask learning (TDNN-MTL) to predict the concurrent vowel scores. The input to the TDNN-MTL were temporal responses of concurrent vowels obtained using the auditory-nerve model. The TDNN-MTL was trained at a 3-Hz F0 difference until the score (80%) became closer to the YNH score (85%). The TDNN-MTL learned the formant coding and aided in the segregation of concurrent vowels into dominant and recessive vowels. The total weighted loss was tailored to mimic the dominant-recessive relationship of the YNH scores. The TDNN-MTL was tested against the other five F0 differences. The TDNN-MTL model scores of both vowels were successful in predicting the YNH scores. The chi-square tests revealed that the TDNN-MTL model scores correlated well with the YNH scores compared with other F0-segregation and multi-layer perceptron models. The TDNN-MTL also correctly predicted the one vowel identification scores and F0 benefit. These findings suggest that the trained TDNN-MTL accurately predicts the formant coding of the concurrent vowels across F0 differences, which aided in capturing the YNH scores. The TDNN-MTL can be extended to validate the behavioral studies on concurrent vowels obtained across acoustic (e.g., vowel duration and level) and auditory changes (e.g., aging and hearing loss).