RESUMEN
Orthogonal frequency division multiplexing (OFDM) is the digital modulation technique used by 4G and many other wireless communication systems. OFDM signals have significant amplitude fluctuations resulting in high peak to average power ratios which can make an OFDM transmitter susceptible to non-linear distortion produced by its high power amplifiers (HPA). A simple and popular solution to this problem is to clip the peaks before an OFDM signal is applied to the HPA but this causes in-band distortion and introduces bit-errors at the receiver. In this paper we discuss a novel technique, which we call the Equation-Method, for correcting these errors. The Equation-Method uses the Fast Fourier Transform to create a set of simultaneous equations which, when solved, return the amplitudes of the peaks before they were clipped. We show analytically and through simulations that this method can, correct all clipping errors over a wide range of clipping thresholds. We show that numerical instability can be avoided and new techniques are needed to enable the receiver to differentiate between correctly and incorrectly received frequency-domain constellation symbols.
RESUMEN
Voice quality assessment is required by healthcare professionals in patients suffering from voice problems. Speech and language therapists (SLTs) use a well-known subjective assessment approach which is called GRBAS, to quantify voice problems. GRBAS is an acronym for a five dimensional scale of measurements of voice properties which were originally recommended by the Japanese Society of Logopeadics and Phoniatrics and the European Research for clinical and research use. The properties are `Grade', `Roughness', `Breathiness', `Asthenia' and `Strain'. In requiring the services of trained SLTs, this subjective assessment make the GRBAS measurement expensive to administer. In this research, computerised objective measurement of `Strain' in voice using two regression prediction models is compared with measurements produced by SLTs according to the GRBAS scale. These regression models are K Nearest Neighbor Regression (KNNR) and Multiple Linear Regression (MLR). These new approaches for prediction of Strain are based on different subsets of features, different sets of data, and different prediction models in comparison with previous approaches in the literature. The best feature subset for predicting Strain objectively was obtained amongst different feature subsets. When compared with the mean of five SLT's scores, over 102 samples, the computerised measurement was found to have a Normalized Root Mean Square Error (NRMSE) averaged over 20 trials, lower than that of each individual SLT. We have achieved a NRMSE of 14.6% and 15.1% for the MLR and KNNR respectively when the best feature subsets were used for predicting Strain objectively.
