Phase Tracking Sequences for 5G NR in 52.6-71 GHz Band: Design and Analysis

This paper presents a novel approach to the phase tracking reference signal (PTRS) design for phase noise impact compensation in the 5G NR communication systems intended to work in a new 52.6 GHz to 71 GHz frequency band. For detailed problem illustration, the phase noise compensation algorithms are discussed and explained, from the basic common phase error (CPE) compensation to the MMSE-base inter-carrier interference (ICI) filtering. Performance of the different phase noise compensation algorithms is investigated for the baseline PTRS accepted in the current 5G NR specification and compared with the newly proposed approach to the PTRS design. This approach is based on nulling the subcarriers adjacent to the reference signals to minimize influence of the ICI on the estimation process. It was shown that new nulling PTRS design outperforms currently used distributed PTRS structure. In addition, numerical results represent a trade-off between the filter size and the amount of the allocated training resources to achieve better performance. It was shown that proposed PTRS structures and processing algorithms give ICI compensation level very close to optimal scheme and thus, different approaches (such as time domain compensation) may be required for further progress.

A neural network combined with sliding mode controller for the two-wheel self-balancing robot

This article presents the sliding control method combined with the selfadjusting neural network to compensate for noise to improve the control system's quality for the two-wheel self-balancing robot. Firstly, the dynamic equations of the two-wheel self-balancing robot built by Euler–Lagrange is the basis for offering control laws with a neural network of noise compensation. After disturbance-compensating, the sliding mode controller is applied to control quickly the two-wheel self-balancing robot reached the desired position. The stability of the proposed system is proved based on the Lyapunov theory. Finally, the simulation results will confirm the effectiveness and correctness of the control method suggested by the authors.

De-Noising Process in Room Impulse Response with Generalized Spectral Subtraction

The generalized spectral subtraction algorithm (GBSS), which has extraordinary ability in background noise reduction, is historically one of the first approaches used for speech enhancement and dereverberation. However, the algorithm has not been applied to de-noise the room impulse response (RIR) to extend the reverberation decay range. The application of the GBSS algorithm in this study is stated as an optimization problem, that is, subtracting the noise level from the RIR while maintaining the signal quality. The optimization process conducted in the measurements of the RIRs with artificial noise and natural ambient noise aims to determine the optimal sets of factors to achieve the best noise reduction results regarding the largest dynamic range improvement. The optimal factors are set variables determined by the estimated SNRs of the RIRs filtered in the octave band. The acoustic parameters, the reverberation time (RT), and early decay time (EDT), and the dynamic range improvement of the energy decay curve were used as control measures and evaluation criteria to ensure the reliability of the algorithm. The de-noising results were compared with noise compensation methods. With the achieved optimal factors, the GBSS contributes to a significant effect in terms of dynamic range improvement and decreases the estimation errors in the RTs caused by noise levels.