Channel Estimation and Prediction Based Adaptive Wireless Communication Systems

Wireless channels are typically much more noisy than wired links and subjected to fading due to multipath propagation which result in ISI and hence high error rate. Adaptive modulation is a powerful technique to improve the tradeoff between spectral efficiency and Bit Error Rate (BER). In order to adjust the transmission rate, channel state information (CSI) is required at the transmitter side.In this paper the performance enhancement of using linear prediction along with channel estimation to track the channel variations and adaptive modulation were examined. The simulation results shows that the channel estimation is sufficient for low Doppler frequency shifts (<30 Hz), while channel prediction is much more suited at high Doppler shifts with same SNR and target BER=10-4. It was shown that the performance at higher Doppler frequency shifts (<30Hz) was improved by more than 2dB over channel estimation at target BER=10-4 and 32QAM constellation used

Performance evaluation of a linear predictor frequency estimator for mobile flat fading wireless channels

A well known frequency estimation algorithm using the linear prediction method is analyzed for flat fading wireless channels. The estimator outputs are statistically analyzed and its jitter performances are compared with the non-fading case and the Cramer-Rao bound. We provide a closed form solution for the distribution and the variance of the frequency estimates under fading conditions by making valid assumptions. We also verify the theoretical model using simulations. Analysis shows that the variance of the estimates for flat fading channels reaches a threshold point and increasing the transmit power does not necessarily improve the performances any further

Parameter Estimation for Real Filtered Sinusoids

This research develops theoretical methods for parameter estimation of filtered, pulsed sinusoids in noise and demonstrates their effectiveness for Electronic Warfare EW applications. Within the context of stochastic modeling, a new linear model, parameterized by a set of Linear Prediction LP coefficients, is derived for estimating the frequencies of filtered sinusoids. This model is an improvement over previous modeling techniques since the effects of the filter and the coefficients upon the noise statistics are properly accounted for during model development. From this linear model, a relationship between LP coefficient estimation and Maximum Likelihood ML frequency estimation is derived and several coefficient estimators, based on fixed point theory and ML techniques, are constructed. A bound for the coefficient estimation error is developed and used to gauge the quality of point estimates directly from the data and knowledge of the noise variance. Furthermore, a multirate implementation of an EW digital channelized receiver is described functionally and probabilistically. When applied to the EW receiver, simulations indicate the new estimators provide unbiased, minimum variance, parameter estimates of filtered sinusoids at lower SNRs than the estimators currently employed. The bounds on the estimation error are then used establish confidence intervals for each point estimate

Enhanced Channel Estimation Algorithm for Dedicated Short-Range Communication Systems

The Dedicated Short-Range Communication (DSRC) has been widely accepted as a promising wireless technology for enhancing traffic safety. In such DSRC-based vehicle-to-vehicle (V2V) communication systems, because of the extremely time-varying characteristic of wireless propagation channels, accurate channel estimation is essential for reliable information exchange between vehicles. In this paper, the characteristics of the propagation channel and several traditional channel estimation schemes for V2V communications are reviewed. Then, a delay-based channel-frequency-response decomposition scheme is proposed to estimate and predict the double-selective V2V channel while adhering to the IEEE 802.11p standard. The proposed method achieves a more favorable performance than the traditional methods in V2V scenarios by combining the least square estimation in the frequency domain with the linear prediction in time domain. The performance advantages of the proposed scheme are verified by the simulation results from three typical scenarios. Furthermore, a reference design on a field-programmable gate array for the proposed channel estimation scheme is presented for the purpose of demonstrating its implementation feasibility and complexity

Neural Speech Phase Prediction based on Parallel Estimation Architecture and Anti-Wrapping Losses

This paper presents a novel speech phase prediction model which predicts wrapped phase spectra directly from amplitude spectra by neural networks. The proposed model is a cascade of a residual convolutional network and a parallel estimation architecture. The parallel estimation architecture is composed of two parallel linear convolutional layers and a phase calculation formula, imitating the process of calculating the phase spectra from the real and imaginary parts of complex spectra and strictly restricting the predicted phase values to the principal value interval. To avoid the error expansion issue caused by phase wrapping, we design anti-wrapping training losses defined between the predicted wrapped phase spectra and natural ones by activating the instantaneous phase error, group delay error and instantaneous angular frequency error using an anti-wrapping function. Experimental results show that our proposed neural speech phase prediction model outperforms the iterative Griffin-Lim algorithm and other neural network-based method, in terms of both reconstructed speech quality and generation speed.Comment: Accepted by ICASSP 2023. Codes are availabl