Robust spatio-temporal partial-response signaling over a frequency-selective fading MIMO channel with imperfect CSI

Partial-response signaling is known to facilitate the equalizer design because a controlled amount of residual interference is permitted. The design of the target impulse response of the partial-response precoder often assumes perfect channel state information, which is unfortunately not available at the transmitter in most practical applications. Consequently, this contribution focuses instead on the robust and joint design of a spatio-temporal target impulse response and the equalization coefficients for a frequency-selective fading multiple-input multiple-output communication channel based on current and/or previous noisy channel estimates. More precisely, the error in the channel estimates is statistically modeled, and robustness is achieved by minimizing the mean-squared estimation error averaged over the joint distribution of the actual channel and the available channel estimates. Numerical results of the bit error rate confirm that the proposed robust partial-response signaling not only provides a significant performance gain compared to traditional full-response signaling, but also outperforms the naive approach, which ignores channel estimation errors

Frequency estimation in multipath rayleigh-sparse-fading channels

Maximum-likelihood (ML) data-aided frequency estimation in multipath Rayleigh-fading channels with sparse impulse responses is investigated. We solve this problem under the assumption that the autocorrelation matrix of the pilot signal can be approximated by a diagonal matrix, the fading of different path amplitudes are independent from each other, and the additive noise is white and Gaussian. The ML frequency estimator is shown to be based on combining nonlinearly transformed path periodograms. We have derived the nonlinear function for the two cases: known and unknown fading variances. The new frequency estimators lead, in particular cases, to known ML frequency estimators for nonsparse multipath fading channels. The use of a priori information about the mean number of paths in the channel allows a significant improvement of the accuracy performance. Exploiting the sparseness of the channel impulse response is shown to significantly reduce the threshold signal-to-noise ratio at which the frequency error departs from the Cramer-Rao lower bound. However, precise knowledge of the channel sparseness is not required in order to realize this improvement

Constant False Alarm Rate (CFAR) detection based estimators with applications to sparse wireless channels

Thesis (Master)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2006Includes bibliographical references (leaves: 87-89)Text in English; Abstract: Turkish and Englishx, 94 leavesWe provide Constant False Alarm Rate (CFAR) based thresholding methods for training based channel impulse response (CIR) estimation algorithms for communication systems which utilize a periodically transmitted training sequence within a continuous stream of information symbols. After obtaining the CIR estimation by using known methods in the literature, there are estimation errors which causes performance loss at equalizers. The channel estimation error can be seen as .noise. on CIR estimations and CFAR based thresholding methods, which are used in radar systems to decide the presence of a target, can effectively overcome this problem. CFAR based methods are based on determining threshold values which are computed by distribution of channel noise. We provide exact and approximate distribution of channel noise appear at CIR estimate schemes. We applied Cell Averaging-CFAR (CA-CFAR) and Order Statistic-CFAR (OSCFAR) methods on the CIR estimations. The performance of the CFAR estimators are then compared by their Least Square error in the channel estimates. The Signal to Interference plus Noise Ratio (SINR) performance of the decision feedback equalizers (DFE), of which the tap values are calculated based on the CFAR estimators, are also provided

Channel Estimation for Diffusive Molecular Communications

In molecular communication (MC) systems, the \textit{expected} number of molecules observed at the receiver over time after the instantaneous release of molecules by the transmitter is referred to as the channel impulse response (CIR). Knowledge of the CIR is needed for the design of detection and equalization schemes. In this paper, we present a training-based CIR estimation framework for MC systems which aims at estimating the CIR based on the \textit{observed} number of molecules at the receiver due to emission of a \textit{sequence} of known numbers of molecules by the transmitter. Thereby, we distinguish two scenarios depending on whether or not statistical channel knowledge is available. In particular, we derive maximum likelihood (ML) and least sum of square errors (LSSE) estimators which do not require any knowledge of the channel statistics. For the case, when statistical channel knowledge is available, the corresponding maximum a posteriori (MAP) and linear minimum mean square error (LMMSE) estimators are provided. As performance bound, we derive the classical Cramer Rao (CR) lower bound, valid for any unbiased estimator, which does not exploit statistical channel knowledge, and the Bayesian CR lower bound, valid for any unbiased estimator, which exploits statistical channel knowledge. Finally, we propose optimal and suboptimal training sequence designs for the considered MC system. Simulation results confirm the analysis and compare the performance of the proposed estimation techniques with the respective CR lower bounds.Comment: to be appeared in IEEE Transactions on Communications. arXiv admin note: text overlap with arXiv:1510.0861

Kalman filter equalization for QPSK communications

The discrete complex Kalman filter is considered as an equalizer for quadrature phase shift keyed (QPSK) systems in the presence of additive noise and intersymbol interference (ISI). For a known linear time-invariant channel with finite duration impulse response, the finite n-dimension complex Kalman filter equalizer is able to reduce the degradation caused by ISI. When the channel is unknown, an adaptive Kalman equalizer is used in which the channel complex tap gains are estimated by decision feedback. A two component multipath channel QPSK system is used as an example. Using the Chernoff upper bound to calculate the error probabilities, the computer simulation shows that both the Kalman filter equalizer and adaptive equalizer have a better performance than the integrate-and-dump correlator with no equalizer --Abstract, page ii

An Improved Computational Model for Adaptive Communication Channel Estimation

Channel estimation is an important and necessary function performed by modern wireless receivers. The goal of channel estimation is to measure the effects of the channel on known or partially known transmission. The usual practice in acquiring knowledge about a channel is to model the channel and then acquire the parameters involved in the model. This paper proposes a variable partial update model for adaptive communication channel estimation with a view to improving signal error at the receiver station. The proposed model is composed of finite impulse response transversal adaptive filter and least mean square adaptation algorithm. The performance of the proposed model was compared with the full update model. The evaluation results indicated that the proposed model performed better than the full update model in terms of computational complexity, memory load, and convergence rate

Kalman interpolation filter for channel estimation of LTE downlink in high-mobility environments

The estimation of fast-fading LTE downlink channels in high-speed applications of LTE advanced is investigated in this article. In order to adequately track the fast time-varying channel response, an adaptive channel estimation and interpolation algorithm is essential. In this article, the multi-path fast-fading channel is modelled as a tapped-delay, discrete, finite impulse response filter, and the time-correlation of the channel taps is modelled as an autoregressive (AR) process. Using this AR time-correlation, we develop an extended Kalman filter to jointly estimate the complex-valued channel frequency response and the AR parameters from the transmission of known pilot symbols. Furthermore, the channel estimates at the known pilot symbols are interpolated to the unknown data symbols by using the estimated time-correlation. This article integrates both channel estimation at pilot symbols and interpolation at data symbol into the proposed Kalman interpolation filter. The bit error rate performance of our new channel estimation scheme is demonstrated via simulation examples for LTE and fast-fading channels in high-speed applications

Channel estimators for HF radio links

The thesis is concerned with the estimation of the sampled impulse-response (SIR), of a time-varying HF channel, where the estimators are used in the receiver of a 4800 bits/s, quaternary phase shift keyed (QPSK) system, operating at 2400 bauds with an 1800 Hz carrier. T= FIF modems employing maximum-likelihood detectors at the receiver require accurate knowledge of the SIR of the channel. With this objective in view, the thesis considers a number of channel estimation techniques, using an idealised model of the data transmission system. The thesis briefly describes the ionospheric propagation medium and the factors affecting the data transmission over BF radio. It then presents an equivalent baseband model of the I-IF channel, that has three separate Rayleigh fading paths (sky waves), with a 2Hz frequency spread and transmission delays of 0,1.1 and 3 milliseconds relative to the first sky wave. Estimation techniques studied are, the Gradient estimator, the Recursive leastsquares (RLS) Kalman estimator, the Adaptive channel estimators, the Efficient channel estimator ( that takes into account prior knowledge of the number of fading paths in the channel ), and the Fast Transversal Filter (F-FF), estimator (which is a simplified form of the Kalman estimator). Several new algorithms based on the above mentioned estimation techniques are also proposed. Results of the computer simulation tests on the performance of the estimators, over a typical worst channel, are then presented. The estimators are reasonably optimized to achieve the minimum mean-square estimation error and adequate allowance has been made for stabilization before the commencement of actual measurements. The results, therefore, represent the steady-state performance of the estimators. The most significant result, obtained in this study, is the performance of the Adaptive estimator. When the characteristics of the channel are known, the Efficient estimators have the best performance and the Gradient estimators the poorest. Kalman estimators are the most complex and Gradient estimators are the simplest. Kalman estimators have a performance rather similar to that of Gradient estimators. In terms of both performance and complexity, the Adaptive estimator lies between the Kalman and Efficient estimators. FTF estimators are known to exhibit numerical instability, for which an effective stabilization technique is proposed. Simulation tests have shown that the mean squared estimation error is an adequate measurement for comparison of the performance of the estimators