Proposed Channel Estimation Research Articles

DFT-based Cluster Analysis Channel Estimation Algorithm for OFDM Systems on Multipath Channels

In this paper, a enhanced DFT-based channel estimation for OFDM systems is proposed. Conventional DFT-based channel estimations improve the performance by suppressing time domain noise beyond the length of the cyclic prefix (CP), so it does not completely suppress the noise. However, they potentially require information on channel impulse responses and may also result in mean-square error (MSE) floor due to incorrect channel information such as channel delay spread. In order to overcome the disadvantage, our proposed channel estimation can improve the performance by deciding significant channel taps adaptively. Significant channel taps are detected on the basis of cluster discriminant analysis. Simulation results demonstrate that the proposed algorithm outperforms the conventional DFT-based estimation in terms of BER and MSE performance.

A Kalman Filtering Channel Estimation Method Based on State Transfer Coefficient Using Threshold Correction for UWB Systems

Aimed at the divergence problem in the traditional Kalman Filtering (KF) channel estimation algorithm due to the inaccurate state transfer coefficient (STC), this paper proposes a novel KF channel estimation method using the STC with threshold correction. By setting a reasonable threshold, the estimation performance of STC can be greatly improved in the time-varying Ultra-Wideband (UWB) channel environment. The simulation results demonstrate that, compared with the traditional estimation methods, the proposed channel estimation method can not only significantly improve the estimation performance but also effectively restrain the traditional KF’s divergence problem.

Artificial Neural Network trained by Genetic Algorithm for Smart MIMO Channel Estimation for Downlink LTE-Advance System

Long-Term Evolution (LTE) is the next generation of current mobile telecommunication networks. LTE has a flat radio-network architecture and significant increase in spectrum efficiency, throughput and user capacity. In this paper, performance analysis of robust channel estimators for Downlink Long Term Evolution-Advanced (DL LTE-A) system using three Artificial Neural Networks: Feed-forward neural network (FFNN), Cascade-forward neural network (CFNN) and Layered Recurrent Neural Network (LRN) are trained separately using Back-Propagation Algorithm and also ANN is trained by Genetic Algorithm (GA). The methods use the information got by the received reference symbols to estimate the total frequency response of the channel in two important phases. In the first phase, the proposed ANN based method learns to adapt to the channel variations, and in the second phase it estimates the channel matrix to improve performance of LTE. The performance of the estimation methods is evaluated by simulations in Vienna LTE-A DL Link Level Simulator in MATLAB software. Performance of the proposed channel estimator, ANN trained by Genetic Algorithm (ANN- GA) is compared with traditional Least Square (LS) algorithm and ANN based other estimator like Feed- forward neural network, Layered Recurrent Neural Network and Cascade-forward neural network for Closed Loop Spatial Multiplexing (CLSM)-Single User Multi-input Multi-output (MIMO-2×2 and 4×4) in terms of throughput. Simulation result shows proposed ANN- GA gives better performance than other ANN based estimations methods and LS. Index Terms — LTE-A, MIMO, Artificial Neural Network (ANN), Back-Propagation, Layered Recurrent Neural Network (LRN), Feed-forward neural network (FFNN), Cascade-forward neural network (CFNN), Genetic Algorithm (GA).

Low Complexity Channel Estimation for MC-CDMA System Based Chaos under Fast Multipath Channel

Based on interpolation methods, a low complexity channel estimation method for MC-CDMA system based chaos under fast fading multipath channel is proposed.Different interpolation methods are used; Linear interpolation (LI), cubic spline interpolation (CSI), and piecewise cubic Hermite interpolation (PCHI). The multi-carrier scheme is established by using a chaotic generator with different initial conditions, while the pilots are established using another chaotic generator with another different initial conditions. The proposed channel estimator without and with interpolation methods for single user and multiusers is tested under fast frequency selective Rayleigh fading channel. Simulation results demonstrate that the channel estimation of MC-CDMA system based chaos using interpolation methods and when the pilots are chaos signal too, achieves better performance improvement in fast multipath environment compared to the conventional MC-CDMA system when the pilots are considered as Walsh- Hadamard codes. The proposed system is also built and tested using Simulink program.

Constructed Data Pilot-Assisted Channel Estimators for Mobile Environments

Nowadays, more and more communication systems are deployed under mobile environments with very high velocity. This paper focuses on the channel estimation problem in vehicular scenarios, which is very challenging in view of the extremely time-varying characteristics of mobile channels. Specifically, we propose a novel channel estimator named constructed data pilot (CDP) estimator for the current communication standards by fully exploiting the channel correlation characteristics across two concatenated symbols. On the basis of the CDP estimator, we further resort to two efficient techniques to improve its performance over the entire signal-to-noise-ratio (SNR) region. For the first technique, the time-variant mobile channel is modeled as a first-order Markov process so that the exact autocorrelation value of the two adjacent symbols can be derived. For the second technique, the SNR is estimated and serves as a priori information. Simulation results reveal that our proposed channel estimators outperform existing alternatives with lower computational complexity.

Subspace-Based Blind Channel Estimation for OFDM by Exploiting Cyclic Prefix

A new subspace-based blind channel estimation (SBCE) approach for cyclically prefixed orthogonal frequency-division multiplexing (CP-OFDM) is proposed to provide enhanced estimation performance. Based on the redundant structure that CP reproduces the tail part of the useful signal vector, a predetermined null subspace corresponding to the transmitted CP-OFDM signal vector subspace is identified and enables the proposed estimator to extract the channel impluse response by projecting the received signal onto the predetermined null subspace. While requiring less implementation complexity, the proposed channel estimator is shown to outperform conventional SBCE approaches in mean-square error performance when the number of received blocks and the received signal-to-noise power ratio are not large.

A New Sparse Channel Estimation and Tracking Method for Time-Varying OFDM Systems

We propose a simple sparse channel estimation and tracking method for orthogonal frequency-division multiplexing (OFDM) systems based on a dynamic parametric channel model, where the channel is parameterized by a small number of distinct paths, each characterized by path delay and path gain, and all parameters are time varying. In the proposed method, we adaptively choose the delay grid and estimate each channel path delay iteratively. To further reduce the complexity, we also propose a tracking algorithm based on the fact that the changes in the channel path number and path delays are small over a few adjacent OFDM symbols. After the physical path delays are estimated, we then estimate the channel path gains by using the polynomial basis expansion model (P-BEM). Simulation results demonstrate the effectiveness of the proposed channel estimation and tracking method in dynamic environments. Compared with the compressive-sensing-based channel estimator using the orthogonal matching pursuit (OMP) algorithm, the new technique proposed here has much lower computational complexity while offering comparable performance.

Cross‐relation‐based frequency‐domain blind channel estimation with multiple antennas in orthogonal frequency division multiplexing systems

Frequency domain blind channel estimation methods for orthogonal frequency division multiplexing (OFDM) systems with multiple antennas are proposed, in which the cross relation between the channel gains and a single snapshot of the received signal on each subcarrier is utilised. Necessary and sufficient conditions for the identifiability of a single-input multiple-output OFDM channel estimator are derived and, furthermore, using a first-order perturbation analysis, the approximation of mean-squared-error (MSE) is analytically obtained, which shows that the proposed estimator is approximately unbiased at high signal-to-noise ratio. The proposed estimator exhibits lower MSEs than those of both the existing subspace-based and deterministic blind channel estimators and is less sensitive to the estimation error of the number of multipaths. Finally, the extension of the proposed channel estimation to multiple-input-multiple-output OFDM is presented.

An Improved DFT-Based Channel Estimation Algorithm for OFDM System in Non-sample-Spaced Multipath Channels

It is well known that channel impairment caused by multipath reflections can deeply degrade the transmission efficiency in wireless communication systems. The conventional DFT-based channel estimation methods improve the performance by neglecting nonsignificant channel taps. However, in multipath channels with non-sample-spaced time delays, this will cause power leakage and result in an error floor. In order to overcome this problem, based on the property of Channel impulse response, we describe a utility of channel estimation technology for OFDM systems using discriminant analysis. Usually, significant channel taps are detected on the basis of a predetermined threshold, so the optimal threshold value becomes a crucial factor. But it is difficult to decide channel taps that are approximately equal to the threshold value. To solve this disadvantage, the proposed algorithm improves performance by using Mahalanobis distance discriminant analysis for channel taps. This approach can mitigate the aliasing effect in the DFT-based channel estimator and reduce the leakage power efficiently when there is non-sample-spaced path delay. Simulation results show that the proposed channel estimators can eliminate the error floor substantially and its performance is better than the LS and conventional DFT estimators.

A Wireless Local Area Network Channel Estimation Scheme

In this paper, a robust channel estimation scheme is proposed for the OFDM based wireless local area network systems. The proposed channel estimation scheme has been done targeting the IEEE 802.11a standard [, but it can be extended to other packet-based OFDM systems. A robust channel estimation scheme is proposed for the OFDM-based wireless local area network systems. Performance evaluation shows that the WLAN system with the proposed channel estimation scheme is with practical system performance under harsh fading channel.

Study of ANN Configuration on Performance of Smart MIMO Channel Estimation for Downlink LTE-Advanced System

Long-Term Evolution (LTE) is the next generation of current mobile telecommunication networks. LTE has a new flat radio-network architecture and significant increase in spectrum efficiency. In this paper, performance analysis of robust channel estimators for Downlink Long Term Evolution- Advanced (DL LTE-A) system using three Artificial Neural Network ANN Architectures: Feed-forward neural network, Cascade-forward neural network and Layered Recurrent Neural Network (LRN) are adopted to train the constructed ANNs models separately using Back-Propagation Algorithm. The methods use the information got by the received reference symbols to estimate the total frequency response of the channel in two important phases. In the first phase, the proposed ANN based method learns to adapt to the channel variations, and in the second phase it estimates the channel matrix to improve performance of LTE. The performance of the estimation methods is evaluated by simulations in Vienna LTE-A DL Link Level Simulator. Performance of the proposed channel estimator, Layered Recurrent Neural Network is compared with traditional Least Square (LS) algorithm and ANN based other estimator like Feed-forward neural network and Cascade-forward neural network for Closed Loop Spatial Multiplexing-Single User Multi-input Multi- output (2×2 and 4×4) (CLSM-SUMIMO) in terms of throughput. Simulation result shows LRN gives better performance than other ANN based estimations methods and LS.

Blind and semi-blind channel estimation with fast convergence for MIMO-OFDM systems

In this paper, the blind subspace channel estimation using the block matrix scheme is proposed for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Based on the Toeplitz structure, the block matrix scheme collects a group of the received OFDM symbols into a vector, and then partitions it into a set of equivalent symbols. The number of equivalent symbols is about N times of OFDM symbols, where N is the size of FFT operation. With those equivalent symbols, the proposed blind subspace channel estimation can converge within a small amount of OFDM symbols. The identifiability of the proposed channel estimation is examined that the channel matrix is determined up to an ambiguity matrix. Besides, the semi-blind channel estimation is also investigated by combining few pilot sequences with the subspace method. Simulation results show that the proposed channel estimators perform very well even in a time-varying scenario. Especially the semi-blind methods achieve almost the same BERs as those by true channels.

Improved least mean square algorithm with application to adaptive sparse channel estimation

Least mean square (LMS)-based adaptive algorithms have attracted much attention due to their low computational complexity and reliable recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods have been proposed based on different sparse penalties, such as ℓ1-norm LMS or zero-attracting LMS (ZA-LMS), reweighted ZA-LMS, and ℓp-norm LMS. However, the aforementioned methods cannot fully exploit channel sparse structure information. To fully take advantage of channel sparsity, in this paper, an improved sparse channel estimation method using ℓ0-norm LMS algorithm is proposed. The LMS-type sparse channel estimation methods have a common drawback of sensitivity to the scaling of random training signal. Thus, it is very hard to choose a proper learning rate to achieve a robust estimation performance. To solve this problem, we propose several improved adaptive sparse channel estimation methods using normalized LMS algorithm with different sparse penalties, which normalizes the power of input signal. Furthermore, Cramer-Rao lower bound of the proposed adaptive sparse channel estimator is derived based on prior information of channel taps' positions. Computer simulation results demonstrate the advantage of the proposed channel estimation methods in mean square error performance.

DFT-Based Channel Estimator for OFDM Systems with Leakage Estimation

In this letter, we present a novel discrete Fourier transform (DFT)-based channel estimator for orthogonal frequency-division multiplexing (OFDM) systems. The conventional DFT-based estimator zeroes out noise-dominant values in the transform domain, which suffers from a poor performance in the presence of channel leakage. Some improved estimators have been proposed, but these estimators all focus on reducing the leaked energy. We propose to estimate the channel leakage by using the partial minimum mean square error (MMSE) filtering. When the channel delay is sufficiently less than the duration of an OFDM symbol, the partial MMSE filtering can be simplified. Simulation results show that the proposed channel estimator achieves much better performance than conventional estimators and well approaches the MMSE estimator.

Low‐complexity channel estimation and turbo equalisation for high frequency channels

A low-complexity channel estimation algorithm is proposed to improve performance of two high frequency standardised waveforms: MIL-STD-188-110B Appendix C standard and STANAG 4285. In the proposed estimation algorithm, the channel impulse response is modelled as a linear-time function within a frame and the least square criterion is adopted. With the estimated CIRs, the authors study and compare bit-error-rate (BER) performance and complexity of three turbo equalisation (TEQ) schemes, including the minimum mean-square error (MMSE), the MMSE with decision feedback equalisation (DFE) (MMSE-DFE) and the soft-feedback interference-cancellation (SFIC) turbo receivers. The authors then propose a hybrid TEQ scheme that uses the MMSE-DFE equaliser for the first iteration and the SFIC equaliser for the rest. Simulation results show that the proposed channel estimation algorithm performs well and the TEQ schemes realise a significant performance improvement as compared with a non-iterative receiver. Moreover, the proposed hybrid TEQ scheme achieves the best trade-off between BER and complexity for the MIL-STD-188-110B Appendix C standard, whereas the SFIC-TEQ scheme is optimal for the STANAG 4285 standard.

Performance Analysis of Downlink W-CDMA Systems in Weibull and Lognormal Fading Channels Using Chaotic Codes

In this paper, a subspace based blind channel estimation scheme for downlink W-CDMA systems using chaotic codes under Weibull and Lognormal fading channel conditions is proposed and compared with W-CDMA system using PN codes. The algorithm provides estimates of multiuser channels by exploiting the structural information of the data output. The subspace of the (data + noise) matrix contains sufficient information for unique determination of channels and, hence, the signature waveforms and signal constellations. The proposed channel estimation algorithm is also implemented for multiuser--orthogonal frequency division multiplexing (OFDM) system. Performance measures like bit error rate (BER) and root mean square error (RMSE) are plotted for Weibull and Lognormal fading channels. Signal constellations under Weibull and Lognormal channels are also plotted. Analytical and Simulation results for BER and RMSE are compared for W-CDMA system using PN codes and chaotic codes. Simulation results show that, chaos-based W-CDMA outperforms the PN-based W-CDMA in terms BER and RMSE. Simulation results of multiuser-OFDM system shows that performance is further improved when compared to the W-CDMA system.

A Generalized Analytical Solution to Channel Estimation With Intersymbol Interference Cancelation and Co-Channel Interference Cancelation for Single Input Single Output/Multiple Input Single Output Digital Terrestrial Multimedia Broadcasting Systems

A novel generalized channel estimation scheme without the need of matrix inversion or iterative operations is proposed for single input single output (SISO)/multiple input single output (MISO) digital terrestrial multimedia broadcasting (DTMB) system, adopting the time-domain synchronous orthogonal frequency division multiplexing technology. Simultaneously exploiting time domain correlation channel estimation and the constant coefficients of the imperfect autocorrelation of m-sequence, a simple and generalized closed-form solution related to each channel tap can be acquired by solving the constructed linear equations. On the basis of the specified frame head structure for the SISO DTMB system, quasi-orthogonal frame head sequences over multiple antennas are designed by employing different cyclic shifts of m-sequence. In addition, in order to effectively recover the transmitted signal in the frequency domain, the tail cancelation and cyclic reconstruction process is elaborated primarily for MISO systems. It is shown that, for the SISO DTMB system, the implementation complexity of the intersymbol interference cancelation (ISIC) of the proposed approach is reduced by approximately 90% over the existing iterative scheme, and for the MISO DTMB system, the implementation complexity of the proposed scheme with the co-channel interference cancelation (CCIC) and ISIC dramatically decreases compared with the existing scheme. Simulation results validate that the proposed channel estimation scheme can achieve almost the same performance as the existing channel estimation methods, but with a significant reduction in computational complexity in both SISO and MISO DTMB systems.

Channel Estimation with Optimal Power Controls in Amplify-and-Forward Relay Networks

In this paper, we present a power control method of relay for improving a channel estimation performance in amplify-and-forward (AF) relay networks over frequency-selective fading channel. We first analyze a channel mean-square error (MSE) by imposing a power control effect on a simple (LS) channel estimation. Then, we find that there is a trade-off between the amplitude of power control and MSE performance. Moreover, we propose the optimal and suboptimal power control methods in order to resolve such a trade-off. Finally, the superiority of the proposed channel estimation with optimal power controls is verified through numerical simulations.

Spectrally Efficient Time-Frequency Training OFDM for Mobile Large-Scale MIMO Systems

Large-scale orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) is a promising candidate to achieve the spectral efficiency up to several tens of bps/Hz for future wireless communications. One key challenge to realize practical large-scale OFDM MIMO systems is high-dimensional channel estimation in mobile multipath channels. In this paper, we propose the time-frequency training OFDM (TFT-OFDM) transmission scheme for large-scale MIMO systems, where each TFT-OFDM symbol without cyclic prefix adopts the time-domain training sequence (TS) and the frequency-domain orthogonal grouped pilots as the time-frequency training information. At the receiver, the corresponding time-frequency joint channel estimation method is proposed to accurately track the channel variation, whereby the received time-domain TS is used for path delays estimation without interference cancellation, while the path gains are acquired by the frequency-domain pilots. The channel property that path delays vary much slower than path gains is further exploited to improve the estimation performance, and the sparse nature of wireless channel is utilized to acquire the path gains by very few pilots. We also derive the theoretical Cramer-Rao lower bound (CRLB) of the proposed channel estimator. Compared with conventional large-scale OFDM MIMO systems, the proposed TFT-OFDM MIMO scheme achieves higher spectral efficiency as well as the coded bit error rate performance close to the ergodic channel capacity in mobile environments.

Soft-input iterative channel estimation for bit-interleaved turbo-coded MIMO-OFDM systems

Bit-interleaved coded modulation (BICM) is a robust multiplexing technique for achieving multiplexing gain in multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. However, in order to benefit maximally from the various advantages offered by BICM-based MIMO-OFDM systems, availability of accurate MIMO channel state information (CSI) at the receiver end of the system is essential. Without accurate MIMO CSI, accurate MIMO demapping and coherent detection and decoding of the transmitted message symbols at the system’s receiver would be impossible. In such cases, the multiplexing gain offered by the BICM technique, as well as the higher data rate made possible by the MIMO-OFDM system, is not benefitted from in full. In this paper, we propose a soft input based decision-directed channel estimation scheme for the provision of MIMO CSI for coherent detection of signals in MIMO-OFDM systems. The proposed channel estimator works in iterative mode with a MIMO demapper and a turbo decoder, and is based on the fast data projection method (FDPM) and the variable step size normalised least mean square (VSSNLMS) algorithm. Simulation results of the proposed estimator based on the FDPM and VSSNLMS algorithms indicate better performance in comparison with the same estimator employing minimum mean square error criteria and deflated projection approximation subspace tracking algorithms for both slow- and fast-fading channel scenarios. The proposed estimator would be suitable for use at the receiver end of MIMO-OFDM wireless communication systems operating in either slow- or fast-fading channels.