Distributed sparse channel estimation for OFDM systems with high mobility

Doubly-selective (DS) fading channel is often occurred in many orthogonal frequency division multiplexing (OFDM) communication systems, such as high-speed rail communication systems and underwater acoustic (UWA) wireless networks. It is challenging to provide an accurate and fast estimation over the doubly-selective channel, due to the strong Doppler shift. This paper addresses the doubly selective channel estimation problem based on complex exponential basis expansion model (CE-BEM) in OFDM systems from the perspective of distributed compressive sensing (DCS). We propose a novel DCS-based improved sparsity adaptive matching pursuit (DCS-IMSAMP) algorithm. The advantage of the proposed algorithm is that it can exploit the joint channel sparsity information using dynamic threshold, variable step size and tailoring mechanism. Simulation results show that the proposed algorithm achieves 5dB performance gain with faster operation speed, in comparison with traditional DCS-based sparsity adaptive matching pursuit (DCS-SAMP) algorithm.

The time and frequency selectivity in doubly-selective channels corrupt the orthogonality between different subcarriers in an orthogonal frequency-division multiplexing(OFDM) symbol and induce significant inter carrier interference(ICI). To mitigate this effect, many channel estimation and equalization based ICI mitigation schemes had been proposed. However, most of them are based on pilot pattern different from comb-type arrangement, therefore not applicable for many widely applied standards of which pilot tones are equispacedly spread in each symbol, such as DVB-T. For these systems, we resort to an two-stage estimation schemes to obtain accurate channel impulse response. In this paper, three fine estimation schemes are proposed and compared. Simulation results indicate that our proposed schemes can substantially reduce the mean squared error (MSE) of channel estimation in doubly-selective channels.

Channel estimation and synchronization are considered the most challenging issues in Orthogonal Frequency Division Multiplexing (OFDM) system. OFDM is highly affected by synchronization errors that cause reduction in subcarriers orthogonality, leading to significant performance degradation. The synchronization errors cause two issues: Symbol Time Offset (STO), which produces inter symbol interference (ISI) and Carrier Frequency Offset (CFO), which results in inter carrier interference (ICI). The aim of the research is to simulate Comb type pilot based channel estimation for OFDM system showing the effect of pilot numbers on the channel estimation performance and propose a modified estimation method for STO with less number of pilot to mitigate ISI effect. Channel estimation is simulated for different pilot number with 16QAM modulation in Ped-A Rayleigh channel. The STO effect assumed less than the cyclic prefix length to minimize ISI and maintain the orthogonality between subcarriers. The simulation results show that the high number of pilots are needed to achieve good channel estimation in case no synchronization used while similar results are achieved with smaller number of pilots if the propose modification on the synchronization method are used aligned with the channel estimation. The proposed system is evaluated in terms of BER and SNR.

In this paper, we will focus on channel estimation (CE) in orthogonal frequency-division multiplexing (OFDM) systems. The time-varying (TV) channelsare modeled by a basis expansion model (BEM). Due to the time-variation, the channel matrix in the frequency domain is no longer diagonal, but approximately banded.We use a pilot-aided algorithm for estimation of rapidly varying wireless channels in OFDM systems. Theperforms is goodwhen the channels vary on the scale of a single OFDM symbol duration, which occurs in mobile communication scenarios such as WiMAX, WAVE, and DVB-T.We recover Fourier coefficients of the channel taps by the pilot information.We then estimate the BEM coefficients of the channel taps from their respective Fourier coefficients using a recently developed inverse reconstruction method.We compare some BEM models in inverse methodsto find out the best ones in certain conditions.

This paper deals with channel estimation for orthogonal frequency-division multiplexing (OFDM) systems with transmit diversity. Space time coded OFDM systems, which can provide transmit diversity, require perfect channel estimation to improve communication quality. In actual OFDM systems, training sequences are usually used for channel estimation. The authors propose a training based channel estimation strategy suitable for space time coded OFDM systems. This novel strategy provides enhanced performance, high spectrum efficiency and relatively low computation complexity.

This work pertains to the use of superimposed training (ST) for channel estimation in orthogonal frequency division multiplexing (OFDM) based systems. A time domain coherent averaging based channel estimator is derived from the least squares criterion. By exploiting the periodicity of the training sequences in the time domain and inserting zeros instead of data at some of the training sequence subcarrier locations depending on the desired estimation accuracy, a controlled superimposition technique is proposed. This method includes the flexibility to trade off between bandwidth efficiency and performance. The mean squared estimation error (MSEE) performance of such a system is mathematically analyzed and a training sequence selection criterion optimizing the same is proposed. The simulation performance of the scheme is presented in terms of the MSEE and the bit error rate (BER) of the OFDM system. Such a scheme is attractive in high data rate scenarios in closed loop OFDM systems.

In orthogonal frequency division multiplexing (OFDM) systems, channel variations within an OFDM block destroy orthogonality between subcarriers, resulting in intercarrier interference (ICl), which increases an error floor in proportional to normalized Doppler frequency. To mitigate the effects of channel variations, many schemes have been proposed, but they are computationally complex or at the price of sacrificing spectral efficiency. In this paper, we first provide an ICI analysis in both time- and frequency-domain while existing literatures analyze the ICI effects mainly in the frequency domain. Based on this analysis, we then propose a novel ICI suppression technique, which uses iterative ICI cancellation in time domain and has a computational complexity that is linear in the OFDM symbol length. Channel estimation in rapidly time-varying scenarios is critical for ICl suppression and coherent demodulation. We also propose a scheme for estimating the channel parameters varying within an OFDM block. Along with the channel estimation scheme, we investigate the issue of optimum pilot tone placement. Theoretical analysis and simulation results show that the proposed ICl suppression method and channel estimation scheme have low computational complexity and good performance for most Doppler frequency of practical importance.

The doubly selective (DS) channel estimation in the large-scale multiple-input multiple-output (MIMO) systems is a challenging problem due to the large number of the channel coefficients to be estimated, which requires unaffordable and prohibitive pilot overhead. In this paper, first we conduct the analysis about the common sparsity of the basis expansion model (BEM) coefficients among all the BEM orders and all the transmit–receive antenna pairs. Then, a novel pilot pattern is proposed, which inserts the guard pilots to deal with the intercarrier interference under the superimposed pilot pattern. Moreover, by exploiting the common sparsity of the BEM coefficients among different BEM orders and different antennas, we propose a block distributed compressive sensing-based DS channel estimator for the large-scale MIMO systems. Its structured sparsity leads to the reduction of the pilot overhead under the premise of guaranteeing the accuracy of the estimation. Furthermore, taking consideration of the block structure, a pilot design algorithm referred to as block discrete stochastic optimization is proposed. It optimizes the pilot positions by reducing the coherence among different blocks of the measurement matrix. Besides, a linear smoothing method is extended to large-scale MIMO systems to improve the accuracy of the estimation. Simulation results verify the performance gains of our proposed estimator and the pilot design algorithm compared with the existing schemes.

Accurate channel estimation at the receiver is critical for subsequent downlink data transmission in orthogonal frequency division multiplexing (OFDM) systems. However, tracking the rich variety of propagation environments is challenging. At the same time, the variation of the channel in time and frequency also makes channel estimation difficult. In this paper, we exploit machine learning techniques as an attractive option to learn improved solutions for channel estimation problem. Instead of training a single large neural network for all channel conditions, we design dedicated sub-networks for channels with varying delay and Doppler shift conditions. Then we propose a hard selection method to estimate the channel with the optimal sub-network which is determined by a channel condition classifier. We further propose a soft selection method to combine multiple sub-networks for better estimation performance. Simulation results show that compared with the conventional least squares (LS) channel estimation method, the proposed networks achieve approximately 7 dB normalized mean square error (NMSE) gain at SNR = 10 dB.

Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels

This thesis focuses on advanced signal processing techniques for multicarrier modulation, in partic- ular, orthogonal frequency division multiplexing (OFDM). OFDM promises a substantial increase in data rate and robustness against the frequency selectivity of multipath channels. For coherent detection, channel estimation is essential for receiver design. In this thesis, we will present a re- ceiver design where the channel estimator exploits the sparse nature of the physical channel. We present the most popular subspace algorithm from the array processing literature, namely root- MUSIC, recent sparse identification algorithms in the form of orthogonal matching pursuit (OMP) and basis pursuit (BP), and a hybrid method called path identification (PI) algorithm which is the main contribution of this thesis. We also compare the performance of these estimators with that of the conventional estimators such as least-squares (LS) estimator and linear minimum-mean-squares estimator (LMMSE).

Orthogonal Frequency Division Multiplexing (OFDM) is extensively employed due to its advantages in countering channel perturbations, such as multipath fading and frequency selective fading. Nevertheless, the intercarrier interference and sensitivity to synchronization error are the issues that OFDM technology has to confront and resolve. To achieve reliable communication, the accuracy of channel estimation is of particular significance. In this paper, the base extension model is utilized for channel estimation on the MATLAB platform. The Base Expansion Model (BEM) can capture the key characteristics of the channel with fewer parameters, thereby reducing the complexity and enhancing the accuracy of channel estimation. In the system simulation, the signal transmission effect of the current channel is evaluated by comparing the mean error variance and bit error rate under different SNR values. The performances of CE-CEM, P-CEM and GCE-BEM under LS, MMSE and LMMSE are compared, and the best performance of GCE-BEM under LMMSE is obtained, realizing accurate channel estimation.

A time-varying channel estimation method for orthogonal frequency division multiplexing (OFDM) systems is presented. The proposed method makes use of two asymmetric pilot symbols to improve channel estimation performance and meanwhile obtain a reasonable pilot overhead. Complete simulations illustrate that the proposed method can have precise estimation result even under very large normalized Doppler frequency.

This paper addresses the effective sparse channel estimation based on the most significant taps (MST) selection with high spectral efficiency and moderate computational complexity for orthogonal frequency division multiplexing (OFDM) systems. The main goal of this paper is to obtain an efficient time domain threshold and achieve the channel estimation performance approaching the least squares (LS) method with exact sparsity (exact number of non-zero channel taps) without using prior knowledge of both channel statistics and noise standard deviation. Firstly, classical LS method is used to get an initial channel impulse response (CIR) estimate. Then a novel effective threshold, estimated from the noise coefficients of the initial estimated CIR, is proposed. Finally, the obtained threshold is used to select the most significant taps. Simulations show that the proposed method is able to achieve good channel estimation performances for sparse channel within a comparatively wide range of sparsity with small number of pilots and moderate computational complexity.

In this paper, a novel DFT-based pilot-symbol-aided channel estimation algorithm for orthogonal frequency division multiplexing (OFDM) systems is presented. By using transform domain cluster and discriminant analysis, this algorithm can adaptively remove the noise in the channel impulse response (CIR). Then as the noise is removed, the accuracy of channel estimation can be improved dramatically. With a number of simulation experiments, the efficacy of the proposed channel estimation algorithm is validated by the numerical results.