Navigate Symbol Assisted Channel Estimation Algorithms under Various Channel Distribution

In order to reduce the effect of elements of noise, navigate symbol assisted (NSA) channel estimation (CE) algorithms based on the transform domain such as discrete Fourier transform (DFT) and discrete cosine transform (DCT)seemenchanting owing to their capacity. Here, an improved algorithm for a better channel estimation has been proposed based on expurgated discrete cosine transform (E-DCT) with additive white Gaussian noise (AWGN), Rayleigh distribution (RD) and Rician distribution (RcD) to obviate the leakage of energy using the property of symmetric and also compared with the conventional channel estimation algorithms such as Least Square (LS), DFT and mirror weighted DCT even that of E-DCT with additive white Gaussian noise (AWGN) distribution. Simulation results demonstrate that the E-DCT-RD can reduce the energy leakage more efficiently, and performed far better than the existing CE algorithms

A Robust Threshold for Iterative Channel Estimation in OFDM Systems

A novel threshold computation method for pilot symbol assisted iterative channel estimation in OFDM systems is considered. As the bits are transmitted in packets, the proposed technique is based on calculating a particular threshold for each data packet in order to select the reliable decoder output symbols to improve the channel estimation performance. Iteratively, additional pilot symbols are established according to the threshold and the channel is re-estimated with the new pilots inserted to the known channel estimation pilot set. The proposed threshold calculation method for selecting additional pilots performs better than non-iterative channel estimation, no threshold and fixed threshold techniques in poor HF channel simulations

OFDM Channel Estimation Along with Denoising Approach under Small SNR Environment using SSA

In this paper, a de-noising approach in conjunction with channel estimation (CE) algorithm for OFDM systems using singular spectrum analysis (SSA) is presented. In the proposed algorithm, the initial CE is computed with the aid of traditional linear minimum mean square error (LMMSE) algorithm, and then further channel is evaluated by considering the low rank eigenvalue approximation of channel correlation matrix related to channel using SSA. Simulation results on bit error rate (BER) revealed that the method attains an improvement of 7 dB, 5 dB and 3 dB compared to common LSE, MMSE and SVD based methods respectively. With the help of statistical correlation coefficient (C) and kurtosis (k), the SSA method utilized to de-noise the received OFDM signal in addition to CE. In the process of denoising, the received OFDM signal will be decomposed into different empirical orthogonal functions (EOFs) based on the singular values. It was established that the correlation coefficients worked well in identifying useful EOFs only up to moderate (SNR geq 12dB). For low SNR<12 dB, kurtosis was found to be a useful measure for identifying the useful EOFs. In addition to outperforming the existing methods, with this de-noising approach, the mean square error (MSE) of channel estimator is further improved approximately 1 dB more in SNR at the cost of computational complexity

A Novel Data-Aided Channel Estimation with Reduced Complexity for TDS-OFDM Systems

In contrast to the classical cyclic prefix (CP)-OFDM, the time domain synchronous (TDS)-OFDM employs a known pseudo noise (PN) sequence as guard interval (GI). Conventional channel estimation methods for TDS-OFDM are based on the exploitation of the PN sequence and consequently suffer from intersymbol interference (ISI). This paper proposes a novel dataaided channel estimation method which combines the channel estimates obtained from the PN sequence and, most importantly, additional channel estimates extracted from OFDM data symbols. Data-aided channel estimation is carried out using the rebuilt OFDM data symbols as virtual training sequences. In contrast to the classical turbo channel estimation, interleaving and decoding functions are not included in the feedback loop when rebuilding OFDM data symbols thereby reducing the complexity. Several improved techniques are proposed to refine the data-aided channel estimates, namely one-dimensional (1-D)/two-dimensional (2-D) moving average and Wiener filtering. Finally, the MMSE criteria is used to obtain the best combination results and an iterative process is proposed to progressively refine the estimation. Both MSE and BER simulations using specifications of the DTMB system are carried out to prove the effectiveness of the proposed algorithm even in very harsh channel conditions such as in the single frequency network (SFN) case