Channel Estimation For OFDM Systems Research Articles

Deep Learning Based Fully Progressive Image Super-Resolution Scheme for Channel Estimation in OFDM Systems

Deep Learning Based Fully Progressive Image Super-Resolution Scheme for Channel Estimation in OFDM Systems

Deep Learning-based Pilot Adaptation and Channel Estimation in OFDM Systems

Deep Learning-based Pilot Adaptation and Channel Estimation in OFDM Systems

Channel Estimation in OFDM Systems based on the Mamdani Fuzzy Genetic Algorithm

Channel Estimation in OFDM Systems based on the Mamdani Fuzzy Genetic Algorithm

Hybrid AE and Bi-LSTM-Aided Sparse Multipath Channel Estimation in OFDM Systems

Hybrid AE and Bi-LSTM-Aided Sparse Multipath Channel Estimation in OFDM Systems

Adversarial Training for Jamming-robust Channel Estimation in OFDM Systems

Adversarial Training for Jamming-robust Channel Estimation in OFDM Systems

Blind channel estimation in ZP-OFDM system using new precoding matrix over channels with memory

Using a new precoding matrix, a new method for blind channel estimation of OFDM systems with zero padding has been investigated. A single vector of the correlation matrix is employed to obtain the normalized estimated channel vector, and one pilot signal is used to resolve the scalar ambiguity. Two models of OFDM systems have been investigated, and their NMSE and BER results have been compared in various scenarios. In particular conditions, the new precoding matrix offers an advantage over the conventional circulant. To aid in the creation of novel precoding matrices, a proof of the full rank property is stated under some conditions.

Sensing-Aided Channel Estimation in OFDM Systems by Leveraging Communication Echoes

Sensing-Aided Channel Estimation in OFDM Systems by Leveraging Communication Echoes

Pilot based channel estimation of OFDM systems using deep learning techniques

Pilot based channel estimation of OFDM systems using deep learning techniques

New precoding blind channel estimation and channel order estimation algorithm in OFDM systems with cyclic prefix

Using different precoding matrices, a new blind channel estimation in OFDM systems with cyclic prefix is presented in this paper. The proposed method employs one column of the correlation matrix directly, unlike the traditional precoding techniques where the elements of precoding matrix is been removed. Results show the impact of this method specially when using the type of precoding matrices which include the circulant property in its design. Since channel order estimation is an important task in blind methods, a new and simple algorithm is also investigated with no additional complexity been added to the system. A proof of diagonalizability property is mentioned which can be implemented for other investigations concerning this type of matrices.

An Effective Generative Model Based Channel Estimation Method With Reduced Overhead

The traditional channel estimation for OFDM systems suffers from big overheads caused by pilots. Current channel estimation methods only consider the correlation among pilots within a time-frequency grid, which is a kind of local channel information. In this article, we propose a Generative Adversarial Network (GAN) based channel estimation method to utilize not only local channel information but also correlation across multiple frames, thus much fewer pilots are needed. We propose to explore GAN to establish a mapping from a low dimensional space to a high dimensional channel space. Then based on very few pilots, we solve an optimization problem to localize the low dimensional vector corresponding to the specified channel and generate the corresponding channel. Furthermore, we propose an improved conditional GAN (cGAN), which takes the multi-path number as conditional information, making the proposed generative channel model more robust in various scenarios. Experiments demonstrate that the proposed cGAN-based method can be applied to multiple channel profiles once trained offline, and it is robust to the mismatch of multi-path number. Besides, the proposed method outperforms the ideal ALMMSE and ChannelNet methods under the same pilot density. In the best case, it can achieve comparable performance with only about half the number of pilots.

A Low-Complexity Channel Estimation Based on a Least-Squares Algorithm in OFDM Systems

As the channel frequency responses (CFRs) at virtual pilot subcarriers are assumed to be zero, the estimated CFRs will have a leakage effect for discrete Fourier transform (DFT)-based channel estimation in OFDM systems. The CFRs at odd pilot subcarriers and even pilot subcarriers are related if the number of maximum channel delay points is smaller than or equal to half the number of pilots (including virtual pilots). According to this correlation, we propose a low-complexity least-squares (LS) method to estimate the CFRs at virtual even and odd pilot subcarriers, respectively. This will solve the problem of the leakage effect in DFT-based channel estimation. The proposed method does not need to know the statistical properties of the channel or insert extra pilots as with some estimation methods. Furthermore, although this method has less computation than the LS method, both have almost the same channel estimation efficiency in simulation. The channel estimation efficiency of our proposed method is still similar to that of the LS method, even if the number of maximum channel delay points is greater than half the number of pilots. Therefore, the proposed low-complexity method is very suitable for equalizer hardware implementation.

A Low-Complexity Channel Estimation for OFDM Systems Based on CIR Length Adaptation

A Low-Complexity Channel Estimation for OFDM Systems Based on CIR Length Adaptation

Adaptive Channel Estimation for OFDM Systems Using CIR Length Estimate

This letter addresses adaptive channel estimation for OFDM systems to reduce computational complexity. There are numerous methods suggested for low-complexity channel estimation, but some of them still suffer from a considerable complexity or performance degradation due to the channel energy leakage. We present an adaptive channel estimator based on the channel impulse response (CIR) length estimate to reduce the complexity without any performance degradation. We first introduce a simple and efficient method based on the sequential hypothesis test (SHT) to estimate the CIR length and then apply it to the adaptive implementation of the SVD-based LMMSE estimator. Simulation results demonstrate that the SHT method performs well with very low complexity and that the adaptive estimator not only reduces the complexity significantly but also provides better performance than the fixed estimators.

MSE analysis for the finite order joint frequency tracking and channel estimation in OFDM systems

ABSTRACT The technique of orthogonal frequency division multiplexing is very sensitive to the carrier frequency offset (CFO). The requirement for high estimation accuracy when we acquire the CFO value is demanding. An efficient high-order approximation algorithm for jointly estimating the CFO and the channel impulse response (CIR) has been presented in the literature. From their simulation results, it is found that the estimator mean-square error (MSE) values are very close to the Cramer-Rao bound values, indicating that the highest estimation accuracy is achieved. However, no theoretical analysis is provided to confirm this assertion. In this article, we derive the theoretical formulas for the CFO MSE and the average CIR MSE for the estimators. It is found that the theoretical MSE for a joint CFO and CIR estimation problem approximates the Cramer-Rao bound when the signal-to-noise ratio (SNR) is high and the generalized CAZA sequence is used as the preamble. From our simulations, the theoretical results still hold true even for an SNR as low as 5 dB. Consequently, the range of SNR for which the theoretical analysis is valid is quite large.

Double Interpolation-Based Linear Fitting for OMP Channel Estimation in OFDM Systems

Conventional orthogonal matching pursuit (OMP) based channel estimation adopts discretized grids to represent path delays, which leads to energy leakage of off-grid paths. From the perspective of linear fitting, utilizing multiple atoms with fractional delays and shortening the distance between these atoms in the off-grid path reconstruction can reduce the energy leakage. To this end, an improved OMP channel estimation scheme is proposed in this letter for higher estimation accuracy. The proposed scheme utilizes trigonometric function based interpolation twice to estimate the delays of two atoms, which are adopted to reconstruct an off-grid path. Furthermore, a two-step search strategy is introduced to keep low computational complexity. The advantage of the proposed scheme is validated in simulations.

LMMSE Channel Estimation in OFDM Systems: A Vector Quantization Approach

The linear minimum mean square error (LMMSE) channel estimation yields the optimal performance in terms of the mean square error (MSE). However, the high computational complexity limits its application in practical systems. To overcome this issue, this letter proposes a vector quantization approach to the LMMSE channel estimation. The LMMSE filtering matrices corresponding to some typical wireless channels are calculated off-line. When performing the LMMSE channel estimation on-line, an appropriate LMMSE filtering matrix is selected according to the MSE criterion. At the expense of a negligible performance degradation, the computational complexity of the LMMSE channel estimation is reduced.

Method of advanced channel estimation in OFDM systems by regression technique

The article considers the task of increasing the efficiency of estimating the parameters of multipath radio communication channels used in the design of mobile remote access systems with orthogonal frequency division with OFDM multiplexing. In the conditions of an ever-growing number of subscribers, an increase in transmission speed and noise immunity of mobile radio communication systems, the task of increasing the efficiency of channel estimation in multipath signal propagation remains an urgent one. It is also essential that the evaluation function was performed with minimal complexity and with high accuracy of signal recovery. Within the framework of the work, the analysis of existing algorithms, models, and methods for estimating the communication channel parameters of OFDM systems is carried out, the main differences and disadvantages are noted. A technique has been developed for estimating the complex envelope of an OFDM signal in a multipath communication channel based on regression analysis. A mathematical model for estimating a discrete multipath channel by a regression method using pilot symbols is presented. The type of the approximating function is selected, taking into account the requirements for the complexity of implementation and performance indicators. We present an algorithm for estimating the complex envelope of an OFDM signal in a multipath communication channel by the regression method.

Optimization of Deterministic Pilot Pattern Placement Based on Quantum Genetic Algorithm for Sparse Channel Estimation in OFDM Systems

Optimization of Deterministic Pilot Pattern Placement Based on Quantum Genetic Algorithm for Sparse Channel Estimation in OFDM Systems

Nonlinear Channel Estimation for OFDM System by Wavelet Transform Based Weighted TSVR

An efficient nonlinear channel estimation method for pilot-aided orthogonal frequency division multiplexing system is proposed in this work. The considered channel is selective in time and frequency domain, that is doubly selective channel. Wavelet transform based weighted twin support vector regression is used for channel frequency response estimation, which is suitable for the regression of nonlinear system. Different from traditional support vector regression algorithm, the proposed algorithm gives samples different weights according to their variance calculated based on wavelet transform. The weights are added into both first and quadratic terms of the objective functions to reduce the impact of outliers, which is likely to appear in the received pilot signal polluted by noise. The proposed channel estimation algorithm has good generalization ability and can reduce the influence of overfitting problem. The results of computational tests show that the proposed algorithm is with better estimating performance compared to the classical pilot-aided channel estimation methods.

Tune Smarter Not Harder: A Principled Approach to Tuning Learning Rates for Shallow Nets

Effective hyper-parameter tuning is essential to guarantee the performance that neural networks have come to be known for. In this work, a principled approach to choosing the learning rate is proposed for shallow feedforward neural networks. We associate the learning rate with the gradient Lipschitz constant of the objective to be minimized while training. An upper bound on the mentioned constant is derived and a search algorithm, which always results in non-divergent traces, is proposed to exploit the derived bound. It is shown through simulations that the proposed search method significantly outperforms the existing tuning methods such as Tree Parzen Estimators (TPE). The proposed method is applied to three different existing applications: a) channel estimation in OFDM systems, b) prediction of the exchange currency rates and c) offset estimation in OFDM receivers, and it is shown to pick better learning rates than the existing methods using the same or lesser compute power.