Estimation In OFDM Systems Research Articles

Fundamental Limits and Optimization of Multiband Delay Estimation in OFDM Systems

Fundamental Limits and Optimization of Multiband Delay Estimation in OFDM Systems

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

Phase noise estimation in OFDM systems

Phase noise 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

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

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

Machine learning‐based methods for joint detection‐channel estimation in OFDM systems

In this work, two machine learning (ML)‐based structures for joint detection‐channel estimation in OFDM systems are proposed and extensively characterized. Both ML architectures, namely Deep Neural Network (DNN) and Extreme Learning Machine (ELM) are developed for provide improved data detection performance and compared with the conventional matched filter (MF) detector equipped with the minimum mean square error (MMSE) and least square (LS) channel estimators. The bit‐error‐rate (BER) performance vs computational complexity trade‐off is analyzed, demonstrating the superiority of the proposed DNN‐OFDM and ELM‐OFDM detectors methodologies.

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.

Carrier Frequency Offset Estimation in 5G NR: Introducing Gradient Boosting Machines

The Beyond fifth Generation (B5G) communication systems imposed several challenges on radio designers. For example, a machine is required to set up a call at a low Signal-to-Noise Ratio (SNR), as low as -10 dB, in the extended coverage mode. Moreover, only one receive antenna will be available, and virtually no frequency diversity. Such requirements present major challenges to maintaining timing and frequency synchronization. Carrier Frequency Offset (CFO) estimation is at the heart of these challenges. Different ways have been proposed for CFO estimation such as maximum likelihood based on a cyclic prefix. Nevertheless, these methods remain limited in various ways. At the same time, Machine Learning (ML) techniques showed outstanding performance in several wireless communication problems. In this work, we propose an ML-based approach for CFO estimation in OFDM systems. Specifically, we propose a Gradient-Boosting Machine (GBM)-based solution to predict the CFO given the received Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS). Furthermore, we make our dataset available for public access to encourage other researchers to pursue this promising direction. We compare our results with different baseline models (i.e., artificial neural networks and support vector machines). The experimental results show that our model outperforms other baseline models due to its ensemble nature which enables ensemble models to obtain a better generalization behavior.

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.

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.

Variable leaky steepest descent algorithm for autoregressive fading estimation in OFDM systems

This paper presents an adaptive algorithm for unbiased estimation of autoregressive Rayleigh flat fading parameters in OFDM systems. To this end, a variable leaky steepest descent algorithm is proposed. The authors use a variable leak parameter in the algorithm to remove the noise induced bias from the autoregressive estimates. Theoretical performance analyses of the proposed method are conducted. Moreover, computer simulations are presented to evaluate the performance of the proposed algorithm and to compare it with other existing methods.

Joint Channel and AoA Estimation in OFDM Systems: One Channel Tap With Multiple AoAs Problem

Recently, a joint channel and angle-of-arrival (AoA) estimation scheme for pilot-assisted multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with digital arrays has been proposed; in that scheme, compressive sensing-based time-domain channel estimation is applied. In the scheme, it is assumed that each channel tap has only one AoA. However, in reality, one channel tap may contain responses from multiple paths with different AoAs. In this letter, we propose an improved approach to solve the aforementioned problem. The underlying concept is to transmit multiple OFDM symbols with different beamforming vectors, rendering the time-invariant channel equivalently time-variant. Subsequently, the correlation matrix, imbedded with AoA information, can be estimated. Finally, subspace-based algorithms, such as multiple signal classification (MUSIC), can be used for AoA estimation. The advantage of the proposed method is that the number of detectable AoAs is much larger than that in conventional MUSIC. The performance of the proposed method was also theoretically analyzed. Simulation results demonstrated that with a small number of OFDM symbols, both channel gains and AoAs could be satisfactorily estimated and the theoretical analysis results were accurate.

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

Two-Stage Channel Frequency Response Estimation in OFDM Systems

This paper proposes two-stage channel frequency response estimation algorithm in communication systems with OFDM technology. Algorithm is based on Kalman filter. Pilots from current and previous OFDM symbols are used for channel estimation. At the first stage data is processed in time and frequency directions. Pilots from the current OFDM symbol are filtered and at the position, where the pilots from the previous OFDM symbols should be placed, predictions are made. Predictions are based on the pilots and channel correlation characteristics. The data processing carried out on both sides relative to the array of processed data in frequency direction and on one side at processing in time direction. The results of processing are optimally combined at the second stage. The autoregressive process was used as a channel model. The analysis of the developed algorithm carried out on a model example by statistical modeling. Modeling showed that application of designed algorithm allows reducing the standard deviation of the estimation error of channel frequency response. The efficiency of designed algorithm studied using Rayleigh channel with Doppler spectrum described by Jakes model. The autocorrelation characteristics of the channel were considered as known. Modeling showed a decrease in the probability of a bit error during reception using the proposed algorithm. It is also shown that an increase in the order of the autoregressive model reduces the error in estimating the frequency response of the communication channel.