OFDM Channel Estimation Research Articles

Learning-Based Block-Wise Planar Channel Estimation for Time-Varying MIMO OFDM

Learning-Based Block-Wise Planar Channel Estimation for Time-Varying MIMO OFDM

Low Complexity Deep Learning Augmented Wireless Channel Estimation for Pilot-Based OFDM on Zynq System on Chip

Low Complexity Deep Learning Augmented Wireless Channel Estimation for Pilot-Based OFDM on Zynq System on Chip

Experimental investigations on the applicability of LASSO based sparse recovery for compressively sensed OFDM channel estimation

Experimental investigations on the applicability of LASSO based sparse recovery for compressively sensed OFDM channel estimation

Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm‐based channel estimation in OFDM

SummaryDual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm‐based channel estimation in OFDM (DiWGAN‐Hyb AOA‐COA‐MIMO‐OFDM) is proposed in this manuscript. In OFDM, there is a non‐stationary channel physical appearance during channel estimation (CE). Therefore in this work, Hyb AOA‐COA is employed to enhance the DiWGAN weight parameters. The proposed DiWGAN‐Hyb AOA‐COA‐MIMO‐OFDM technique is executed in network simulator (NS2) tool. The proposed technique attains lower computational cost 99.67%, 92.34%, and 97.45%; lesser bit error rate 98.33%, 83.12%, and 88.96%; and lesser mean square error 93.15%, 79.90%, and 92.88% compared with existing methods, like MIMO‐OFDM system using deep neural network and MN‐based improved AMO model (DNN‐IAMO‐MIMO‐OFDM), MIMO‐OFDM systems using the deep learning and optimization (RBFNN‐PSO‐MIMO‐OFDM), and MIMO‐OFDM systems using hybrid neural network (HNN‐CSI‐MIMO‐OFDM) respectively.

Deep Residual Learning with Attention Mechanism for OFDM Channel Estimation

In this paper, we apply deep learning to the channel estimation problem of OFDM. Precisely speaking, to reduce the influence of noise on LS channel estimation, we design a channel estimation model based on attention mechanism and residual network. AttRNet-Conv and AttRNet-FC use attentional mechanism to change the weight of the feature to suppress noise. Compared with other channel estimation algorithms based on deep learning, AttRNet-Conv and AttRNet-FC avoid damaging channel information. The simulation results show that AttRNet-Conv and AttRNet-FC are superior to existing channel estimation algorithms based on deep learning in both high and low pilot frequency conditions. Even at low SNR, the estimation accuracy is higher than that of LMMSE. Experimental results show thatAttRNet-Conv and AttRNet-FC has strong robustness. In addition, we find that AttRNet-Conv is more suitable for channel estimation at low SNR in EPA, and the AttRNet-FC model is used in other cases.

A Spatial-Specific Neural Network-Based OFDM Channel Estimation Under Time-Varying Channels

A Spatial-Specific Neural Network-Based OFDM Channel Estimation Under Time-Varying Channels

Multi‐input fully CNN for joint pilot decontamination and symbol detection in 5G massive MIMO

AbstractThis paper presents a multi‐input deep learning‐based joint pilot decontamination and symbol detection (SD) technique for 5G massive multiple‐input multiple‐output (MAMIMO) systems. It consists of a fully convolutional neural network (FCNN) that finds the 5G channel coefficients using pre‐known sounding reference signals (SRSs) under pilot contamination and a symbol detector derived from projected gradient descent iterations. The study considers pilot contamination caused by inter‐cell interferences between SRSs during channel estimation (CE). The proposed scheme accepts entire 5G orthogonal frequency modulated (OFDM) data and least square estimates and produces the transmitted OFDM signal. Simulation experiments demonstrated that the proposed technique has better CE and SD performance with reduced trainable parameters. Moreover, it is faster due to the lowest elapsed time during end‐to‐end OFDM symbol detection. This paper proposes a joint pilot decontamination and signal detection for 5G MAMIMO systems. It achieves better detection performance with the lowest number of trainable parameters and memory requirements. It is applicable in 5G OFDM symbol detection and channel estimation under pilot contamination.

Phase-Domain Injected Training for Channel Estimation in Constant Envelope OFDM

Constant envelope orthogonal frequency division multiplexing (CE-OFDM) is a multi-carrier waveform with 0 dB peak-to-average power ratio (PAPR). This property enables the exploitation of multi-carrier waveforms with non-linear power amplifiers, avoiding the undesirable clipping effects. However, the existing channel estimation techniques designed for OFDM cannot be reused, since the use of a phase modulator makes CE-OFDM a non-linear waveform. Previous works assumed that the channel estimation process relies on the transmission of preambles, and the data symbols are equalized using a frequency domain equalizer (FDE). To avoid the overhead induced by preambles, a phase-domain injected training (PIT) is proposed, where the pilot sequence is embedded in the phase dimension of the data symbols. This novel approach does not waste time and/or frequency resources as in preamble-based schemes. Moreover, it does not require additional power for the training. The received symbols are averaged with a dual procedure, and owing to the particular structure of CE-OFDM, the channel estimates are recovered. Also, the analytical expression of the channel estimation mean squared error (MSE) is derived. Finally, several numerical results illustrate the performance of the proposal, showing that the MSE, bit error rate (BER) and achievable rate are improved, as compared to the existing works.

Channel estimation for underwater acoustic OFDM communications via temporal sparse Bayesian learning

Channel estimation is a challenging task for underwater acoustic (UWA) communications. In this paper, we investigate the channel estimation for OFDM based UWA communications by utilizing the channel characteristics as auxiliary information. Firstly, the channel estimation problem is formulated as a sparse signal recovery problem using block-wise single measurement vector (SMV) model and multiple measurement vector (MMV) model. Then we develop a novel channel estimators under the temporal sparse Bayesian learning (TSBL) framework by jointly exploiting the temporal correlation and sparsity based on the block-wise SMV model. For the MMV model, a variational Bayesian inference based TSBL (VBI-TSBL) method is introduced that considers the inter-block correlations and exploits the row-sparsity by a linear temporal correlation model. The two Bayesian inference methods realize the posterior and parameter estimation of channel via expectation maximization (EM) and variational EM algorithm, respectively. Simulation and sea-trial experiments show the proposed channel estimators outperform the benchmarks in terms of bit error rate, mean square error of channel estimation and the robustness.

Deep Learning for OFDM Channel Estimation in Impulsive Noise Environments

Deep Learning for OFDM Channel Estimation in Impulsive Noise Environments

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

A Novel Adaptive Pilot Placement Algorithm for Channel Estimation in OFDM Based Systems

Çok taşıyıcılı haberleşme sistemlerinden biri olan dikgen frekans bölmeli çoğullama (orthogonal frequency division multiplexing, OFDM) sistemi, bant genişliğini etkin kullanarak yüksek hızlı veri haberleşmesine olanak sağlaması ve yankı gecikmelerine karşı dayanıklılığı nedeniyle günümüzde yaygın olarak kullanılmaktadır. OFDM sisteminde gezgin iletim kanalının bozucu etkileri alıcıda kanal kestirimi yapılarak azaltılabilir. Bu çalışmada OFDM sistemi için kanalın yapısına göre uyarlamalı olarak değişebilen yeni bir pilot yerleşimli kanal kestirim algoritması önerilmiştir. Önerilen algoritma kanalın derin sönümlenmelerinin olduğu bölgelere daha sık pilot yerleştirilmesi temeline dayanmaktadır. Benzetim sonuçlarından, önerilen uyarlamalı kanal kestirim algoritmasının klasik kanal kestirim algoritmalarına göre daha iyi sonuç verdiği ve bit hata oranı (BHO) başarımında 11.6 dB’e varan bir iyileşme sağladığı görülmüştür. Ayrıca literatürdeki çalışmalardan farklı olarak bu çalışmada frekans düzleminde pilot sıklığının artırılacağı bölgelerin eşik seviyesini belirlemek için yeni bir matematiksel ifade önerilmiştir.

Sparse channel estimation for OFDM based on IOMP algorithm under unknown sparsity

Sparse channel estimation for OFDM based on IOMP algorithm under unknown sparsity

An improved channel estimation for underwater acoustic OFDM and COFDM systems

An improved channel estimation for underwater acoustic OFDM and COFDM systems

An improved channel estimation for underwater acoustic OFDM and COFDM systems

Recently, there has been a tremendous increase in research and development of underwater communication systems. Orthogonal frequency division multiplexing has been widely employed for underwater acoustic communication because of its numerous advantages. Coding as well as interleaving techniques can be used along with orthogonal frequency division multiplexing for improving the error correction capability of the receiver. For demodulating the signal with fairly acceptable accuracies, the channel parameters need to be estimated at the receiver. Channel estimation can be performed by inserting pilots into the sub-carriers of an OFDM symbol. Underwater channels exhibit sparse channel impulse response and hence, the channel can be estimated using the compressive sensing techniques for properly reconstructing the received signals. Dictionary-based sparse channel estimation is presented in this paper which outperforms the channel estimation by LS and MMSE estimations. The results have been compared for various environmental conditions as well as for various numbers of pilots. It has been observed that the number of pilots is to be chosen as a trade-off between channel estimation accuracy and bandwidth efficiency.

Constant response window channel estimation method and LUT-based digital predistortion applied to OFDM VLC links

In this work, we investigate a LUT-based digital predistortion technique applied to a Visible Light Communication system. We study the performance of the predistortion technique for OFDM signals with up to 2048 subcarriers, 16-QAM modulation, and three levels of DC bias considering the voltage–current LED response of a commercially available device. In addition, we propose a new and simpler pilot-assisted approach for the OFDM channel estimation, named Constant Response Window, the performance being tested with up to 64-QAM modulation and 2048 IFFT size OFDM signals. BER and MSE results show that the combination of the LUT-based digital predistortion technique with the new estimation method presents an improved performance over the Least Squares and the Minimum Mean Square Error channel estimation techniques in the considered scenario.

Frame splitting and data-aided decision direct channel estimation for OFDM in fast varying fading channels

This article proposes a novel wireless channel estimation method suited for fast fading environment. Generally, forward error correction (FEC) requires long block code length to raise error correction capability while it deteriorates the channel tracking accuracy in pilot-aided estimation. To meet these trade-off demands, we propose a frame splitting and decision directed channel estimation (DDCE). It splits information data in half and estimate channel state information (CSI) of the former part. That of latter is simply extrapolated using the former CSI. Computer simulation result shows that the proposed method can improve bit error rate performance even at fast fading environment.

Joint channel estimation and data detection for OFDM based cooperative system

Orthogonal frequency division multiplexing based cooperative system using Alamouti space–time block coding at relay node represents an alternative solution to achieve better connectivity and significant enhancement to the data rate in wireless fading channels. But, these advantages cannot be achieved without an efficient estimation of the channels which becomes more challenging for cooperative communications. This paper addresses the joint channel estimation and data detection for cooperative communication systems. Indeed, equispaced pilot symbols are used by maximum likelihood (ML) algorithm to derive channels estimator, and then equalizers are calculated and applied to improve receiver data detection. The main contribution of our work is the development of the ML estimator, the corresponding Cramer–Rao lower bound, mean square error, signal to interference plus noise ratio, outage probability, bit error probability and the use of simulations to demonstrate the superior performances of the proposed methods.

Compressed Sensing Channel Estimation for OFDM With Non-Gaussian Multipath Gains

This paper analyzes the impact of non-Gaussian multipath component (MPC) amplitude distributions on the performance of Compressed Sensing (CS) channel estimators for OFDM systems. The number of dominant MPCs that any CS algorithm needs to estimate in order to accurately represent the channel is characterized. This number relates to a Compressibility Index (CI) of the channel that depends on the fourth moment of the MPC amplitude distribution. A connection between the Mean Squared Error (MSE) of any CS estimation algorithm and the MPC amplitude distribution fourth moment is revealed that shows a smaller number of MPCs is needed to well-estimate channels when these components have large fourth moment amplitude gains. The analytical results are validated via simulations for channels with lognormal MPCs such as the NYU mmWave channel model. These simulations show that when the MPC amplitude distribution has a high fourth moment, the well known CS algorithm of Orthogonal Matching Pursuit performs almost identically to the Basis Pursuit De-Noising algorithm with a much lower computational cost.

머신 러닝을 이용한 고속 시변 OFDM 채널 예측의 성능 개선

CE-BEM을 이용하는 저복잡도 고속 시변 OFDM채널 예측은 성능 개선에 있어 시스템 파라미터 값을 결정하는 문제가 매우 복잡한 양상을 띠고 있다. 본 논문에서는 머신 러닝에 의한 CE-BEM 문제점을 개선하는 방안을 제시한다. The determination of system parameter values is very complicated for improving the performance of fast time-varying OFDM channel estimation with low complexity CE-BEM. In this letter, we propose a method to mitigate the problem of the CE-BEM by machine learning.