Channel Estimation For Orthogonal Frequency Division Multiplexing Systems Research Articles

Evaluating Performance: A Study of Encrypted vs. Unencoded Signals in SISO-OFDM with LS and MMSE Estimations

The continuous progress in digital communication has played a crucial role in meeting the increasing need for faster data rates. Orthogonal Frequency Division Multiplexing (OFDM), a pivotal methodology in this progression, attains improved data rates through the efficient utilisation of densely packed carriers within a specified channel bandwidth. This article focuses on the investigation of channel estimation in OFDM systems. the study of search lies in its examination of the performance consequences associated with the incorporation or lack thereof of a convolutional encoder in OFDM systems and looks at how well two well-known channel estimation algorithms, Least Square (LS) and Minimum Mean Square Error (MMSE), work in 4-Quadrature amplitude modulation (4-QAM) OFDM systems with and without a convolutional encoder with a comprehensive evaluation of the efficacy of the OFDM system across various channel conditions. It uses MATLAB implementations as its main tool. The findings of the study indicate that the MMSE algorithm, despite its higher complexity, exhibits superior performance in comparison to the LS algorithm when combined with a convolutional encoder. The gain in terms of bit error rate (BER) improvement approximately 12 dB. This represents the logarithmic scale improvement in BER from BER1(uncoded) to BER2(coded) at the same the energy per bit to noise power spectral density ratio (Eb/N0) of 40 dB.

Attention Guided Multi-Task Network for Joint CFO and Channel Estimation in OFDM Systems

The existence of high carrier frequency offset (CFO) and fading channels degrades the performance of communication systems in high mobility environments significantly. To address these challenges, in this paper, we propose an attention guided multi-task network for joint CFO and channel estimation in orthogonal frequency division multiplexing (OFDM) systems. Specifically, considering the correlation between CFO estimation and channel estimation, we construct a multi-task neural network framework which consists of a shared branch (SB), a CFO estimation branch (CFOEB) and a channel estimation branch (CEB). Among them, the SB extracts common features of two tasks, and the CFOEB and CEB make more detailed CFO and channel estimation, respectively. To further suppress the influence of CFO estimation error on channel estimation, we introduce an attention guided module (AGM) which completes the re-calibration of the channel dimension on the basis of different CFO characteristics. We train the whole network by an end-to-end paradigm and construct a suitable multi-task loss function to balance the losses between different tasks. Simulation results demonstrate that the proposed attention guided multi-task learning based joint CFO and channel estimation scheme outperforms the conventional methods and has greater robustness under various conditions.

Bayesian Learning-Assisted Joint Frequency Tracking and Channel Estimation for OFDM Systems

Orthogonal frequency division multiplexing (OFDM) is very sensitive to the carrier frequency offset (CFO). The CFO estimation precision heavily makes impacts on the OFDM performance. In this paper, a new Bayesian learning-assisted joint CFO tracking and channel impulse response estimation is proposed. The proposed algorithm is modified from a Bayesian learning-assisted estimation (BLAE) algorithm in the literature. The BLAE is expectation-maximization (EM)-based and displays the estimator mean square error (MSE) lower than the Cramer-Rao bound (CRB) when the CFO value is near zero. However, its MSE value may increase quickly as the CFO value goes away from zero. Hence, the CFO estimator of the BLAE is replaced to solve the problem.

Deep Learning-Based Joint Pilot Design and Channel Estimation for OFDM Systems

We propose a non-uniform joint pilot design and channel estimation (JPDCE) scheme in closed-loop orthogonal frequency-division multiplexing (OFDM) systems. Specifically, the encoder of the proposed JPDCE scheme is composed of two sub-networks which are used for pilot location assignment and pilot power allocation, respectively. To ensure accurate learning of the pilots, the OFDM block consisting of intelligently customized layers is designed to constrain the output of the encoder. Furthermore, the decoder is used to learn the channel state information (CSI) based on the output of OFDM layers by minimizing the mean square error (MSE) of the channel estimation. The numerical results indicate that the JPDCE scheme considerably outperforms the traditional methods as well as two state-of-the-art deep learning (DL)-based ones, demonstrating its excellent ability to learn the statistical characteristics of the wireless channel. In addition, we demonstrate that the JPDCE scheme shows excellent robustness against various channel distortion and interference: when (i) there are limited pilots, (ii) the cyclic prefix (CP) is removed, and/or (iii) clipping noise is introduced.

A Pilot-Based Integration Method of Ranging and LS Channel Estimation for OFDM Systems

In the design of unmanned aerial vehicle (UAV) communication systems, orthogonal frequency division multiplexing (OFDM) is a commonly used communication technology. An efficient channel estimation and equalization algorithm is required to recover the amplitude, phase, and frequency of the signal in OFDM systems. At present, the more precise channel estimation method is based on the pilot. However, its spectrum utilization is relatively low. Therefore, this paper presents the design of a new pilot based on the LS channel estimation, which extends the role of the traditional pilot and improves the utilization of the spectrum. In addition to the channel estimation and equalization, the new pilot can also be utilized for ranging. Simulation results show that the proposed scheme can achieve both channel estimation and communication ranging functions by using the new pilot, and it outperforms the conventional method in channel estimation performance. The proposed method can complete ranging when the bit error rate (BER) is above 0 dB. Moreover, compared with the traditional channel estimation, it reduces the requirement for SNR by about 1 dB under the same BER.

Deep Learning Based Channel Estimation for OFDM Systems With Doubly Selective Channel

In wireless communication orthogonal frequency division multiplexing (OFDM) systems with high mobility, channel estimation becomes challenging due to the double selectivity of channels. To solve this problem, a deep learning based method which fully considers the channel features and noise effects is proposed in this letter. In the pilot-based channel estimation scheme, a compression-and-reconstruction channel estimation network (CRCENet) is designed to improve the channel estimation performance, which fuses not only the low-level features of the rapid changes between adjacent subcarriers in the short term and the high-level features of the overall channel changes under large time spans in the backbone, but also the features using the attention mechanism in the up-sampling branches. Meanwhile, considering that the samples with different signal-to-noise ratios (SNRs) have different credibility, loss weights based on credibility are assigned to data with different SNRs during network training, to further improve the estimation performance of the network. The experimental results revealed that CRCENet performed better than the traditional least squares estimation algorithm and state-of-the-art learning networks.

Transfer learning-based channel estimation in orthogonal frequency division multiplexing systems using data-nulling superimposed pilots.

Data-nulling superimposed pilot (DNSP) effectively alleviates the superimposed interference of superimposed training (ST)-based channel estimation (CE) in orthogonal frequency division multiplexing (OFDM) systems, while facing the challenges of the estimation accuracy and computational complexity. By developing the promising solutions of deep learning (DL) in the physical layer of wireless communication, we fuse the DNSP and DL to tackle these challenges in this paper. Nevertheless, due to the changes of wireless scenarios, the model mismatch of DL leads to the performance degradation of CE, and thus faces the issue of network retraining. To address this issue, a lightweight transfer learning (TL) network is further proposed for the DL-based DNSP scheme, and thus structures a TL-based CE in OFDM systems. Specifically, based on the linear receiver, the least squares estimation is first employed to extract the initial features of CE. With the extracted features, we develop a convolutional neural network (CNN) to fuse the solutions of DL-based CE and the CE of DNSP. Finally, a lightweight TL network is constructed to address the model mismatch. To this end, a novel CE network for the DNSP scheme in OFDM systems is structured, which improves its estimation accuracy and alleviates the model mismatch. The experimental results show that in all signal-to-noise-ratio (SNR) regions, the proposed method achieves lower normalized mean squared error (NMSE) than the existing DNSP schemes with minimum mean square error (MMSE)-based CE. For example, when the SNR is 0 decibel (dB), the proposed scheme achieves similar NMSE as that of the MMSE-based CE scheme at 20 dB, thereby significantly improving the estimation accuracy of CE. In addition, relative to the existing schemes, the improvement of the proposed scheme presents its robustness against the impacts of parameter variations.

Fast Fading Channel Estimation for OFDM Systems with Complexity Reduction

Channel estimation plays a significant role in the Inter-carrier interference (ISI) mitigation and symbol detection in Orthogonal frequency division multiplexing (OFDM) systems. As the speed of mobile terminal increases, the channel changes rapidly, causing fast fading which will degrade the system performance. In this paper, a low complexity fast fading channel estimation method suited for scattered pilot OFDM systems is proposed. This method is characterized by estimating channel matrix directly in the frequency domain, which can significantly reduce the channel estimation complexity with good system performance. Simulation results verify its superiority over the existing estimation methods.

A Low Complexity Learning-Based Channel Estimation for OFDM Systems With Online Training

In this paper, we devise a highly efficient machine learning-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems, in which the training of the estimator is performed online. A simple learning module is employed for the proposed learning-based estimator. The training process is thus much faster and the required training data is reduced significantly. Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission. The feasibility of this novel construction approach is verified by theoretical analysis and simulations. Based on this construction approach, two alternative training data generation schemes are proposed. One scheme transmits additional block pilot symbols to create training data, while the other scheme adopts a decision-directed method and does not require extra pilot overhead. Simulation results show the robustness of the proposed channel estimation method. Furthermore, the proposed method shows better adaptation to practical imperfections compared with the conventional minimum mean-square error (MMSE) channel estimation. It outperforms the existing machine learning-based channel estimation techniques under varying channel conditions.

LMMSE channel estimation for OFDM systems with channel correlation function selection

In the linear minimum mean square error (LMMSE) estimation for orthogonal frequency division multiplexing (OFDM) systems, the problem about the determination of the algorithm's parameters, especially those related with channel frequency response (CFR) correlation, has not been readily solved yet. Although many approaches have been proposed to determine the statistic parameters, it is hard to choose the best one within those approaches in the design phase, since every approach has its own most suitable application conditions and the real channel condition is unpredictable. In this paper, we propose an enhance LMMSE estimation capable of selecting parameters by itself. To this end, sampled noise MSE is first proposed to evaluate the practical performance of interpolation. Based on this evaluation index, a novel parameter comparison scheme is proposed to determine the parameters which can endow LMMSE estimation best performance within a parameter set. After that, the structure of the enhanced LMMSE is illustrated, and it is applied in OFDM systems. Besides, the issues about theoretical analysis on accuracy of the parameter comparison scheme, the parameter set design and algorithm complexity are explained in detail. At last, our analyses and performance of the proposed estimation method are demonstrated by simulation experiments.

A General Framework for Estimating Channel of Orthogonal Frequency Division Multiplexing Systems by Utilizing Sparse Representation

Channel estimation is a crucial task for orthogonal frequency division multiplexing (OFDM) modulation-based systems since this estimation is used for compensating impacts of a wireless channel. Recently, sparse representation (SR) is proposed for this task as wireless channels are considered as a sparse signal. However, SR considers sparse as the main feature and omit other features of the channel while estimating the channel. In this paper, we propose a general framework for utilizing other features of the channel in sparse channel estimation for OFDM systems, while these features are omitted in conventional sparse methods. In this regard, by utilizing maximum a posterior (MAP) estimation and defining new parameters, these features are conveyed into sparse channel estimation process to improve channel estimation. The simulation results indicate that our proposed framework not only improves the estimated parameter, but also reduces the number of resources such as the number of estimation pilots or transmitted power.

A novel delay dictionary design for compressive sensing-based time varying channel estimation in OFDM systems

Compressive sensing (CS) is a new attractive technique adopted for Linear Time Varying channel estimation. orthogonal frequency division multiplexing (OFDM) was proposed to be used in 4G and 5G which supports high data rate requirements. Different pilot aided channel estimation techniques were proposed to better track the channel conditions, which consumes bandwidth, thus, considerable data rate reduced. In order to estimate the channel with minimum number of pilots, compressive sensing CS was proposed to efficiently estimate the channel variations. In this paper, a novel delay dictionary-based CS was designed and simulated to estimate the linear time varying (LTV) channel. The proposed dictionary shows the suitability of estimating the channel impulse response (CIR) with low to moderate Doppler frequency shifts with acceptable bit error rate (BER) performance.

Joint CFO and Channel Estimation in OFDM Systems Using Sparse Bayesian Learning

In this article, we study the problem of joint carrier frequency offset (CFO) and sparse channel estimation in orthogonal frequency-division multiplexing (OFDM) communication systems, from the perspective of sparse Bayesian learning (SBL) framework. We first consider the problem in the compressed sensing (CS) context and reformulate it as the problem of recovering a sparse vector from the received signal when CFO and noise variance are unknown parameters that should also be estimated. A novel SBL-based scheme has then been designed to iteratively estimate the CFO, channel impulse response (CIR), and variance of the noise jointly, using the expectation-maximization (EM) algorithm. Both theoretical analysis and simulation results confirm that the proposed scheme outperforms the existing methods while requiring a lower computational cost.

Pilot Pattern Design for Deep Learning-Based Channel Estimation in OFDM Systems

In this letter, we present a downlink pilot design scheme for Deep Learning (DL) based channel estimation (ChannelNet) in orthogonal frequency-division multiplexing (OFDM) systems. Specifically, in the proposed scheme, a feature selection method named Concrete Autoencoder (ConcreteAE) is used to find the most informative locations for pilot transmission. This autoencoder consists of a concrete layer as the encoder and a multilayer perceptron (MLP) as the decoder. During the training, the concrete layer selects the most informative pilot locations, and the decoder reconstructs an approximate estimation of the channel. Eventually, the ChannelNet is trained on the output of the ConcreteAE aiming to reconstruct the ideal channel response. The estimation error results show that this approach outperforms the previously presented ChannelNet with a uniformly distributed pilot pattern, and its performance is comparable to the minimum mean square error (MMSE).

Pilot Based Channel Estimation and Synchronization in OFDM System

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.

Performance Enhancement of OFDM System using Hybrid Channel Estimation with Pseudo Pilot

For high spectral efficiency and robustness against intersymbol interference (ISI), orthogonal frequency division multiplexing (OFDM) is the best suited technology for many applications such as digital audio broadcasting (DAB), wireless networks, digital video broadcasting (DVB) and LAN. For efficient recovery of the original transmitted signal we require the coherent demodulator as the frequency selective fading channels affects the transmitted signal in OFDM. At the receiver end for coherent demodulation we require channel state information (CSI), which is done by channel estimation. To find the channel characteristics which varies with time and frequency, a reference signal is transmitted with payload signal know as pilot or training sequence and technique or process is known as channel estimation. The use of pilots in the OFDM system leads to pilots overhead. So we propose a new technique which will reduces the transmission delay, reduce pilot overhead, increases transmission rate and improves the BER performance without any computational complexity, which is implemented using the Hybrid algorithm for channel based OFDM system. In this proposed technique, for the channel estimation in OFDM system over AWGN fading channel pseudo-pilots are used. At the transmitter end, pseudo-pilots are employed to breed bank of pseudo random symbol (PRS). In this OFDM system with pseudo-pilot the channel estimation is done by the Hybrid (LS + RLS) estimation approach to recover the original transmitted information. The performance of the proposed techniques and the weighted scheme are compared and verified using computerized simulation carried out using Matrix Laboratory (MATLAB) software.

Wireless channel estimation in OFDM systems based on collaborative filtering techniques

Abstract In this paper, a new channel estimation algorithm in Orthogonal Frequency Division Multiplexing (OFDM) systems is proposed. The proposed algorithm is suitable for cases with low density of pilot sub-carriers, where standard interpolation methods (linear, second order and cubic spline interpolation) are inaccurate. The algorithm improves the interpolation methods by employing memory based collaborative filtering (CF) techniques which are less sensitive to the number and location of the pilot subcarriers. CF algorithms are usually used in the context of recommender systems (e-commerce) for predictions of the unknown user-item ratings based on known values of similar users. The advantage of CF is the ability to e ciently produce quality predictions with highly sparse data. Computer simulations are used to verify the proposed channel estimation algorithm and demonstrate that the proposed algorithm improves predictive accuracy metrics, such as Root Mean Squared Error (RMSE), compared to usual estimation methods.

A Comprehensive Review on Channel Estimation in OFDM System

In wireless communication, orthogonal frequency division multiplexing (OFDM) plays a major role because of its high transmission rate. Channel estimation and tracking have many different techniques available in OFDM systems. Among them, the most important techniques are least square (LS) and minimum mean square error (MMSE). In least square channel estimation method, the process is simple but the major drawback is it has very high mean square error. Whereas, the performance of MMSE is superior to LS in low SNR, its main problem is it has high computational complexity. If the error is reduced to a very low value, then an exact signal will be received. In this paper an extensive review on different channel estimation methods used in MIMO-OFDM like pilot based, least square (LS) and minimum mean square error method (MMSE) and least minimum mean square error (LMMSE) methods and also other channel estimation methods used in MIMO-OFDM are discussed.

Efficient Compressed Sensing Based Non-Sample Spaced Sparse Channel Estimation in OFDM System

This paper aims to develop an efficient compressed sensing (CS) based channel estimation method for non-sample spaced sparse channels in orthogonal frequency division multiplexing (OFDM) systems, which can effectively balance the channel estimation performance, spectral efficiency and computational complexity. To realize this goal, a novel delay tracking and residual norm minimization (DTRNM) method is proposed. In this method, the idea of reference delay grids (RDG) inspired by the delay tracking (DT) method proposed in [1] is formulated, which is proven to be much more efficient than current algorithms in estimating non-sample spaced sparse channels. Both theoretical derivation and simulation results show the effectiveness of the proposed method.

Enhanced channel estimation in OFDM systems with neural network technologies

Orthogonal frequency division multiplexing (OFDM) provides an effective and low complexity means of eliminating inter-symbol interference for transmission over frequency selective fading channels. In OFDM systems, channel state information (CSI) is required for the OFDM receiver to perform coherent detection or diversity combining, if multiple transmit and receive antennas are deployed. In practice, CSI can be reliably estimated at the receiver by transmitting pilots along with data symbols. In this paper, we investigate and compare various efficient pilot-based channel estimation schemes by neural network technologies for OFDM systems. We present further the application of functional link neural fuzzy network (FLNFN) for channel estimation in the investigated OFDM systems. We compared bit error rates of the proposed neural network with that of the other neural network technologies, the least square (LS) algorithm, and the minimum mean square error (MMSE) algorithm. Our results demonstrate that the proposed FLNFN algorithm can enhance the performance of channel estimation in existing OFDM channel environments.