Parametric Channel Estimation for OFDM Systems in Time-Varying Environment Based on Subspace Projecting and Tracking

The Channel estimation in Vehicle-to-Vehicle (V2V) communications is critical due to the time-varying characteristics of the V2V channel. Hence, the authors propose a hybrid approach towards channel estimation by combining Pilot Symbol Aided Channel Estimation (PSACE), Improved Crow Search Algorithm (ICSA), and Decision Directed Channel Estimation (DDCE) by employing Weight Regularized Convolutional Neural Network (WCNN). Hybrid channel estimation by combining the properties of PSACE and DDCE. PSACE suffers from pilot overhead issue due to the demand of huge number of pilots for channel estimation. On the other hand, DDCE suffers from error propagation in fast fading environments. The proposed system employs an ICSA and WCNN to overcome the issues faced by channel estimation methods. Further, the proposed strategy replaces the analytical modeling of the channel with the help of the neural network. Optimized pilots obtained from ICSA along with data symbols are fed to WCNN to estimate the channel in a non-stationary environment. Finally, the performance of the proposed method is evaluated in terms of Bit Error Rate, Mean Square Error, and Packet Error Rate and the simulations are carried out under various V2V scenarios for testing its sturdiness.

Channel estimation can be classified into pilot‐assisted channel estimation, blind channel estimation, and decision DDCE. This chapter focuses on pilot‐assisted channel estimation in OFDM systems. There are two main design issues for channel estimation in OFDM systems. The first one is pilot design and the second one is channel estimator design with both low complexity and accuracy. An interpolation technique should be performed to obtain a channel estimation value at each data subcarrier. Channel estimation for MIMO‐OFDM systems is more complicated than single antenna systems but the basic principle is similar to the single antenna systems. The purpose of equalizers is to find the inverse function of the channel response and eliminate an Inter‐Symbol Interference (ISI) caused by the dispersive nature of the channel. The channel estimator and the equalizer are designed according to the system requirements such as pilot assignment, antenna array, system complexity, latency, and power.

In order to solve the problem that noise and signal interference have an effect on the channel estimation of receiver, a channel estimation method based on integrated neural network has been proposed in this paper. Firstly, improved neural network would be used to extract nonlinear relationship between pilot signal with noise and original pilot signal. Secondly, the integrated neural network model would be obtained by selective integrating according to diversity factor, then channel estimation results could be obtained by model. Compared with the traditional methods, not only this method could reduce the computational complexity and obtain more accurate channel estimation results to reduce the bit error rate of communication system, but also reduce the cost of pilot and increase the bandwidth utilization rate. The simulation results show that the mean square error of channel estimation and bit error rate of communication are reduced by about 5% and 3% respectively, when compared with the traditional channel estimation method under the same signal-to-noise ratio.

In this paper, we propose a robust channel estimation method in OFDM systems over fast fading channels. In consideration of the effect of null subcarriers, the proposed channel estimation is first performed in the frequency domain. By making efficient use of channel correlation and the property of ICI to reduce the number of needed parameters for channel estimation, robust channel estimation for ICI is accomplished in parallel, pilot cluster by pilot cluster. It is then transformed into the time domain to obtain a gain in noise reduction and to reduce interpolation error as well. Simulation results show that the proposed channel estimation has good performance compared to previous works.

This paper presents SLRTNet, a deep learning model designed for channel estimation in OFDM systems. SLRTNet employs a super-resolution network in combination with a transformer architecture, which features an improved encoder-decoder structure. To extract essential input information, a transformer encoder with a multi-head attention mechanism and a Feedforward Net that utilizes LRR Blocks are used. A multi-level residual network is implemented as a transformer decoder to improve the model's generalization ability. The simulation results show that SLRTNet outperforms other deep learning-based neural network schemes. Compared with the conventional LS algorithm, SLRTNet exhibits a performance gain of 0.7 to 2 dB in the low signal-to-noise ratio (SNR) regime and 5.5 to 8 dB in the high SNR regime. This indicates that SLRTNet has good performance in channel estimation for OFDM systems and improves the accuracy of channel estimation effectively.

The minimum mean-square error (MMSE) criterion is often used for channel estimation in wireless OFDM systems. The MMSE channel estimators perform better than the least-square (LS) criterion-based techniques but their computational complexity is one of the main drawbacks for practical implementation, especially when the number of subcarriers is large. To address this issue while maintaining the performance advantage, we present a new simplified MMSE channel estimator for OFDM systems in multipath fading channels. The proposed method is primarily based on the rank-reduction of the channel correlation matrix in the frequency domain. Simulation results show that the proposed method achieves almost the same performance as the full-rank MMSE method, while significantly reducing the computational complexity.

In OFDM systems, channel estimation is a crucial consideration for improving system performance. The channel fading effects are eliminated by the equalization of received signal, and the equalizer coefficients are determined according to the results of channel estimation. In this paper, the channel estimation of OFDM systems in time domain for fast fading channel is investigated and developed. The time-domain channel estimation scheme is achieved by inserting pseudo-noise (PN) sequences in the prefix of each OFDM symbol, and therefore the channel impulse response can be estimated. These proposed algorithms include both least-squares and maximum-likelihood estimation techniques. The proposed techniques can perform better than the conventional channel estimation with correlators. Some comparative simulations are given to illustrate the superior performance of the proposed channel estimation techniques.

In this paper, we investigate the tracking of time-varying channels in OFDM systems using both preamble and pilot symbols. A regularized least-squares method will be used to incorporate the initial channel estimate from the preamble to improve the performance of pilot-assisted channel estimation. Instead of using a fixed regularization parameter, the proposed method adaptively determines the regularization parameter according to mean-squared error of the latest channel estimate and that of the standard LS estimate. Simulation results demonstrate the proposed method can outperform the standard LS method with lesser pilots, thus achieving the reduction of pilot overhead especially at lower SNR environments.

The acquisition of accurate channel estimation is important for Orthogonal Frequency-Division Multiplexing (OFDM) system to reliably recover the transmitted data symbols. Decision directed channel estimation (DDCE) has been utilized to improve the accuracy of conventional training-based channel estimation. However, this method can only provide limited improvement when the data decision feedback comprises a large portion of decision errors at low signal-to-noise (SNR) range. A new Iterative DDCE (IDDCE) scheme with reliable data feedback selection is proposed in this paper. The unreliable data decisions are eliminated on the subcarriers where noise components are enhanced due to the impact of frequency selective channel. An explicit expression of the variance of channel estimation error is derived, based on which the optimal selection criteria are also derived to minimize the variance. Simulation results show that the accuracy of channel estimation is significantly improved with the proposed algorithm.

OFDM is one of the most promising techniques to achieve high data rates over mobile wireless channels. Transmitter diversity and channel estimation play important roles in improving the performance of OFDM systems. We develop a special interleaver, called OOC (only-one-cycle) interleaver, at the transmitter to implement frequency domain diversity. At the receiver, a combination of MMSE channel estimation and linear interpolation is proposed to provide the channel parameters. Iterative dynamic detection and the combination estimation are employed to enhance system performance. Computer simulation results are shown to prove the effectiveness.

To solve the problem of low accuracy of channel estimation in OFDM systems in the scenario of high-speed multipath channels, it is proposed in the paper that a noise reduction scheme of channel estimation is based on DFT (Discrete Fourier Transform) algorithm. By analyzing the algorithm principles, the mathematical model of the system is built, and the performance of the scheme is analyzed. Simulation results reveal that in high-speed scenario, the scheme has effectively achieved low bit error rate and low mean square error, thus having good application value.

Channel estimation in low SNRs is challenging for multiple-input multiple-output single carrier frequency domain equalization (MIMO SC-FDE) systems. This paper presents a novel design of training sequences with circular orthogonal properties, which can be applied to accurate channel estimation. The orthogonality of training sequences help to eliminate the interference between the transmit antennas, while circular orthogonality guarantees the identification of every resolvable delay bin of each multipath channel at the receiver. Compared to the traditional frequency-domain channel estimation methods, the proposed algorithm not only suppresses the interference between the antennas but also decreases the variance of the noise, resulting in the improvement in MSE of channel estimation and BER performance of the system. Simulation results demonstrate the effectiveness and feasibility of the proposed algorithm.

Vehicle-to-Vehicle (V2V) communication is crucial for intelligent transportation system (ITS). Accurate channel estimation is important for realizing reliable V2V communication. However, these existing channel estimation methods cannot provide an accurate estimation performance in low and high signal-to-noise ratio (SNR) simultaneously. To address this problem, an improved channel estimation method was proposed in this paper. The method we proposed is based on constructed data-pilots (CDP) channel estimation method, Spectral Temporal Averaging (STA), and SNR threshold determined by minimum square error (MSE) performance. Simulation results show our proposed method can get better Bit Error Rate (BER) performance than STA channel estimation method and CDP channel estimation method in a low SNR range, and get an almost same BER performance as the CDP channel estimation method in a high SNR range.

A novel data-aided channel estimation for OFDM systems based on selective superimposed pilot sequences is proposed in this paper, where the receiver correlates the received signal sequence with the pilot symbol sequence, and obtains the channel estimation. However, the pilot sequence is superimposed on a data sequence and transmitted together, and thus it has a high spectral efficiency. The selective pilot sequence owns low correlation with data sequence to reduce the interference. In the case of Pilot=1 and L=31, BER of the proposed channel estimation is improved about 2%. With the increase of L and pilot, BER gets lower. So by taking advantage of the correlation between pilot sequences themselves, the proposed algorithm can achieve the good channel estimation.

In practical OFDM systems, some virtual subcarriers are reserved for easing the requirements on the filter. In this case, the channel impulse response (CIR) estimated by DFT approaches appears a spectral leakage, which results in an error floor for the MSE performance. This paper proposes two enhanced DFT interpolation-based channel estimation methods, termed as EDFTI-CE-A and EDFTI-CE-B, based on the robust Wiener filtering, which can eliminate most of the leakage effect owning to the absence of pilot symbols in the virtual subcarriers and further suppress the effect of additive noise. The analysis and simulation results show that the proposed EDFTI-CE-A can almost eliminate the MSE floor of conventional DFTI-CE with small complexity increase and the proposed EDFTI-CE-B can achieve the same performance as linear MMSE channel estimator but with much lower computational complexity.