A Comprehensive Review on Channel Estimation in OFDM System

Dubey, Neha; Pandit, Ankit

doi:10.24113/ijoscience.v5i3.201

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…Training sequences are known sequences that are sent over the channel and used to estimate the channel characteristics, its suitable for estimating both the frequency-selective and time-varying characteristics of the channel, such as Wi-Fi. Blind methods use algorithms to estimate the channel characteristics without any prior knowledge of the transmitted signal, it may be explored, especially when channel conditions are expected to change rapidly or when the channel is not well-characterized in advance [19]. LTE typically use a combination of pilot symbols and training sequences to strike a balance between accurate channel estimation and efficient use of resources.…”

Section: Channel Estimationmentioning

confidence: 99%

Enhancement of BER of LTE System by Using DCT-Neural Network in Different Channel Models

Ahmed,

Al-Thahab

2024

MMEP

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In this work the discrete cosine transform is proposed for LTE systems with the aid of feed-forward neural network as a suitable equalizer to retrieve the effect of channel within Rayleigh Faded channels. This system was implemented using the Quadrature-Phase Shift Keying as a modulation technique, and using different Maximum Doppler Shift, which represents the highest Doppler shift that can occur between the transmitter and the receiver in a given wireless channel. by using DCT-FFNN with different MDS values effectively mitigates signal distortion resulting from multipath propagation and common issues in wireless communication networks and demonstrates higher accuracy in predicting BER values. According to the research, the performance will be better at MDS 50 when compared to the rest of the MDS used in the paper. These advantages come with minimal loss in data rate and bandwidth and no additional expense in terms of power. The simulation results indicate that an FFNNs-based channel estimator outperforms the pilot-based channel estimator in LTE systems operating over a Rayleigh fading channel, because FFNNs have low complexity and can quickly and accurately adjust the signal strength of incoming signals based on their input. All LTE system models were implemented using MATLAB 2016.

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Section: Channel Estimationmentioning

confidence: 99%

Enhancement of BER of LTE System by Using DCT-Neural Network in Different Channel Models

Ahmed,

Al-Thahab

2024

MMEP

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“…According to prior knowledge for the algorithm, the channel estimation methods can be divided into the blind method and the training-sequence-based method. In the blind channel estimation method, the statistical property of received signals is utilized to estimate the channel state information without any prior information of the transmitted signal [18]. The training-sequence-based method performs the channel estimation by utilizing the known training sequence part of the received signals [19].…”

Section: Introductionmentioning

confidence: 99%

Doppler and Channel Estimation Using Superimposed Linear Frequency Modulation Preamble Signal for Underwater Acoustic Communication

Lv,

Sun,

Chen

et al. 2024

JMSE

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Due to the relative motion between transmitters and receivers and the multipath characteristic of wideband underwater acoustic channels, Doppler and channel estimations are of great significance for an underwater acoustic (UWA) communication system. In this paper, a preamble signal based on superimposed linear frequency modulation (LFM) signals is first designed. Based on the designed preamble signal, a real-time Doppler factor estimation algorithm is proposed. The relative correlation peak shift of two LFM signals in the designed preamble signal is utilized to estimate the Doppler factor. Moreover, an enhanced channel estimation algorithm, the correlation-peak-search-based improved orthogonal matching pursuit (CPS-IOMP) algorithm, is also proposed. In the CPS-IOMP algorithm, the excellent autocorrelation characteristic of the designed preamble signal is used to estimate the channel sparsity and multipath delays, which are utilized to construct the simplified dictionary matrix. The simulation and sea trial data analysis results validated the designed preamble, the proposed Doppler estimation algorithm, and the channel estimation algorithm. The performance of the proposed Doppler factor estimation is better than that of the block estimation algorithm. Compared with the original OMP algorithm with known channel sparsity, the proposed CPS-IOMP algorithm achieves a similar estimation accuracy with a smaller computational complexity, as well as requiring no prior knowledge about the channel sparsity.

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“…Two sorts of techniques are involved in CE; they are blind along with pilot‐based estimation. In blind CE, the received signal's statistical properties without the requirement to resort to a preamble or pilot signal are utilized 17 . Various known training symbols are implanted between data symbols in a pilot‐based estimation technique.…”

Section: Introductionmentioning

confidence: 99%

“…In blind CE, the received signal's statistical properties without the requirement to resort to a preamble or pilot signal are utilized. 17 Various known training symbols are implanted between data symbols in a pilot-based estimation technique. Then, these subcarrier symbols are extracted by the receiver and utilized for synchronization and channel state estimation.…”

Section: Introductionmentioning

confidence: 99%

A new optimized least‐square sparse channel estimation scheme for underwater acoustic communication

Kumar

2023

Int J Communication

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Summary In underwater acoustic (UWA) communication, orthogonal frequency division multiplexing (OFDM) is a promising technology that is highly essential to get channel state information meant for channel estimation (CE). Nevertheless, higher complexity, slower convergence, and poor performance, which degrade the performance estimation, are the limitations of the traditional CE methodologies. Thus, by amalgamating the least square (LS)‐CE algorithm along with polynomial interpolated black widow optimization (PI‐BWO) model, an optimized least square sparse (OLSS) CE algorithm has been proposed to intend for a UWA‐OFDM communication system. Formerly, by utilizing the 2's complement shift left turbo encoding (2CSL‐TE) methodology, the input signal is encoded. After that, the modulated encoded signal is provided for inverse fast Fourier transform (IFFT) operations; subsequently, they are transferred over the UWA channel toward the receiver OFDM. By employing the OLSS methodology, the received OFDM signal's interference‐free region is utilized for sparse CE at the receiver. Regarding symbol error rate (SER), bit error rate (BER), mean square error (MSE), and peak signal‐to‐noise ratio (PSNR), the proposed model's experiential outcome is evaluated and analogized with the other prevailing methodologies. When analogized with the conventional models, the proposed estimation methodologies achieved better performance.

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A Comprehensive Review on Channel Estimation in OFDM System

Cited by 4 publications

References 14 publications

Enhancement of BER of LTE System by Using DCT-Neural Network in Different Channel Models

Enhancement of BER of LTE System by Using DCT-Neural Network in Different Channel Models

Doppler and Channel Estimation Using Superimposed Linear Frequency Modulation Preamble Signal for Underwater Acoustic Communication

A new optimized least‐square sparse channel estimation scheme for underwater acoustic communication

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