Adaptive Channel Estimation for Underwater Acoustic OFDM System in Impulsive Noise Environment

Wu, Yaohui; Shao, Pengfei; Zhang, Shaozhong; Li, Youming

doi:10.1155/2022/1455526

Cited by 4 publications

(7 citation statements)

References 29 publications

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“…The result is that this algorithm has been showing better performance than the actual MMP algorithm; when analogizing with other techniques, the processing time is high. Furthermore, Wu et al 37 presented a joint time‐frequency OMP (JTF‐OMP) technique where the affected channels' estimation profited adaptively from that of neighboring channels in time or frequency. It is generally known that the following OMP method, which is challenging to deal with because of the impulsive noise (IN), relies heavily on preliminary estimates of the Doppler scale.…”

Section: Literature Surveymentioning

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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Section: Literature Surveymentioning

confidence: 99%

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

Kumar

2023

Int J Communication

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“…Since each signal is a wave signal and is combined to be sent across the medium transporter, the transmitted data is encoded into the frequency domain to split the bandwidth into orthogonal parts before entering the Inverse Fast Fourier Transform (IFFT) to convert the signal to the time domain [9] [10].…”

Section: -Ofdm Transmission and Receivingmentioning

confidence: 99%

“…As for the noise, it can be deleted when the data reaches the receiver by discovering the data frame. The main problem lies in the multiple paths and their effects [9].…”

Section: -Ofdm Transmission and Receivingmentioning

confidence: 99%

“…Usually when sending data from the sender to the receiver, most of the time there is a line of site signal, which is the signal that is directly from the sender to the receiver without any obstacle, but there are also signals called fade path, in which the signal is subjected to collision with obstacles such as buildings or Trees, and thus those signals may be reflected or broken and then reach the receiver. Each of the paths crossed by the signal will be exposed to path gain and path delay, which differ from one path to another [8,9].…”

Section: -Ofdm Transmission and Receivingmentioning

confidence: 99%

“…Slow shift can be manipulated by CFO, by which we mean control shift, where we scuff the carrier frequency by a certain amount [9][10], which we calculate mathematically through the following equations:…”

Section: Synchronization Problem In Ofdmmentioning

confidence: 99%

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A New Approach to Improve Transmitting and Receiving Timing in Orthogonal Frequency Division Multiplexing (OFDM) Systems

Abed

2023

ijcsm

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Nowadays, most wireless communications employ OFDM technology to reduce signal interference. Its sensitivity to timing faults, however, can cause a severe performance reduction. This study suggests a new approach to enhance communication systems' transmitting and receiving time. Although OFDM is a widely used modulation strategy in communication systems, it is vulnerable to timing faults, which can seriously affect performance. A frequency interference processing method is created that uses OFDM technology and goes through numerous steps, such as correcting CFO, adding a periodic prefix to prevent frequency distortion, calculating the time delay between transmission and reception, and spotting timing mistakes. It is clear that the lack of synchronization between the transmitter and channel interference has caused the Signal-to-Noise Ratio (SNR) to decline correspondingly. The initial approach had a piece of code that computed the transmission and reception times while recording their differences. As a result, it handles signal interference and frequency and temporal synchronization, two key aspects of OFDM. Timing precision varies from 0 to 1, and the time gap between transmission and reception is only a fraction of a second. When the SNR was available, timing errors and the lag between transmission and reception timing were seen, which suggests that there is a small probability of having too many locks or losing codes while utilizing OFDM. Simulation exercises show that the suggested method considerably enhances the system's Bit Error Rate (BER) performance in a variety of time offset conditions. The outcomes imply that the suggested strategy could be a workable remedy for OFDM systems with timing issues.

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Hybrid AE and Bi-LSTM-Aided Sparse Multipath Channel Estimation in OFDM Systems

Kondepogu,

Bhattacharyya

2024

IEEE Access

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OFDM is a powerful modulation technique that efficiently transmits high-speed digital data over frequencyselective fading channels. It divides the signal into multiple subcarriers, each experiencing flat fading, and compensates for independent noise using a simple one-tap equalizer. In wireless systems, coherent detection is often employed, and accurate channel knowledge is crucial for optimizing performance. Traditional channel estimation methods, such as Least-Squares (LS), utilize randomly spaced pilot patterns, which can compromise spectral efficiency. Significant losses in BER and MSE affect many such existing methods in detrimental manner. To counter such failure, this paper proposes a new hybrid deep learning-aided sparse multipath channel estimation approach for OFDM communication systems. Additionally, a hybrid deep learning HA-Bi-LSTM model is developed by combining Bidirectional Long Short-Term Memory (Bi-LSTM) and Auto-Encoder (AE) to enhance data communication performance. Further, an efficient Grasshopper Electric Fish Optimization Algorithm (G-EFOA) is developed to optimize the parameters of AE and Bi-LSTM, resulting in superior solutions. AE handles feature extraction, while Bi-LSTM performs estimation. The proposed model aims to minimize BER and MSE values. Results show about an average 77 % improvement in BER across varied modulation schemes, along with a significant drop in MSE values by 84 %. Also, a sizable drop-in simulation time proves the contribution of the algorithm. Finally, the proposed model's effectiveness is demonstrated through performance evaluations.

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Adaptive Channel Estimation for Underwater Acoustic OFDM System in Impulsive Noise Environment

Cited by 4 publications

References 29 publications

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

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

A New Approach to Improve Transmitting and Receiving Timing in Orthogonal Frequency Division Multiplexing (OFDM) Systems

Hybrid AE and Bi-LSTM-Aided Sparse Multipath Channel Estimation in OFDM Systems

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