Sparse channel recovery with inter-carrier interference self-cancellation in OFDM

Song, Zuxun; Guo, Siyu; Qian, Zhenghong; Dong-dong, Shui

doi:10.21629/jsee.2018.04.02

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…Hence, it has been utilized in mobile acoustic communication, in which the channel has exhibited long multipath delays that help to neglect the requirement for the time-domain equalizers (Dun et al 2021). This system can separate the frequency selection channels into several series of frequency-flat subcarriers along with scalar gains (Jiansheng et al 2018). The number of multipath components (MPC) is essentially less than the amount of channel tap in the subcarriers or frequency-time domain.…”

Section: A Importance Of the Workmentioning

confidence: 99%

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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Section: A Importance Of the Workmentioning

confidence: 99%

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

Kondepogu,

Bhattacharyya

2024

IEEE Access

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“…Underwater acoustic communication is playing an increasingly important role in marine engineering, but underwater acoustic channels have a multipath fading effect, causing intersymbol interference. In order to combat frequency selective fading and overcome the effects of multipath transmission, orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain equalization (SC-FDE) techniques have be used [1][2][3][4] . SC-FDE has similar complexity to OFDM, while retaining the advantages of a low peak-to-average power ratio (PAPR) of a single-carrier signal, reducing sensitivity to phase noise and frequency shift, so it can be applied to underwater acoustic communications [5][6][7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

SC-FDE timing synchronization improvement algorithm based on genetic algorithm

liu

Liang

et al. 2023

Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022)

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Aiming at the timing synchronization problem in underwater acoustic single-carrier frequency domain equalization system (SC-FDE), the training sequence and new metric function is constructed by using the well-related Zadoff-Chu (ZC) sequence, and the genetic algorithm is used to optimize the combination structure, which solves the problems of peak platform and false peak interference in the commonly used synchronization methods. Simulation results show that the improved synchronization algorithm is more accurate at the timing point under low signal-to-noise ratio and has high synchronization accuracy.

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