ELM-Based Frame Synchronization in Nonlinear Distortion Scenario Using Superimposed Training

Qing, Chaojin; Wang, Yu; Tang, Shuhai; Chuangui, Rao,; Wang, Jiafan

doi:10.1109/access.2021.3070336

Cited by 7 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inspired by CNN, a graph neural network was constructed in [43] to improve the performance of CE by extracting the correlations of the CSI. Nevertheless, these DL-based CEs still face many challenges, such as low generalization with the environment change [44], long training time, complex parameter tuning, and large memory requirements [45], etc. Relative to the general DL method, the TL features many advantages [24,46], e.g., huge amount of data is not required, the training time is short, and the network effectively adapts to the new environment without network retraining, etc.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…where x dis denotes the distorted version of x, and f dis (•) represents the nonlinear-distortion function [26].…”

Section: A Transmit Signal Modelmentioning

confidence: 99%

Joint Model and Data Driven Receiver Design for Data-Dependent Superimposed Training Scheme with Imperfect Hardware

Qing¹,

Dong²,

Wang³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Data-dependent superimposed training (DDST) scheme has shown the potential to achieve high bandwidth efficiency, while encounters symbol misidentification caused by hardware imperfection. To tackle these challenges, a joint model and data driven receiver scheme is proposed in this paper.Specifically, based on the conventional linear receiver model, the least squares (LS) estimation and zero forcing (ZF) equalization are first employed to extract the initial features for channel estimation and data detection. Then, shallow neural networks, named CE-Net and SD-Net, are developed to refine the channel estimation and data detection, where the imperfect hardware is modeled as a nonlinear function and data is utilized to train these neural networks to approximate it. Simulation results show that compared with the conventional minimum mean square error (MMSE) equalization scheme, the proposed one effectively suppresses the symbol misidentification and achieves similar or better bit error rate (BER) performance without the second-order statistics about the channel and noise.

show abstract

Timing synchronization of LACO-OFDM under non-linear distortions using ELM

K.S.,

Miriyala,

Mallaiah

et al. 2024

AEU - International Journal of Electronics and Communications

View full text Add to dashboard Cite

ELM-Based Frame Synchronization in Nonlinear Distortion Scenario Using Superimposed Training

Cited by 7 publications

References 34 publications

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

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

Joint Model and Data Driven Receiver Design for Data-Dependent Superimposed Training Scheme with Imperfect Hardware

Timing synchronization of LACO-OFDM under non-linear distortions using ELM

Contact Info

Product

Resources

About