Constellation rotation and symbol detection for data‐dependent superimposed training

Dou, Gaoqi; Li, Congying; Gao, Jun; Guo, Fang

doi:10.1049/el.2014.1681

Cited by 5 publications

(6 citation statements)

References 3 publications

(8 reference statements)

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“…In other words, the CE accuracy of DNSP scheme is obtained at the cost of degradation of data detection performance. Thus, the works in [13,[29][30][31][32][33][34][35][36] are proposed to tackle this issue. In [13], a partially data-dependent ST scheme was proposed to reduce symbol misidentification by researching the trade-off between interference cancellation and frequency integrity.…”

Section: Related Workmentioning

confidence: 99%

“…[32] investigated a data coding scheme to relieve the symbol misidentification. [33] utilized a constellation rotation scheme to preserve the partial symbol information discarded by the superimposed scheme. By considering the signal sub-space technique [34], theoretically analyzed that the symbol misidentification was related to both the modulation scheme and the pilot pattern.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

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“…In [5], the discarded symbol information was retrieved according to the relevancy between encoding and decoding, which avoids partial symbol misidentification. In [6], to avoid symbol misidentification, a constellation rotation method was proposed and used to preserve the partial symbol information discarded by DDST scheme. By using the signal sub-space technique, [7] theoretically analyzed the phenomenon of symbol misidentification (also called data identification problem).…”

Section: A Related Workmentioning

confidence: 99%

“…with Q and P being constants, satisfying Q = N/P , and t being the shift variable for constellation rotation [6].…”

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

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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.

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“…represent the equivalent noise vectors at D. Note that v t includes the propagated noise α t H 2 n 1t that is related to the specific realisation of h 2 , while v b contains the extra data interference α b H 12 x b besides the propagated noise α b H 2 n 1b . Similar to superimposed training scheme in the conventional pointto-point transmission [18][19][20][21], the data-induced interference is the dominant effect on channel estimation performance.…”

Section: Design Of Ibstmentioning

confidence: 99%

Individual channel estimation for amplify‐and‐forward relay networks using in‐band superimposed training

et al. 2018

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Constellation rotation and symbol detection for data‐dependent superimposed training

Cited by 5 publications

References 3 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

Individual channel estimation for amplify‐and‐forward relay networks using in‐band superimposed training

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