A Precoding Scheme for Eliminating Data Identification Problem in Single Carrier System Using Data-Dependent Superimposed Training

Chan, Kuei-Cheng; Li, Chih-Peng; Hung, Chen-Yen; Huang, Wan-Jen

doi:10.1109/access.2019.2909012

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…Similar orthogonal precoder-pilot design is obtained via an iterative algorithm in [25]. Five criterion for data identification is given in [26], including proportionality to DFT matrix; same magnitude of entries; orthogonal columns; orthogonal to pilots; and periodic auto-correlation function of columns of precoder. Authors in [27], [28] select square precoder matrix (e.g., Hadamard), and analyzes the channel estimation performance.…”

Section: Introductionmentioning

confidence: 99%

Generalized Superimposed Training Scheme in Cell-Free Massive MIMO Systems

Garg

Ratnarajah

2022

IEEE Trans. Wireless Commun.

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Regular pilots in a massive multi-input multi-output (MIMO) system with large number of users suffer from the pilot contamination effect due to limited training time. In this paper, for a cell-free massive MIMO system, we have proposed a generalized superimposed pilot (GSP) scheme, where the available number of pilots are equal to the coherence time slots, and the transmitting data symbols are spread over the coherence time with the help of simple precoding. Further, in order to keep the system scalable, a low complexity and distributed time processing approach is employed, and the corresponding rate components are analyzed. It is shown that with careful design of precoding matrix and number of data symbols, the GSP symbols can provide much better channel estimation and data detection performance, as compared to the regular pilot scheme and the conventional superimposed scheme. These results have been verified via simulations. It is also inferred that centralized processing in cell free system improves the data detection performance than localized processing. Iterative data detection at the central node also improves the MSE of data estimates. The pilot contamination effect, is significantly reduced due to availability of larger number of pilots, as compared to regular pilots transmission.

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Section: Introductionmentioning

confidence: 99%

Generalized Superimposed Training Scheme in Cell-Free Massive MIMO Systems

Garg

Ratnarajah

2022

IEEE Trans. Wireless Commun.

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

Qing

Dong

Wang

et al. 2022

IEEE Trans. Wireless Commun.

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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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A Precoding Scheme for Eliminating Data Identification Problem in Single Carrier System Using Data-Dependent Superimposed Training

Cited by 4 publications

References 23 publications

Generalized Superimposed Training Scheme in Cell-Free Massive MIMO Systems

Generalized Superimposed Training Scheme in Cell-Free Massive MIMO Systems

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

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

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