A low complexity turbo receiver for data nulling superimposed pilots in OFDM

Zaarour, Farah; Simon, Eric Pierre; Zwingelstein-Colin, Marie; Dayoub, Iyad

doi:10.1109/dictap.2015.7113166

Cited by 4 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…+ A similar complexity analysis for DNST was done in [16]. Note that CM refers to complex multiplications.…”

Section: B Channel Estimationmentioning

confidence: 99%

“…2) Receiver: This proposal differs from previous related works in data detection process. Its complexity lies in the number of complex multiplications (CM), which we calculate similarly as in [16]. The analysis is provided in Table II and Table III.…”

Section: Data Detectionmentioning

confidence: 99%

“…In the literature, there are similar methods that also allow adding sequences for channel estimation by using spread codes [14] [15]. In [16], a turbo receiver is proposed for DNST, using a Bit Interleaved Coded Modulation (BICM) coder and an interference cancellation method, but with a prohibitive complexity. The Peak-to-Average power Ratio (PAR) in ST-OFDM is investigated in [17] and [18] for semiblind channel estimation in MIMO OFDM systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Partial-Data Superimposed Training With Data Precoding for OFDM Systems

Estrada-Jiménez

García

2019

IEEE Trans. on Broadcast.

View full text Add to dashboard Cite

Superimposed Training (ST) is a recently addressed technique used for channel estimation where known training sequences are arithmetically added to data symbols, avoiding the use of dedicated pilot subcarriers, and thus, increasing the available bandwidth compared with traditional Pilot Symbol Assisted Modulation (PSAM) schemes. However, the system handles data interference over channel estimation as a result of the ST process; also, data detection is degraded by pilot interference. Recent ST methods have analyzed the data interference and presented schemes that deal with it. We propose a novel superimposed model over a precoded data scheme, named Partial-Data Superimposed Training (PDST), where an interference control factor assigns the adequate information level to be added to the training sequence in Orthogonal Frequency Division Multiplexing (OFDM) systems. Also, a data detection method is introduced to improve the Symbol Error Rate (SER) performance. Moreover, a capacity analysis of the system has been derived. Finally, simulation results confirm that performance of PDST is superior to previous proposals.

show abstract

“…+ A similar complexity analysis for DNST was done in [16]. Note that CM refers to complex multiplications.…”

Section: B Channel Estimationmentioning

confidence: 99%

Section: Data Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Partial-Data Superimposed Training With Data Precoding for OFDM Systems

Estrada-Jiménez

García

2019

IEEE Trans. on Broadcast.

View full text Add to dashboard Cite

show abstract

“…Regretfully, some information-bearing data at certain frequency bins in DNSP are removed prior to transmission, resulting in the symbol misidentification [13]. To tackle the issue of the symbol misidentification, the classic estimation algorithms such as minimum mean square error (MMSE) and maximum likelihood are introduced into DNSP [14]. Yet they are still facing many challenges, such as the availability for the second-order statistics of the channel and noise [15], the low estimation accuracy, and the high computational complexity [16].…”

Section: Introductionmentioning

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

mentioning

confidence: 99%

Transfer Learning-based Channel Estimation in Orthogonal Frequency Division Multiplexing Systems Using Data-nulling Superimposed Pilots

Qing,

Dong,

Wang

et al. 2022

Preprint

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 DLbased 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.Index Terms-Transfer learning (TL), channel estimation (CE), orthogonal frequency division multiplexing (OFDM), data-nulling superimposed pilot (DNSP) I. INTRODUCTIONO RTHOGONAL frequency division multiplexing (OFDM) has been widely applied in wireless communication systems, due to its attractive solution to combat multipath fading [1]. To guarantee the reliable communication in OFDM systems, channel estimation (CE) plays a critical role [2] to eliminate the impact of wireless channels, and thus inspires many CE methods, e.g., non-pilotaided CE [3], [4] and pilot-aided CE [5]. Without employing the pilot sequence (PS), the non-pilot-aided CE saves the valuable bandwidth resources [6]. Yet the high computational

show abstract

A low complexity turbo receiver for data nulling superimposed pilots in OFDM

Cited by 4 publications

References 15 publications

Partial-Data Superimposed Training With Data Precoding for OFDM Systems

Partial-Data Superimposed Training With Data Precoding for OFDM Systems

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

Contact Info

Product

Resources

About