Neural Network Based Joint Carrier Frequency Offset and Sampling frequency Offset Estimation and Compensation in MIMO OFDM-OQAM Systems

Kumar, Abhishek; Saha, Seemanti; Abhiranjan,; Bhattacharya, Rajarshi

doi:10.23919/ursigass49373.2020.9232207

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…A DNN-based joint CFO and SFO estimation along with filter bank compensation in MIMO OFDM offset quadrature amplitude modulation (OQAM) systems is proposed, in which CFO and sampling frequency offset (SFO) ranges are evenly divided into several subranges and are estimated using a DNN-based classifier. 44 Moreover, algorithms based on deep learning to implicitly eliminate the effects of CFO and STO have also emerged as an attractive solution to the synchronization problem. In the OFDM system with CFO and phase offset impaired, a DNN-based signal detection method is proposed and implicitly eliminates the influence of CFO and phase offset.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, deep learning‐based joint time and frequency synchronization algorithms have received attention. A DNN‐based joint CFO and SFO estimation along with filter bank compensation in MIMO OFDM offset quadrature amplitude modulation (OQAM) systems is proposed, in which CFO and sampling frequency offset (SFO) ranges are evenly divided into several subranges and are estimated using a DNN‐based classifier 44 . Moreover, algorithms based on deep learning to implicitly eliminate the effects of CFO and STO have also emerged as an attractive solution to the synchronization problem.…”

Section: Introductionmentioning

confidence: 99%

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Timing‐deviation and frequency‐offset estimations for multicarrier transmission in high mobility environments using deep neural network

Wu,

Geng,

Chen

et al. 2023

Int J Communication

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SummaryThe multicarrier technology used in fourth‐generation (4G) and fifth‐generation (5G) communications is quite susceptible to multipath fading and Doppler frequency shift, which is more prominent in high mobility environments. Therefore, the accuracy of timing and frequency synchronization impacts the overall system performance significantly. Existing synchronization methods rely on the correlation of the preamble sequences and are, hence, vulnerable to severe multipath effect and Doppler effect. In this article, we propose a novel scheme leveraging deep neural network (DNN) to achieve high‐precision synchronization in high mobility environments. Concretely, a convolutional neural network (CNN) architecture is proposed to extract the hidden features of received signals for timing deviation estimation. In the following, a fully connected (FC) model is demonstrated to classify the optimal carrier frequency offset (CFO) estimate from several CFO candidates. The proposed synchronization scheme is assessed under extended vehicular A (EVA) channel with various Doppler frequency shifts. Simulation results corroborate that the proposed DNN‐based scheme achieves a significant performance gain over the conventional correlation methods, and the proposed timing deviation estimation scheme exhibits an excellent complexity reduction; wherefore, it is extremely promising for multicarrier transmission systems in high mobility environments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Timing‐deviation and frequency‐offset estimations for multicarrier transmission in high mobility environments using deep neural network

Wu,

Geng,

Chen

et al. 2023

Int J Communication

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“…As a result, non-idealities such as the fading channel effect and carrier frequency offset (CFO) caused by oscillator frequency mismatch reduce the symbol error rate (SER) performance for signal detection in downlink power domain multi-user NOMA-OFDM [2,4,9,10,14,15]. As a multi-carrier multiple-access technique, it has a high peak-to-average power ratio (PAPR).…”

Section: Introductionmentioning

confidence: 99%

Deep neural network based downlink power domain multi-user NOMA-OFDM signal detection

Singh,

Saha

2023

Eng. Res. Express

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Non-orthogonal multiple access-based orthogonal frequency division multiplexing (NOMA-OFDM) is a promising waveform-based multiple access technology for future wireless networks for multiple-user symbol transmission (MUST) in the same time-frequency resource block. However, it differs in the power domain, enhancing its spectrum efficiency. This is essential to meet the high data rate required for ever-increasing connected devices and the Internet of Things (IoT). However, NOMA-OFDM systems suffer from impairments such as imperfect successive interference cancellation (SIC) caused by channel impairments like channel fading, carrier frequency offset, and non-linearity caused by non-linear power amplifiers. This paper identifies and addresses the key impairments mentioned in the NOMA-OFDM system and proposes DNN-based estimation in offline training and detection in online testing for downlink power domain multi-user NOMA-OFDM symbols. The reported 2 dB SNR gain compared to least square-SIC/minimum mean square error SIC-based methods is a significant finding and demonstrates the robustness of the proposed DNN-aided approach against various channel impairments.

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Machine/deep learning based estimation and detection in OFDM communication systems with various channel imperfections

Singh

Saha

2022

Wireless Netw

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Neural Network Based Joint Carrier Frequency Offset and Sampling frequency Offset Estimation and Compensation in MIMO OFDM-OQAM Systems

Cited by 4 publications

References 11 publications

Timing‐deviation and frequency‐offset estimations for multicarrier transmission in high mobility environments using deep neural network

Timing‐deviation and frequency‐offset estimations for multicarrier transmission in high mobility environments using deep neural network

Deep neural network based downlink power domain multi-user NOMA-OFDM signal detection

Machine/deep learning based estimation and detection in OFDM communication systems with various channel imperfections

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