Adaptive and joint frequency offset and carrier phase estimation based on Kalman filter for 16QAM signals

Xiang, Qian; Yang, Yanfu; Cao, Juntao; Yao, Yong

doi:10.1016/j.optcom.2018.08.041

Cited by 16 publications

(5 citation statements)

References 8 publications

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“…On the other hand, the tracking capability of the MKF with adaptive Q is comparatively low but its estimation accuracy is better. This result is in agreement to the performance of the adaptive Kalman filter (AKF) proposed in 18 for B2B system. Poor tracking capability of the filter can be attributed to the choice of .…”

Section: Resultssupporting

confidence: 87%

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Reduced sampling rate Kalman filters for carrier phase and frequency offset tracking in 200 Gbps 16 QAM coherent communication system

Sharma

Krishnamurthy

2021

Sci Rep

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We propose 1 state and 2 state multi-step Kalman filters (MKFs) to estimate and compensate CFO, LPN and NLPN in long-haul coherent fiber-optic communication systems. The proposed filters generate state estimates once every m symbols and therefore operate at a reduced sampling rate compared to conventional KFs that perform symbol by symbol processing. No computations are performed to obtain phase estimates of the intermediate $$m-1$$ m - 1 samples; instead, the present and previous estimates are averaged and used to derotate the intermediate $$m-1$$ m - 1 samples which are then demodulated to recover the transmitted symbols. This reduces the computational load on the receiver DSP. Further, in order to improve estimation accuracy, we adaptively vary the process noise covariance Q. Simulation results of 200 Gbps PDM 16 QAM system over 12 spans shows that the proposed 1 state MKF can reduce the sampling rate requirement by a factor of $$m = 20$$ m = 20 with Q-factor degradation of 1.32 dB compared to single-step KF at linewidth of 100 kHz. The 2 state MKF tracks PN and CFO with a maximum step size of $$m=10$$ m = 10 for a CFO of 100 MHz at linewidth of 100 kHz. We also study the dynamic performance of the proposed algorithms by applying step change to CFO. The 2 state MKF with adaptive Q is able to track a step change of 400 MHz of CFO with $$m = 1$$ m = 1 and 3 with high estimation accuracy but slower convergence time compared to the non-adaptive 2 state MKF. Finally, we study the computational requirements of the proposed MKFs and show that they offer significant reduction in computations compared to single-step KF thus making the proposed filters suitable for hardware implementation.

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Section: Resultssupporting

confidence: 87%

“…( 7 ), the expectation operator is approximated by the time average value of . In 18 , a forgetting factor is proposed to obtain the average value of over time. Therefore Q in Eq.…”

Section: Principle Of 1 and 2 State Multi-step Kalman Filtersmentioning

confidence: 99%

Reduced sampling rate Kalman filters for carrier phase and frequency offset tracking in 200 Gbps 16 QAM coherent communication system

Sharma

Krishnamurthy

2021

Sci Rep

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“…The research study conducted by Xiang et al [29] has provided brief information regarding the frequency offset carrier phase estimation. In the research study, a carrier recovery scheme was proposed by the researchers.…”

Section: Related Workmentioning

confidence: 99%

“…In the research study [29], the researchers have provided detailed information about machine learning and its application in the modulation format transparent carrier recovery. The researchers in the study have stated that the usage of the internet has increased up to a lot of extents in recent years.…”

Section: Related Workmentioning

confidence: 99%

Accurate Phase Detection for ZigBee Using Artificial Neural Network

Alqahtani¹,

Alsulami²,

Alahmari³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability, scalability, and enhancement of wireless mesh networking. This standard uses a physical layer of binary phase-shift keying (BPSK) modulation and can be operated with two frequency bands, 868 and 915 MHz. The frequency noise could interfere with the BPSK signal, which causes distortion to the signal before its arrival at receiver. Therefore, filtering the BPSK signal from noise is essential to ensure carrying the signal from the sender to the receiver with less error. Therefore, removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor networks. Moreover, researchers have reported a positive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems, including ZigBee. Meanwhile, artificial neural network (ANN) and machine learning (ML) models outperformed results for predicting signals for detection and classification purposes. This paper develops a neural network predictive detection method to enhance the performance of BPSK modulation. First, a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network (WPAN) standard. Then, Gaussian noise was injected into the BPSK simulation model. To reduce the noise of BPSK phase signals, a recurrent neural networks (RNN) model is implemented and integrated at the receiver side to estimate the BPSK's phase signal. We evaluated our predictive-detection RNN model using mean square error (MSE), correlation coefficient, recall, and F1-score metrics. The result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient (R-value) metric for different signal-to-noise (SNR) values. In addition, our RNN-based model scored 98.71% and 96.34% based on recall and F1-score, respectively.

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“…Despite being attractive, FBMC-OQAM is heavily subject to Carrier frequency offset (CFO), due to the orthogonality loss among the subcarriers [11,12] . Moreover, the orthogonality condition in FBMC-OQAM is imposed only in the real field.…”

Section: Introductionmentioning

confidence: 99%

Frequency Synchronization Based on a Cascading Cross‐Correlation Function for CO‐FBMC‐OQAM

Wang

Chen

Lyu³

et al. 2020

Chin. j. electron.

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Adaptive and joint frequency offset and carrier phase estimation based on Kalman filter for 16QAM signals

Cited by 16 publications

References 8 publications

Reduced sampling rate Kalman filters for carrier phase and frequency offset tracking in 200 Gbps 16 QAM coherent communication system

Reduced sampling rate Kalman filters for carrier phase and frequency offset tracking in 200 Gbps 16 QAM coherent communication system

Accurate Phase Detection for ZigBee Using Artificial Neural Network

Frequency Synchronization Based on a Cascading Cross‐Correlation Function for CO‐FBMC‐OQAM

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