Performance of ML-based carrier frequency offset estimation in CO-OFDM systems

Balogun, Muyiwa B.; Oyerinde, Olutayo O.; Takawira, Fambirai

doi:10.1109/afrcon.2017.8095477

Cited by 2 publications

(5 citation statements)

References 22 publications

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“…In the initial stage, which is around 20 sets of simulated data, it reached a relatively stable value. It clearly shows that when the data volume reaches 300, the curves of the estimated results by the two algorithms are basically the same, which proves Equation (15). From the above simulation's results, it is obvious that, compared to the MLE, the estimated result of IBR can reach a stable value in a short amount time.…”

Section: Algorithm and Simulationsupporting

confidence: 54%

“…In the initial stage, which is around 20 sets of simulated data, it reached a relatively stable value. It clearly shows that when the data volume reaches 300, the curves of the estimated results by the two algorithms are basically the same, which proves Equation (15). S-parameter estimation results, which start around 8444.75 MHz, decreases at the third set of simulated data, fluctuates up and down as the simulated data increases, and finally stays around 8444.79 MHz.…”

Section: Algorithm and Simulationsupporting

confidence: 52%

“…Inspired by similar methods [12][13][14][15][16][17][18][19], we refer to the theory of Bayesian estimation and use an effective estimation method to improve accuracy in the presence of noise. Since only the frequency of the valley point of the S-parameter is required for demodulation, all of the parameters in the algorithm are related to the frequency of the valley point of S-parameter.…”

Section: Principlementioning

confidence: 99%

“…Balogun, M.B. et al [15] propose an effective estimation scheme based on the ML approach. The scheme mitigates and compensates the undesirable effect of carrier frequency offset on the coherent optical OFDM system.…”

Section: Introductionmentioning

confidence: 99%

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Iteration Bayesian Reweighed Algorithm for Optical Carrier-Based Microwave Interferometry Sensing

Zhou

Fan

et al. 2020

Sensors

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This paper proposes a novel iteration Bayesian reweighed (IBR) algorithm to obtain accurate estimates of a measurement parameter that uses only a few noisy measurement data. The method is applied to optimize the frequency fluctuation in an optical carrier-based microwave interferometry (OCMI) system. The algorithm iteratively estimates the frequency of the S-parameter valley point by collecting training samples to rebalance the weights between prior samples, which reduces the impact of noise in the system. Simulation shows that the estimated result of this algorithm is closer to the true value than that of the maximum likelihood estimation (MLE) using the same amount of measured data. Under the influence of system noise, this algorithm optimizes the frequency fluctuation of the S-parameter and reduces the impact of individual measured data. In this study, we applied the algorithm in the strain sensing experiment and compared it with the MLE. When axial strain changes 240 με, the IBR algorithm yields a deviation of 36 με, which is a significant reduction from 138 με (using the MLE method). Moreover, the average error rate decreases from 25% to 3% (with the MLE method), suggesting that the linear fitting degree of the estimated results and accuracy of the system are improved. Moreover, the algorithm has a wide range of applicability, for it can handle different application models in the OCMI system and the systems with frequency fluctuation problems.

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Section: Algorithm and Simulationsupporting

confidence: 54%

“…In the initial stage, which is around 20 sets of simulated data, it reached a relatively stable value. It clearly shows that when the data volume reaches 300, the curves of the estimated results by the two algorithms are basically the same, which proves Equation (15). S-parameter estimation results, which start around 8444.75 MHz, decreases at the third set of simulated data, fluctuates up and down as the simulated data increases, and finally stays around 8444.79 MHz.…”

Section: Algorithm and Simulationsupporting

confidence: 52%

Section: Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Iteration Bayesian Reweighed Algorithm for Optical Carrier-Based Microwave Interferometry Sensing

Zhou

Fan

et al. 2020

Sensors

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show abstract

“…The overall reliability, efficiency and system performance called CO-OFDM are degraded by the phase noise (PN) constitutes is a major impairment [2]. While improving overall efficiency and system performance, it decreases system complexity [3]. Then, a shorter OFDM symbol duration (lower number of subcarriers) is preferable to increase the PN tolerance [4].…”

Section: Introductionmentioning

confidence: 99%

Estimation of channel distortion in orthogonal frequency division multiplexing system using pilot technique

Joseph

Gowda

2022

IJEECS

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Orthogonal frequency-division multiplexing (OFDM) is resistant to frequency selective fading due to the longer symbol duration. However, mobile applications channel timing fluctuations in one OFDM signal cause intercarrier-interference (ICI), which reduces performance. This research presented the support vector regression (SVR) model-based channel estimation technique for coherent optical communication systems. Due to the coherent optical orthogonal frequency-division-multiplexed (COOFDM) system, a channel model is developed that includes linear fibre dispersion effects, noise from optical amplifiers, and inter-carrier interference generated by laser phase noise. As a result, for such a system, an accurate channel estimate is essential. Based on this concept, derivation of channel estimation and phase estimation for the system of CO-OFDM. The proposed method is tested and evaluated using MATLAB software. Computer simulation results for several standard methods such as extreme learning machines (ELM) and artificial neural networks (ANN) validate the feasibility of the suggested methodology. The CO-OFDM system’s transmission experiments and computer simulations prove that the support vector machine-based model following pilot-assisted phase estimation gives the optimal performance. Therefore, results depicted that the channel estimation utilizing the SVR model gives good performance than the other methods, thus the proposed model gives an accurate CE process, respectively.

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Performance of ML-based carrier frequency offset estimation in CO-OFDM systems

Cited by 2 publications

References 22 publications

Iteration Bayesian Reweighed Algorithm for Optical Carrier-Based Microwave Interferometry Sensing

Iteration Bayesian Reweighed Algorithm for Optical Carrier-Based Microwave Interferometry Sensing

Estimation of channel distortion in orthogonal frequency division multiplexing system using pilot technique

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