OFDM tolerance to additive white Gaussian and laser phase noises in optical heterodyning systems accompanied by the feasible pilot-assisted equalization

Zabala-Blanco, David; Azurdia-Meza, César A.; Montejo‐Sánchez, Samuel; Firoozabadi, Ali Dehghan

doi:10.1007/s10043-020-00587-8

Cited by 8 publications

(12 citation statements)

References 37 publications

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“…As future research, results in the case of optical channel model presence by including both linearities and non-linearities must be considered, where the impact of the chromatic dispersion and fiber nonlinearities in the BER depends on the subcarrier position. By considering that the coherent detection implies of heterodyning the optical signal with a continuous-wave optical field E LO (t) previously it falls on the photodetector, the electrical current may be written as [7,32]:…”

Section: Ofdm Transmittermentioning

confidence: 99%

“…Additionally, n(t) refers to the AWGN signal for accounting thermal and shot noises, still present in a perfect receiver [37]. After secreting the low frequencies, transforming to an analytic representation with the assistance of the Hilbert transform, and converting to the baseband frequency, the normalized input to the OFDM demodulator is given by [7]:…”

Section: Ofdm Transmittermentioning

confidence: 99%

“…As mentioned in the Introduction, the channel frequency response for data subcarriers is usually estimated through linear interpolation from the values given by pilots [39], technique knows as PAE. This kind of equalization has demonstrated high performance and low complexity [4,6,7]. Here, the power of pilot subcarriers comes from the average power of the constellation while the quadrature component (the imaginary part of the symbol in the constellation diagram) of pilots is fixed to 0 for simplicity purposes [5].…”

Section: θ(0)mentioning

confidence: 99%

“…CPE is relatively easy to diminish contrasted with ICI since the former induces the same rotation in all constellation symbols during the OFDM symbol, instead the latter behaves like Gaussian noise and depends on the subcarrier frequency. Fortunately, the CPE may be controlled through the pilot-assisted equalization (PAE) method [4][5][6][7], which is usually inserted in OFDM modems in order to correct the multipath channel effects. Here, the channel frequency response for data subcarriers is estimated by interpolation from the values provided by pilot subcarriers.…”

Section: Introductionmentioning

confidence: 99%

“…To prevent time limitations and algebraic requirements related to the hidden layer output matrix in the training process, the singular value decomposition approach is adopted [20]. Simulated results report that for the QPSK constellation subject to an additive white Gaussian noise (AWGN) channel, the fully-complex ELM outperforms the fully-real ELM and feasible PAE [4,7] in terms of the BER as well as the complexity modem.…”

Section: Introductionmentioning

confidence: 99%

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Relaxation of the Radio-Frequency Linewidth for Coherent-Optical Orthogonal Frequency-Division Multiplexing Schemes by Employing the Improved Extreme Learning Machine

et al. 2020

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A coherent optical (CO) orthogonal frequency division multiplexing (OFDM) scheme gives a scalable and flexible solution for increasing the transmission rate, being extremely robust to chromatic dispersion as well as polarization mode dispersion. Nevertheless, as any coherent-detection OFDM system, the overall system performance is limited by laser phase noises. On the other hand, extreme learning machines (ELMs) have gained a lot of attention from the machine learning community owing to good generalization performance, negligible learning speed, and minimum human intervention. In this manuscript, a phase-error mitigation method based on the single-hidden layer feedforward network prone to the improved ELM algorithm for CO-OFDM systems is introduced for the first time. In the training step, two steps are distinguished. Firstly, pilots are used, which is very common in OFDM-based systems, to diminish laser phase noises as well as to correct frequency-selective impairments and, therefore, the bandwidth efficiency can be maximized. Secondly, the regularization parameter is included in the ELM to balance the empirical and structural risks, namely to minimize the root mean square error in the test stage and, consequently, the bit error rate (BER) metric. The operational principle of the real-complex (RC) ELM is analytically explained, and then, its sub-parameters (number of hidden neurons, regularization parameter, and activation function) are numerically found in order to enhance the system performance. For binary and quadrature phase-shift keying modulations, the RC-ELM outperforms the benchmark pilot-assisted equalizer as well as the fully-real ELM, and almost matches the common phase error (CPE) compensation and the ELM defined in the complex domain (C-ELM) in terms of the BER over an additive white Gaussian noise channel and different laser oscillators. However, both techniques are characterized by the following disadvantages: the CPE compensator reduces the transmission rate since an additional preamble is mandatory for channel estimation purposes, while the C-ELM requires a bounded and differentiable activation function in the complex domain and can not follow semi-supervised training. In the same context, the novel ELM algorithm can not compete with the CPE compensator and C-ELM for the 16-ary quadrature amplitude modulation. On the other hand, the novel ELM exposes a negligible computational cost with respect to the C-ELM and PAE methods.

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Section: Ofdm Transmittermentioning

confidence: 99%

Section: Ofdm Transmittermentioning

confidence: 99%