QAM equalization and symbol detection in OFDM systems using extreme learning machine

Muhammad, Ishaq Gul; Tepe, Kemal; Abdel-Raheem, Esam

doi:10.1007/s00521-011-0796-y

Cited by 18 publications

(13 citation statements)

References 28 publications

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“…To find the output weights matrix, pilot signals are utilized. With the assistance of 1000 hidden neurons, simulations establish that compared to the previous ELM based methods [13,21], the multiple split-complex ELM owns the benefit of higher detection accuracy with a slight complexity increase. On the other hand, the multi-layer perceptron, generalized radial basis function, and robust ELM are proposed in Reference [23] for mitigating nonlinearities in CO-OFDM systems under the 16QAM signaling.…”

Section: Introductionmentioning

confidence: 94%

“…An equalizer based on the standard ELM can be robust against the multipath effects introduced by the wireless channel [13,21,22]. In our work, the improved ELM algorithm is used to increase the RF-linewidth tolerance in CO-OFDM signals, by considering pilot and data subcarriers as training and testing samples, respectively.…”

Section: Proposed Extreme Learning Machine Algorithm For Laser Phase-mentioning

confidence: 99%

“…Note that in our previous study (ELMs to reduce phase error in radio-over-fiber OFDM schemes with direct detection [24]), H † does not consider the regularization parameter and, consequently, its inclusion could be explored in a next paper. Indeed, this constant is either discarded or not optimized in the rest of the manuscripts about OFDM schemes with the ELM algorithm as equalizer [13,[21][22][23]. In the next Subsection, an analysis of the complexity of the proposed ELM and other phase-error mitigation mechanisms is presented.…”

Section: Parameters' Optimization Of the Extreme Learning Machinementioning

confidence: 99%

“…The merits owned by the ELM algorithm make it a suitable alternative to perform channel equalization in OFDM schemes in wireless [13,21,22] and optical fiber [23,24] communications. On the one hand, a fully real-valued ELM to perform equalization and symbol detection processes in OFDM-based systems is introduced in Reference [21]. In the following, the methodology and results of this study are briefly exposed.…”

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: Introductionmentioning

confidence: 94%

Section: Proposed Extreme Learning Machine Algorithm For Laser Phase-mentioning

confidence: 99%

Section: Parameters' Optimization Of the Extreme Learning Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…In an additive white Gaussian noise (AWGN) channel, the standard ELM algorithm is utilized as a novel classification framework to perform correct symbol detection for amplitude phase shift keying constellations [16] by showing the same symbol and bit error rates as the well-known max log maximum a posteriori method (the optimal solution for symbol detection in single carrier). For OFDM signals over a frequency selective fading channel subject to diverse values of the signal-to-noise ratio (SNR), an algorithm that employs real-valued ELM that jointly solves the issue of equalization and symbol detection for QPSK and 16 quadrature amplitude modulation formats is presented [17]. Simulation results show that the proposed technique outperforms other learning-based equalizers in terms of the symbol error rate and the training speed.…”

Section: Introductionmentioning

confidence: 99%

Extreme Learning Machines to Combat Phase Noise in RoF-OFDM Schemes

et al. 2019

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Radio-over-fiber (RoF) orthogonal frequency division multiplexing (OFDM) systems have been revealed as the solution to support secure, cost-effective, and high-capacity wireless access for the future telecommunication systems. Unfortunately, the bandwidth-distance product in these schemes is mainly limited by phase noise that comes from the laser linewidth, as well as the chromatic fiber dispersion. On the other hand, the single-hidden layer feedforward neural network subject to the extreme learning machine (ELM) algorithm has been widely studied in regression and classification problems for different research fields, because of its good generalization performance and extremely fast learning speed. In this work, ELMs in the real and complex domains for direct-detection OFDM-based RoF schemes are proposed for the first time. These artificial neural networks are based on the use of pilot subcarriers as training samples and data subcarriers as testing samples, and consequently, their learning stages occur in real-time without decreasing the effective transmission rate. Regarding the feasible pilot-assisted equalization method, the effectiveness and simplicity of the ELM algorithm in the complex domain are highlighted by evaluation of a QPSK-OFDM signal over an additive white Gaussian noise channel at diverse laser linewidths and chromatic fiber dispersion effects and taking into account several OFDM symbol periods. Considering diverse relationships between the fiber transmission distance and the radio frequency (for practical design purposes) and the duration of a single OFDM symbol equal to 64 ns, the fully-complex ELM followed by the real ELM outperform the pilot-based correction channel in terms of the system performance tolerance against the signal-to-noise ratio and the laser linewidth.

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OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects

Sousa

Pires

Del-Moral-Hernandez

2017

Neural Comput & Applic

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QAM equalization and symbol detection in OFDM systems using extreme learning machine

Cited by 18 publications

References 28 publications

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

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

Extreme Learning Machines to Combat Phase Noise in RoF-OFDM Schemes

OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects

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