The Experts below are selected from a list of 2193 Experts worldwide ranked by ideXlab platform
Changyong Pan - One of the best experts on this subject based on the ideXlab platform.
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Clipping Noise estimation based on deep complex neural network with sparsity constraint
Vehicular Technology Conference, 2020Co-Authors: Xudong Zhang, Yu Zhang, Xiaohua Chang, Changyong PanAbstract:Clipping Noise estimation and cancellation are essential in orthogonal frequency division multiplexing (OFDM) systems when Clipping is performed to reduce the peak-to-average power ratio (PAPR). Motivated by the richer representational capacity of complex numbers and the fact that communication is a complex-valued problem, a novel Clipping Noise estimation scheme based on deep complex neural network is proposed in this paper. Specifically, the Clipping Noise is determined by a deep complex network, namely Clipping Noise estimation network (CNE-Net), such that the mean square error (MSE) and the sparsity of the estimated Clipping Noise are jointly optimized. Besides, an ordering based zero-forcing scheme is utilized to further ensure the sparsity of the estimated Clipping Noise. Simulation results show that the proposed CNE-Net shows comparable performance with the conventional decision-aided reconstruction (DAR) scheme and can achieve better performance than the one-iteration DAR scheme when the Clipping Noise is not sparse enough. In summary, the CNE-Net has a good capability to estimate the Clipping Noise from Noise-affected features.
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robust Clipping Noise cancellation based on location aware compressed sensing
Vehicular Technology Conference, 2020Co-Authors: Xudong Zhang, Yu Zhang, Xiaohua Chang, Changyong PanAbstract:For OFDM systems, to cope with the remain problems of high complexity and low performance in conventional Clipping Noise cancellation methods, a robust scheme based on location-aware compressed sensing (CS) and phase correction is proposed in this paper. Based on CS theory, a simple and configurable selection criterion is utilized to choose reliable observations for the Clipping Noise reconstruction. The transceiver is redesigned to transmit both the data and the Clipping location. With the aid of Clipping location information and phase information from the receiver, the proposed scheme improves both the accuracy and computational complexity. Simulation results show that the proposed scheme achieves excellent performance even in low signal-to-Noise ratio (SNR) environments. Besides, due to the low computational complexity and excellent adaptivity, the proposed scheme is more feasible in practical engineering applications than other CS-based methods.
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VTC Spring - Clipping Noise Estimation Based on Deep Complex Neural Network with Sparsity Constraint
2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 2020Co-Authors: Xudong Zhang, Yu Zhang, Xiaohua Chang, Changyong PanAbstract:Clipping Noise estimation and cancellation are essential in orthogonal frequency division multiplexing (OFDM) systems when Clipping is performed to reduce the peak-to-average power ratio (PAPR). Motivated by the richer representational capacity of complex numbers and the fact that communication is a complex-valued problem, a novel Clipping Noise estimation scheme based on deep complex neural network is proposed in this paper. Specifically, the Clipping Noise is determined by a deep complex network, namely Clipping Noise estimation network (CNE-Net), such that the mean square error (MSE) and the sparsity of the estimated Clipping Noise are jointly optimized. Besides, an ordering based zero-forcing scheme is utilized to further ensure the sparsity of the estimated Clipping Noise. Simulation results show that the proposed CNE-Net shows comparable performance with the conventional decision-aided reconstruction (DAR) scheme and can achieve better performance than the one-iteration DAR scheme when the Clipping Noise is not sparse enough. In summary, the CNE-Net has a good capability to estimate the Clipping Noise from Noise-affected features.
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VTC Spring - Robust Clipping Noise Cancellation Based on Location-Aware Compressed Sensing
2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 2020Co-Authors: Xudong Zhang, Yu Zhang, Xiaohua Chang, Changyong PanAbstract:For OFDM systems, to cope with the remain problems of high complexity and low performance in conventional Clipping Noise cancellation methods, a robust scheme based on location-aware compressed sensing (CS) and phase correction is proposed in this paper. Based on CS theory, a simple and configurable selection criterion is utilized to choose reliable observations for the Clipping Noise reconstruction. The transceiver is redesigned to transmit both the data and the Clipping location. With the aid of Clipping location information and phase information from the receiver, the proposed scheme improves both the accuracy and computational complexity. Simulation results show that the proposed scheme achieves excellent performance even in low signal-to-Noise ratio (SNR) environments. Besides, due to the low computational complexity and excellent adaptivity, the proposed scheme is more feasible in practical engineering applications than other CS-based methods.
Seunghwan Lee - One of the best experts on this subject based on the ideXlab platform.
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reliable data aided sparsity aware approaches to Clipping Noise estimation in ofdm systems
Vehicular Technology Conference, 2012Co-Authors: Junho Lee, Seunghwan LeeAbstract:In this paper, we propose reliable data aided sparsity-aware approaches to estimate and cancel the Clipping Noise in OFDM systems. Those are motivated by the fact that reliable data can be exploited to estimate the Clipping Noise in a successive interference cancellation (SIC) manner. When the Clipping Noise has relatively large support, a data non-aided method is not class enough to estimate the Clipping Noise well due to the compressed sensing (CS) based measurement shortages. Simulation results demonstrate the effectiveness of our proposed methods in estimating the Clipping Noise and approaching the performance with no Clipping Noise.
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VTC Fall - Reliable Data Aided Sparsity-Aware Approaches to Clipping Noise Estimation in OFDM Systems
2012 IEEE Vehicular Technology Conference (VTC Fall), 2012Co-Authors: Junho Lee, Seunghwan LeeAbstract:In this paper, we propose reliable data aided sparsity-aware approaches to estimate and cancel the Clipping Noise in OFDM systems. Those are motivated by the fact that reliable data can be exploited to estimate the Clipping Noise in a successive interference cancellation (SIC) manner. When the Clipping Noise has relatively large support, a data non-aided method is not class enough to estimate the Clipping Noise well due to the compressed sensing (CS) based measurement shortages. Simulation results demonstrate the effectiveness of our proposed methods in estimating the Clipping Noise and approaching the performance with no Clipping Noise.
Kamran Kiasaleh - One of the best experts on this subject based on the ideXlab platform.
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Statistical Modelling of the Clipping Noise in OFDM-based Visible Light Communication System.
arXiv: Signal Processing, 2019Co-Authors: Nima Taherkhani, Kamran KiasalehAbstract:This paper analyses the statistics of the Clipping Noise in orthogonal frequency-division-multiplex (OFDM) based visible light Communication systems. The clipped signal is generally modelled as the summation of the scaled original signal and Clipping Noise, which is treated by the linear equalizer in the receiver. Generally, it is assumed that the clipped and original signal share the same statistics. Although valid in some cases, we show that such assumption is invalid when the transmitter is tightly constrained. We derive closed-form probability distribution function (pdf) for the Clipping Noise and use the pdf for statistical hypothesis testing in an optimum receiver
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reed solomon encoding for the mitigation of Clipping Noise in ofdm based visible light communications
2018 International Conference on Computing Networking and Communications (ICNC), 2018Co-Authors: Nima Taherkhani, Kamran KiasalehAbstract:In this paper, we present a Clipping Noise mitigation technique in orthogonal frequency-division multiplexing (OFDM) based visible light communication (VLC) system employing Reed Solomon (RS) coding. In this technique, the data is encoded using an RS codeword and then part of the redundancy introduced by the frame which exceeds the Clipping range are removed by applying different number of puncturing, and the redundancy left in the data are used at the receiver side to retrieve the data block. The puncturing of the redundancy aids in mitigating Clipping Noise generated by truncating the OFDM symbols at the transmitter by shortening the number of active subcarriers, while the residual redundancy are exploited at the receiver for the reconstruction and correction of errors.
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ICNC - Reed Solomon Encoding for the Mitigation of Clipping Noise in OFDM-Based Visible Light Communications
2018 International Conference on Computing Networking and Communications (ICNC), 2018Co-Authors: Nima Taherkhani, Kamran KiasalehAbstract:In this paper, we present a Clipping Noise mitigation technique in orthogonal frequency-division multiplexing (OFDM) based visible light communication (VLC) system employing Reed Solomon (RS) coding. In this technique, the data is encoded using an RS codeword and then part of the redundancy introduced by the frame which exceeds the Clipping range are removed by applying different number of puncturing, and the redundancy left in the data are used at the receiver side to retrieve the data block. The puncturing of the redundancy aids in mitigating Clipping Noise generated by truncating the OFDM symbols at the transmitter by shortening the number of active subcarriers, while the residual redundancy are exploited at the receiver for the reconstruction and correction of errors.
Xudong Zhang - One of the best experts on this subject based on the ideXlab platform.
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Clipping Noise estimation based on deep complex neural network with sparsity constraint
Vehicular Technology Conference, 2020Co-Authors: Xudong Zhang, Yu Zhang, Xiaohua Chang, Changyong PanAbstract:Clipping Noise estimation and cancellation are essential in orthogonal frequency division multiplexing (OFDM) systems when Clipping is performed to reduce the peak-to-average power ratio (PAPR). Motivated by the richer representational capacity of complex numbers and the fact that communication is a complex-valued problem, a novel Clipping Noise estimation scheme based on deep complex neural network is proposed in this paper. Specifically, the Clipping Noise is determined by a deep complex network, namely Clipping Noise estimation network (CNE-Net), such that the mean square error (MSE) and the sparsity of the estimated Clipping Noise are jointly optimized. Besides, an ordering based zero-forcing scheme is utilized to further ensure the sparsity of the estimated Clipping Noise. Simulation results show that the proposed CNE-Net shows comparable performance with the conventional decision-aided reconstruction (DAR) scheme and can achieve better performance than the one-iteration DAR scheme when the Clipping Noise is not sparse enough. In summary, the CNE-Net has a good capability to estimate the Clipping Noise from Noise-affected features.
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robust Clipping Noise cancellation based on location aware compressed sensing
Vehicular Technology Conference, 2020Co-Authors: Xudong Zhang, Yu Zhang, Xiaohua Chang, Changyong PanAbstract:For OFDM systems, to cope with the remain problems of high complexity and low performance in conventional Clipping Noise cancellation methods, a robust scheme based on location-aware compressed sensing (CS) and phase correction is proposed in this paper. Based on CS theory, a simple and configurable selection criterion is utilized to choose reliable observations for the Clipping Noise reconstruction. The transceiver is redesigned to transmit both the data and the Clipping location. With the aid of Clipping location information and phase information from the receiver, the proposed scheme improves both the accuracy and computational complexity. Simulation results show that the proposed scheme achieves excellent performance even in low signal-to-Noise ratio (SNR) environments. Besides, due to the low computational complexity and excellent adaptivity, the proposed scheme is more feasible in practical engineering applications than other CS-based methods.
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VTC Spring - Clipping Noise Estimation Based on Deep Complex Neural Network with Sparsity Constraint
2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 2020Co-Authors: Xudong Zhang, Yu Zhang, Xiaohua Chang, Changyong PanAbstract:Clipping Noise estimation and cancellation are essential in orthogonal frequency division multiplexing (OFDM) systems when Clipping is performed to reduce the peak-to-average power ratio (PAPR). Motivated by the richer representational capacity of complex numbers and the fact that communication is a complex-valued problem, a novel Clipping Noise estimation scheme based on deep complex neural network is proposed in this paper. Specifically, the Clipping Noise is determined by a deep complex network, namely Clipping Noise estimation network (CNE-Net), such that the mean square error (MSE) and the sparsity of the estimated Clipping Noise are jointly optimized. Besides, an ordering based zero-forcing scheme is utilized to further ensure the sparsity of the estimated Clipping Noise. Simulation results show that the proposed CNE-Net shows comparable performance with the conventional decision-aided reconstruction (DAR) scheme and can achieve better performance than the one-iteration DAR scheme when the Clipping Noise is not sparse enough. In summary, the CNE-Net has a good capability to estimate the Clipping Noise from Noise-affected features.
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VTC Spring - Robust Clipping Noise Cancellation Based on Location-Aware Compressed Sensing
2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 2020Co-Authors: Xudong Zhang, Yu Zhang, Xiaohua Chang, Changyong PanAbstract:For OFDM systems, to cope with the remain problems of high complexity and low performance in conventional Clipping Noise cancellation methods, a robust scheme based on location-aware compressed sensing (CS) and phase correction is proposed in this paper. Based on CS theory, a simple and configurable selection criterion is utilized to choose reliable observations for the Clipping Noise reconstruction. The transceiver is redesigned to transmit both the data and the Clipping location. With the aid of Clipping location information and phase information from the receiver, the proposed scheme improves both the accuracy and computational complexity. Simulation results show that the proposed scheme achieves excellent performance even in low signal-to-Noise ratio (SNR) environments. Besides, due to the low computational complexity and excellent adaptivity, the proposed scheme is more feasible in practical engineering applications than other CS-based methods.
Harald Haas - One of the best experts on this subject based on the ideXlab platform.
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Clipping Noise mitigation using partial transmit sequence for optical OFDM systems
2014 3rd International Workshop in Optical Wireless Communications (IWOW), 2014Co-Authors: Z. Esat Ankarali, S. Imtiaz Hussain, Mohamad Abdallah, Khalid A. Qaraqe, Huseyin Arslan, Harald HaasAbstract:Orthogonal frequency division multiplexing (OFDM) is a promising technology to achieve higher data rates in optical wireless communication (OWC) systems. Unlike single carrier techniques, OFDM can easily handle inter-symbol interference (ISI) by adding a small redundancy, i.e., cyclic prefix (CP). However, due to the imperfections of optical transmitters, i.e., light-emitting-diodes (LEDs), and insufficient direct current (DC) biasing, OFDM signal with a large peak-to-average power ratio (PAPR) can be typically clipped double sidedly. Clipping results in a Noise effect in frequency domain and degrades the performance. In this paper, we propose to mitigate Clipping Noise effect in waveform domain using a partial transmit sequence (PTS) based approach. In this approach, at the transmitter, the input data is multiplied by a sequence of phase factors that are properly selected to reduce the PAPR of the signals and thus minimize the Clipping Noise. Our results show that, at the expense of a reasonable bandwidth penalty due to the transmission of the phase factors to the receiver and extra complexity exponentially proportional to the number of the phase factors, Clipping Noise is decreased and a considerable improvement is obtained in the BER performance.
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Clipping Noise in OFDM-based optical wireless communication systems
IEEE Transactions on Communications, 2012Co-Authors: Svilen Dimitrov, Sinan Sinanovic, Harald HaasAbstract:In this paper, the impact of Clipping Noise on optical wireless communication (OWC) systems employing orthogonal frequency division multiplexing (OFDM) is investigated. The two existing optical OFDM (O-OFDM) transmission schemes, asymmetrically clipped optical OFDM (ACO-OFDM) and direct-current-biased optical OFDM (DCO-OFDM), are studied. Time domain signal Clipping generally results from direct current (DC) biasing and/or from physical limitations of the transmitter front-end. These include insufficient forward biasing and the maximum power driving limit of the emitter. The Clipping Noise can be modeled according to the Bussgang theorem and the central limit theorem (CLT) as attenuation of the data-carrying subcarriers at the receiver and addition of zero-mean complex-valued Gaussian Noise. Analytical expressions for the attenuation factor and the Clipping Noise variance are determined in closed-form and employed in the derivation of the electrical signal-to-Noise ratio (SNR). The validity of the model is verified through a Monte Carlo bit-error ratio (BER) simulation. Finally, the BER performance of ACO-OFDM with DCO-OFDM is compared for different Clipping levels and multi-level quadrature amplitude modulation (M-QAM) schemes.
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On the Clipping Noise in an ACO-OFDM optical wireless communication system
GLOBECOM - IEEE Global Telecommunications Conference, 2010Co-Authors: Svilen Dimitrov, Harald HaasAbstract:In this paper, the Clipping Noise in an asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) wireless communication system is derived semi-analytically. Clipping Noise in ACO-OFDM is introduced either because of insufficient forward biasing of the emitter, or a low power sensitivity of the detector. Following the Bussgang theorem, the non-linear distortion caused by the Clipping of the time domain signal attenuates the frequency domain subcarriers at the receiver and adds zero-mean Gaussian Noise. The attenuation factor and the Clipping Noise variance are determined and verified through simulation. Finally, bit-error ratio (BER) and goodput simulations compare the performance of ACO-OFDM with a direct-current-biased optical OFDM (DCO-OFDM) system for different Clipping levels and multilevel quadrature amplitude modulation (M-QAM) schemes.
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GLOBECOM - On the Clipping Noise in an ACO-OFDM Optical Wireless Communication System
2010 IEEE Global Telecommunications Conference GLOBECOM 2010, 2010Co-Authors: Svilen Dimitrov, Harald HaasAbstract:In this paper, the Clipping Noise in an asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) wireless communication system is derived semi-analytically. Clipping Noise in ACO-OFDM is introduced either because of insufficient forward biasing of the emitter, or a low power sensitivity of the detector. Following the Bussgang theorem, the non-linear distortion caused by the Clipping of the time domain signal attenuates the frequency domain subcarriers at the receiver and adds zero-mean Gaussian Noise. The attenuation factor and the Clipping Noise variance are determined and verified through simulation. Finally, bit-error ratio (BER) and goodput simulations compare the performance of ACO-OFDM with a direct-current-biased optical OFDM (DCO-OFDM) system for different Clipping levels and multilevel quadrature amplitude modulation ($M$-QAM) schemes.