To mitigate the mismatch of granularities between fixed grid and client traffic, the elastic optical network (EON) was proposed by using optical orthogonal frequency division multiplexing (OFDM). In EONs, every optica...
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To mitigate the mismatch of granularities between fixed grid and client traffic, the elastic optical network (EON) was proposed by using optical orthogonal frequency division multiplexing (OFDM). In EONs, every optical signal is generated with just enough sub-carriers. However, due to the consideration of optical OFDM, the fiber dispersion and cross talk between channels severely limit the communication system performance in EONs. Thus, it is very necessary to design effective channel estimation algorithms for the purpose of ensuring reliable and long-distance signal transmission. Therefore, in this paper, we design an identified channel estimation algorithm called as combined channel estimation algorithm (CCEA), with the joint consideration of improved discrete Fourier transformation and wavelet threshold de-noising method. Our CCEA avoids the generation of high-frequency components for the purpose of obtaining the accurate channelestimation and eliminates the noise information owned by effective sampling points using wavelet threshold de-noising method. The simulation results in our communication system for EONs demonstrate that: whether the bit error rate is higher or lower than the forward error correction limit, our algorithm always satisfies the requirements of communication systems well.
The maximum-likelihood (ML) time-frequency synchronisation algorithm combined with channelestimation for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems in frequency sele...
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The maximum-likelihood (ML) time-frequency synchronisation algorithm combined with channelestimation for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems in frequency selective fading channels is addressed. In the proposed algorithm, the authors use two steps to maximise an ML metric to obtain first the frequency offset and then timing. A fast Fourier transform algorithm is used to estimate the frequency offset. Using these two estimates, the channel is identified. A simple iterative algorithm is proposed to improve the frequency offset estimation. The performance of the proposed synchronisation approach, in terms of timing failure probability and mean square error of the estimated frequency offset and bit error rate, is compared with others in the literature. Comparison of simulation results with the Cramer-Rao lower bound clearly illustrates the accuracy of the proposed algorithm, which outperforms the state-of-the-art synchroniser devices in the open literature.
A new adaptive channel estimation algorithm for large-scale multiple input multiple output (MIMO) systems is proposed by combining the block sparsity adaptive matching pursuit (BSAMP) technique with adaptive beamformi...
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A new adaptive channel estimation algorithm for large-scale multiple input multiple output (MIMO) systems is proposed by combining the block sparsity adaptive matching pursuit (BSAMP) technique with adaptive beamforming. Firstly, the structure model based on continuous constant is optimised randomly, and it is used alternately with the basic denoising optimisation scheme to find the sparse characteristic channel. Then the sparse matrix is optimised based on adaptive beamforming to enhance the channel sparsity. Furthermore, based on BSAMP technology, using the joint sparsity of large-scale MIMO system subchannels, we set threshold and find the maximum backward difference position to select the atoms of the support set quickly and preliminarily. At the same time, the energy dispersion caused by the non-orthogonality of the observation matrix is considered to improve the estimation performance of the algorithm. Finally, the atoms are filtered by regularisation to improve the stability of the algorithm. Simulation results show that the algorithm can recover large-scale MIMO channel information with unknown sparsity quickly and accurately, and the average running time is only 0.12 s.
Orthogonal frequency division multiplexing (OFDM) is a transmission technique that is based on many orthogonal carriers that are transmitted simultaneously. OFDM effectively mitigates inter symbol interference (ISI) a...
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Orthogonal frequency division multiplexing (OFDM) is a transmission technique that is based on many orthogonal carriers that are transmitted simultaneously. OFDM effectively mitigates inter symbol interference (ISI) and has high-efficient frequency utilization. OFDM channelestimation is one of the key technologies in the OFDM system. In this paper, channelestimation techniques for OFDM systems, based on pilot-aided channel estimation algorithm, over mountain wireless environment are investigated. It is well known that least square (LS) and linear minimum mean square error (LMMSE) algorithms are effectual channelestimation methods for OFDM. We propose a novel modified LS channelestimation method, which is based on noise reduction procedure to improve the channelestimation accuracy. The simulation results demonstrate that comparing to LS channel estimation algorithm, LMMSE algorithm and the proposed modified LS algorithm provide better performances. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology
A semi-blind Expectation-Maximization (EM) channel estim ation algorithm is proposed for 50 Gb/s quadrature phase shift keying (QPSK)-Discrete MultiTone (DMT) signal transmission systems using intensity modulation/dir...
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ISBN:
(纸本)9781510625808
A semi-blind Expectation-Maximization (EM) channel estim ation algorithm is proposed for 50 Gb/s quadrature phase shift keying (QPSK)-Discrete MultiTone (DMT) signal transmission systems using intensity modulation/direct detection (IM/DD) over 100 km standard single mode fiber (SSMF). The reported channelestimation methods for DMT systems can be roughly divided into two categories: semi-blind channelestimation and blind channelestimation. Due to the low accuracy of traditional blind channel estimation algorithm and lower spectral efficiency of the semi-blind channel estimation algorithm relying on more training sequences (TS), EM algorithm is proposed that is a two-step iterative procedure to maximize the likelihood function for achieving channelestimation instead of classical semi-blind channelestimation methods with more TS. Also, we assume the channel of the IM/DD DMT system as an additive white gaussian noise (AWGN) channel. Simulation results show that using EM algorithm yields about 2 dB optical signal noise ratio (OSNR) improvement at a bit error ratio (BER) of 3.8x10(-3) compared to classical channelestimation based on TS under the same number of TS and has the similar performance compared to classical channelestimation relying on more TS. In addition, it is shown that at high OSNR (> 19 dB), the performance of EM algorithm outperforms that of LMS algorithm. On the contrary, the performance of least mean square (LMS) algorithm outperforms that of EM algorithm at low OSNR (< 19 dB).
Orthogonal frequency division multiplexing (OFDM) is a transmission technique that is based on many orthogonal carriers that are transmitted simultaneously. OFDM effectively mitigates inter symbol interference (ISI) a...
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Orthogonal frequency division multiplexing (OFDM) is a transmission technique that is based on many orthogonal carriers that are transmitted simultaneously. OFDM effectively mitigates inter symbol interference (ISI) and has high-efficient frequency utilization. OFDM channelestimation is one of the key technologies in the OFDM system. In this paper, channelestimation techniques for OFDM systems, based on pilot-aided channel estimation algorithm, over mountain wireless environment are investigated. It is well known that least square (LS) and linear minimum mean square error (LMMSE) algorithms are effectual channelestimation methods for OFDM. We propose a novel modified LS channelestimation method, which is based on noise reduction procedure to improve the channelestimation accuracy. The simulation results demonstrate that comparing to LS channel estimation algorithm, LMMSE algorithm and the proposed modified LS algorithm provide better performances.
This paper describes a general windowing method to improve the channelestimation of ultra-wideband communication systems, then proposes a new low-complexity channel estimation algorithm which can effectively resist t...
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ISBN:
(纸本)9781450365246
This paper describes a general windowing method to improve the channelestimation of ultra-wideband communication systems, then proposes a new low-complexity channel estimation algorithm which can effectively resist the inter-symbol interference. The algorithm can resist the inter-symbol interference caused by the channel impulse response. The algorithm only requires a 32-point FFT module. It is verified that the algorithm can effectively reduce the interference caused by multi-path channel and noise.
A comparative investigation on various channel estimation algorithms for OFDM system in the mobile communication environment is presented and analyzed in terms of computational complexity, mean square error, and bit e...
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A comparative investigation on various channel estimation algorithms for OFDM system in the mobile communication environment is presented and analyzed in terms of computational complexity, mean square error, and bit error rate in this paper. As a result, Wiener filter estimation shows the best error performance. Concerning the computational complexity as well as the performance, however, the piecewise linear estimator is considered as a proper choice when the reference signal spacing is relatively narrow. And the cubic-spline estimator is a good alternative to the Wiener filter estimation if the reference signal spacing is wider than the coherent bandwidth of transmission channel.
As the 5G technology is applied indoors, the antenna array aperture and the distance from the source to the antenna array become comparable, which makes the far-field signal model no longer valid. In this case, the co...
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ISBN:
(纸本)9781728135175
As the 5G technology is applied indoors, the antenna array aperture and the distance from the source to the antenna array become comparable, which makes the far-field signal model no longer valid. In this case, the complexity of signal model is increased because the channel parameters are not only the angle of arrival, but also the distance. In this paper, a near-field signal model based on a uniform circular array (UCA) is studied to avoid the estimation error caused by the plane wave model. Moreover, channel estimation algorithms based on near-field signal model are investigated and compared in terms of estimation accuracy and algorithm complexity. Finally based on these two aspects, the algorithm that is more suitable for indoor communication will be selected.
Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm-based channelestimation in OFDM (DiWGAN-Hyb AOA-COA-MIMO-OFDM) is proposed i...
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Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm-based channelestimation in OFDM (DiWGAN-Hyb AOA-COA-MIMO-OFDM) is proposed in this manuscript. In OFDM, there is a non-stationary channel physical appearance during channelestimation (CE). Therefore in this work, Hyb AOA-COA is employed to enhance the DiWGAN weight parameters. The proposed DiWGAN-Hyb AOA-COA-MIMO-OFDM technique is executed in network simulator (NS2) tool. The proposed technique attains lower computational cost 99.67%, 92.34%, and 97.45%;lesser bit error rate 98.33%, 83.12%, and 88.96%;and lesser mean square error 93.15%, 79.90%, and 92.88% compared with existing methods, like MIMO-OFDM system using deep neural network and MN-based improved AMO model (DNN-IAMO-MIMO-OFDM), MIMO-OFDM systems using the deep learning and optimization (RBFNN-PSO-MIMO-OFDM), and MIMO-OFDM systems using hybrid neural network (HNN-CSI-MIMO-OFDM) respectively. Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm-based channelestimation in OFDM (DiWGAN-Hyb-AOA-COA-MIMO-OFDM) is proposed in this manuscript. During channelestimation in OFDM, there is a non-stationary channel physical appearance, problems arise to overcome this issue, and DiWGAN is used. Therefore in this work, hybrid Archimedes optimization algorithm and chimp optimization algorithm (Hyb AOA-COA) are employed to enhance the DiWGAN weight parameters. The proposed is compared with existing methods, like DNN-IAMO-MIMO-OFDM, MIMO OFDM RBFNN-PSO-MIMO-OFDM, and HNN-CSI-MIMO-OFDM, respectively. image
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