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.
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).
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.
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
The field of wireless communication is experiencing rapid advancements, leading to remarkable achievements in terms of both high data rates and low latency. Notably, the emergence of 5G has significantly influenced th...
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ISBN:
(纸本)9783031538292;9783031538308
The field of wireless communication is experiencing rapid advancements, leading to remarkable achievements in terms of both high data rates and low latency. Notably, the emergence of 5G has significantly influenced the landscape, boasting impressive speeds of up to 20 Gb/s. Furthermore, the utilization of technologies like Orthogonal Frequency DivisionMultiplexing (OFDM) has played a crucial role in optimizing the efficiency of spectral utilization within communication networks. However, despite these advancements, the progress of wireless communication has introduced challenges in accurately estimating channels. This is primarily due to the presence of varied factors such as distortion, attenuation, fading, scattering, and other interruptions that affect the transmission of radio waves to their intended destinations. To address this issue, researchers have put forward suggestions aimed at enhancing the accuracy of channelestimation and strengthen the signal. In this paper, an extensive and comprehensive review of various channelestimation techniques provided, categorizing them based on their unique characteristics. Additionally, the role of the upcoming technologies, like artificial intelligence in the context ofwireless communicationwill be investigated. Such a review will be invaluable in making informed decisions regarding the selection of channelestimation techniques for emerging wireless communication systems.
Mountaionus terrain with moderate-to-heavy tree densities generally falls under maximum path loss category, during signal strength prediction and simulation in macrocellular environment. Traditional path loss models l...
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ISBN:
(数字)9781665452984
ISBN:
(纸本)9781665452984
Mountaionus terrain with moderate-to-heavy tree densities generally falls under maximum path loss category, during signal strength prediction and simulation in macrocellular environment. Traditional path loss models like Hata-Okumura model etc. are not suitable for hilly terrain with moderate-to-heavy tree densities. Recently, replecating the actual terrain scenarios, various Stanford University interim (SUI) channel models are proposed. The contribution of this work are two fold. First, we propose a modified least square (LS) algorithm which can efficiently estimate the channel model SUI-6. Secondly, to satisfy the required conditions of the proposed modified LS algorithm, we propose a new class of Z-complementary pairs (ZCPs). Numerical simulations ensure the efficacy of the proposed ZCPs under modified LS algorithm.
The increasing road traffic demands innovative safety solutions, and researchers are exploring V2V communication via 5G massive MIMO at 60 GHz using roadside lamp-based base stations. This approach, with its numerous ...
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ISBN:
(纸本)9783031538292;9783031538308
The increasing road traffic demands innovative safety solutions, and researchers are exploring V2V communication via 5G massive MIMO at 60 GHz using roadside lamp-based base stations. This approach, with its numerous antennas, offers benefits like improved spectral efficiency, higher throughput, expanded coverage, energy efficiency, and reduced latency. However, deploying massive MIMO in mmWave technology poses challenges, particularly in selecting a suitable channel estimation algorithm. This research paper addresses the channelestimation issue and introduces a sparsity adaptive algorithm that balances accuracy and computational complexity effectively. The algorithm optimizes channelestimation in 60 GHz scenarios relevant for vehicular communications by leveraging channel sparsity, reducing complexity while maintaining accuracy. It is well-suited for real-time vehicular communication applications. To validate the proposed algorithm's efficiency, the paper presents a comprehensive survey of existing channelestimation methods. A comparison table discusses various scenarios, particularly those in the 60 GHz range, providing insights into the algorithm's performance in different settings. These results hold promise for enhancing V2V communication systems, contributing to safer and more efficient road networks amid growing vehicular density.
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.
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.
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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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.
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