To compensate for the severe pathloss in satellite broadcasting system, the phased array with a half wavelength aperture is the conventional scheme. However, it is difficult to further scale up the phased array to pro...
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To compensate for the severe pathloss in satellite broadcasting system, the phased array with a half wavelength aperture is the conventional scheme. However, it is difficult to further scale up the phased array to provide high data rate communication. The reconfigurable holographic surface (RHS) with continuous or quasi-continuous aperture is a promising alternative to phased array for satellite communications. Unlike the amplitude ratio controlled RHS, the amplitude-controlled RHS is firstly modelled in the satellite broadcasting system. To reduce the computational complexity of the RHS sum rate maximization problem, the unrolled gradient projection (GP) method is proposed. The simulation results show that the RHS scheme has larger sum rate than the phased array method, and the unrolled GP method for RHS is faster than the phased array method.
Owing to the manually fixed step size, the conventional gradient projection (GP) method requires relatively long time to solve the reconfigurable intelligent surface (RIS) aided hybrid beamforming problem. In order to...
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Owing to the manually fixed step size, the conventional gradient projection (GP) method requires relatively long time to solve the reconfigurable intelligent surface (RIS) aided hybrid beamforming problem. In order to speed up the GP method, we propose to learn the step sizes by using deep learning. Since the proposed deep learning architecture has a coordinate ascent structure, every step in the deep learning is explainable. Due to the simple multi-layer architecture, the proposed unrolled GP method has a strong out-of-distribution generalization capability. Under a single training setting, the unrolled GP approach is tested under thirty nine different out-of-distribution settings. The extensive simulation results show that the unrolled GP method has larger achievable rate than the GP method under middle-to-high signal-to-noise ratio (SNR) settings, and the proposed method is ten times faster than the GP method for all settings.
This paper introduces an unrolled Expectation Maximization (EM) algorithm for sparse image reconstruction from radio interferometric measurements in the presence of a compound Gaussian distribution noise. Traditional ...
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ISBN:
(纸本)9789464593617;9798331519773
This paper introduces an unrolled Expectation Maximization (EM) algorithm for sparse image reconstruction from radio interferometric measurements in the presence of a compound Gaussian distribution noise. Traditional model-based reconstruction methods, rooted in inference and optimization fields, provide an initial foundation with theoretical guarantees, but their performance is highly linked to the model accuracy and the choice of hyperparameter values. The popularity of supervised machine learning rose over the last decade, yet they faced hurdles related to interpretability and theoretical foundations. The emergence of unrolled algorithms addresses these limitations by combining the strengths of both approaches. We specifically focus on unrolling a regularized EM algorithm as a feedforward neural network with a residual connection. Experimental results showcase improvements over the iterative EM version.
Time-frequency analysis (TFA) is widely used to describe local time-frequency (TF) features of seismic data. Among the commonly used TFA tools, sparse TFA (STFA) is an excellent one, which can obtain a TF spectrum wit...
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Time-frequency analysis (TFA) is widely used to describe local time-frequency (TF) features of seismic data. Among the commonly used TFA tools, sparse TFA (STFA) is an excellent one, which can obtain a TF spectrum with good readability. However, many STFA algorithms suffer from expensive calculation time and unavoidable prior knowledge, such as the iterative shrinkage-thresholding algorithm (ISTA) and the sparse reconstruction by separable approximation (SpaRSA). Inspired by the unrolled algorithm and its successful applications in signal processing, we propose a deep learning (DL)-based ISTA unrolled algorithm, which is named the sparse time-frequency analysis network (STFANet). The STFANet contains two parts, i.e., the sparse TF spectrum generator and the reconstruction module. The former learns how to transform a 1-D seismic signal from a large amount of unlabeled data into a 2-D sparse TF spectrum, which is implemented based on the proposed unrolled iterative dynamic shrinkage-thresholding (UIDST) algorithm. Note that the UIDST algorithm is carried out by using a simplified DL network. The latter serves as a physical constraint of model training to ensure that our generator obtains an accurate TF spectrum, which is actually an inverse TF transform. In this study, the traditional inverse short-time Fourier transform (STFT) is utilized in the reconstruction module. To test the effectiveness of the proposed model, we apply it to 3-D poststack field data. The results show that, compared with the traditional TFA tools, the STFANet can availably compute the TF spectrum with better readability, which benefits seismic attenuation delineation.
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