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Speech Enhancement Based on approximate message passing

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China Communications 2020年第8期17卷 187-198页

作者： Chao Li Ting Jiang Sheng Wu School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

To overcome the limitations of conventional speech enhancement methods, such as inaccurate voice activity detector(VAD) and noise estimation, a novel speech enhancement algorithm based on the approximate message passing(AMP) is adopted. AMP exploits the difference between speech and noise sparsity to remove or mute the noise from the corrupted speech. The AMP algorithm is adopted to reconstruct the clean speech efficiently for speech enhancement. More specifically, the prior probability distribution of speech sparsity coefficient is characterized by Gaussian-model, and the hyper-parameters of the prior model are excellently learned by expectation maximization(EM) algorithm. We utilize the k-nearest neighbor(k-NN) algorithm to learn the sparsity with the fact that the speech coefficients between adjacent frames are correlated. In addition, computational simulations are used to validate the proposed algorithm, which achieves better speech enhancement performance than other four baseline methods-Wiener filtering, subspace pursuit(SP), distributed sparsity adaptive matching pursuit(DSAMP), and expectation-maximization Gaussian-model approximate message passing(EM-GAMP) under different compression ratios and a wide range of signal to noise ratios(SNRs).

关键词： speech enhancement approximate message passing Gaussian model expectation maximization algorithm

Multi-Panel Extra-Large Scale MIMO Based Joint Activity Detection and Channel Estimation for Near-Field Massive IoT Access

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China Communications 2023年第5期20卷 232-243页

作者： Zhen Gao Hanlin Xiu Yikun Mei Anwen Liao Malong Ke Chun Hu Mohamed-Slim Alouini School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of TechnologyBeijing 100081China The Advanced Technology Research Institute of Beijing Institute of Technology Jinan 250307China Yangtze Delta Region Academy of Beijing Institute of Technology(Jiaxing) Jiaxing 314019China Electrical Engineering Program Division of Physical Sciences and EngineeringKing Abdullah University of Science and TechnologyThuwal 23955Saudi Arabia

The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication systems.Meanwhile,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider coverage.By combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)access.First,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV algorithm.The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.

关键词： extra-large scale MIMO massive IoT access active user detection channel estimation multipanel approximate message passing

Deep Learning-Based AMP for Massive MIMO Detection

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China Communications 2022年第10期19卷 69-77页

作者： Yang Yang Shaoping Chen Xiqi Gao Hubei Key Laboratory of Intelligent Wireless Communications South-Central Minzu UniversityWuhan 430074China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China

Low-complexity detectors play an essential role in massive multiple-input multiple-output (MIMO) transmissions. In this work, we discuss the perspectives of utilizing approximate message passing (AMP) algorithm to the detection of massive MIMO transmission. To this end, we need to efficiently reduce the divergence occurrence in AMP iterations and bridge the performance gap that AMP has from the optimum detector while making use of its advantage of low computational load. Our solution is to build a neural network to learn and optimize AMP detection with four groups of specifically designed learnable coefficients such that divergence rate and detection mean squared error (MSE) can be significantly reduced. Moreover, the proposed deep learning-based AMP has a much faster converging rate, and thus a much lower computational complexity than conventional AMP, providing an alternative solution for the massive MIMO detection. Extensive simulation experiments are provided to validate the advantages of the proposed deep learning-based AMP.

关键词： approximate message passing convergence machine learning

Denoising enabled channel estimation for underwater acoustic communications:A sparsity-aware model-driven learning approach

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Intelligent and Converged Networks 2023年第1期4卷 1-14页

作者： Sicong Liu Younan Mou Xianyao Wang Danping Su Ling Cheng School of Informatics Xiamen UniversityXiamen 361005China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China the Information Engineering University of the WitwatersrandJohannesburg 2050South Africa

It has always been difficult to achieve accurate information of the channel for underwater acoustic communications because of the severe underwater propagation conditions,including frequency-selective property,high relative mobility,long propagation latency,and intensive ambient noise,etc.To this end,a deep unfolding neural network based approach is proposed,in which multiple layers of the network mimic the iterations of the classical iterative sparse approximation algorithm to extract the inherent sparse features of the channel by exploiting deep learning,and a scheme based on the Sparsity-Aware DNN(SA-DNN)for UAC estimation is proposed to improve the estimation accuracy.Moreover,we propose a Denoising Sparsity-Aware DNN(DeSA-DNN)based enhanced method that integrates a denoising CNN module in the sparsity-aware deep network,so that the degradation brought by intensive ambient noise could be eliminated and the estimation accuracy can be further improved.Simulation results demonstrate that the performance of the proposed schemes is superior to the state-of-the-art compressed sensing based and iterative sparse recovery schems in the aspects of channel recovery precision,pilot overhead,and robustness,particularly under unideal circumstances of intensive ambient noise or inadequate measurement pilots.

关键词： Orthogonal Frequency Division Multiplexing(OFDM) Underwater Acoustic Communications(UAC) sparse recovery deep learning sparse learning denoising approximate message passing