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Sparse Bayesian Learning Using Complex t-Prior for Beam-Domain Massive MIMO Channel Estimation

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 5905-5920页

作者： Furuta, Kengo Takahashi, Takumi Ochiai, Hideki Osaka Univ Grad Sch Engn Suita 5650871 Japan

This paper proposes a novel beam-domain channel estimation (CE) algorithm via sparse Bayesian learning (SBL) using complex t-prior for massive multi-user multiple-input multiple-output (MIMO) systems. Due to the sidelobe leakage and insufficient observation resolution resulting from physical constraints, the equivalent channel after digital beamforming at the receiver has a structure with many small but non-zero elements, which cannot be modeled strictly as a sparse signal. To fully capture this pseudo-sparse structure characterized by the signal strength variations among elements, we design a novel SBL algorithm that incorporates a complex t-distribution using a hierarchical Bayesian model. By utilizing a high degree of adaptability of this heavy-tailed prior, it is possible to efficiently learn the signal strength, accounting for elements with non-zero but small values, which is verified by the regularization analysis based on an equivalent optimization problem. The efficacy of the proposed CE algorithm is confirmed by numerical simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) sparse signal recovery (SSR)-based algorithms but also achieves the performance of a genie-aided scheme over a wide signal-to-noise ratio (SNR) range in both sub-6 GHz and millimeter-wave (mmWave) wireless communication scenarios.

关键词： Massive MIMO Bayes methods signal processing algorithms Channel estimation Millimeter wave communication Estimation Accuracy channel estimation sparse Bayesian learning hierarchical Bayesian model complex t-distribution beam-domain signal processing

Efficient Nonnegative Tensor Decomposition Using Alternating Direction Proximal Method of Multipliers

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Chinese Journal of Electronics 2024年第5期33卷 1308-1316页

作者： Deqing WANG Guoqiang HU State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory of Marine Robotics Liaoning Province College of Artificial Intelligence Dalian Maritime University

Nonnegative CANDECOMP/PARAFAC（NCP） tensor decomposition is a powerful tool for multiway signal processing. The alternating direction method of multipliers（ADMM） optimization algorithm has become increasingly popular for solving tensor decomposition problems in the block coordinate descent framework. However,the ADMM-based NCP algorithm suffers from rank deficiency and slow convergence for some large-scale and highly sparse tensor data. The proximal algorithm is preferred to enhance optimization algorithms and improve convergence properties. In this study, we propose a novel NCP algorithm using the alternating direction proximal method of multipliers（ADPMM） that consists of the proximal algorithm. The proposed NCP algorithm can guarantee convergence and overcome the rank deficiency. Moreover, we implement the proposed NCP using an inexact scheme that alternatively optimizes the subproblems. Each subproblem is optimized by a finite number of inner iterations yielding fast computation speed. Our NCP algorithm is a hybrid of alternating optimization and ADPMM and is named *** experimental results on synthetic and real-world tensors demonstrate the effectiveness and efficiency of our proposed algorithm.

关键词： Tensors Neuroscience signal processing algorithms Linearization techniques signal processing Fluorescence Data processing

Point Cloud Resampling via Hypergraph signal processing

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IEEE signal processing LETTERS 2021年 28卷 2117-2121页

作者： Deng, Qinwen Zhang, Songyang Ding, Zhi Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA

Three-dimensional (3D) point clouds are important data representations in visualization applications. The rapidly growing utility and popularity of point cloud processing strongly motivate a plethora of research activities on large-scale point cloud processing and feature extraction. In this work, we investigate point cloud resampling based on hypergraph signal processing (HGSP). We develop a novel method to extract sharp object features and reduce the data size of point cloud representation. By directly estimating hypergraph spectrum based on hypergraph stationary processing, we design a spectral kernel-based filter to capture high-dimensional interactions among point signal nodes and to better preserve object surface outlines. Experimental results validate the effectiveness of hypergraph in representing point clouds, and demonstrate the robustness of the proposed algorithm under noise.

关键词： Three-dimensional displays Kernel Surface reconstruction Tensors Feature extraction signal processing algorithms signal processing Compression hypergraph signal processing point cloud resampling virtual reality

Robust Estimation of the Covariance Matrix From Data With Outliers

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IEEE OPEN JOURNAL OF signal processing 2024年 5卷 1061-1072页

作者： Stoica, Petre Babu, Prabhu Varshney, Piyush Uppsala Univ Dept Informat Technol Div Syst & Control S-75237 Uppsala Sweden Indian Inst Technol Delhi Ctr Appl Res Elect CARE New Delhi 110016 India

The robust estimation of the covariance matrix is a frequent task in practical applications in which, more often than not, some data samples are outliers. There are several methods that can be used to robustly estimate a covariance matrix from corrupted data, a representative example of which is the minimum covariance determinant (MCD) method. In this paper we present a maximum conditional likelihood interpretation of MCD that provides a new motivation of as well as further insights into this method. To perform at its best MCD requires information on the number of outliers in the data, which usually is not available. We propose two new methods for covariance matrix estimation from data with outliers that do not suffer from this problem: TEST (multiple-hypothesis testing method) which uses the FDR (false discovery rate) to test a set of model hypotheses and hence estimate the number of outliers and their locations, and LIKE (penalized likelihood method) that solves the outlier estimation problem using a GIC (generalized information criterion) to penalize the complexity of a high-dimensional data model. We show by means of numerical simulations that the performances of TEST and LIKE are relatively similar to one another as well as to the performance of the oracle MCD (which uses the true number of outliers) and significantly better than the performance of MCD that uses an upper bound on the outlier number.

关键词： Covariance matrices Estimation Testing signal processing algorithms Minimization Upper bound signal processing Symbols Numerical simulation Numerical models Robust covariance matrix estimation outlier detection minimum covariance determinant false discovery rate

ICASSP 2023 Acoustic Echo Cancellation Challenge

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IEEE OPEN JOURNAL OF signal processing 2024年 5卷 675-685页

作者： Cutler, Ross Saabas, Ando Parnamaa, Tanel Purin, Marju Indenbom, Evgenii Ristea, Nicolae-Catalin Guzvin, Jegor Gamper, Hannes Braun, Sebastian Aichner, Robert Microsoft Corp Redmond WA 98052 USA

The ICASSP 2023 Acoustic Echo Cancellation Challenge is intended to stimulate research in acoustic echo cancellation (AEC), which is an important area of speech enhancement and is still a top issue in audio communication. This is the fourth AEC challenge and it is enhanced by adding a second track for personalized acoustic echo cancellation, reducing the algorithmic + buffering latency to 20 ms, as well as including a full-band version of AECMOS (Purin et al., 2020). We open source two large datasets to train AEC models under both single talk and double talk scenarios. These datasets consist of recordings from more than 10,000 real audio devices and human speakers in real environments, as well as a synthetic dataset. We open source an online subjective test framework and provide an objective metric for researchers to quickly test their results. The winners of this challenge were selected based on the average mean opinion score (MOS) achieved across all scenarios and the word accuracy (WAcc) rate.

关键词： Measurement signal processing algorithms Speech enhancement Echo cancellers Acoustics Training Recording Acoustic echo cancellation deep learning speech enhancement speech quality assessment subjective test

FM-RTDETR: Small Object Detection Algorithm Based on Enhanced Feature Fusion With Mamba

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IEEE signal processing LETTERS 2025年 32卷 1570-1574页

作者： Yang, Yuchuan Dai, Jiahui Wang, Yong Chen, Yafei Chongqing Univ Technol Sch Artificial Intelligence Chongqing 401135 Peoples R China

Traditional real-time object detection networks deployed in autonomous aerial vehicles (AAVs) struggle to extract features from small objects in complex backgrounds with occlusions and overlapping objects. To address this challenge, we propose FM-RTDETR, a real-time object detection algorithm optimized for small object detection. We redesign the encoder of RT-DETRv2 by integrating the Feature Aggregation and Diffusion Network (FADN), improving the algorithm's ability to capture contextual information. Subsequently, we introduce the Parallel Atrous Mamba Feature Fusion Module (PAMFFM), which combines shallow and deep semantic information to better capture small object features. Furthermore, we propose the Cross-stage Enhanced Feature Fusion Module (CEFFM), merging features for small objects to provide richer and more detailed information. Finally, we propose STIoU Loss, which incorporates a penalty term to adjust the scaling of the loss function, improving detection granularity for small objects. FM-RTDETR achieves AP(50) scores of 54.0% and 56.3% on the VisDrone2019-DET and AI-TOD datasets. Compared with other state-of-the-art methods, our method shows great potential in small object detection from drones.

关键词： Object detection signal processing algorithms Feature extraction Transformers Training Real-time systems Accuracy Decoding Convolution Artificial intelligence Feature fusion RT-DETRv2 small object detection VisDrone

Low Cost Variable Step-Size LMS With Maximum Similarity to the Affine Projection Algorithm

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IEEE OPEN JOURNAL OF signal processing 2024年 5卷 82-91页

作者： Ferrer, Miguel De Diego, Maria Gonzalez, Alberto Univ Politecn Valencia UPV Inst Telecommun & Multimedia Applicat iTEAM Valencia 46022 Spain

The LMS algorithm is widely employed in adaptive systems due to its robustness, simplicity, and reasonable performance. However, it is well known that this algorithm suffers from a slow convergence speed when dealing with colored reference signals. Numerous variants and alternative algorithms have been proposed to address this issue, though all of them entail an increase in computational cost. Among the proposed alternatives, the affine projection algorithm stands out. This algorithm has the peculiarity of starting from $N$ data vectors of the reference signal. It transforms these vectors into as many data vectors suitably normalized in energy and mutually orthogonal. In this work, we propose a version of the LMS algorithm that, similar to the affine projection algorithm, starts from $N$ data vectors of the reference signal but corrects them by using only a scalar factor that functions as a convergence step. Our goal is to align the behavior of this algorithm with the behavior of the affine projection algorithm without significantly increasing the computational cost of the LMS.

关键词： signal processing algorithms Convergence Projection algorithms Computational efficiency Approximation algorithms Costs Robustness Adaptive filters affine projection algorithm variable step-size

Binaural Multichannel Blind Speaker Separation With a Causal Low-Latency and Low-Complexity Approach

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IEEE OPEN JOURNAL OF signal processing 2024年 5卷 238-247页

作者： Westhausen, Nils L. Meyer, Bernd T. Carl Von Ossietzky Univ Oldenburg Commun Acoust D-26129 Oldenburg Germany Cluster Excellence Hearing4All D-26129 Oldenburg Germany

In this article, we introduce a causal low-latency low-complexity approach for binaural multichannel blind speaker separation in noisy reverberant conditions. The model, referred to as Group Communication Binaural Filter and Sum Network (GCBFSnet) predicts complex filters for filter-and-sum beamforming in the time-frequency domain. We apply Group Communication (GC), i.e., latent model variables are split into groups and processed with a shared sequence model with the aim of reducing the complexity of a simple model only containing one convolutional and one recurrent module. With GC we are able to reduce the size of the model by up to 83% and the complexity up to 73% compared to the model without GC, while mostly retaining performance. Even for the smallest model configuration, GCBFSnet matches the performance of a low-complexity TasNet baseline in most metrics despite the larger size and higher number of required operations of the baseline.

关键词： Complexity theory Low latency communication Speech enhancement Convolution signal processing algorithms Microphones MIMO communication Binaural low-latency multi-channel real-time speaker-separation

Deep Learning Model for CS-Based signal Recovery for IRS-Assisted Near-Field THz MIMO System

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IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY

IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY 2024年 5卷 1326-1335页

作者： Sharma, Vaishali Keshari, Prakhar Sharma, Sanjeev Deka, Kuntal Krejcar, Ondrej Bhatia, Vimal Indian Inst Technol Indore Dept Elect Engn Indore 453552 India Indian Inst Technol BHU Varanasi Dept Elect Engn Varanasi 221005 India Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati 781039 India Skoda Auto Univ Mlada Boleslav 29301 Czech Republic Univ Teknol Malaysia Malaysia Japan Int Inst Technol Skudai 81310 Malaysia Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Jiangsu Peoples R China

Terahertz (THz) communication is a cutting-edge technology for the sixth-generation (6G) networks, offering vast bandwidths and data rates up to terabits per second, significantly advancing vehicular connectivity and services. However, THz signals are impacted by attenuation, path loss, and beam misalignment. Furthermore, the requisite high Nyquist sampling rates for THz systems increase the computational and system complexity of the receiver. A promising solution to navigate these obstacles involves the use of intelligent reflecting surfaces (IRS)-enhanced multiple-input multiple-output (MIMO) technology, which steers THz wave propagation. However, the substantial dimensions associated with IRS and MIMO extend the near-field, particularly at THz frequencies, as indicated by the Rayleigh distance and suffer from beam squint. To reduce system complexity and reduce sampling to sub-Nyquist rate, we propose a novel receiver design for an IRS-assisted near-field MIMO THz system that employs low-complexity compressed sensing. This method introduces an IRS signal-matched (IRSSM) measurement matrix with beam squint for capturing the transmitted signal at a sub-Nyquist rate, taking advantage of the sparsity in the signal and THz channels, and signal recovery using the deep learning (DL) model. Simulation results for symbol error rate (SER) and normalized mean square error (NMSE) performance indicate that the proposed DL-based receiver outperforms conventional recovery algorithms based on orthogonal matching pursuit (OMP) CS-recovery and dictionary-shrinkage estimation (DSE).

关键词： Terahertz communications Receivers Terahertz radiation Channel estimation Array signal processing Symbols signal processing algorithms THz band symbol detection compressed sensing MIMO DNN near-field beam squint