Multi-Channel Acoustic Echo Cancellation Based on Direction-of-Arrival Estimation

Acoustic echo cancellation (AEC) is an important speech signal processing technology that can remove echoes from microphone signals to enable natural-sounding full-duplex speech communication. While single-channel AEC is widely adopted, multi-channel AEC can leverage spatial cues afforded by multiple microphones to achieve better performance. Existing multi-channel AEC approaches typically combine beamforming with deep neural networks (DNN). This work proposes a two-stage algorithm that enhances multi-channel AEC by incorporating sound source directional cues. Specifically, a lightweight DNN is first trained to predict the sound source directions, and then the predicted directional information, multi-channel microphone signals, and single-channel far-end signal are jointly fed into an AEC network to estimate the near-end signal. Evaluation results show that the proposed algorithm outperforms baseline approaches and exhibits robust generalization across diverse acoustic environments.

Target Localization with Coprime Multistatic MIMO Radar via Coupled Canonical Polyadic Decomposition Based on Joint Eigenvalue Decomposition

This paper investigates target localization using a multistatic multiple-input multiple-output (MIMO) radar system with two distinct coprime array configurations: coprime L-shaped arrays and coprime planar arrays. The observed signals are modeled as tensors that admit a coupled canonical polyadic decomposition (C-CPD) model. For each configuration, a C-CPD method is presented based on joint eigenvalue decomposition (J-EVD). This computational framework includes (semi-)algebraic and optimization-based C-CPD algorithms and target localization that fuses direction-of-arrivals (DOAs) information to calculate the optimal position of each target. Specifically, the proposed (semi-)algebraic methods exploit the rotational invariance of the Vandermonde structure in coprime arrays, similar to the multiple invariance property of \added{estimation of signal parameters via rotational invariance techniques} (ESPRIT), which transforms the model into a J-EVD problem and reduces computational complexity. The study also investigates the working conditions of the algorithm to understand model identifiability. Additionally, the proposed method does not rely on prior knowledge of non-orthogonal probing waveforms and is effective in challenging underdetermined scenarios. Experimental results demonstrate that our method outperforms existing tensor-based approaches in both accuracy and computational efficiency.

WiCAL: Accurate Wi-Fi-Based 3D Localization Enabled by Collaborative Antenna Arrays

Accurate 3D localization is essential for realizing advanced sensing functionalities in next-generation Wi-Fi communication systems. This study investigates the potential of multistatic localization in Wi-Fi networks through the deployment of multiple cooperative antenna arrays. The collaborative gain offered by these arrays is twofold: (i) intra-array coherent gain at the wavelength scale among antenna elements, and (ii) inter-array cooperative gain across arrays. To evaluate the feasibility and performance of this approach, we develop WiCAL (Wi-Fi Collaborative Antenna Localization), a system built upon commercial Wi-Fi infrastructure equipped with uniform rectangular arrays. These arrays are driven by multiplexing embedded radio frequency chains available in standard access points or user devices, thereby eliminating the need for sophisticated, costly, and power-hungry multi-transceiver modules typically required in multiple-input and multiple-output systems. To address phase offsets introduced by RF chain multiplexing, we propose a three-stage, fine-grained phase alignment scheme to synchronize signals across antenna elements within each array. A bidirectional spatial smoothing MUSIC algorithm is employed to estimate angles of arrival (AoAs) and mitigate performance degradation caused by correlated interference. To further exploit inter-array cooperative gain, we elaborate on the synchronization mechanism among distributed URAs, which enables direct position determination by bypassing intermediate angle estimation. Once synchronized, the distributed URAs effectively form a virtual large-scale array, significantly enhancing spatial resolution and localization accuracy.

Directional Sparsity Based Statistical Channel Estimation for 6D Movable Antenna Communications

Six-dimensional movable antenna (6DMA) is an innovative and transformative technology to improve wireless network capacity by adjusting the 3D positions and 3D rotations of antennas/surfaces (sub-arrays) based on the channel spatial distribution. For optimization of the antenna positions and rotations, the acquisition of statistical channel state information (CSI) is essential for 6DMA systems. In this paper, we unveil for the first time a new \textbf{\textit{directional sparsity}} property of the 6DMA channels between the base station (BS) and the distributed users, where each user has significant channel gains only with a (small) subset of 6DMA position-rotation pairs, which can receive direct/reflected signals from the user. By exploiting this property, a covariance-based algorithm is proposed for estimating the statistical CSI in terms of the average channel power at a small number of 6DMA positions and rotations. Based on such limited channel power estimation, the average channel powers for all possible 6DMA positions and rotations in the BS movement region are reconstructed by further estimating the multi-path average power and direction-of-arrival (DOA) vectors of all users. Simulation results show that the proposed directional sparsity-based algorithm can achieve higher channel power estimation accuracy than existing benchmark schemes, while requiring a lower pilot overhead.

T-Rex: Fitting a Robust Factor Model via Expectation-Maximization

Over the past decades, there has been a surge of interest in studying low-dimensional structures within high-dimensional data. Statistical factor models $-$ i.e., low-rank plus diagonal covariance structures $-$ offer a powerful framework for modeling such structures. However, traditional methods for fitting statistical factor models, such as principal component analysis (PCA) or maximum likelihood estimation assuming the data is Gaussian, are highly sensitive to heavy tails and outliers in the observed data. In this paper, we propose a novel expectation-maximization (EM) algorithm for robustly fitting statistical factor models. Our approach is based on Tyler's M-estimator of the scatter matrix for an elliptical distribution, and consists of solving Tyler's maximum likelihood estimation problem while imposing a structural constraint that enforces the low-rank plus diagonal covariance structure. We present numerical experiments on both synthetic and real examples, demonstrating the robustness of our method for direction-of-arrival estimation in nonuniform noise and subspace recovery.

Mobile Jamming Mitigation in 5G Networks: A MUSIC-Based Adaptive Beamforming Approach

Mobile jammers pose a critical threat to 5G networks, particularly in military communications. We propose an intelligent anti-jamming framework that integrates Multiple Signal Classification (MUSIC) for high-resolution Direction-of-Arrival (DoA) estimation, Minimum Variance Distortionless Response (MVDR) beamforming for adaptive interference suppression, and machine learning (ML) to enhance DoA prediction for mobile jammers. Extensive simulations in a realistic highway scenario demonstrate that our hybrid approach achieves an average Signal-to-Noise Ratio (SNR) improvement of 9.58 dB (maximum 11.08 dB) and up to 99.8% DoA estimation accuracy. The framework's computational efficiency and adaptability to dynamic jammer mobility patterns outperform conventional anti-jamming techniques, making it a robust solution for securing 5G communications in contested environments.

A Novel Sparse Sum and Difference Co-Array With Low Redundancy and Enhanced DOF for Non-Circular Signals

Array structures based on the sum and difference co-arrays provide more degrees of freedom (DOF). However, since the growth of DOF is limited by a single case of sum and difference co-arrays, the paper aims to design a sparse linear array (SLA) with higher DOF via exploring different cases of second-order cumulants. We present a mathematical framework based on second-order cumulant to devise a second-order extended co-array (SO-ECA) and define the redundancy of SO-ECA. Based on SO-ECA, a novel array is proposed, namely low redundancy sum and difference array (LR-SDA), which can provide closed-form expressions for the sensor positions and enhance DOF in order to resolve more signal sources in the direction of arrival (DOA) estimation of non-circular (NC) signals. For LR-SDA, the maximum DOF under the given number of total physical sensors can be derived and the SO-ECA of LR-SDA is hole-free. Further, the corresponding necessary and sufficient conditions of signal reconstruction for LR-SDA are derived. Additionally, the redundancy and weight function of LR-SDA are defined, and the lower band of the redundancy for LR-SDA is derived. The proposed LR-SDA achieves higher DOF and lower redundancy than those of existing DCAs designed based on sum and difference co-arrays. Numerical simulations are conducted to verify the superiority of LR-SDA on DOA estimation performance and enhanced DOF over other existing DCAs.

A Deep Learning-Based Supervised Transfer Learning Framework for DOA Estimation with Array Imperfections

In practical scenarios, processes such as sensor design, manufacturing, and installation will introduce certain errors. Furthermore, mutual interference occurs when the sensors receive signals. These defects in array systems are referred to as array imperfections, which can significantly degrade the performance of Direction of Arrival (DOA) estimation. In this study, we propose a deep-learning based transfer learning approach, which effectively mitigates the degradation of deep-learning based DOA estimation performance caused by array imperfections. In the proposed approach, we highlight three major contributions. First, we propose a Vision Transformer (ViT) based method for DOA estimation, which achieves excellent performance in scenarios with low signal-to-noise ratios (SNR) and limited snapshots. Second, we introduce a transfer learning framework that extends deep learning models from ideal simulation scenarios to complex real-world scenarios with array imperfections. By leveraging prior knowledge from ideal simulation data, the proposed transfer learning framework significantly improves deep learning-based DOA estimation performance in the presence of array imperfections, without the need for extensive real-world data. Finally, we incorporate visualization and evaluation metrics to assess the performance of DOA estimation algorithms, which allow for a more thorough evaluation of algorithms and further validate the proposed method. Our code can be accessed at https://github.com/zzb-nice/DOA_est_Master.

An Artificial Intelligence Enabled Signature Estimation of Dual Wideband Systems in Ultra-Low Signal-to-Noise Ratio

Millimeter-wave (mmWave) massive Multiple Input Multiple Output (MIMO) systems encounter both spatial wideband spreading and temporal wideband effects in the communication channels of individual users. Accurate estimation of a user's channel signature -- specifically, the direction of arrival and time of arrival -- is crucial for designing efficient beamforming transceivers, especially under noisy observations. In this work, we propose an Artificial Intelligence (AI)-enabled framework for estimating the channel signature of a user's location in mmWave massive MIMO systems. Our approach explicitly accounts for spatial wideband spreading, finite basis leakage effects, and significant unknown receiver noise. We demonstrate the effectiveness of a denoising convolutional neural network with residual learning for recovering channel responses, even when channel gains are of extremely low amplitude and embedded in ultra-high receiver noise environments. Notably, our method successfully recovers spatio-temporal diversity branches at signal-to-noise ratios as low as -20 dB. Furthermore, we introduce a local gravitation-based clustering algorithm to infer the number of physical propagation paths (unknown a priori) and to identify their respective support in the delay-angle domain of the denoised response. To complement our approach, we design tailored metrics for evaluating denoising and clustering performance within the context of wireless communications. We validate our framework through system-level simulations using Orthogonal Frequency Division Multiplexing (OFDM) with a Quadrature Phase Shift Keying (QPSK) modulation scheme over mmWave fading channels, highlighting the necessity and robustness of the proposed methods in ultra-low SNR scenarios.

Array Partitioning Based Near-Field Attitude and Location Estimation

This paper studies a passive source localization system, where a single base station (BS) is employed to estimate the positions and attitudes of multiple mobile stations (MSs). The BS and the MSs are equipped with uniform rectangular arrays, and the MSs are located in the near-field region of the BS array. To avoid the difficulty of tackling the problem directly based on the near-field signal model, we establish a subarray-wise far-field received signal model. In this model, the entire BS array is divided into multiple subarrays to ensure that each MS is in the far-field region of each BS subarray. By exploiting the angles of arrival (AoAs) of an MS antenna at different BS subarrays, we formulate the attitude and location estimation problem under the Bayesian inference framework. Based on the factor graph representation of the probabilistic problem model, a message passing algorithm named array partitioning based pose and location estimation (APPLE) is developed to solve this problem. An estimation-error lower bound is obtained as a performance benchmark of the proposed algorithm. Numerical results demonstrate that the proposed APPLE algorithm outperforms other baseline methods in the accuracy of position and attitude estimation.