Complex ResNet Aided DoA Estimation for Near-Field MIMO Systems

Abstract

The near-field effect of short-range multiple-input multiple-output (MIMO) systems imposes many challenges on direction-of-arrival (DoA) estimation. Most conventional scenarios assume that the far-field planar wavefronts hold. In this paper, we investigate the DoA estimation problem in short-range MIMO communications, where the effect of near-field spherical wave is non-negligible. By converting it into a regression task, a novel DoA estimation framework based on complex-valued deep learning (CVDL) is proposed for the near-field region in shortrange MIMO communication systems. Under the assumption of a spherical wave model, the array steering vector is determined by both the distance and the direction. However, solving this regression task containing a massive number of variables is challenging, since datasets need to capture numerous complicated feature representations. To overcome this, a virtual covariance matrix (VCM) based on received signals is constructed, and thus such features extracted from the VCM can deal with the complicated coupling relationship between the direction and the distance. Although the emergence of wireless big data driven by future communication networks promotes deep learning-based wireless signal processing, the learning algorithms of complexvalued signals are still ongoing. This paper proposes a onedimensional (1-D) residual network that can directly tackle complex-valued features due to the inherent 1-D structure of signal subspace vectors. In addition, we put forth a cropped VCM based policy which can be applied to different antenna sizes. The proposed method is able to fully exploit the complex-valued information. Our simulation results demonstrate the superiority of the proposed CVDL approach over the baseline schemes in terms of the accuracy of DoA estimation.

Convolution Neural Networks for Localization of Near‐Field Sources via Symmetric Double‐Nested Array

“…Based on the prediction of the autoencoder, we employ the classification CNN to obtain the spatial spectrum and estimate the AOAs of the near-field sources. Herein, the CNN of the pth subregion for the gth data is depicted in (3,0) g u (3,1) g u (3,2) g u (3,3) g u (3,4) g u (3,5) g u (3,6) g u (3,7) M (M+1) Construct the phase difference matrix of each near-field source in (8).…”

“…By treating the assignment of angle-of-arrival (AOA) label as a separate binary classification, a convolutional neural network (CNN) method in [1] is presented for multispeaker AOA estimation. By employing an end-to-end regression rather than a classification, the complex-valued residual network (ResNet) in [2] is proposed to improve the training performance for AOA estimation in shortrange multiple-input multiple-output (MIMO) communications. In [3], some small deep feedforward networks are trained for AOA estimation under uniform circular array, which can reduce computational complexity and have similar satisfactory performance.…”

Efficient Joint Estimation of DOA, Range and Reflectivity in Near-Field by Using Mixed-Order Statistics and a Symmetric MIMO Array

“…The mechanism used in [28] requires additional operations to pair the parameters. In [29], by considering the problem as a regression task, a DOA estimation framework based on complex-valued deep learning is presented for short-range MIMO communication systems. Solving this regression task containing a massive number of variables is challenging since datasets need to capture many complicated feature representations.…”

Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks

“…Many DOA estimation methods based on neural networks have been developed in recent years to reduce the computational burden. References [12][13][14][15] using a convolutional neural network (CNN), references [16,17] using a support vector regression (SVR), reference [18] using a residual network, reference [19] using a fully connected neural network (FNN), reference [20] using a long short-term memory network, and references [21,22] using a radial basis function (RBF) achieve high accuracy DOA estimation. However, they can only be used in single-source scenarios, which may be extremely limited in practical applications.…”