This letter proposes a low-complexity deep-learning-based direction-of-arrival (DOA) estimation method for a hybrid massive multiple-input multiple-output (MIMO) system with a uniform circular array at the base station. In the proposed method, we first input the received signal vector into some small deep feedforward networks that are trained offline. Based on the outputs of the networks, we then generate a set of candidate angles. By selecting the optimal one from all candidate angles, we finally obtain the DOA estimation. Simulation results demonstrate that, compared with the conventional maximum likelihood (ML) method, the proposed DOA estimation method can achieve similar or even better performance with much less complexity.

Domain adaptation (DA) is widely used in learning problems lacking labels. Recent studies show that deep adversarial DA models can make markable improvements in performance, which include symmetric and asymmetric architectures. However, the former has poor generalization ability, whereas the latter is very hard to train. In this article, we propose a novel adversarial DA method named adversarial residual transform networks (ARTNs) to improve the generalization ability, which directly transforms the source features into the space of target features. In this model, residual connections are used to share features and adversarial loss is reconstructed, thus making the model more generalized and easier to train. Moreover, a special regularization term is added to the loss function to alleviate a vanishing gradient problem, which enables its training process stable. A series of experiments based on Amazon review data set, digits data sets, and Office-31 image data sets are conducted to show that the proposed ARTN can be comparable with the methods of the state of the art.

We propose a simple sparse channel estimation and tracking method for orthogonal frequency-division multiplexing (OFDM) systems based on a dynamic parametric channel model, where the channel is parameterized by a small number of distinct paths, each characterized by path delay and path gain, and all parameters are time varying. In the proposed method, we adaptively choose the delay grid and estimate each channel path delay iteratively. To further reduce the complexity, we also propose a tracking algorithm based on the fact that the changes in the channel path number and path delays are small over a few adjacent OFDM symbols. After the physical path delays are estimated, we then estimate the channel path gains by using the polynomial basis expansion model (P-BEM). Simulation results demonstrate the effectiveness of the proposed channel estimation and tracking method in dynamic environments. Compared with the compressive-sensing-based channel estimator using the orthogonal matching pursuit (OMP) algorithm, the new technique proposed here has much lower computational complexity while offering comparable performance.

This work investigates the problem of undersampled face recognition (i.e., insufficient training data) encountered in practical Internet-of-Things (IoT) applications. Insufficient and uncertain samples captured by IoT devices may include background and facial disguise that makes face recognition more challenging than that with sufficient and reliable images. Many models work well in face recognition on a big data set, but when training data are insufficient, they achieve unsatisfactory performance. This work proposes a novel method named sparse common feature-based representation (SCFR) that provides a unique and stable result and completely avoids very time-consuming training required by a deep learning model. Specially, it constructs a common feature dictionary using both training and test images. Thereinto, a common feature is based on a discriminative common vector and learned by a Gaussian mixture model for both training and test images in a semisupervised learninig manner, which would reduce the difference among samples in each class. In the optimization, the latent indicator of test data is initialized by the estimated label. This can avoid learning invalid information and lead to good prototype images. A new variation dictionary characterizes variables that can be shared by different classes. Finally, this work adopts minimum reconstruction residuals to recognize test images, thus bringing about a substantial improvement in SCFR’s performance. Extensive results on benchmark face databases demonstrate that the proposed method is better than the state-of-the-art methods handling undersampled face recognition.

Typical adversarial-training-based unsupervised domain adaptation (UDA) methods are vulnerable when the source and target datasets are highly complex or exhibit a large discrepancy between their data distributions. Recently, several Lipschitz-constraint-based methods have been explored. The satisfaction of Lipschitz continuity guarantees a remarkable performance on a target domain. However, they lack a mathematical analysis of why a Lipschitz constraint is beneficial to UDA and usually perform poorly on large-scale datasets. In this article, we take the principle of utilizing a Lipschitz constraint further by discussing how it affects the error bound of UDA. A connection between them is built, and an illustration of how Lipschitzness reduces the error bound is presented. A local smooth discrepancy is defined to measure the Lipschitzness of a target distribution in a pointwise way. When constructing a deep end-to-end model, to ensure the effectiveness and stability of UDA, three critical factors are considered in our proposed optimization strategy, i.e., the sample amount of a target domain, dimension, and batchsize of samples. Experimental results demonstrate that our model performs well on several standard benchmarks. Our ablation study shows that the sample amount of a target domain, the dimension, and batchsize of samples, indeed, greatly impact Lipschitz-constraint-based methods' ability to handle large-scale datasets. Code is available at https://github.com/CuthbertCai/SRDA.

In this letter, we investigate secure transmission for multi-cell multi-user massive multiple-input multiple-output systems with an active eavesdropper. During the uplink (UL) training, the eavesdropper can attack the training phase to cause pilot contamination. To resist such pilot contamination attack (PCA), we propose a secure transmission that does not require statistical information of the channels. In the proposed transmission strategy, we first detect PCA by a simple method. If the PCA is not detected, we then estimate the UL data of the users by a semi-blind method based on an asynchronous protocol. After that, we perform the data-aided channel estimation and design the corresponding precoder. Simulation results demonstrate that the proposed transmission can effectively resist the PCA even when the PCA detection method fails.