Millimeter wave/Terahertz (mmWave/THz) communication with extremely large-scale antenna arrays (ELAAs) offers a promising solution to meet the escalating demand for high data rates in next-generation communications. A large array aperture, along with the ever increasing carrier frequency over the mmWave/THz bands, leads to a large Rayleigh distance. As a result, the traditional planar-wave assumption may not hold valid for mmWave/THz systems featuring ELAAs. In this paper, we consider the problem of hybrid near/far-field channel estimation by taking spherical wave propagation into account. By analyzing the coherence properties of any two near-field steering vectors, we prove that the hybrid near/far-field channel admits a block-sparse representation on a specially designed unitary matrix. Specifically, the percentage of nonzero elements of such a block-sparse representation is in the order of 1/√N, which tends to zero as the number of antennas,N, grows. Such a block-sparse representation allows to convert channel estimation into a block-sparse signal recovery problem. Simulation results are provided to verify our theoretical results and illustrate the performance of the proposed channel estimation approach in comparison with existing state-of-the-art methods.

In this paper, we consider the problem of downlink beam training for extremely large-scale millimeter wave (mmWave)/Terahertz (THz) systems, where the far-field assumption which treats wavefronts as planar waves may not hold valid. For such hybrid far/near-field channels, beam training needs to identify the best beam alignment on a two-dimensional angle-range domain. An exhaustive search scheme sequentially scanning the entire angle-range space incurs a high training overhead. To address this issue, in this paper, we propose an efficient hybrid far/near-field beam training method. By utilizing the approximate orthogonality of near-field steering vectors of the same effective distance, we devise a multi-directional beam training sequence which can more efficiently scan the entire angle-range space. Based on the devised beam training sequence, we develop a simple estimation method at the receiver that can simultaneously identify the angle and the range associated with the dominant path. Simulation results show that the proposed method achieves better performance than the exhaustive search scheme, while with a much lower overhead cost. The proposed method also presents a clear advantage over other existing state-of-the-art hybrid far/near-field beam training methods in terms of performance and generality.

We consider the problem of spatial channel covariance matrix (CCM) estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication systems. Spatial CCM is essential for two-timescale beamforming in IRS-assisted systems; however, estimating the spatial CCM is challenging due to the passive nature of reflecting elements and the large size of the CCM resulting from massive reflecting elements of the IRS. In this paper, we propose a CCM estimation method by exploiting the low-rankness as well as the positive semi-definite (PSD) 3-level Toeplitz structure of the CCM. Estimation of the CCM is formulated as a semidefinite programming (SDP) problem and an alternating direction method of multipliers (ADMM) algorithm is developed. Our analysis shows that the proposed method is theoretically guaranteed to attain a reliable CCM estimate with a sample complexity much smaller than the dimension of the CCM. Thus the proposed method can help achieve a significant training overhead reduction. Simulation results are presented to illustrate the effectiveness of our proposed method and the performance of two-timescale beamforming scheme based on the estimated CCM.

We study the problem of joint wideband spectrum sensing and recovery of multi-band signals in a multi-antenna-based sub-Nyquist sampling framework. Specifically, the multi-band signal is composed of a number of uncorrelated narrowband signals spreading over a wide frequency band. Unlike existing works which assume the source signals impinge on the receiver via a line-of-sight (LOS) path, we consider a more practical unknown MIMO channel which results from multipath propagation. A new sub-Nyquist sampling architecture is proposed, where each antenna output passes through two channels, namely, a direct path and a delayed path with a controlled amount of time delay. The signal at each channel is then sampled by a synchronized low-rate analog-to-digital converter (ADC). We utilize the collected data samples to build a set of cross-correlation matrices with different time lags and develop a CANDECOMP/PARAFAC (CP) decomposition-based method to recover the carrier frequencies, power spectra as well as the source signals themselves. Recovery conditions of the proposed method are analyzed, and Cramér-Rao bound (CRB) results for our estimation problem are derived. Simulation results are presented to illustrate the effectiveness of the proposed method.

We consider the problem of max-min power control for downlink cell-free (CF) massive MIMO systems. Under the bisection framework, solving this problem amounts to solving a sequence of convex conic feasibility checking problems (CCFCP). Unfortunately, the problem size of the CCFCP of the CF massive MIMO system grows rapidly as the number of users and access points (AP) increases. Existing feasibility checking methods become computationally intractable even when the system consists of only a moderate number of users and APs. To address this limitation, we propose to reformulate the CCFCP as a two-set feasibility problem, which is then solved by the averaged alternating reflection (AAR) algorithm. The proposed method outperforms existing methods in terms of computational efficiency.

In this article, we consider the problem of decentralized composite optimization over a connected and symmetric graph, in which each node holds its own agent-specific private convex functions, and communications are only allowed between nodes with direct links. A variety of algorithms has been proposed to solve such a problem in an alternating direction method of multiplier (ADMM) framework. Many of these algorithms, however, need to include some extra proximal term in the augmented Lagrangian function such that the resulting algorithm can be implemented in a decentralized manner. The use of the extra proximal term slows down the convergence speed because it forces the current solution to stay close to the solution obtained in the previous iteration. To address this issue, in this article, we first introduce the notion of simplest bipartite graph, which is defined as a bipartite graph that has a minimum number of edges to keep the graph connected. A simple two-step message passing-based procedure is proposed to find a simplest bipartite graph associated with the original graph. We show that the simplest bipartite graph has some interesting properties. By utilizing these properties, an ADMM without involving extra proximal terms can be developed to perform decentralized composite optimization over the simplest bipartite graph. The simulation results show that our proposed method achieves a much faster convergence speed than the existing state-of-the-art decentralized algorithms.

In this letter, we consider channel estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) systems, where an IRS is deployed to assist the data transmission from the base station (BS) to a user. It is shown that for the purpose of joint active and passive beamforming, the knowledge of a large-size cascade channel matrix needs to be acquired. To reduce the training overhead, the inherent sparsity in mmWave channels is exploited. By utilizing properties of Katri-Rao and Kronecker products, we find a sparse representation of the cascade channel and convert cascade channel estimation into a sparse signal recovery problem. Simulation results show that our proposed method can provide an accurate channel estimate and achieve a substantial training overhead reduction.

Instrument recognition plays very important roles in music information retrieval, sound source separation and automatic music transcription. However, due to different playing styles and audio qualities, this task cannot be accomplished easily. Simultaneous existence of multiple instruments in polyphonic music increases the challenge to a greater extent. This article mainly focus on the identification of the predominant instruments in polyphonic music. We propose to construct a network with an auxiliary classification designed based on the onset groups and instrument families. The principal classification and the auxiliary classification enable the network to learn the instrument categories and groups jointly in a pattern of multitask learning. The IRMAS datasetis adopted in the experiment to extract the mel-spectrogram and six other types of features. The micro and macro average of precisions, recalls and F1 measures are used to evaluate the classification results. The effect of multitask learning, batch normalization and center loss in the predominant instrument recognition are demonstrated by various experiments. By selecting the loss ratios through a development set, the micro and macro F1 measures of our proposed network can reach 0.685 and 0.597, which are 10.7% and 16.4% higher than those obtained by the baseline, the ConvNet presented in [1].

In this paper, we consider the problem of channel estimation for uplink multiuser massive MIMO systems, where, in order to significantly reduce the hardware cost and power consumption, one-bit analog-to-digital converters (ADCs) are used at the base station (BS) to quantize the received signal. We first extend the conventional Bussgang linear minimum mean square error (BLMMSE) estimator to the general nonzero threshold case. We then study the problem of one-bit quantization design, aiming at minimizing the mean squared error of the generalized BLMMSE estimator. A set partition scheme is proposed to devise the quantization thresholds. The rationale behind the proposed scheme is to divide each antenna's received samples into a number of disjoint subsets according to their pairwise correlation and assign diverse thresholds to those highly correlated data samples. In addition to the set partition scheme, a gradient descent scheme is developed to search for optimal quantization thresholds. The proposed schemes only require the statistical information of the received signals to devise the quantization thresholds, which can be calculated in advance before the training process begins. Simulation results show that the generalized BLMMSE estimator can achieve a significant performance improvement over the conventional Bussgang LMMSE estimator.

There has been a growing interest in wideband spectrum sensing due to its applications in cognitive radios and electronic surveillance. To overcome the sampling rate bottleneck for wideband spectrum sensing, in this paper, we study the problem of compressed power spectrum estimation whose objective is to reconstruct the power spectrum of a wide-sense stationary signal based on sub-Nyquist samples. By exploring the sampling structure inherent in the multicoset sampling scheme, we develop a computationally efficient method for power spectrum reconstruction. An important advantage of our proposed method over existing compressed power spectrum estimation methods is that our proposed method, whose primary computational task consists of fast Fourier transform (FFT), has a very low computational complexity. Such a merit makes it possible to efficiently implement the proposed algorithm in a practical field-programmable gate array (FPGA)-based system for real-time wideband spectrum sensing. Our proposed method also provides a new perspective on the power spectrum recovery condition, which leads to a result similar to what was reported in prior works. Simulation results are presented to show the computational efficiency and the effectiveness of the proposed method.

The problem of beam alignment for millimeter wave (mm-Wave) communications is studied in this paper. We show that, by exploiting the sparse scattering nature of mmWave channels, the beam alignment problem can be formulated as a sparse encoding and phaseless decoding problem, which involves finding a sparse sensing matrix and an efficient recovery algorithm to recover the support and magnitude of the s-parse signal from compressive phaseless measurements. We develop a general function-Code (GF-Code) algorithm for s-parse encoding and phaseless decoding. Simulation results are provided to corroborate the effectiveness of the proposed GF-Code method.

In this letter, we propose a proximal alternating direction method of multiplier (ADMM) to solve the composite optimization problem over a decentralized network. Compared with existing methods, such as PG-EXTRA and IC-ADMM, the proposed decentralized proximal ADMM method does not rely on assuming a smooth + nonsmooth structure on the objective functions, thus covering a wider range of composite optimization problems. Simulation results show that the proposed proximal ADMM presents a considerable performance advantage over existing state-of-the-art algorithms for both nonsmooth + nonsmooth and smooth + nonsmooth composite optimization problems.

Sparse Beyesian learning is a popular approach for sparse signal recovery, and has demonstrated superior performance in a series of experiments. Nevertheless, the sparse Bayesian learning algorithm involves a matrix inverse at each iteration. Its associated computational complexity grows significantly with the problem size, which hinders its application to many practical problems even with moderately large datasets. To address this issue, in this letter, we develop a fast inverse-free sparse Bayesian learning method. Specifically, by invoking a fundamental property for smooth functions, we obtain a relaxed evidence lower bound (relaxed-ELBO) that is computationally more amiable than the conventional ELBO used by sparse Bayesian learning. A variational expectation-maximization (EM) scheme is then employed to maximize the relaxed-ELBO, which leads to a computationally efficient inverse-free sparse Bayesian learning algorithm. Simulation results show that the proposed algorithm has a fast convergence rate and achieves lower reconstruction errors than other state-of-the-art fast sparse recovery methods in the presence of noise.

In face recognition for criminal identification, the training data are always clean while the probe data are occluded by sunglasses, scarf or other facial accessories. Occlusions in the probe data severely degrade the recognition performance. We find that introducing artificial occlusions into the training data is helpful in this situation. The incremental training data is decomposed into a class-specific dictionary, a non-class-specific dictionary and a sparse noise or corruption by the sparse and dense hybrid representation framework (SDR). The artificially introduced occlusions play an important role in building the discriminative faces for classification during SDR. Experimental results demonstrate that the proposed method can provide higher recognition accuracy under benchmark face database.

We propose a pattern-coupled sparse Bayesian learning method for inverse synthetic aperture radar (ISAR) imaging by exploiting a block-sparse structure inherent in ISAR target images. A two-dimensional pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring scatterers on the target scene. An expectation-maximization (EM) algorithm is developed to infer the maximum a posterior (MAP) estimate of the hyperparameters, along with the posterior distribution of the sparse signal. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.

In this paper, we develop a new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns. A pattern-coupled hierarchical Gaussian prior model is introduced to characterize the statistical dependencies among coefficients, where a set of hyperparameters are employed to control the sparsity of signal coefficients. Unlike the conventional sparse Bayesian learning framework in which each individual hyperparameter is associated independently with each coefficient, in this paper, the prior for each coefficient not only involves its own hyperparameter, but also the hyperparameters of its immediate neighbors. In doing this way, the sparsity patterns of neighboring coefficients are related to each other and the hierarchical model has the potential to encourage structured-sparse solutions. The hyperparameters, along with the sparse signal, are learned by maximizing their posterior probability via an expectation-maximization (EM) algorithm.

Approximate formulations for the 3-dB beamwidth are derived in this letter to measure the spatial resolution of the broadband nonredundant array (NRA) and minimum redundancy array (MRA), which assume the ideal continuous-time, infinite-length filters with the frequency responses obtained by the linearly constrained minimum variance (LCMV) optimization. By these formulations, the beamwidths of NRA and MRA are compared with that of the uniform linear array (ULA). Moreover, the accuracy of the derived formulations is assessed by numerical studies.

In this letter, a new broadband beamformer using infinite impulse response (IIR) filters is proposed. The unique feature of our letter is that by replacing all the delay elements with tap-to-tap IIR filters under some structural restrictions, the Frost processor is naturally extended from the finite impulse response (FIR) beamformer to the IIR beamformer. In the new beamformer, the feedforward and feedback weights are computed with the constrained and unconstrained least-mean-squares algorithms, respectively. The stability of the IIR filters in the proposed beamformer could be monitored easily. Simulation results demonstrate the improved performance of the new IIR beamformer relative to the Frost beamformer.

We present a closed-form solution for blind single-input multiple-output finite impulse response channel estimation driven by colored sources. The second-order statistics of the input source are known a priori. The uniqueness of the system solution is proved by exploiting the derived property of companion matrices that are constructed from the inherent structural relationship between the source autocorrelation matrices. Numerical simulation results are presented to illustrate the performance of the proposed algorithm.