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.

Wireless-powered Internet of Things networks face critical challenges due to the limited energy of sensor nodes and the difficulty of maintaining data freshness. To address these issues, this paper proposes a multi–unmanned aerial vehicle (UAV)-assisted data collection framework to minimize the age of information (AoI). A hierarchical multiple decision strategy (HMDS) is developed to optimize UAV hovering time by dynamically selecting non-orthogonal multiple access or orthogonal frequency division multiple access for intra-cluster transmission according to energy constraints and channel conditions. In addition, a multi-collaborative trajectory optimization (MCTO) scheme is proposed to reduce UAV flight time through joint clustering, partitioning, and trajectory planning. Specifically, radius-adaptive K-means++ clustering, load-balanced spectral partitioning, and a hybrid K-nearest neighbors and elite ant strategy are employed. Simulation results show that the proposed HMDS and MCTO significantly reduce hovering and flight times, thereby effectively improving AoI performance.

This paper considers channel estimation for extremely large-scale intelligent reflecting surface (XL-IRS)-assisted terahertz (THz) communication systems. Specifically, an XL-IRS is deployed close to users (UEs) to enhance communication performance between the base station (BS) and UE. With its large aperture, the XL-IRS has a Rayleigh distance of tens of meters. Therefore, the users are likely located in the near-field region of the XL-IRS, while the BS is in its far-field region. Consequently, a spherical wavefront propagation model should be considered to characterize the propagation property between the XL-IRS and the UE, while the planar wavefront propagation model is utilized in the BS-IRS link. By leveraging Khatri-Rao product and Kronecker product properties, we rephrase the channel estimation problem. In addition, we construct an orthogonal dictionary, which essentially modifies the well-known Discrete Fourier Transform (DFT) matrix. We further find that the considered channel can be block-sparsely represented by this dictionary. Hence, the channel estimation can be converted into a block sparse recovery problem, which can be efficiently solved by several off-the-shelf methods. The simulation results show that our proposed method achieves better estimation performance than the conventional polar-domain-based method.

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.

In this paper, we consider state estimation in the Kalman filtering framework with unlimited sensing measurements (USMs), which are obtained from sensors equipped with a self-reset analog-to-digital (SR-ADC). SR-ADC was recently introduced to deal with the saturation issue frequently encountered in a conventional ADC. To tackle the nonlinearity of the USM, we present a unique decomposition property of the USM. Leveraging this property and a multiple model adaptive estimation strategy, we propose a novel USF-based Kalman filtering (KF-USM) algorithm. Numerical results reveal that the proposed KF-USM filter is an effective alternative to the conventional ADC-based KF to deal with high dynamic range input signals, offering more accurate state estimation in the presence of saturation.

In this paper, we consider channel estimation for millimeter wave/Terahertz (mmWave/THz) communication systems equipped with extremely large antenna arrays. As the number of antennas increases, users may locate either in the near-field region or in the far-field region, resulting in a hybrid near/far-field channel model. By analyzing the properties of coherence of two near/far-field steering vectors, we construct an orthogonal dictionary and prove that the hybrid near/far-field channel vector has a block-sparse representation on this dictionary. Based on this observation, hybrid near/far field channel estimation for mmWave/THz systems with extremely large-scale antennas can be formulated as a block-sparsity compressed sensing problem, which can be solved by many block-sparse signal recovery algorithms such as the B-SBL and PC-SBL. Simulation results reveal that our proposed method can achieve a performance improvement over the existing polar-domain based solution with a substantial reduction of training overhead.

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 joint wideband spectrum sensing and carrier frequency estimation in a sub-Nyquist sampling framework. Specifically, a multi-antenna receiver is used to estimate the carrier frequencies and power spectra of multiple narrowband transmissions that spread over a wide frequency band. Unlike existing works that assume the source signals impinge on the receiver via a line-of-sight (LOS) path, we consider a more practical multiple-input multiple-output (MIMO) channel characterized by 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 predetermined 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 and power spectra of the source signals. Simulation results are presented to illustrate the effectiveness of the proposed method.

We consider the nonlinear robust filtering problem where the measurements are partially disturbed by outliers. A new robust Kalman filter based on a detect-and-reject idea is developed. To identify and exclude outliers automatically, each measurement is assigned an indicator variable, which is modeled by a beta-Bernoulli prior. The mean-field variational Bayesian method is then utilized to estimate the state of interest as well as the indicator in an iterative manner at each time instant. Simulation results reveal that the proposed algorithm outperforms several recent robust solutions with higher computational efficiency and better accuracy.

In this paper, we attack the estimation problem in Kalman filtering when the measurements are contaminated by outliers. We employ the Laplace distribution to model the underlying non-Gaussian measurement process. The maximum posterior estimation is solved by the majorization minimization (MM) approach. This yields an MM based robust filter, where the intractable ℓ1norm problem is converted into an ℓ2norm format. Furthermore, we implement the MM based robust filter in the Kalman filtering framework and develop a Laplace ℓ1robust Kalman filter. The proposed algorithm is tested by numerical simulations. The robustness of our algorithm has been borne out when compared with other robust filters, especially in scenarios of heavy outliers.

Dynamic compressed sensing (DCS) has recently gained popularity as a successful approach to recovering dynamic sparse signals. In this paper, we attack the problem from a Bayesian perspective. The proposed model imposes sparse constraints on both the unknown sparse signal and its temporal innovation via t priors. Due to the conjugacy between the priors and likelihoods, we are able to propose a computationally efficient mean-field variational Bayes algorithm to learn the model without parameter tuning. We consider both the online and offline scenarios, and demonstrate via numerical experiments that the proposed methods are superior to alternatives in terms of both reconstruction accuracy and computational time.