27 accepted papers
Single-snapshot signal processing in sparse linear arrays has become increasingly vital, particularly in dynamic environments like automotive radar systems, where only limited snapshots are available. These arrays are often utilized either to cut manufacturing costs or result from unintended antenna…
Hybrid analog-digital beamforming is an effective approach for practical implementations of a massive multiple-input multiple-output (MIMO) system by reducing the number of radio frequency (RF) chains. Fully connected hybrid beam-forming (F-HBF), where each RF chain is connected to each antenna, can…
This paper presents a comprehensive analysis of two-channel modulo analog-to-digital converters (ADCs) systems, focusing on the sensitivity of ADC thresholds. By exploiting analytic number theory, we first investigate the relationship among ADC threshold precision, maximum signal dynamic range, and…
When the line-of-sight between the base station and mobile users is unavailable, reconfigurable intelligent surfaces (RIS) can be exploited to ensure connectivity and improve data transmission performance. The objective of this paper is to estimate and predict timevarying user-RIS channels with low…
In this paper, we analyze the effect of sensor placement to the achievable number of degrees-of-freedom (DOFs) when the sensors deviate from a half-wavelength grid. More specifically, we consider two variations of a uniform linear array (ULA), namely, when one or more sensors are shifted from half-w…
This paper addresses the direction-of-arrival (DOA) estimation problem of mixed coherent and uncorrelated signals using a sparse rectangular array, where tensor reconstruction is employed to preserve the structure of multi-dimensional array signals. In the proposed approach, we first estimate the DO…
This paper considers a wireless network assisted by an intelligent reflecting surface (IRS) to enhance data transmission between the base station and mobile users. Our objective is to estimate and predict the user-IRS channels by exploiting a small number of sparsely distributed active elements with…
In this paper, we develop a deep learning framework to optimize the compressive sampling matrix in a massive multiple-input multiple-output (MIMO) system. The optimized compressive sampling matrix is utilized to project high-dimensional data received at the massive MIMO system into a lower-dimension…
We consider an integrated sensing-communication system operating in a dynamic environment, such as an autonomous vehicle scenario. We propose a novel, low-cost, low power consumption and low-computation approach for designing a beam that can simultaneously reach the radar target of interest and the…
In this paper, we analyze the Cramer-Rao bound of the distributed direction-of-arrival (DOA) estimation problem where the covariance matrix is formulated in a mixed-precision manner. In this scheme, the self-covariance matrix of a subarray is locally computed using the full-precision data received a…
Distributed array consisting of multiple subarrays is attractive for high-resolution direction-of-arrival (DOA) estimation when a large-scale array is infeasible. To achieve effective distributed DOA estimation, it is required to transmit information observed at the subarrays to the fusion center, w…
We propose a novel automotive radar imaging technique to provide high-resolution information in four dimensions, i.e., range, Doppler, azimuth, and elevation, by exploiting a joint sparsity design in frequency spectrum and array configurations. Random sparse step-frequency waveform is proposed to sy…
Sensor array-based joint radar-communication (JRC) systems exploit adaptive beamforming to transmit radar and communication signals in their respective directions. Optimal sensor selection is anticipated as an attractive means to achieve superior performance with a low hardware cost because of the e…
Moving platforms enable sparse arrays to assume higher degrees of freedom and lead to increased number of lags. In essence, array motion can fill the holes in the spatial auto-correlation lags associated with a fixed platform and, therefore, increase the number of sources detectable by the same phys…
In this paper, we propose a new sparse coprime array design that achieves a higher number of degrees-of-freedom for direction-of-arrival (DOA) estimation. The proposed array design completely avoids lag redundancies between the two constituting subarrays of the coprime array, thus achieving the maxi…
We propose a distributed unmanned aerial vehicle (UAV) network performing collaborative radar sensing for multi-target localization and motion parameter estimation. Two UAV network topologies are considered for data propagation and information fusion. In the former, we form a sequential UAV node cha…
Multi-task compressive sensing is a framework that, by leveraging the useful information contained in multiple tasks, significantly reduces the number of measurements required for sparse signal recovery and achieves improved sparse reconstruction performance of all tasks. In this paper, a novel mult…
Coprime arrays are capable of achieving an increased number of degrees-of-freedom by operating the coarray signals. However, their non-uniform coarrays prevent the full utilization of the available signals. To address this problem, a novel coarray interpolation-based direction-of-arrival (DOA) estim…
Massive multiple-input multiple-output (MIMO) is a promising technique for 5G communications due to its superior spectrum and energy efficiencies. Despite its many advantages, the high number of antennas used in massive MIMO brings many challenges in practical implementations. Among them, the pilot…
As a new emerging technology for wireless communications, massive multiple-input multiple-output (MIMO) faces a significant challenge to deploy a separate receiver chain of front-end circuits in a dense circuit board. In this paper, we apply the compressive sensing technique to reduce the required n…
In this paper, we consider robust direction-of-arrival (DOA) estimation for an array that contains mis-calibrated sensors with unknown gain and phase uncertainties. We develop two robust DOA estimation algorithms based on the maximum correntropy criterion (MCC). In the first algorithm, adaptively op…
For radar echoes sampled at sub-Nyquist rates, it is impractical, if not impossible, to recover full-range Nyquist samples because of huge storage and computational loads. By exploiting the banded structure of the measurement matrix, we develop a novel segment-sliding reconstruction (SegSR) scheme t…
Fast and accurate detection of elderly falls can significantly reduce the rate of morbidity and mortality. In the past decade, extensive research has been performed to achieve real-time fall monitoring solutions. In this paper, we consider the radar-based modality and utilize the family of fractiona…
Increased demand on spectrum sensing over a broad frequency band requires a high sampling rate and thus leads to a prohibitive volume of data samples. In some applications, e.g., spectrum estimation, only the second-order statistics are required. In this case, we may use a reduced data sampling rate…
In this paper, we address the suppression of frequency modulated jammers in a multi-sensor Global Navigation Satellite System (GNSS) receiver. In particular, we consider the case of sparsely sampled signals and compressed observations. In this case, applying conventional time-frequency (TF) analysis…
For spatially distributed targets encountered in radar and sonar applications, direct application of subspace-based methods usually do not lead to an accurate estimation of the direction and angular extent of the signal arrivals. If the spatial distribution of the targets can be parameterized with a…
In this paper, we propose a novel structured compressive sensing algorithm based on non-parametric Bayesian framework for the reconstruction of sparse entries with a continuous structure. A paired spike-and-slab prior is first employed to impose signal sparsity. A logistic Gaussian kernel model, whi…