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Several methods have been recently proposed for estimating sparse Gaussian graphical models using $\ell_{1}$ regularization on the inverse covariance matrix. Despite recent advances, contemporary applications require methods that are even…
Reconfigurable intelligent surfaces (RISs) have recently received widespread attention in the field of wireless communication. An RIS can be controlled to reflect incident waves from the transmitter towards the receiver; a feature that is…
We study the performance of the simulated bifurcation (SB) algorithm for signal detection in multiple-input multiple-output (MIMO) system, a problem of key interest in modern wireless communication systems. Our results show that SB…
Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for enhancing wireless communications through dense antenna arrays. Accurate channel estimation is critical to unlocking their full performance potential. To…
Reconfigurable intelligent surface (RIS) has been anticipated to be a novel cost-effective technology to improve the performance of future wireless systems. In this paper, we investigate a practical RIS-aided multiple-input-multiple-output…
A joint robust transmit/receive adaptive beamforming for multiple-input multipleoutput (MIMO) radar based on probability-constrained optimization approach is developed in the case of Gaussian and arbitrary distributed mismatch present in…
In this paper, we study the low-complexity iterative soft-input soft-output (SISO) detection algorithm in a large-scale distributed multiple-input multiple-output (MIMO) system. The uplink interference suppression matrix is designed to…
This paper seeks an efficient algorithm for stochastic precoding to maximize the long-term average weighted sum rates throughout a multiple-input multiple-output (MIMO) network. Unlike many existing works that assume a particular…
Near-field U-MIMO communications require carefully optimized sampling grids in both angular and distance domains. However, most existing grid design methods neglect the influence of base station height, assuming instead that the base…
In the context of a time-varying multiuser multiple-input-multiple-output (MIMO) system, we design recursive least squares based adaptive predictors and differential quantizers to minimize the sum mean squared error of the overall system.…
Community detection is an important problem in unsupervised learning. This paper proposes to solve a projection matrix approximation problem with an additional entrywise bounded constraint. Algorithmically, we introduce a new differentiable…
In this work, the uplink channel estimation problem is considered for a millimeter wave (mmWave) multi-input multi-output (MIMO) system. It is well known that pilot overhead and computation complexity in estimating the channel increases…
In diffusion-based communication, as for molecular systems, the achievable data rate is very low due to the slow nature of diffusion and the existence of severe inter-symbol interference (ISI). Multiple-input multiple-output (MIMO)…
This paper proposes a novel learning to learn method, called learning to learn iterative search algorithm (LISA), for signal detection in a multi-input multi-output (MIMO) system. The idea is to regard the signal detection problem as a…
The D-band offering an untapped wide bandwidth is promising for high data rate communication and high-resolution wireless sensing. However, these potentials are hindered by the low performance and energy efficiency of the D-band circuits…
This work presents blind constrained constant modulus (CCM) adaptive algorithms based on the set-membership filtering (SMF) concept and incorporates dynamic bounds {for interference suppression} applications. We develop stochastic gradient…
In this paper, we propose a MIMO receiver algorithm that exploits {\em channel hardening} that occurs in large MIMO channels. Channel hardening refers to the phenomenon where the off-diagonal terms of the ${\bf H}^H{\bf H}$ matrix become…
For many applications in signal processing and machine learning, we are tasked with minimizing a large sum of convex functions subject to a large number of convex constraints. In this paper, we devise a new random projection method (RPM) to…
We propose a novel scheme that allows MIMO system to modulate a set of permutation matrices to send more information bits, extending our initial work on the topic. This system is called Permutation Matrix Modulation (PMM). The basic idea is…
Large-scale multiple-input-multiple-output (MIMO) systems typically operate in dense array deployments with limited scattering environments, leading to highly correlated and ill-conditioned channel matrices that severely degrade the…