Related papers: Joint Channel Estimation and User Grouping for Mas…
We study the problem of estimating multiple linear regression equations for the purpose of both prediction and variable selection. Following recent work on multi-task learning Argyriou et al. [2008], we assume that the regression vectors…
In practical massive MIMO systems, a substantial portion of system resources are consumed to acquire channel state information (CSI), leading to a drastically lower system capacity compared with the ideal case where perfect CSI is…
Multiple-input multiple-output (MIMO) systems require efficient and accurate channel estimation with low pilot overhead to unlock their full potential for high spectral and energy efficiency. While deep generative models have emerged as a…
This paper begins with considering the identification of sparse linear time-invariant networks described by multivariable ARX models. Such models possess relatively simple structure thus used as a benchmark to promote further research. With…
This paper investigates the uplink cascaded channel estimation for intelligent-reflecting-surface (IRS)-assisted multi-user multiple-input-single-output systems. We focus on a sub-6 GHz scenario where the channel propagation is not sparse…
In this paper, the uplink adaptation for massive multiple-input-multiple-output (MIMO) networks without the knowledge of user density is considered. Specifically, a novel cooperative uplink transmission and detection scheme is first…
We consider the problem of estimating the channel in reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) systems. We propose two variational expectation maximization (VEM) based algorithms for channel estimation in…
The problem of user scheduling with reduced overhead of channel estimation in the uplink of Massive multiple-input multiple-output (MIMO) systems has been considered. A geometry-based stochastic channel model (GSCM), called the COST 2100…
Extremely large-scale massive multiple-input multiple-output (MIMO) has shown considerable potential in future mobile communications. However, the use of extremely large aperture arrays has led to near-field and spatial non-stationary…
Base station (BS) cooperative transmission can improve the spectrum efficiency of cellular systems, whereas using which the channels will become asymmetry. In this paper, we study the impact of the asymmetry on the performance of channel…
Block transmission systems have been proven successful over frequency-selective channels. For time-varying channel such as in high-speed mobile communication and underwater communication, existing equalizers assume that channels over…
Extra-large scale MIMO (XL-MIMO) is a key technology for meeting sixth-generation (6G) requirements for high-rate connectivity and uniform quality of service (QoS); however, its deployment is challenged by the prohibitive complexity of…
This paper considers the uplink pilot overhead in a time division duplexing (TDD) massive Multiple Input Multiple Output (MIMO) mobile systems. A common scenario of conventional massive MIMO systems is a Base Station (BS) serving all user…
This paper presents a novel radio frequency (RF) beam training algorithm for sparse multiple input multiple output (MIMO) channels using unitary RF beamforming codebooks at transmitter (Tx) and receiver (Rx). The algorithm leverages…
Channel and frequency offset estimation is a classic topic with a large body of prior work using mainly maximum likelihood (ML) approach together with Cram\'er-Rao Lower bounds (CRLB) analysis. We provide the maximum a posteriori (MAP)…
Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel estimation is essential. However, due to massive number of…
We address the design of pilot sequences for channel estimation in the context of multiple-user Massive MIMO; considering the presence of channel correlation, and assuming that the statistics are known, we seek to exploit the spatial…
Energy consumption is an important issue in continuous wireless telemonitoring of physiological signals. Compressed sensing (CS) is a promising framework to address it, due to its energy-efficient data compression procedure. However, most…
Channel estimation problem is one of the key technical issues in time-variant multiple-input single-output (MSIO) communication systems. To estimate the MISO channel, least mean square (LMS) algorithm is applied to adaptive channel…
This work focuses on the downlink communication of a multiuser MIMO system where the base station antennas and the users' receiving antennas are all active, but at each transmission, only a subset of the receive antennas is selected by the…