MIMO Channel Research Articles

Deep Joint Source-Channel Coding for Adaptive Image Transmission Over MIMO Channels

Deep Joint Source-Channel Coding for Adaptive Image Transmission Over MIMO Channels

Peak Cancellation Signal Generation Considering Variance in Signal Power among Transmitter Antennas in PAPR Reduction Method Using Null Space in MIMO Channel for MIMO-OFDM Signals

Peak Cancellation Signal Generation Considering Variance in Signal Power among Transmitter Antennas in PAPR Reduction Method Using Null Space in MIMO Channel for MIMO-OFDM Signals

Spatial MIMO Channel Characterization Under Different Vehicular Distributions

Spatial MIMO Channel Characterization Under Different Vehicular Distributions

Deep CSI Compression for Dual-Polarized Massive MIMO Channels With Disentangled Representation Learning

Deep CSI Compression for Dual-Polarized Massive MIMO Channels With Disentangled Representation Learning

Efficient Ray-Tracing Simulation for Near-Field Spatial Non-Stationary mmWave Massive MIMO Channel and Its Experimental Validation

Efficient Ray-Tracing Simulation for Near-Field Spatial Non-Stationary mmWave Massive MIMO Channel and Its Experimental Validation

Multipath Angle Spread and Performance in Indoor Measured Tri-Polarized Massive MIMO Channels

Multipath Angle Spread and Performance in Indoor Measured Tri-Polarized Massive MIMO Channels

Parametric channel estimation for RIS-assisted mmWave MIMO-OFDM systems with low pilot overhead

We consider the channel estimation problem in a millimeter-wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system aided by a passive reconfigurable intelligent surface (RIS). Initially, leveraging the inherent sparsity of mmWave channels, we decouple the channel estimation into the recovery of multipath parameters, including spatial frequencies of angles, delays, and complex gains. Subsequently, we employ two training schemes: the first involves a full row rank constrained RIS phase shift pattern, and the second incorporates a Kronecker structure constrained RIS phase shift pattern. These schemes reform channel estimation into estimating parameters from two fifth-order tensor signals, which admit constrained Canonical Polyadic decomposition. Moreover, both tensor signals share Vandermonde-constrained factor matrices, enabling algebraic algorithms for tensor decomposition problems and recovering channel multipath parameters. Theoretical analyses show that the first training scheme has a lower signal processing complexity for channel estimation, while the second attains a higher estimation accuracy. Also, we prove that our proposed two schemes have the same minimum pilot overhead, which is as low as proportional to the product of the number of paths in the two MIMO channels (transmitter-RIS and RIS-receiver). Numerical results validate the effectiveness of our proposed methods.

From Data-Driven Learning to Physics-Inspired Inferring: A Novel Mobile MIMO Channel Prediction Scheme Based on Neural ODE

From Data-Driven Learning to Physics-Inspired Inferring: A Novel Mobile MIMO Channel Prediction Scheme Based on Neural ODE

Polarization Diversity and Transfer Learning-Based Modulation Optimization for High-Speed Dual Channel MIMO Backscatter Communication

Polarization Diversity and Transfer Learning-Based Modulation Optimization for High-Speed Dual Channel MIMO Backscatter Communication

Underwater Wireless Optical Communication for IOT using Coding MIMO Diversity

The present study examined and simulated the coding or no coding performance for different configurations of multi-input, multioutput underwater wireless optical communication systems. MIMO diversity and channel coding were used to reduce the power transmitter, assuming lognormal distribution random for weak ocean turbulence. The simulation tested for a link range of 30 m in low ocean turbulence and 500 Mbps on–off-keying was also presented using Monte-Carlo. The results showed improvement in power transmitted (gain) for 2x5 compared to Uncoded SISO, which was about 33.60db using the concatenated code. So that the underwater wireless optical communications (UWOC) even now will also be significantly impacted by the Internet of Underwater Things (IoUT) and ultimate mobile technologies five generation communication systems because of their extraordinarily high demand for data safety, bit rate, energy consumption, so it is used in combined data transfer between underwater communication devices. The UWOC system based on coded MIMO may successfully be used in IoUT due to its high robustness and power qualification.

Characteristics and Channel Capacity Studies of a Novel 6G Non-Stationary Massive MIMO Channel Model Considering Mutual Coupling

Characteristics and Channel Capacity Studies of a Novel 6G Non-Stationary Massive MIMO Channel Model Considering Mutual Coupling

Identity Authentication for Ground Control Station Based on MmWave MIMO Channel Sparsity

Identity Authentication for Ground Control Station Based on MmWave MIMO Channel Sparsity

Moment analysis on channel eigenvalue for DAS‐to‐DAS system

AbstractIn this paper, we study on channel eigenvalue distribution of MIMO system, namely, DAS‐to‐DAS system, of which transmit and receive nodes are geometrically distributed in unplanned manner. It offers key to performance upper bound analysis of collaborative transmission in unplanned network and multi‐user transmission of distributed antenna system. Previous studies on stochastic geometry have analyzed performance of single typical receiver, while transmitters are geometrically distributed. And random matrix theory has unveiled probabilistic behavior of MIMO channel eigenvalues, while transmit and receive antennas are collocated. Our study integrates knowledge from the two different research fields to extend the applicability to analysis on channel eigenvalue distribution of DAS‐to‐DAS system. As a preliminary research on such channel eigenvalue distribution, its first few moments are derived in analytic form. And its coherency of the analysis results with Monte‐Carlo simulation results has examined.

Secret-Key Capacity From MIMO Channel Probing

Secret-Key Capacity From MIMO Channel Probing

Compressing MIMO Channel Submatrices with Tucker Decomposition: Enabling Efficient Storage and Reducing SINR Computation Overhead

Compressing MIMO Channel Submatrices with Tucker Decomposition: Enabling Efficient Storage and Reducing SINR Computation Overhead

Parametrization and Estimation of High-Rank Line-of-Sight MIMO Channels With Reflected Paths

Parametrization and Estimation of High-Rank Line-of-Sight MIMO Channels With Reflected Paths

RIS-Assisted MIMO Channel Modeling with Spatially Consistent Sparsified Properties

RIS-Assisted MIMO Channel Modeling with Spatially Consistent Sparsified Properties

Channel estimation and MIMO combining architecture in millimeter wave cellular system with few ADC bits

Hybrid combiner and precoder architectures, radio frequency (RF) chain, analog phase shifters, digital-to-analog converter (DAC), and analog-to-digital converter (ADC) are components of a millimeter wave cellular system. Prior works in the area of millimeter wave cellular system design employ receiver with infinite bit and large amount of RF chain that scales linearly with the quantity of transmitting and receiving antennas. This mode of design no doubt increases power demand or requirement of a typical millimeter wave system. In this work, hybrid architecture with few RF chains and small number of ADC bits are proposed and are used as candidate for millimeter wave channel estimation and cellular communication. In that connection, least square (LS), orthogonal matching pursuit (OMP), compressed sampling matching pursuit (CoSAMP), and deep learning (DL) techniques are utilized for analytical investigation. Indeed, computational results reveal that, when ADC consisting of uniform mid- rise quantizer is employed, the performance of 4 and 6 bits at signal-to-noise ratio (SNR) values of − 10 dB and 20 dB is at par with infinite bit (unquantized case). As a validation, DL compares favorably well with adaptive compressed sensing (ACS) technique previously used in the literature for channel estimation, while OMP and CoSAMP show better performance than ACS.

Rank Optimization for MIMO Channel With RIS: Simulation and Measurement

Rank Optimization for MIMO Channel With RIS: Simulation and Measurement

Joint PAPR reduction using null space in MIMO channel and predistortion for MIMO-OFDM signals in multi-antenna AF-type relay transmission

Joint PAPR reduction using null space in MIMO channel and predistortion for MIMO-OFDM signals in multi-antenna AF-type relay transmission