MIMO Channel Research Articles

Numerical Evaluation of the MU-MIMO Beamforming Performance with Channel Estimation

This paper presents the numerical evaluation of the ZF beamforming algorithm and DFT precoding using the LS channel estimation in the multiuser multiantenna (MU-MIMO) downlink system. The sum rate performance depending on a number of users are presented. The arising inter user correlation degrades the sum rate (spectral efficiency) performance of multiuser MIMO system especially in scenarios where the number of users is larger than the number of antennas at the BS. For MIMO channel simulation the QuaDRiGa channel model reflecting the real propagation conditions is used. The obtained performance of MU-MIMO ZF precoding in spatially correlated channel are compared with DFT precoding based on the empirical cumulative density function of the sum rate of multiple users. Numerical results show that the ZF precoder outperforms the DFT precoder in channel with less spatial correlation. The DFT precoder in spatially more correlated channel has advantage over ZF precoder.

Robust Transceiver Design for RIS-Aided Spatially Correlated MIMO Channels With Uncertainty

Robust Transceiver Design for RIS-Aided Spatially Correlated MIMO Channels With Uncertainty

FPGA based modified multi-repeat distribution matcher for probabilistic amplitude shaping

The technique of probabilistic amplitude modulation, based on distribution matching, has garnered considerable attention in recent years as a means to enhance spectral efficiency and diminish the constellation energy of coded modulation. This paper introduces the implementation of Probabilistic Amplitude Modulation (PAS) using a Modified Multi-Repeat Distribution Matcher (MMRDM) on a Field Programmable Gate Array (FPGA). The Modified Multiple Repetition Distribution Matcher (MMRDM) is integrated into a 2×2 Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system, realized through the Xilinx System Generator (XSG). Simple Zero-Forced and Minimum Mean Squared Error (MMSE) equalizers are applied to the receiver for signal detection across the MIMO channel. The system incorporates enhanced security through chaos-based scrambling with 16 and 64 Quadrature Amplitude Modulation (QAM). VHDL code files for this system are generated for the Xilinx Kintex-7 (xc7k325t-3fbg676) for hardware implementation. Performance evaluation includes an assessment of required storage capacity, complexity, and bit error rate (BER). Using Vivado 2017.4, the system is successfully routed with resource utilization, for example, 0.67% Block RAM (BRAM), 68.6% Look-Up Tables (LUT), 83% DSP 48s, and 1.5% registers for 64-QAM uniform modulation. Similarly, for 64-QAM 10 level (shaper output 60 bit) shaped modulation, the resource utilization is 0.67% BRAM, 68.8% LUT, 83% DSP 48s, and 1.6% registers on the specified device. Simulation results demonstrate an improvement in the net shaping gain of approximately (2-4 dB) at 1×10^(-4) for different equalizer cases compared to uniform QAM, along with a notable reduction in required storage capacity and computational complexity.

Hybrid Deep Learning Approach for 6G MIMO Channel Estimation and Interference Alignment HetNet Environments

Hybrid Deep Learning Approach for 6G MIMO Channel Estimation and Interference Alignment HetNet Environments

Superposed three-dimensional 64QAM constellation design for MIMO-OFDM visible light communication systems.

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) has been widely used to improve data rate in visible-light communication (VLC) systems. However, the high correlation of channels restricts the application of MIMO. A superposed constellation, combined with MIMO, can achieve considerable diversity gain even in highly correlated MIMO channels. In this study, what we believe to be novel superposed three-dimensional 64-quadrature amplitude modulation (3D-64QAM) constellation schemes are proposed for MIMO-OFDM VLC systems. First, a superposed 3D-64QAM constellation scheme using two transmitted light emitting diodes (LEDs) is proposed, where two independent signals with 3D-4QAM and 3D-16QAM modulation formats are superposed to form a 3D-64QAM signal at the receiver. Then, for what we believe is the first time, we expand the superposed constellation solution to three-LED application scenarios, wherein the 3D-64QAM constellation is superposed by three different 3D-4QAM constellations. Both schemes benefit from a higher minimum Euclidean distance of 3D-64QAM constellation, 1.67 times that of traditional two-dimensional (2D) 64QAM constellation. Meanwhile, the equal-power superposition design of transmitted signals reduces the nonlinearity of LEDs and power competition of photodiodes. Moreover, the three-LED scheme further improves the transmitted power without increasing the risk of nonlinear distortion. To improve spectral efficiency and reduce complexity, we also propose a 3D OFDM modulation scheme. The superposed 3D-64QAM schemes are first studied through theoretical analysis and computer simulation. Then, an experimental demonstration is established to investigate the system performance comprehensively. Experimental results prove that the superposed 3D-64QAM constellation schemes achieve a superior bit error rate (BER) performance than the traditional superposed 2D-64QAM constellation scheme. Compared with two-LED scheme, the three-LED scheme not only obtains a lower BER, but also improves the dynamic range of driving peak-to-peak voltage significantly.

Low-complex channel estimation in extra-large scale MIMO with the spherical wave properties

This paper investigates the low-complex linear minimum mean squared error (LMMSE) channel estimation in an extra-large scale MIMO system with the spherical wave model (SWM). We model the extra-large scale MIMO channels using the SWM in the terahertz (THz) line-of-sight propagation, in which the transceiver is a uniform circular antenna array. On this basis, for the known channel covariance matrix (CCM), a low-complex LMMSE channel estimation algorithm is proposed by exploiting the spherical wave properties (SWP). Meanwhile, for the unknown CCM, a similar low-complex LMMSE channel estimation algorithm is also proposed. Both theoretical and simulation results show that the proposed algorithm has lower complexity without reducing the accuracy of channel estimation.

Gigantic MIMO Channel Characterization: Challenges and Enabling Solutions

Gigantic MIMO Channel Characterization: Challenges and Enabling Solutions

A quantitative analysis of MIMO channel performance in new generation networks: Prospects and limitations

The objective of this article is to analyze the behavior of channels in mobile radio transmission chains and propose a solution for reducing propagation attenuation and ensuring Quality of Service (QoS) in telecommunications. As network coverage and throughput demands continue to grow, a thorough characterization of channels is essential. The primary contribution of this work is to investigate the different phases of the digital transmission chain jointly to achieve optimal reliability and QoS. Spatial and temporal representations must be considered at the same level when characterizing and modeling MIMO system channels. Accurate and realistic modeling can be achieved through transmission channel characterization in the spatial domain, which is critical in a MIMO context to improve throughput and performance compared to conventional systems. The article explores the behavior of information carriers in the propagation environment and the impact of fading, diversity, multiplexing, and multipath phenomena.

Massive MIMO Channel Measurement Data Set for Localization and Communication

Channel state information (CSI) needs to be estimated for reliable and efficient communication, however, user location information is hidden inside and can be further exploited. This article presents a detailed description of a Massive Multi-Input Multi-Output (MaMIMO) testbed and provides a set of experimental location-labeled CSI data. We first focus on the design of the hardware and software of a MaMIMO testbed for gathering multiple CSI data sets. We then show this data can be used for learning-based localization and enhanced communication research. The presented data set is made fully available to the research community. We illustrate that a CSIbased joint communication and sensing processing pipeline can be evaluated and designed based on the collected data set. Specifically, the localization output obtained by a convolutional neural network (CNN) trained on the data sets is used to schedule users to improve the spectral efficiency (SE) of the communication system. Finally, we pose promising directions for further exploiting this data set and creating more data sets.

FDD Massive MIMO Channel Training: Optimal Rate-Distortion Bounds and the Spectral Efficiency of “One-Shot” Schemes

We study the problem of providing channel state information (CSI) at the transmitter in multi-user “massive” MIMO systems operating in frequency division duplexing (FDD). The wideband MIMO channel is a vector-valued random process correlated in time, space (antennas), and frequency (subcarriers). The base station (BS) broadcasts periodically β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tr</sub> pilot symbols from its <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> antenna ports to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> single-antenna users (UEs). Correspondingly, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> UEs send feedback messages about their channel state using β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fb</sub> symbols in the uplink (UL). Using results from remote rate-distortion theory, we show that, as snr → ∞, the optimal feedback strategy achieves a channel state estimation mean squared error (MSE) that behaves as Θ(1) if β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tr</sub> < <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> and as Θ(snr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–α</sup> ) when β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tr</sub> ⩾ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> , where α = min(β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fb</sub> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> , 1), where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> is the rank of the channel covariance matrix. The MSE-optimal rate-distortion strategy implies encoding of long sequences of channel states, which would yield completely stale CSI and therefore poor multiuser precoding performance. Hence, we consider three practical “one-shot” CSI strategies with minimum one-slot delay and analyze their large-SNR channel estimation MSE behavior. These are: (1) digital feedback via entropy-coded scalar quantization (ECSQ), (2) analog feedback (AF), and (3) local channel estimation at the UEs via compressed sensing and digital feedback. These schemes have different requirements in terms of knowledge of the channel statistics at the UE and at the BS. In particular, the latter strategy requires no statistical knowledge and is closely inspired by a CSI feedback scheme currently proposed in 3GPP standardization. It is shown that ECSQ achieves optimal MSE at the price of a slight increase in feedback rate which vanishes for large SNR. AF achieves the optimal MSE decay rate of Θ(snr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–1</sup> ) whenever β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tr</sub> , β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fb</sub> ⩾ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> but is sub-optimal if β⩾ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> and β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fb</sub> < <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> . The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3GPP-inspired</i> scheme is shown, via numerical simulations, to achieves performance similar to ECSQ and AF when the multipath channel is sufficiently sparse in the angle-delay domain, but suffers from a large performance gap if this requirement is not met.

Investigation of the Effects of Atmospheric Attenuation and Frequency on MIMO Channel Capacity

The efficiencies of 5G channels, which are highly affected by atmospheric attenuation, are still being investigated. The effects of frequency and atmospheric attenuation parameters such as humidity, temperature, rain, and pressure were investigated in this study using the NYUSIM program. The spatial consistency mode of the NYUSIM channel simulator was turned off, and unnormalized channel capacities were calculated at 28, 45, 60, and 73 GHz frequencies. According to the research results, the rain rate was the atmospheric attenuation parameter that significantly affected MIMO channel capacity. In contrast, the humidity percentage had the slightest impact. The frequency where the channel capacity is most affected by the four determined atmospheric attenuation parameters is 60 GHz, while the frequency where it is least affected is 28 GHz. The study found that using frequencies with high atmospheric attenuation reduces communication efficiency significantly. Furthermore, rain rate has a significant impact on 5G channel performance.

A tensor-based approach for frequency-selective MIMO channel equalization

A tensor-based approach for frequency-selective MIMO channel equalization

Channel Hardening of IRS-Aided Multi-Antenna Systems: How Should IRSs Scale?

It is widely believed that large IRS-aided MIMO settings maintain the fundamental features of massive MIMO systems. This work gives a rigorous proof that confirms this belief. We show that using a large passive IRS, the end-to-end MIMO channel between the transmitter and the receiver always hardens, even if the IRS elements are strongly correlated. For fading direct and reflection links between the transmitter and the receiver, our derivations demonstrate that for a large number of reflecting elements on the IRS, the capacity of the end-to-end channel is accurately approximated by a real-valued Gaussian random variable whose variance goes to zero as the number of IRS elements grows unboundedly large. The order of this drop depends on how the physical dimensions of the IRS grow. We derive this order explicitly. Numerical experiments show that the closed-form approximation very closely matches the histogram of the capacity term, even in practical scenarios. As a sample application of the results, we characterize the dimensional trade-off between the transmitter and the IRS. The result is intuitive: For a target performance, the larger the IRS is, the fewer transmit antennas are required.

Fast Beam Search Based on Recorded Combinations for Distributed Massive MIMO Systems Using Sub-THz Wave Bands

Massive multiple-input multiple-output (mMIMO) systems using sub-Terahertz (sub-THz) wave bands are viewed as a promising way to increase greatly the capacity of future mobile communications because they can provide a wider bandwidth than conventional wireless communications. Distributed mMIMO systems can improve the spatial multiplexing transmission performance by reducing spatial correlations among the MIMO channels in line-of-sight environments. Unfortunately, beam search is more complex than in conventional wireless communications, because each access point (AP) requires a very narrow beamwidth to compensate for the significant propagation attenuation in sub-THz waves and beam search must involve all distributed APs. To address this problem, this paper proposes a fast beam search method to limit the beam-sweep directions of beam sweep based on the beam combinations selected for the distributed APs in the past. Simulations show that the proposed method maintains the transmission capacity while greatly reducing the number of search beams compared to conventional all-AP exhaustive beam search. The results show based on 16 APs that while matching the transmission capacity attained by all-AP exhaustive or two-stage beam search methods, the proposed method reduces the number of search beams by APs (excluding the initial AP executed exhaustive beam search) by 90.8% at the 50th percentile and 62.5% at the 95th percentile compared to two-stage beam search for 50,000 sets of user equipment used for recording beam combinations before evaluating the performance.

Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning.

In order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input-multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The method applies the reciprocity of the uplink and downlink, uses uplink channel state information in the base station to help users give feedback on unknown downlink information, and compresses and restores the channel state information. First, an encoder-decoder structure is established. The encoder reduces the network depth and uses multi-resolution convolution to increase the accuracy of channel state information extraction while reducing the number of computations relating to user equipment. Afterward, the channel state information is compressed to reduce feedback overhead in the channel. At the decoder, with the help of the reciprocity of the uplink and downlink, the feature extraction of the uplink's magnitudes is carried out, and the downlink channel state information is integrated into a channel state information feature matrix, which is restored to its original size. The simulation results show that compared with CSINet, CRNet, CLNet, and DCRNet, indoor reconstruction precision was improved by an average of 16.4%, and outside reconstruction accuracy was improved by an average of 21.2% under all compressions.

Three-Dimensional Motion Compensation Method Based on Sparse Array Designed for Time-Division Multiplexing Multiple-Input-Multiple-Output Through-Wall Radar

A large-aperture radar composed of a multiple-input-multiple-output (MIMO) planar array can complete 3D through-wall imaging (TWI), but the simultaneous work of the multiple transceiver channels leads to difficulties in designing the hardware. In engineering, multiple transceiver channels are usually realized by time-division multiplexing (TDM) in MIMO radar, which is called TDM MIMO radar. A time delay will be introduced when the channels are switched, which will cause high sidelobes and position deviation in the 3D imaging of moving targets, also known as range migration. This paper proposes a motion compensation algorithm based on sparse array, designed to eliminate range migration in moving targets in 3D TWI scenes. In the proposed algorithm, the coincident array elements of the equivalent array are used as the compensation channels to calculate the position difference of the target, which can correct the remaining MIMO channels. The proposed algorithm is compared with no compensation, and the reference-channel-based motion compensation algorithm (RCMCA). According to the simulation and experimental results, the proposed motion compensation algorithm can effectively eliminate sidelobes, and keep the position deviation within 0.30 m in the 3D TWI of moving targets under the TDM MIMO radar, without increasing the system complexity.

Extremely-Fast, Energy-Efficient Massive MIMO Precoding With Analog RRAM Matrix Computing

Signal processing in wireless communications, such as precoding, detection, and channel estimation, are basically about solving inverse matrix problems, which, however, are slow and inefficient in conventional digital computers, thus requiring a radical paradigm shift to achieve fast, real-time solutions. Here, for the first time, we apply the emerging analog matrix computing (AMC) to the linear precoding of massive MIMO. The real-valued AMC concept is extended to process complex-valued signals. In order to adapt the MIMO channel models to RRAM conductance mapping, a new matrix inversion circuit is developed. In addition, fully analog dataflow and optimized operational amplifiers are designed to support AMC precoding implementation. Simulation results show that the zero-forcing precoding is solved within 20 ns for a 16×128 MIMO system, which is two orders of magnitude faster than the conventional digital approach. Meanwhile, the energy efficiency is improved by 50×.

Opportunistic Schedule Selection for Multiuser MIMO FSO Communications: A Security- Reliability Trade-Off Perspective

The security and reliability of free space optical (FSO) communications and the trade-off between the two are the most critical characteristics to emphasize in FSO systems, especially as optical wireless communications continue to evolve. In this work, we investigate the impact of opportunistic transmit aperture selection (TAS) on the security-reliability trade-off (SRT) of the multiuser multiple-input multiple-output (MU-MIMO) free space optical system. We consider a general scenario in which the FSO system consists of a transmitter side with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> users and a single receiver with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> apertures communicating over generalized Malaga-M turbulence channels. We derive the TAS combined signal-to-noise ratio (SNR) distributions for the FSO MIMO channel between the transmitting users and the receiver. A statistical analysis of the TAS scheme is performed, where the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> th best user with the highest SNR is selected. Based on this analysis, the closed-form expressions for the outage probability, average bit error rate, intercept probability, and SRT are derived. Then, the diversity advantages provided by the channel variations considering the atmospheric turbulence and generalized nonzero boresight pointing errors are further discussed. The results show that the considered TAS significantly improves the security, reliability, and SRT performance and has the potential to solve existing FSO communication problems. Monte Carlo simulations are used to verify the correctness of the numerical results.

On the Latent Space of mmWave MIMO Channels for NLOS Identification in 5G-Advanced Systems

On the Latent Space of mmWave MIMO Channels for NLOS Identification in 5G-Advanced Systems

Numerical Evaluation of the MU-MIMO Beamforming Performance with User Selection Algorithm

This paper presents the numerical evaluation of the ZF beamforming algorithm using the user selection in the multiuser multiantenna (MU-MIMO) downlink system. Two user selection algorithm – semiorthogonal user selection and greedy user selection are numerically evaluated based on the open source MIMO channel model. The sum rate performance depending on number of users are presented. The arising inter user correlation degrades the sum rate (spectral efficiency) performance of multiuser MIMO system especially in scenarios where the number of users is larger than the number of antennas at the BS. The selection of users is based on the orthogonality of the channels among selected users. For MIMO channel simulation the QUADRIGA channel model reflecting the real propagation conditions is used. The obtained performance of MU-MIMO ZF precoding in spatially correlated channel are compared based on the empirical cumulative density function of the sum rate of multiple users. Numerical results show that the ZF precoder using user selection (G ZF) outperforms the ZF precoder with random user selection in spectral efficiency. The greedy user selection in spatially correlated channel has advantage to semi-orthogonal user selection. It isobserved that as the increasing the number of served users used for selection the greedy user selection gives better performance than semi-orthogonal algorithm.