Design Of Multiple-input Multiple-output Systems Research Articles

MIMO system identification and uncertainty calibration with a limited amount of data using transfer learning

Multiple-input multiple-output (MIMO) systems are fundamental in numerous advanced engineering applications, from aerospace to telecommunications, where precise system identification is critical for optimal performance. However, the identification of such systems often faces significant hurdles due to data scarcity, with existing approaches typically requiring substantial amounts of data for effective training. Addressing this challenge, this paper introduces a novel transfer learning framework designed specifically for MIMO system identification under conditions of limited data and inherent uncertainties. The proposed framework is applied to two case studies: the first in metal additive manufacturing, specifically the laser-blown powder-directed energy deposition as the source domain and the laser hot wire-directed energy deposition as the target domain, and the second involving a nonlinear case study of a continuous stirred-tank reactor (CSTR) with a temperature-dependent reaction. The results underscore the framework's effectiveness in capturing the dynamics of the target systems, including the ability to effectively model nonlinear dynamics. Comparative analyses highlight the benefits of employing dimensionless numbers in dynamic system modelling, offering reduced dimensionality, more physical meaning, and increased model accuracy. Overall, the proposed framework presents a promising approach to enhance system identification in MIMO systems with limited data and uncertainties, with potential applications across diverse domains.

Highly-efficient filtered hierarchical identification algorithms for multiple-input multiple-output systems with colored noises

Multiple-input multiple-output (MIMO) systems have extensive applications in industrial processes and systems engineering. This letter applies the filtering identification idea to establish a filtered identification model and investigate a filtered auxiliary model-based recursive least squares (F-AM-RLS) algorithm for parameter identification of MIMO systems with colored noises. To improve the computational efficiency, this work further proposes a four-stage filtered auxiliary model-based recursive least squares (4S-F-AM-RLS) algorithm by means of the hierarchical identification principle. Then, by incorporating the forgetting factor, a four-stage filtered auxiliary model-based forgetting factor recursive least squares (4S-F-AM-FF-RLS) algorithm is given to improve the convergence speed and the estimation accuracy. Additionally, the computational complexity analysis of the proposed algorithms indicates that the 4S-F-AM-RLS algorithm effectively reduces the computational burden and improves computational efficiency. Finally, the effectiveness of the F-AM-RLS, 4S-F-AM-RLS and 4S-F-AM-FF-RLS algorithms is validated through a numerical example.

Performance analysis and precoder design for IRS-assisted MIMO system with non-linear power amplifiers

In this work, a point-to-point intelligent-reflecting-surface (IRS) assisted single input single output (SISO) model is first developed and later modified to the multiple-input multiple-output (MIMO) case. For the SISO case, the transmitter or the access point (AP) is assumed to be equipped with a non-linear memory-less power amplifier (PA). A PA is assumed to be present for each data stream at the AP or base station (BS) side for the MIMO case. For both cases, three different types of PAs (A) Soft envelope limiter (SEL), (B) Solid-state power amplifier (SSPA), and (C) Travelling wave tube amplifier (TWTA) are considered. A Bussgang decomposition of the transmitted signal is then utilized for further analysis. A modified channel model is conceived for the AP-IRS-User equipment (UE) link based on the reflecting IRS elements’ radar cross-section (RCS). For the SISO system, both perfect and imperfect geometries of IRS elements with uniformly distributed element size errors are considered. An approximate closed-form expression for the upper bound on the above system’s spectral efficiency (SE) with ideal IRS elements is derived. The expression of the SE for two special cases of (A) Infinitely large IRS and (B) Ideal PA is deduced. Approximate closed-form expressions of various quality parameters for the IRS-assisted MIMO system with ideal and non-ideal PAs are also derived and verified numerically. Maximum ratio transmission (MRT) based precoder for this system is designed assuming that the information about the BS-UE link, BS-IRS-UE link, and the non-linear PA statistics is available at the BS. A closed-form expression for the SE of this system with this precoder is then derived and verified for various parametric situations.

A Decomposition-Based Analysis of Wireless Multipath Channel With Full-Wave Simulation

Along with the adaptation of the modern communication theory, the in-depth understanding of multipath fading channel is necessary to guarantee the efficient design of both algorithms and systems, especially the multiple-input--multiple-output (MIMO) system. This letter proposes a novel decomposition-based method aiming to achieve the accurate multipath channel analyses with full-wave simulation. Through the sequential deleting of scatters in the environments, the complex multipath channels can be decomposed, where the accurate delay and strength can be calculated from the recorded electronic fields along the sweeping of incident frequency. Then, the latent multipath components can be simply located along the decomposition. The proposed method can also provide the geographical distributions of channel responses, which are important to guild the MIMO system design.

Design and Analysis of MIMO Systems Using Energy Detectors for Sub-THz Applications

The significant amount of unused spectrum in sub-TeraHertz frequencies is contemplated to realize high rate wireless communications for beyond 5G networks. Yet, the performance of radio-frequency sub-TeraHertz systems is severely degraded by strong oscillator phase noise. Therefore, we investigate in this paper the use of multiple-input multiple-output (MIMO) systems with energy detection receivers to achieve high rate communications robust to phase noise. First, the design of the receiver detection algorithm is addressed. Two detectors are proposed for the studied nonlinear MIMO channel, either derived from the maximum likelihood decision rule by using a Gaussian approximation, or based on the use of neural networks. Second, the communication performance is assessed through numerical simulations for uncoded and coded systems. We consider a realistic scenario modeling an indoor wireless link in D-band with directive antennas and strongly correlated line-of-sight channels. Our results demonstrate that spatial multiplexing with non-coherent sub-TeraHertz transceivers can be realized on strongly correlated line-of-sight channels using the proposed detection schemes. Thereby, we highlight that high-rate radio-frequency sub-TeraHertz systems can be implemented with low-complexity and low-power architectures using MIMO systems with energy detection receivers.

A Non-Stationary Geometry-Based MIMO Channel Model for Millimeter-Wave UAV Networks

Unmanned aerial vehicle (UAV) communications are expected to play a major role in future space-air-ground integrated networks (SAGINs). In this paper, a geometric three-dimensional (3D) non-stationary channel model operating at millimeter-wave (mmWave) band is proposed for wideband UAV multiple-input multiple-output (MIMO) communications based on a multiple-layer cylinder reference model, where both stationary and moving clusters around transmitter (Tx) and receiver (Rx) are considered. Unlike the existing UAV-based GBSMs, the proposed model considers both local and far clusters in the propagation environments. On this basis, a continuous-time Markov model with two states is used to model the dynamic properties of clusters (i.e., clusters appear/disappear with time), and the closed-form expressions of the survival probabilities of clusters are derived. Furthermore, we derive and investigate some significant statistical properties, including space-time-frequency correlation function, quasi-stationary interval, and Doppler power spectrum. Numerical results show that the local mobile cluster (LMC) component leads to higher time correlation compared with the local stationary cluster (LSC) component. In addition, it is found that the LMC component leads to larger quasi-stationary interval compared with the LSC component. Finally, it is found that the motion of transceivers and clusters, and the changes of carrier frequency introduce significant fluctuations in Doppler power spectrum. These observations and conclusions can be considered as a guidance for mmWave UAV MIMO system design.

System Modeling and Precoding Design for Multi-beam Dual-polarized Satellite MIMO System

Background: As the multimedia service develops and the transmission rate in terrestrial communication systems increases rapidly, satellite communication needs to improve the transmission rate and throughput. Multiple Input Multiple Output (MIMO) techniques can increase the system capacity significantly by introducing the space dimension, as the system bandwidth remains the same. Therefore, utilization of MIMO for satellite communications to increase the capacity is an important research topic. So MIMO techniques for multibeam satellite communications are researched in the dissertation. Objective: The goal of this work is improving the capacity of the satellite system. Multi-beam and dual-polarized technologies are applied to a satellite system to improve the capacity further. Methods: In this paper, we first introduce a multi-beam dual-polarized satellite multi-put and multiout (MBDP-S-MIMO) system which combines the full frequency multiplexing and dual-polarization technologies. Then the system model and channel model are first constructed. At last, to improve the capacity further, BD and BD-ZF precoding algorithms are applied to MBDP-S-MIMO and their performance is verified by simulation. Results: Simulation results show the performance of the BD precoding algorithm gets better with the growth of the XPD at the receiver and is almost not affected by the growth of the channel polarization correlation coefficient. In addition, with the growth of the users’ speed, the performance becomes worse. Conclusion: The multi-beam dual-polarized satellite MIMO system has high capacity, and it has certain application prospects for satellite communication.

Transmit Signal Design for Large-Scale MIMO System With 1-bit DACs

Deploying low-resolution (e.g. one-bit) digital-to-analog converters (DACs) is of great importance in the multiple-input multiple-output (MIMO) system equipped with a large-scale antenna array since such a hardware architecture brings low-cost and circuit power saving for each antenna. In this paper, the problem of transmit signal design in a large-scale MIMO system with 1-bit DACs is investigated. To ensure directional transmission, we propose to design the transmit signal by minimizing the weighted mean-squared error (MSE) between the formed beampattern and a given one. The resulting design problem, which involves a nonconvex fourth-order objective and a set of nonconvex discrete constraints, is NP-hard, and therefore, an alternating minimization (AM) method is devised. In order to obtain a high-quality 1-bit solution, we propose a continuous and differentiable function to approximate the 1-bit signal, such that the problem with discrete 1-bit constraint is recast to an unconstrained optimization problem with a penalty term, which can be effectively solved via the limited-memory Broyden, Fletcher, Goldfarb, and Shanno (L-BFGS) approach. Moreover, it is found that a closed-form solution can be obtained when equal weights are applied. In addition, low-complexity schemes are developed based on the fast Fourier transform (FFT). The numerical simulations are conducted to demonstrate the effectiveness and superiority of the proposed method.

Optimal finite-dimensional spectral densities for the identification of continuous-time MIMO systems

This paper presents a method for designing inputs to identify linear continuous-time multiple-input multiple-output (MIMO) systems. The goal here is to design a T-optimal band-limited spectrum satisfying certain input/output power constraints. The input power spectral density matrix is parametrized as the product $$\phi_u(\text{j}\omega)=\frac{1}{2}H(\text{j}\omega)H^\text{H}(\text{j}\omega)$$ , where H(jω) is a matrix polynomial. This parametrization transforms the T-optimal cost function and the constraints into a quadratically constrained quadratic program (QCQP). The QCQP turns out to be a non-convex semidefinite program with a rank one constraint. A convex relaxation of the problem is first solved. A rank one solution is constructed from the solution to the relaxed problem. This relaxation admits no gap between its solution and the original non-convex QCQP problem. The constructed rank one solution leads to a spectrum that is optimal. The proposed input design methodology is experimentally validated on a cantilever beam bonded with piezoelectric plates for sensing and actuation. Subspace identification algorithm is used to estimate the system from the input-output data.

MIMO beamforming system for speech enhancement in realistic environment with multiple noise sources

Multiple noise sources in a realistic environment severely degrade the quality and intelligibility of the desired speech signal, thus posing a severe problem for many speech applications. Several noise reduction algorithms have been proposed with a main goal to solve this problem. However, the good performances of such algorithms are severely impaired in realistic environment under multi-noise sources condition. In this paper, the author treats the noise cancellation system as a multiple-input multiple-output (MIMO) beamformer system. The proposed approach consists of two steps. First, the noise signals are generated by applying the white noise sources to a MIMO AR system. Then, the noisy microphone signals are sequentially processed by employing multi-channel linear prediction error filters (MCLPEFs) and multi-channel adaptive noise estimation filters (MCANEFs) in the lower path of the proposed beamformer. The MCLPEFs are used to whiten the input signals, while the MCANEFs are used as a MIMO system identification to perform the modeling process of the noise signals. Finally, the noise signals in the upper path are subtracted from the estimated noises in the lower path to recover an enhanced speech signal. Moreover, the performance of the proposed MIMO approach was validated under a realistic environment with real noise sources.

Transmit design and DOA estimation for wideband MIMO system with colocated nested arrays

Abstract We consider transmit design and direction-of-arrival (DOA) estimation for a wideband multiple-input multiple-output (MIMO) system equipped with a colocated nested array for both transmit and receive. We present a new transmit scheme which separates the transmitted waveforms in the frequency domain, and allows us to form a sum coarray in the wideband case. Based on the proposed scheme, two coherent DOA estimation methods which construct focusing matrices from the difference coarray of the MIMO virtual array are presented to take full advantage of the degrees of freedom (DOF) and improve the accuracy of DOA estimation. Simulation results illustrate that the proposed system exhibits better performance than several competing configurations.

Hardware Efficient Architecture for Element-Based Lattice Reduction Aided K-Best Detector for MIMO Systems

Multiple-Input Multiple-Output (MIMO) systems are characterised by increased capacity and improved performance compared to the single-input single-output (SISO) systems. One of the main challenge in the design of MIMO systems is the detection of the transmitted signals due to the interference caused by the multiple simultaneously transmitted symbols from the multiple transmit antennas. Several detection techniques have been proposed in the literature in order to reduce the detection complexity, while maintaining the required quality of service. Among these low-complexity techniques is the Lattice Reduction (LR), which can provide good performance and significantly lower complexity compared to Maximum Likelihood (ML) detector. In this paper we propose to use the so-called Element-based Lattice Reduction (ELR) combined with K-Best detector for the sake of attaining a better Bit Error Ratio (BER) performance and lower complexity than the conventional Lenstra, Lanstra, and Lovasz (LLL) LR-aided detection. Additionally, we propose a hardware implementation for the ELR-aided K-Best detector for a MIMO system equipped with four transmit and four receive antennas. The ELR-aided K-Best detector requires an extra 18% increase in power consumption and an extra 20% in area overhead compared to a regular K-Best detector dispensing with ELR, where this increase in the hardware requirements is needed in order to achieve a 2 dB performance improvement at a bit error rate of 10−5.

Geometrical-Based Modeling for Millimeter-Wave MIMO Mobile-to-Mobile Channels

A geometric multiple-input multiple-output (MIMO) channel model is proposed for millimeter-wave (mmWave) mobile-to-mobile (M2M) applications based on the two-ring reference model, where cluster-based nonisotropic scattering at both ends of the radio link is considered. The proposed model employs a few clusters of scatterers located on two rings centered on the transmitter and receiver, and intracluster azimuth spread of scatterers is further characterized according to mmWave channel characteristics. From the model, the time-frequency correlation function, power delay profile (PDP), and the Doppler power spectrum are derived. By adjusting the cluster number, cluster center position, and the degree of intracluster nonisotropic scattering, the model is adaptable to a variety of mmWave M2M scenarios. Model validation is further conducted by comparing the simulated PDPs with mmWave outdoor measurements. Based on a detailed investigation of channel correlation functions, it is found that a small number of clusters leads to high channel correlation, and the degree of intracluster nonisotropic scattering has major impact on correlation only if cluster number is small. In addition, the Doppler power spectrum is similar to the U-shaped spectrum, and several factors (e.g., small cluster number, high intracluster azimuth spread, and large antenna spacing) introduce significant fluctuations in the Doppler power spectrum. Finally, the model is implemented with directional antennas for safety related M2M scenarios, which leads to high channel correlation compared with using omnidirectional antennas. These observations and conclusions can be considered as a guidance for the mmWave M2M MIMO system design.

Spectral-Efficiency Analysis of Regular- and Large-Scale (Massive) MIMO With a Comprehensive Channel Model

This paper provides a generalized analysis for the spectral efficiency of both regular- and large-scale (massive) multiple-input multiple-output (MIMO) systems, where major radio-propagation characteristics and antenna-array parameters are taken into account, including path loss, shadowing effect, multipath fading, antenna correlation, antenna polarization, environmental cross-polarization coupling, and antenna cross-polarization discrimination. After developing the channel model, where the Weichselberger method is exploited to reformulate the Kronecker model and allow closed-form analysis, an upper bound on the spectral efficiency of the MIMO channel is first established, by using the Hadamard's determinant inequality for positive semidefinite matrices. Then, an asymptotic analysis in high signal-to-noise ratio regime is conducted, which explicitly reveals the effects of the aforementioned parameters on the spectral efficiency. Moreover, the asymptotic behavior of the spectral efficiency in the sense of massive MIMO, i.e., as the number of transmit and/or receive antennas approaches infinity, is investigated. Monte Carlo simulation results corroborate the effectiveness of the analysis and the accuracy of the obtained upper bound on the spectral efficiency. Thanks to its high generality, the analysis developed in this paper benefits system designers in the design and performance evaluation of either regular or massive MIMO systems while accounting for realistic phenomena characterizing both the propagation environment and the antenna arrays.

Design framework for spherical microphone and loudspeaker arrays in a multiple-input multiple-output system.

Spherical microphone arrays (SMAs) and spherical loudspeaker arrays (SLAs) facilitate the study of room acoustics due to the three-dimensional analysis they provide. More recently, systems that combine both arrays, referred to as multiple-input multiple-output (MIMO) systems, have been proposed due to the added spatial diversity they facilitate. The literature provides frameworks for designing SMAs and SLAs separately, including error analysis from which the operating frequency range (OFR) of an array is defined. However, such a framework does not exist for the joint design of a SMA and a SLA that comprise a MIMO system. This paper develops a design framework for MIMO systems based on a model that addresses errors and highlights the importance of a matched design. Expanding on a free-field assumption, errors are incorporated separately for each array and error bounds are defined, facilitating error analysis for the system. The dependency of the error bounds on the SLA and SMA parameters is studied and it is recommended that parameters should be chosen to assure matched OFRs of the arrays in MIMO system design. A design example is provided, demonstrating the superiority of a matched system over an unmatched system in the synthesis of directional room impulse responses.

Design and Beamforming Performance of the Compact ESPAR Antenna for the Beam Space MIMO System

Multiple-input multiple-output (MIMO) system is essential to wireless and mobile communication for the higher channel capacity. However, this MIMO system requires many active RF (radio frequency) stages, and is very difficult to be used in mobile devices. It produces the high energy consumption at RF stage as well as the high hardware complexity. Especially, it will be more serious in the massive MIMO system that is strongly considered as an enabling technology for the 5G mobile communication system. In order to solve this problem, beam-space MIMO (BS-MIMO) systems were proposed using the electronically steerable parasitic array radiator (ESPAR) antenna, which work as the MIMO system using single RF stage with many passive parasitic elements. However, there are some limits in this ESPAR based BS-MIMO system because of this many passive parasitic elements. The conventional BS-MIMO system basically uses two parasitic elements for one dimension of MIMO. So, in this paper we like to propose a design method for reducing the passive elements number in this BS-MIMO system. In the proposed system, we cut down the number of the parasitic elements into half. So, the proposed BS-MIMO system uses one parasitic element for one dimension of MIMO. To design 8 ? 8 MIMO system, only eight elements of parasitic elements are needed instead of 16 elements in conventional design. Since this proposed ESPAR based BS-MIMO system requires only the half number of parasitic elements, we can design more compact ESPAR antenna design. Also, we can easily increase the MIMO dimension with compact size by this proposed ESPAR antenna. Simulation results can confirm that the proposed ESPAR BS-MIMO system with half number of elements keeps the very similar performance of the conventional system and acceptable beamforming performance as well. This proposed system could be very useful for the massive MIMO system design for the 5G mobile communication.

Design of Adaptive Millimeter-Wave MIMO Systems in Rain Scattering

In this paper, we consider the low-cost design for point-to-point millimeter-wave (mmW) multiple-input–multiple-output (MIMO) systems in rainfall environments. We first show that rain does not always have a negative impact on system performance, provided that accurate instantaneous channel state information (CSI) is available at both the transmitter and receiver. In addition, we propose a practical and low-cost transmission scheme, referred to as hardware-constrained statistical precoding (HC-SP), for mmW MIMO systems in rainfall environments. This scheme only requires statistical channel information at the transmitter and takes into consideration the hardware constraints on transceiver imposed by the high radio-frequency chain cost. Simulation results demonstrate that the HC-SP scheme allows the mmW MIMO system to have near-optimal performance.

Low‐complexity large‐scale multiple‐input multiple‐output channel estimation using affine combination of sparse least mean square filters

Large-scale multiple-input multiple-output (MIMO) system is considered one of promising technologies to realise next-generation wireless communication system (5G). So far, channel estimation problem is a big obstacle to develop large-scale MIMO system design due to high computational complexity and curse of dimensionality, which are caused by the long delay spread as well as a large number of antennas. Hence, devising any low-complexity channel estimation method could promote the successful development of the large-scale MIMO system. Due to the fact that, large-scale MIMO channels often exhibit sparse or/and cluster-sparse structure, in this study, the authors propose an effective low-complexity large-scale MIMO channel estimation method by using affine combination of sparse adaptive filtering filters. First, problem formulation and standard affine combination of adaptive least mean square (LMS) filters are introduced. Then they propose an effective affine combination method with two sparse LMS filters and design an approximate optimum affine combiner according to stochastic gradient search method. Later, to verify the proposed algorithm for large-scale MIMO channel estimation, both theoretical analysis and numerical simulations are provided to confirm effectiveness of the proposed algorithm which can achieve better estimation performance than the traditional methods.

Stochastic multiple‐input multiple‐output channel model based on singular value decomposition

A novel stochastic multiple-input multiple-output (MIMO) channel model based on the singular value decomposition of the channel matrix is proposed in this study. Under the framework of the proposed model, each of the right singular vectors can be modelled as the product of a stochastic scalar and a non-random vector, as is each of the left singular vectors. The non-random vectors, defined as the eigenmodes of the transmitter and receiver, respectively, can be easily extracted from the measurements, so are the singular values of the channel matrix. The implications of the proposed model's parameters that provide further insight into the MIMO channel are interpreted and a way of exploiting the parameters is given. To validate the proposed model, MIMO channel measurement is carried out under different indoor environments and the channel capacity is analysed. It is shown that the proposed model provides a better fit to the measurement results than the other popular stochastic channel models. The proposed stochastic MIMO channel model can be used for the MIMO communication system design and evaluation.

Characterization of Angular Spread in Underground Tunnels Based on the Multimode Waveguide Model

One of the biggest challenges facing wireless designers is determining how to configure multiple-input–multiple-output (MIMO) antennas in underground-environments, such as mines, so that they deliver best performance. Here, we showed that angular dispersion of the signal, a phenomenon that greatly impacts channel capacity, can be accurately predicted in both near-field and far-field of underground tunnels using a multimode waveguide. Not only this analytical model is much faster than other methods, but also it does not have other shortcomings of measurement-based modeling (e.g., poor resolution for far distances in the tunnel). Further, we characterized angular-spread for different tunnel sizes and showed that the power-azimuth-spectrum can be modeled by a zero-mean Gaussian distribution whose standard-deviation (angular-spread) is dependent on the tunnel dimension and the transmitter-receiver distance. Angular-spread is found to be a decreasing function of axial distance. After a certain distance, it becomes very small (about 4 $^{\circ}$ ) and independent of tunnel transverse dimension. Our results are useful for design of MIMO systems in underground tunnels and sufficient to permit the correlation-matrix required to extend the IEEE 802.11n MIMO channel model to underground environments.