Multiple-input And Single-output Research Articles

Effects of Erroneous CSI on the Performance of Multiple-Transmit Antenna Selection

The paper assumes that the channel state information (CSI) is available at the receiver and is known partially at the transmitter through a feedback channel. The bit error rate (BER) performance of multiple transmit antenna selection (MTAS) for the multiple-input and single-output (MISO) system with STBC (MTAS/MISO-STBC) will be investigated in detail. Meanwhile, the selection criterion that maximizes the channel Frobenius norm or minimizes the error probability of MTAS/MISO-STBC system is employed. In two cases of erroneous CSI (ECSI) and inerrable CSI (ICSI), wireless channels of the MTAS systems are modeled and their analytical expressions are derived. For the case of ICSI, the exact BER expressions of the Chernoff upper bound (CUB) for both the MISO-STBC system of full complexity and MTAS system are evaluated, respectively. Next, for the ECSI case, a comprehensive analytical expression of BER CUB for the same system is derived in detail. Finally, extensive Monte-Carlo simulations are presented to support and validate our numerical analysis proposed in this paper. Both the simulating results and the numerical results show that MTAS can reduce effectively the effect of the erroneous CSI.

Capacity Loss Due to Polarization-Mismatch and Space-Correlation on MISO Channel

This paper analyzes the effects of polarization-mismatch and space-correlation to a multiple-input and single-output (MISO) channel which is observable in the near future cellular communications environments such as large-scale antenna arrays and small cells. The analysis is based on a polarization-mismatched and space-correlated MISO channel which is modeled from the conventional dual-polarized channel. In the MISO channel, polarization-mismatch is described by the polarization-mismatch angle which is uniformly distributed from 0 to the maximum polarization-mismatch angle and space-correlation is described by the exponential correlation model. Assuming high SNR, approximate expressions of the ergodic capacity are derived as a function of the transmit power, number of transmit antennas, maximum polarization-mismatch angle, and space-correlation coefficient in four representative environments: narrowly or widely spread polarization-mismatch angles and slightly or highly correlated channels. Further, the capacity loss introduced by polarization-mismatch and space-correlation is derived with respect to the maximum polarization-mismatch angle and space-correlation coefficient. It is shown that the capacity loss introduced by polarization-mismatch is upper bounded by 2 bit/s/Hz. Whereas, the capacity loss introduced by space-correlation increase with the number of transmit antennas and is upper bounded by 0.832 bit/s/Hz. Required resources to compensate for the capacity loss is derived as well.

Developing Models of Lithuanian Speech Vowels and Semivowels

Lithuanian vowel and semivowel phoneme modelling framework is proposed. Using this framework, the phoneme signal is described as the output of a linear multiple-input and single-output (MISO) system. The MISO system is a parallel connection of single-input and single-output (SISO) systems whose input impulse amplitudes vary in time. Within this framework two synthesis methods are proposed: harmonic and formant. The synthesized sounds obtained by the harmonic synthesis method are compared with those obtained by the formant method. Application of this modelling framework to all of Lithuanian vowel and semivowel synthesis gives naturally sounding result.

Ergodic capacity analysis of spatially modulated systems

Spatial Modulation (SM) is a newly introduced wireless transmission scheme, and Space Shift Keying (SSK) is its simplified version. Their capacity performance has not been well examined to date. Starting from basic definitions of information theory, through theoretical analyses and numerical calculations, this paper evaluates the capacity of SM systems in various channels. Our conclusion is that under Rayleigh fading channels, the capacity of the SSK system is limited by the Signal-to-Noise Ratio (SNR) or the number of transmit antennas, whichever one is smaller. From the capacity point of view, the SM system is better than the Single-Input and Single-Output (SISO) system, but worse than the Multiple-Input and Single-Output (MISO) system. The correlation between transmit antennas will degrade the performance of the SSK system. Line-of-Sight (LOS) may cause performance degradation for SSK, but capacity increases for SM.

Hybrid system identification for high-performance structural control

Structural systems installed with active or semi-active control devices usually require availability of a high-fidelity model to determine appropriate control designs. Use of traditional system identification techniques has proven to be challenging, primarily due to the fact that such models must be multiple-input and multiple-output (MIMO) systems; the inputs correspond to the excitation and the control commands, while the outputs correspond to the measured responses. Even if a model is identified that can represent well the response of the structure, this model is often a non-minimal realization (i.e., the dynamics of various modes are duplicated in the model); these extra dynamics can degrade the performance of the resulting controller. This paper presents a hybrid system identification approach that can result in minimal (or near-minimal) realizations of the MIMO structure–control system models that are effective for structural control design. The first step in this approach develops a simplified model which can portray adequately the system characteristics found in the experimental data. Using information about the pole–zero relationship of the transfer function in the simplified model, a frequency-domain system identification strategy is employed subsequently to derive multiple single-input and multiple-output (SIMO) models with respect to each system input. The systems are then combined to determine the complete MIMO system model that is accurate over the frequency range of interest for the control applications. To demonstrate this hybrid approach, an example is provided to illustrate a high-fidelity identification result from the experiment of an actively isolated building system. Successful control implementation demonstrates the efficacy of this hybrid system identification approach.Structural systems installed with active or semi-active control devices usually require availability of a high-fidelity model to determine appropriate control designs. Use of traditional system identification techniques has proven to be challenging, primarily due to the fact that such models must be multiple-input and multiple-output (MIMO) systems; the inputs correspond to the excitation and the control commands, while the outputs correspond to the measured responses. Even if a model is identified that can represent well the response of the structure, this model is often a non-minimal realization (i.e., the dynamics of various modes are duplicated in the model); these extra dynamics can degrade the performance of the resulting controller. This paper presents a hybrid system identification approach that can result in minimal (or near-minimal) realizations of the MIMO structure–control system models that are effective for structural control design. The first step in this approach develops a simplified model which can portray adequately the system characteristics found in the experimental data. Using information about the pole–zero relationship of the transfer function in the simplified model, a frequency-domain system identification strategy is employed subsequently to derive multiple single-input and multiple-output (SIMO) models with respect to each system input. The systems are then combined to determine the complete MIMO system model that is accurate over the frequency range of interest for the control applications. To demonstrate this hybrid approach, an example is provided to illustrate a high-fidelity identification result from the experiment of an actively isolated building system. Successful control implementation demonstrates the efficacy of this hybrid system identification approach.

Right coprime factorizations of MISO fractional time-delay systems

Fractional order models have been more and more used as they offer better characterization of real systems of lumped as well as distributed nature. In this paper, we are interested in multiple-input and single-output (MISO) fractional systems of commensurate order with input or output delays, which have not been well understood so far. With the intent of using Youla parametrization for controller synthesis, we need coprime factorizations and Bézout factors of the systems. In a previous work, we have derived explicit expressions of left coprime factors. The right counterparts are determined in this paper, which allows us to obtain the set of all stabilizing controllers for some classes of delay fractional systems.

An Empirical Stationary Fuel Cell Model Using Limited Experimental Data for Identification

New technologies for efficient operation of fuel cells require modern techniques in system modeling. Such fuel cell models do not require giving any information about physical mechanisms or internal states of the system. They must be rather precise and should consume less computing time. From the point of view of system theory, polymer electrolyte membrane fuel cells (PEMFC) are multiple input and single output (MISO) systems. The inputs of a fuel cell are the drawn current, the gas pressures at anode and cathode side, and the humidity of these gases which influence the system output, namely the cell voltage, in a nonlinear way. The state of the art in the industry is to describe such nonlinear systems by the usage of lookup tables with a large amount of data. An alternative way to model the input-output behavior of nonlinear systems is the usage of so called black-box and gray-box model approaches. In the last decade, artificial neuronal networks (ANN) became more popular in black-box modeling of nonlinear systems with multiple inputs. Further, if some of the internal processes of a nonlinear system can be mathematically described, a gray-box model is more preferred. In the first part of this paper, the suitability of ANN's in the form of a multilayer perceptron (MLP) network with different numbers of hidden neurons is investigated. A way to confirm the validity for the identified network was worked out. In the second part of this contribution, a gray-box model, valid for a large operating area, based on published semi-empirical models is introduced. Six experimental campaigns for parameter identification and model validation were carried out. The five inputs previously described were varied in a wide range to cover a large operating range. In the last part of this paper, both modeling approaches are investigated with respect to their ability to identify model parameters using a limited number of experimental data.

The design of the MISO Model Predictive Controller for Bioreactor

Bioprocesses have the inherent characteristics of high nonlinearity, parameter uncertainty and information imperfection. These specific natures may cause some difficulties in control. Jacobian linearization method was used in this paper to linearize the bioreactor model firstly, and then a Multiple-input and single output (MISO) model predictive controller was designed for the reactor. PID controller and fuzzy-PID controller was also introduced to compare with the MPC controller. Simulation results prove that the model predictive control has several advantages over the other controls for bioreactors. DOI: http://dx.doi.org/10.11591/telkomnika.v10i6.1444

Blind Equalization Algorithm Based on the Orthogonal Wavelet Transform for SIMO Systems

In order to improve the equalization effects of the constant modulus blind equalization algorithm (CMA) for Single-Input and Multiple-Output (SIMO) systems, orthogonal wavelet transform constant modulus algorithm (WT-CMA) based on SIMO is proposed. This proposed algorithm uses the orthogonal wavelet transform to decrease the autocorrelation of the input signals to accelerate the convergence rate and reduce the steady-state error. Theoretical analysis and computer simulations shows that the proposed algorithm has better performance and smaller steady-state error in SIMO systems, it is very easy to achieve in engineering.

Scattering simulation and reconstruction of a 3D complex target above an underlying surface using SIMO radar with plane array

A SIMO (single input and multiple output) system of a step-frequency (SF) radar is used. It works in downward-looking spotlight mode and moves within a 2D synthetic plane array. A 3D (three-dimensional) matrix of bistatic scattering fields is produced in both the amplitude and phase from a 3D complex-shaped electric-large target above background surface.

A New View on Channel Capacity of MISO Frequency Selective Channels

A new view of multiple-input and single-output (MISO) technology in the frequency selective channels has been expressed in this letter. The definition of the virtual multiple receive antennas is proposed, which shows the maximum degree of freedom in the MISO frequency selective channel is more than one. Analytical and simulation results show that channel capacity can be improved by increasing the number of transmit antennas in the MISO frequency selective environment.

Performance of SIMO FM-DCSK UWB System Based on Chaotic Pulse Cluster Signals

Recently, an ultra-wideband (UWB) system with frequency-modulated differential chaos shift keying (FM-DCSK) modulation has attracted increasing interest for its many distinctive superiorities over its conventional counterparts, especially in low-rate and low-power wireless personal area network (WPAN) applications. However, some of its drawbacks, such as low energy efficiency, complex implementation and weak multiaccess capacity, have also been noticed, which restrict its further acceptance and applications. To overcome these problems, an architecture, named single-input and multiple-output (SIMO) FM-DCSK UWB system, is introduced in this paper. With chaotic transmitted signals based on a high-order Walsh function and multiple antennas diversity reception, this paper demonstrates the superiorities of the new system in bit error rate (BER) performance as well as in moderation complexity. Furthermore, by transmitting chaotic pulse cluster signals, an improved emission signal structure of the SIMO FM-DCSK UWB system is proposed so as to overcome the delay line implementation constraints and to further enhance the BER performance. Based on this new signal format, a method of combining time division and Walsh function division is introduced into the existing Walsh function division scheme, thereby resolving the inherent obstacle in user capability which was known to be limited by the order of the Walsh function.

Effect of Channel Estimation Errors on Arbitrary Transmit Antenna Selection for Cognitive MISO Systems

This letter analyzes the effect of channel estimation errors on the performance of cognitive multiple input and single output (MISO) systems with arbitrary transmit antenna selection. In particular, the channel estimation errors are caused by the partial channel information between the cognitive transmitter and the primary receiver. Utilizing the derived probability density function (PDF) of the received signal-to-noise ratio (SNR), the effective capacity of the considered systems is numerically evaluated, and a new closed-form expression for the case of perfect channel estimation is also derived. Simulation results show that the system performance is not sensitive to the channel estimation error, especially for strict delay quality of service (QoS) requirement of the cognitive user (CU).

Ergodic Capacity for the SIMO Nakagami- Channel

This paper presents closed-form expressions for the ergodic channel capacity of SIMO (single-input and multiple output) wireless systems operating in a Nakagami-m fading channel. As the performance of SIMO channel is closely related to the diversity combining techniques, we present closed-form expressions for the capacity of maximal ratio combining (MRC), equal gain combining (EGC), selection combining (SC), and switch and stay (SSC) diversity systems operating in Nakagami-m fading channels. Also, the ergodic capacity of a SIMO system in a Nakagami-m fading channel without any diversity technique is derived. The latter scenario is further investigated for a large amount of receive antennas. Finally, numerical results are presented for illustration.

A High Resolution Wide Swath SAR method based on intra-pulse null steering and MIMO

High Resolution Wide Swath (HRWS) Synthetic Aperture Radar (SAR) often suffers from low Signal-to-Noise Ratio (SNR) due to small transmitting antenna, especially in phased array antenna systems. Digital Beam Forming (DBF) based on Single Input and Multiple Output (SIMO) achieves receiving array gain at the cost of increasing data rate. This letter proposes a new HRWS SAR method, which employs intra-pulse null steering to get receiving gain in elevation and decrease the data rate, and Multiple Input and Multiple Output (MIMO) using Space-Time Block Coding (STBC) in azimuth to get transmitting gain and receiving array gain simultaneously. The feasibility is verified by deduction and simulations.

An IV-QR Algorithm for Neuro-Fuzzy Multivariable Online Identification

In this paper, a new algorithm for neuro-fuzzy identification of multivariable discrete-time nonlinear dynamic systems, more specifically applied to consequent parameters estimation of the neuro-fuzzy inference system, is proposed based on a decomposed form as a set of coupled multiple input and single output (MISO) Takagi-Sugeno (TS) neuro-fuzzy networks. An on-line scheme is formulated for modeling a nonlinear autoregressive with exogenous input (NARX) recurrent neuro-fuzzy structure from input-output samples of a multivariable nonlinear dynamic system in a noisy environment. The adaptive weighted instrumental variable (WIV) algorithm by QR factorization based on the numerically robust orthogonal Householder transformation is developed to modify the consequent parameters of the TS multivariable neuro-fuzzy network

The Best Achievableℋ2Tracking Performances for SIMO Feedback Control Systems

This paper is concerned with the inherentℋ2tracking performance limitation of single-input and multiple-output (SIMO) linear time-invariant (LTI) feedback control systems. The performance is measured by the tracking error between a step reference input and the plant output with additional penalty on control input. We employ the plant augmentation strategy, which enables us to derive analytical closed-form expressions of the best achievable performance not only for discrete-time system, but also for continuous-time system by exploiting the delta domain version of the expressions.

OPTIMAL TRACKING AND REGULATION ACCURACY OF FEEDBACK CONTROL SYSTEMS

This paper deals with intrinsic performance limits achievable by feedback control. We give analytical expressions of the optimal tracking and regulation problems for linear shift- invariant single-input and multiple-output (SIMO) discrete-time systems. For the former, we modify the existing results by means of the delta operator and show that the continuous-time counter- part results can be properly recovered from this point. For the latter, we derive a discrete-time result first and show the conver- gence property.