Design of a mixed fuzzy controller for multiple-input multiple-output systems

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.

Consider a multiple input multiple output (MIMO) system with pulse amplitude modulation (PAM), where each transmit antenna is allowed to choose a different transmission delay. (We refer to this system as a MIMO system with asynchronous PAM.) Such MIMO systems have been studied before for increasing diversity and avoiding unintentional beamforming. We quantify the capacity/outage capacity improvement due to asynchronism and the corresponding spectral leakage (if any) for MIMO point-to-point and MIMO broadcast channels. We show that under commonly used transmitter spectral mask constraints, the channel has unbounded degrees of freedom. Synchronous PAM exploits only a finite number of these degrees of freedom, and asynchronism helps to improve capacity. For most cases of interest, two distinct shifts attain most of the capacity improvement. These gains are robust to interference from adjacent carriers. Analysis of the spectrum of the asynchronous PAM suggests that optimal water-filling based schemes are useful for improving capacity only in directional links due to possible spectral leakage, while uniform power allocation leads to capacity improvement without any extra spectral leakage.

In conventional ways of fast responding and high performing model predictive control using Field Programmable Gate Arrays (FPGA), the approaches developed are applicable only to Single Input Single Output System (SISO). Many real time systems are Multiple Input Multiple Output (MIMO) systems and loop interaction is a major concern and has to be reduced in control of MIMO systems. Also, direct digital control with ability to solve the Quadratic Programming (QP) problem online, which can be applied to complex systems with fast response time and/or embedded applications and with limited computational resource for supervisory control approach, is required in many complex industrial MIMO systems. A novel direct digital control of Model predictive controller with FPGA for a MIMO system which considers and reduces loop interaction and which can also be implemented with very less investment is proposed here. The system was modeled from first principles and then simulated in Labview environment for MIMO system. Implementation of the system and response was verified for a stirred tank system. The proposed system has good performance in terms of reduction in loop interaction which is a major concern in implementation of controller for MIMO systems. Also, direct digital control of MIMO system instead of supervisory control was accomplished.

The present work is concerned to solve the problem of nonlinear robotic control systems on the basis of Takagi- Sugeno (T-S) fuzzy models. In addition, controllability and observability studies for T-S fuzzy model systems are considered for Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. In particular, the robust pole assignment approach is extended to the fuzzy field. This approach is used to design fuzzy stabilizers and fuzzy observers directly on the overall T-S fuzzy system. The latter one is the representation of the nonlinear robotic system, which is linearized around certain operation regions. The suggested method is considered for either SISO or MIMO systems. Furthermore, the method allows inducing an arbitrary behavior into the fuzzy plant in a relatively easy way. Two examples are used to verify the effectiveness of the proposed approach. The results are compared with the well-known Parallel Distributed Compensation (PDC) method, which is designed on the basis of Linear Matrix Inequalities (LMIs).

Model reference robust control (MRRC) is derived for MIMO systems that have a right Hermite normal form which is SPR and diagonal, and then for systems whose right Hermite normal form is diagonal but not SPR. It is shown that MRRC of multiple input multiple output (MIMO) systems is an extension of model reference control (MRC) of MIMO systems and MRRC of SISO systems. Unwanted coupling exists in many physical MIMO systems. The recursive backstepping procedure used in non-SPR SISO systems cannot be directly applied to diagonal MIMO non-SPR systems without the introduction of the augmented matrix or a precompensator. It is shown that MRRC decouples MIMO systems using only input and output measurements rather than state feedback.

Orbital Angular Momentum (OAM) has been considered as a new dimension in wireless transmission. In Multiple Input Multiple Output (MIMO) system, OAM can also be utilized as the new Degrees of Freedom (DoF) to manipulate the beamforming instead of the traditional Channel State Information (CSI) feedback, so that the computation complexity of the beamforming dramatically decreases, as well as the capacity of the transmission is maintained approaching the upper bound of the MIMO system. For example, this beamforming manipulation with OAM is extremely efficient in the Line of Sight (LoS) MIMO system. In this paper, the mathematical model of the DoF in beamforming manipulation in MIMO with OAM is established. The computation complexity is evaluated, and compared in LoS environment within MIMO, MIMO-OAM, and OAM. The quantitatively simulation confirms the fact that MIMO-OAM has the best trade-off between the capacity and the computation complexity, which gives a consolidated evidence that the MIMOOAM scheme can be adopted as an efficient beamforming scheme in LoS backhaul links between the base station and the self backhaul small cell station, or Near Field Communications (NFC) directly between mobile terminals. Due to these promising applications, MIMO-OAM may become one of the candidate technologies in Beyond 5G (B5G) mobile communications.

Everyone in the world always wants to use a better wireless network which provide high capacity and high data rates. High data rates and capacity can be produce by Multiple Input Multiple Output(MIMO) systems. This paper mainly focused on MIMO systems because the MIMO systems have multiple number of antennas in transmitting and receiving end which can produce more data rates and capacity compared to SISO, SIMO, MISO systems. The multiple antennas allow MIMO systems to produce multiplexing gain and diversity gain. The capacity of MIMO systems will be increased by increased the number of antennas in transmitter and receiver end.

Multiple-Input Multiple-Output (MIMO) systems have been endorsed to enable future wireless communication requirements. The efficient system designing appeals an appropriate channel model that considers all the dominating effects of the wireless environment. Therefore, some complex or less analytically acquiescent composite channel models have been proposed typically for Single-Input Single-Output (SISO) systems. These models are explicitly employed for mobile applications, though, we need a specific study of a model for the MIMO system which can deal with radar clutters and different indoor/outdoor and mobile communication environments. Subsequently, the performance enhancement of the MIMO system is also required in such a scenario. The system performance enhancement can be examined by low error rate and high capacity using spatial diversity and spatial multiplexing respectively. Furthermore, for a more feasible and practical system modeling, we require a generalized noise model along with a composite channel model. Thus, all the patents related to MIMO channel models are revised to achieve the nearoptimal system performance in a real-world scenario. This review paper offers the methods to improve MIMO system performance in less and severe fading as well as shadowing environment and focused on a composite Weibull-gamma fading model. The development is the collective effects of selecting the appropriate channel models, spatial multiplexing/detection, and spatial diversity techniques both at the transmitter and the receivers in the presence of arbitrary noise.

In order to better handle the coupling effects when controlling multiple-input multiple-output (MIMO) systems, taking the decentralized control structure as the basis, this paper proposes a sparse control strategy and a decoupling control strategy. Type-1 and type-2 Takagi-Sugeno (T-S) fuzzy models are used to describe the MIMO system, and the relative normalized gain array-based criterion is employed to measure the coupling effects. The main contributions include: 1) compared to the previous studies, a manner with less computational cost to build fuzzy models for the MIMO systems is provided, and a more accurate method to construct the so-called effective T-S fuzzy model (ETSM) to express the coupling effects is developed; 2) for the sparse control strategy, four indexes are defined in order to extend a decentralized control structure to a sparse one. Afterward, an ETSM-based method is presented that a sparse control system can be realized by designing multiple independent single-input single-output (SISO) control loops; and 3) for the decoupling control strategy, a novel and simple ETSM-based decoupling compensator is developed that can effectively compensate for both steady and dynamic coupling effects. As a result, the MIMO controller design can be transformed to multiple noninteracting SISO controller designs. Both of the sparse and decoupling strategies allow to use linear SISO control algorithms to regulate a closely coupled nonlinear MIMO system without knowing its exact mathematical functions. Two examples are used to show the effectiveness of the proposed strategies.

The research of closed form expressions for the pdf of the lth ordered eigenvalue of a Wishart matrix has received a great attention in the past years owing to its applications in the performance analysis of multiple input multiple output (MIMO) systems in fading environments. Although several closed form expressions for this pdf were obtained in the past years, to the authors' knowledge, no one was very friendly for further analysis. We propose a methodology to obtain the pdf for the lth largest eigenvalue whose expression is given as a sum of terms xβe-xδ. This expression is easily usable to obtain closed form results for the performance of many MIMO systems, such as, for instance, MIMO beamforming, and MIMO with Singular Value Decomposition (SVD). The methodology is valid for both uncorrelated and correlated central Wishart, allowing the investigation of MIMO systems with uncorrelated and correlated Rayleigh fading. © 2008 IEEE.

For higher order modulation, the reliabilities of the bits Gray-mapped onto the modulated symbol vary from the most significant bits (MSBs) to the least significant bits (LSBs). This paper introduces one constellation rearrangement method for SISO (Single-Input Single Output) system. For MIMO (Multiple-Input Multiple Output) system, the constellation rearrangement works also for lower order modulation by the construction of spatial signal. One constellation remapping method for MIMO system is also introduced in the paper. The simulation results show the performance gain for the SISO constellation remapping method.

Multiple-Input Multiple-Output (MIMO) systems that realize high-speed data transmission with multiple antennas at both transmitter and receiver are drawing much attention. In line-of sight (LOS) environments, the performance of MIMO systems depends largely on the difference of the phase difference of direct paths from transmit antennas to each receive antenna. When the phase difference of direct paths are close to each other, the spatial division multiplexing (SDM) channels are not orthogonal to each other so signal detection becomes difficult. In this paper, we propose a MIMO system with relative phase difference time-shift modulation (RPDTM) in Rician fading environments. The proposed scheme transmits independent signals from each antenna at each time slot where the relative phase difference between signal constellations used by transmit antennas varies in a pre-determined pattern. This transmission virtually changes the phase difference of direct paths from transmit antennas to each receive antenna without lowering data rate and without knowledge of the channels. In addition, forward error correction coding (ECC) is applied to exploit the time slots where the receiver can detect the signals easily to improve the detection performance. If there are time slots where the receiver can separate the received signal, the receiver can decode the data by using the time slots and the correlation between data. From the results of computer simulation, we show that MIMO system with RPDTM can achieve the better bit error rate (BER) than the conventional MIMO system. We also show that the MIMO system with RPDTM is effective by about Rician factor K=10dB.

Wireless communications using multiple input multiple output (MIMO) systems enable increased spectral efficiency for a given total transmit power. The increased capacity is achieved through the introduction of additional spatial channels (space-time coding). In this paper, MIMO capacity is calculated as a function of environmental factors, including channel complexity, external interference and channel estimation error. The capacity of MIMO systems, where both the transmitter and receiver know the channel (channel estimate feedback), is compared with single input multiple output (SIMO) and MIMO systems, where only the receiver knows the channel. The channel complexity is studied using a simple statistical physical scattering model. Finally, an expression for capacity loss particular to channel estimation error at the transmitter is introduced.

Wireless Sensor Network (WSN) is used in various applications. Sensors acquire samples of physical data and send them to a central node in different topologies to process the data and makes decisions. A main performance factor for WSN is the battery life that depends on energy consumption on the sensor. To reduce the energy consumption, an energy efficient transmission technique is required. Multiple Input Multiple Output (MIMO) systems showed good utilization of channel characteristics. This leads to enhance the transmission and hence reduce energy consumed by the sensor. In MIMO systems multiple signals are combined at the transmitter and transmitted using multiple antennas. This provides each receiver the whole combined signal and hence, array processing techniques helps in getting better performance. To further enhance the transmission of data, a Low Density Parity Check (LDPC) Coded MIMO wireless sensor network is proposed. The system implements space diversity through Multiple Antennas and temporal diversity through LDPC code and uses a clustering procedure to optimize the forming of the MIMO system. Results showed that, if the number of sensors is greater than the number of receiving antennas, time or frequency multiplexing is possible to keep good performance for the devised system. And by controlling the encoder we can create a temporal and spatial code among the transmitted signals enhancing the BER results in longer battery life at sensor nodes.

In recent years, multiple-input multiple-output (MIMO) systems, which use several transmit and receive antennas, have attracted much attention for high performance radio systems. In MIMO systems, the channel estimation is important to distinguish transmit signals from multiple transmit antennas. While the space-alternating generalized expectation-maximization (SAGE) algorithm is known to offer good channel estimation and data detection. We proposed earlier the minimum mean square error (MMSE)-based SAGE algorithm for MIMO systems where the MMSE estimation is used for channel estimation. We showed that the proposed MMSE-based SAGE algorithm can achieve the better bit error rate (BER) than the maximum likelihood (ML) detection with training symbols. The MMSE channel estimation needs the knowledge of the maximum Doppler frequency F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> for deriving the covariance matrix of the channel and the variance sigma <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of additive white Gaussian noise (AWGN). Additionally, the computation of the MMSE channel estimation requires O(L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) operations where L is the transmitted frame length. Thus, its computational complexity is high. In this paper, we propose a zero-forcing (ZF)-based SAGE algorithm for channel estimation and data detection in MIMO systems that does not need the knowledge of Fd and sigma <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Since the computation of the proposed ZF-based SAGE algorithm requires O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) operations where N is the number of transmit antennas, its computational complexity is low. We show that the proposed ZF-based tracking SAGE algorithm with less computational complexity can achieve almost the same BER as that of the MMSE-based tracking SAGE algorithm