Multiple Input and Multiple Output (MIMO)

In this paper, we propose a feedback-efficient hybrid precoding framework for wideband millimeter-wave (mmWave) multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. To mitigate the high cost of radio frequency (RF) chains and channel state information (CSI) feedback in large-scale antenna arrays, we first construct frequency-flat analog precoders by extracting dominant angle-of-arrival (AoA) and angle-of-departure (AoD) directions from sparse frequency-domain channels. For digital precoding, we design a quantized codebook using the Lloyd algorithm and develop a binary-search-based hierarchical interpolation algorithm that adaptively assigns codewords according to subcarrier correlation. The proposed method achieves sublinear feedback scaling by reducing the feedback overhead from O(K) to O(K/M +log M), where K is the number of subcarriers and M is the pilot spacing. Simulation results demonstrate that the proposed method achieves comparable or superior spectral efficiency and bit error rate (BER) performance to existing clustering and interpolation schemes, while significantly reducing computational complexity and exhibiting robustness under imperfect CSI.

Weight and size are the bottlenecks of portable wireless systems which are in turn dependent on area and power consumption of electronic systems. The situation becomes even worse if wireless systems are equipped with Multiple Input and Multiple Output (MIMO) technology which involves highly complex signal processing. This paper proposes an area and power efficient VLSI architecture for computing the pseudo inverse of augmented channel matrix used in MIMO systems. Results indicate 25% area and 24% power reduction compared to previous architecture in the literature without degrading the BER performance.

Subcarrier based space processing was conventionally employed in Orthogonal Frequency Division Multiplexing (OFDM) systems under Multiple-Input and Multiple-Output (MIMO) channels to achieve optimal performance. At the receiver of such systems, multiple Discrete Fourier Transform (DFT) blocks, each corresponding to one receive antenna, are required to be used. This induces considerable complexity. In this paper, we propose a pre-DFT processing scheme for the receiver of MIMO-OFDM systems with space-time-frequency coding. With the proposed scheme, the number of DFT blocks at the receiver can be any number from one to the number of receive antennas, thus enabling effective complexity and performance tradeoff. Using the pre-DFT processing scheme, the number of input signals to the space-time-frequency decoder can be reduced compared with the subcarrier based space processing. Therefore, a high dimensional MIMO system can be shrunk into an equivalently low dimension one. Due to the dimension reduction, both the complexity of the decoder and the complexity of channel estimation can be reduced. In general, the weighting coefficients calculation for the pre-DFT processing scheme should be relevant to the specific space-time-frequency code employed. In this paper, we propose a simple universal weighting coefficients calculation algorithm that can be used to achieve excellent performance for most practical space-time-frequency coding schemes. This makes the design of the pre-DFT processing scheme independent of the optimization of the space-time-frequency coding, which is desirable for multiplatform systems.

In multiple-input and multiple-output (MIMO) multiuser detection theory, the QR decomposition of the channel matrix H can be used to form the back-cancellation detector. In this paper we propose an optimal QR decomposition, which we call the equal-diagonal QR decomposition, or shortly the QRS decomposition. We apply the decomposition to precoded successive-cancellation detection, where we assume that both the transmitter and the receiver have perfect channel knowledge. We show that, for any channel matrix H, there exists a unitary precoder matrix S, such that HS = QR, where the non-zero diagonal entries of the upper triangular matrix R in the QR decomposition of HS are all equal to each other. The precoder and the resulting successive-cancellation detector have the following properties. a) The minimum Euclidean distance between two signal points at the channel output is equal to the minimum Euclidean distance between two constellation points at the precoder input up to a multiplicative factor that equals the diagonal entry in the R-factor. b) The superchannel HS naturally exhibits an optimally ordered column permutation, i.e., the optimal detection order for the V-BLAST detector is the natural order. c) The precoder S minimizes the block error probability of the QR successive cancellation detector. d) A lower and an upper bound for the free distance at the channel output is expressible in terms of the diagonal entries of the R-factor in the QR decomposition of a channel matrix. e) The precoder S maximizes the lower bound of the channel's free distance subject to a power constraint. f) For the optimal precoder S, the performance of the QR detector is asymptotically (at large signal-to-noise ratios) equivalent to that of the maximum likelihood detector (MLD) that uses the same precoder. Further, in this paper we consider two multiplexing schemes: time division multiple access (TDMA) and orthogonal frequency division multiplexing (OFDM). We design the optimal precoder for binary phase shift keying (BPSK) with these multiplexing schemes, but outline the procedure to extend the method to non-binary schemes such as PAM, PSK and QAM. Finally, examples are given that illustrate the performance of the precoder and the corresponding successive cancellation detector.

Enterprise Application Integration (EAI) solutions cope with two kinds of problems within software ecosystems, namely: keeping a number of application's data in synchrony or creating new functionality on top of them. ESBs provide the technology required to implement a variety of EAI solutions at sensible costs, but they are still far from negligible. It is not surprising then that many authors are working on proposals to endow them with domain-specific tools to help software engineers reduce integration costs. In this article, we introduce a proposal called Guaraná. Its key features are as follows: it provides explicit support to devise EAI solutions using enterprise integration patterns by means of a graphical model; its DSL enables software engineers to have not only the view of a process, but also a view of the whole set of processes of which an EAI solution is composed; both processes and tasks can have multiple inputs and multiple outputs; and, finally, its runtime system provides a task-based execution model that is usually more efficient than the process-based execution models in current use. We have also implemented a graphical editor for our DSL and a set of scripts to transform our models into Java code ready to be compiled and executed. To set up a solution from this code a software engineer only needs to configure a number of adapters to communicate with the applications being integrated.

Terahertz (THz) transmission is a promising strategy for future 6G networks, offering ultra-wide bandwidth. Effective channel modeling and precoding techniques are essential for achieving required coverage and addressing significant path loss in THz communications. In this paper, we comprehensively examine the major THz precoding algorithms for future 6G networks, focusing on their significant challenges and prospects. We discriminate between millimeter-wave and THz channels and uncover issues with THz precoding, such as distance-dependent direction loss, beam split impact, and excessive power usage. To solve these issues, three distinct THz precoding systems, such as hybrid precoding, analog beamforming, and delay-phase precoding, are introduced and their performance is compared.

The Physical layer (PHY) is implemented with the new Generalized Frequency Division Multiplexing (GFDM) scheme to increase the throughput of wireless systems in the present communication era. The GFDM system is combined with Multi-Input and Multi Output antenna system (MIMO GFDM) to achieve higher channel capacity and low out-of-coverage probability. Therefore, the MIMO-GFDM system is suffering from internal Inter-Carrier-Interference (ICI) and external Inter-Symbol-Interference (ISI) at the receiver. This makes the receiver design more complex and challenging. Therefore, to cancel the symbol interference in the MIMO-GFDM system, we have proposed a Singular Value Decomposition (SVD) based Zero-Forcing detection scheme for future wireless communications. To decouple unwanted symbols from the transmitted signal, we employed the precoding technique at the transmitter section and the weighted beam forming technique at the receiver section. Furthermore, a famous Water Filling Algorithm (WFA). The proposed scheme reduces the bit error rate to 1.98×10⁻⁴ at 10 dB, outperforming existing detection methods. The WFA improves power allocation efficiency, enhancing channel capacity by up to 3.74 bps/Hz/u in 4x4 MIMO setups.

Enterprise Application Integration (EAI) solutions cope with two kinds of problems within software ecosystems, namely: keeping a number of application's data in synchrony or creating new functionality on top of them. ESBs provide the technology required to implement a variety of EAI solutions at sensible costs, but they are still far from negligible. It is not surprising then that many authors are working on proposals to endow them with domain-specific tools to help software engineers reduce integration costs. In this article, we introduce a proposal called Guaraná. Its key features are as follows: it provides explicit support to devise EAI solutions using enterprise integration patterns by means of a graphical model; its DSL enables software engineers to have not only the view of a process, but also a view of the whole set of processes of which an EAI solution is composed; both processes and tasks can have multiple inputs and multiple outputs; and, finally, its runtime system provides a task-based execution model that is usually more efficient than the process-based execution models in current use. We have also implemented a graphical editor for our DSL and a set of scripts to transform our models into Java code ready to be compiled and executed. To set up a solution from this code a software engineer only needs to configure a number of adapters to communicate with the applications being integrated.

Data envelopment analysis (DEA) measures the relative efficiency of decision making units (DMUs) with multiple inputs and multiple outputs. DEA-based Malmquist productivity index measures the productivity change over time. In this paper, unlike the original DEA models,it has been assumed that the levels of inputs and outputs are not known exactly. The true data be only known as in forms of bounded or fuzzy data. Hence, we provide a computational method for extension to the DEA-based Malmquist productivity index for all DMUs with interval and fuzzy data. A numerical examples for illustration purpose are presented.

In a downlink multiuser multiple-input and multiple-output (MU-MIMO) system, a base station (BS) communicates with multiple mobile stations (MS) simultaneously in a given spectrum band. The performance of a MU-MIMO system depends on the choice of user selection, power allocation and precoding schemes. In this paper, we study user selection in Zero-Forcing (ZF) precoding based MU-MIMO systems to maximize the sum rate for high data rate applications. We derive analytical results for simultaneous transmission to two MSs with ZF precoding, based on which we propose an extended lowcomplexity algorithm that jointly considers the noise power, the channel gain of the candidate MSs and orthogonality with respect to the selected MSs' channels. Analysis shows that the proposed scheme requires much lower complexity than current schemes. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms in terms of higher throughput.

Many semiconductor manufacturing processes have, by nature, Multiple-Input and Multiple-Output (MIMO) variables. For a drifted MIMO process, the double Multivariate Exponentially Weighted Moving Average (dMEWMA) controller is a popular run-to-run controller for adjusting the process mean to a desired target. The stability conditions of a dMEWMA controller have received considerable attention in the literature. There are two limitations in the existed literature. First, these approaches only demonstrate that the asymptotic mean will meet a desired target; however, they do not show that mean square error matrix for a process is bounded. Second, they do not have an explicit expression of the process output of the dMEWMA controller for any production run t. Hence, by using a conventional state space formulation, it is impossible to address the process performance (such as process fallout rate of the dMEWMA controller) analytically. Using a different approach, this paper derives an analytical expression for the process output of a dMEWMA controller. The problems of the stability conditions and the performance of the dMEWMA controller can then be addressed successfully.

Many manufacturing processes have, by nature, Multiple Input and Multiple Output (MIMO) variables. However, despite its frequent use in practice, MIMO process feedback control has not been fully investigated in the literature. Under a linear MIMO model, this paper proposes a Multivariate Exponentially Weighted Moving Average (MEWMA) controller. The stability conditions of this controller and the feasible region of its associated discount factor are derived. A discount factor which minimizes the total Mean Square Error (MSE) of the process output is obtained under the assumption that the process disturbance is either a white noise series or an IMA(1,1) series. An example is given to demonstrate the applicability of the proposed method. Some related issues are also discussed.

Weight and size are the bottlenecks of portable wireless systems which are in turn dependent on area and power consumption of electronic systems. The situation becomes even worse if wireless systems are equipped with Multiple Input and Multiple Output (MIMO) technology which involves highly complex signal processing. This paper proposes an area and power efficient VLSI architecture for computing the pseudo inverse of augmented channel matrix used in MIMO systems. Results indicate 25% area and 24% power reduction compared to previous architecture in the literature without degrading the BER performance.

5G network is an enhanced communication network, designed to converge the requirements of quality of service (QoS) parameters, data and capacity, by means of signals with high QoS and high-speed data rate. Several state-of-art technologies are involved to obtain the requirement of energy efficient communication system with increased number of users, devices, higher data rate with low latency. This paper presents a system which demonstrates the energy and spectral efficiency achieved for various number of nodes in a specific area. This study addresses the improvement in energy and spectral efficiency when the proposed algorithm is used. The proposed algorithm is a combination of swarm based artificial bee colony (ABC) algorithm with neural network. Experimental results have been carried out to observe the
performance of QoS parameters such as bit error rate (BER), throughput,
power consumption and mean square error (MSE). The maximum energy
efficiency achieved is 34% and Spectral efficiency is 36%.

Neural network and fuzzy logic reasoning can complement each other to form an integrated model which capitalizes on the merits and at the same time offsets the pitfalls of the involved computational intelligence technologies. This article presents a neural-fuzzy model which consists of a neural network for suggesting the change of process parameters, together with a fuzzy reasoning mechanism for acquiring modified parameter values based on the induced parameter values from the neural network. This model is particularly useful in parameterbased control situations where there may be multiple inputs and multiple outputs involved. This model, which serves to learn from sample data and allows to extract rules which are then fuzzified prior to fuzzy inference, is implemented for the dimensional control of injection molding parts, the dimensions of which are primarily determined by the molding process parameters such as injection time and cooling temperature.

Data envelopment analysis is a mathematical programming technique for identifying efficient frontiers for peer decision making units with multiple inputs and multiple outputs. These performance factors (inputs and outputs) are classified into two groups: desirable and undesirable. Obviously, undesirable factors in production process should be reduced to improve the performance. In the current paper, we present a data envelopment analysis (DEA) model in which can be used to improve the relative performance via increasing undesirable inputs and decreasing undesirable outputs.

This paper presents the design and implementation of robust control schemes for two applications of a nanopositioning stage (1) reference trajectory tracking with high resolution over a given bandwidth (2) control design for repetitive motions. The stage has a low degree of freedom monolithic parallel kinematic mechanism using flexure hinges. It is driven by piezoelectric actuators and its displacement is detected by capacitance gauges. The design has strongly coupled dynamics with each actuator input producing in multiaxis motions. The nano-positioner is modeled as a multiple input and multiple output (MIMO) system, and the MIMO plant model is identified by time-domain identification methods. The design of the nano-positioner relies heavily on the control design to account for the high coupling in the system. The proposed H ∞ MIMO controller achieves a good performance in terms of resolution, bandwidth and robustness to the modeling uncertainty. In the second part of the paper, we present control design for tasks that require repetitive motion of nano positioning system. These tasks are quite common in micro/nano manipulation and manufacturing. This paper presents a robust control design that gives a significant (over thirty fold) improvement in tracking of repetitive motions on a prespecified frequency band. This design, unlike other schemes, is robust to modeling uncertainties that arise in flexure based mechanisms, and does not require any learning steps during its real time implementation. This design scheme is implemented on a parallel-kinematics XYZ nano positioning stage for repetitive nano-manipulation and nano-manufacturing applications.

Through intensive study, it has been explored that multiple-input single-output (MISO) antenna systems have the potential to dramatically improve the performance of communication systems over single antenna systems. With the idea of implementing ...

We present a Bayesian game-theoretic approach for the distributed resource allocation problem in the context of K-user fading multiple access channels (MAC). We assume that users have incomplete information about the channel state information (CSI), i.e., each user knows his own channel state, but does not know the states of other users. All users (transmitters) are considered to be rational, selfish, and each one carries the objective of maximizing its own achievable data rate. In such a game-theoretic study, the central question is whether a Bayesian equilibrium (BE) exists. Based on the assumption of two channel states, we prove that there exists exactly one BE in this game.

Data Envelopment Analysis (DEA) is a powerful quantitative tool that provides a means to obtain useful information about efficiency and performance of firms, organizations, and all sorts of functionally similar, relatively autonomous operating units, known as Decision Making Units (DMU). Usually the investigated DMUs are characterized by a vector of multiple inputs and multiple outputs. Unfortunately, not all inputs and/or outputs are deterministic; some could be stochastic. The main concern in this paper is to develop an algorithm to help any organization for evaluating their performance given that some inputs are stochastic. The developed algorithm is for a Stochastic Input Oriented Model based on the Chance Constrained Programming, where the stochastic inputs are normally distributed, while the remaining inputs and all outputs are deterministic.

Data envelopment analysis (DEA) measures the relative efficiency of decision making units (DMU) with multiple inputs and multiple outputs. These inputs and outputs are classified into two groups: desirable and undesirable. In this paper, we consider the problem of evaluating the efficiency of a set of interdependent decision making sub-units (DMSU) that interdependent DMSUs make up a larger DMU with desirable and undesirable factors.

Making optimal use of available resources has always been of interest to humankind, and different approaches have been used in an attempt to make maximum use of existing resources. Limitations of capital, manpower, energy, etc., have led managers to seek ways for optimally using such resources. In fact, being informed of the performance of the units under the supervision of a manager is the most important task with regard to making sensible decisions for managing them. Data envelopment analysis (DEA) suggests an appropriate method for evaluating the efficiency of homogeneous units with multiple inputs and multiple outputs. DEA models classify decision making units (DMUs) into efficient and inefficient ones. However, in most cases, managers and researchers are interested in ranking the units and selecting the best DMU. Various scientific models have been proposed by researchers for ranking DMUs. Each of these models has some weakness(es), which makes it difficult to select the appropriate ranking model. This paper presents a method for ranking efficient DMUs by the voting analytic hierarchy process (VAHP). The paper reviews some ranking models in DEA and discusses their strengths and weaknesses. Then, we provide the method for ranking efficient DMUs by VAHP. Finally we give an example to illustrate our approach and then the new method is employed to rank efficient units in a real world problem.

This paper describes a conceptual approach to measure and compare productivity of resource utilization at the firm level, adapting a set of techniques known as Data Envelopment Analysis (DEA). Within this approach, the paper addresses the issues of multiple inputs and multiple outputs of a construction firm, level of detail for data collection, and the required transformations to correct for differences among projects. In particular, we focus on the resource management of subcontractors. Subcontractors manage multiple, concurrent projects and must allocate limited resources across these projects. Interaction between projects and resource allocation creates non-linear effects, and therefore the productivity of the firm is not simply the productivity of its projects. The proposed measurement methodology will allow assessment of the impact of different management policies (including many of those proposed by lean construction researchers) on firm performance. It is hoped that this novel approach to productivity measurement will help subcontractors identify efficient practices and superior management policies, and will promote adoption of these policies.

Background Multiple input and multiple output systems (MIMO) radiate multiple probing signals through it their transmit antennas and receive multiple coded waveforms from multiple locations. MIMO radar systems have many advantages including high resolution target detection/estimation (Haimovich et al. 2008), significantly improved parameter identifiability (Li et al. 2007). The performance of the transmitted waveforms is judged by their correlation properties (Deng et al. 2004; Liu et al. 2007, 2008). The waveforms with good autocorrelation properties provide high range resolution and good crosscorrelation helps in multiple target return separability. So, there is a need to design MIMO radar waveforms as orthogonal pulses with low correlation properties. In literature, the orthogonal sequences are generated with low autocorrelation and crosscorrelation peak sidelobe levels using various algorithms. Deng et al. (2004) has proposed simulated annealing (SA) algorithm to optimize the frequency sequences for the development of the orthogonal discrete frequency coding waveforms frequency hopping (DFCW_FF) for netted radar systems. Liu has proposed orthogonal DFCW_FF (Liu et al. 2007) and orthogonal discrete frequency coding waveforms linear frequency modulation (DFCW_LFM) (Liu et al. 2008) using a modified genetic algorithm (MGA).

This paper proposes orthogonal Discrete Frequency Coding Space Time Waveforms (DFCSTW) for Multiple Input and Multiple Output (MIMO) radar detection in compound Gaussian clutter. The proposed orthogonal waveforms are designed considering the position and angle of the transmitting antenna when viewed from origin. These orthogonally optimized show good resolution in spikier clutter with Generalized Likelihood Ratio Test (GLRT) detector. The simulation results show that this waveform provides better detection performance in spikier Clutter.

This paper proposes orthogonal Discrete Frequency Coding Space Time Waveforms (DFCSTW) for Multiple Input and Multiple Output (MIMO) radar detection in compound Gaussian clutter. The proposed orthogonal waveforms are designed considering the position and angle of the transmitting antenna when viewed from origin. These orthogonally optimized show good resolution in spikier clutter with Generalized Likelihood Ratio Test (GLRT) detector. The simulation results show that this waveform provides better detection performance in spikier Clutter.

Our work is developed in context of studing Massive MIMO in a 5G context. The aim is to optimize spectral efficiency of several users hyper MIMO system during Uplink communication in a multi-cell contaminated pilot environment, using a new type of precoders called single cell-minimum mean square eroor (S-MMSE) and multicell-minimum mean square eroor (M-MMSE). Indeed, we address two key and well-known issues of massive multiuser MIMO (MU-MIMO) environments in a test-driven development (TDD) operation scheme, namely acquisition of uplink channel state information (UL) and optimisation of the bit stream per unit frequency, the spectral efficiency (SE). From a practical point of view, these two notions are inclusively linked. Indeed, a very good channel estimation leads to a better spectral efficiency. In our approcah, we derive from the minimum mean square error estimator (MMSE) to two new types of precoders that can operate in a multicell environment with a contaminated pilot sequence, namely the S-MMSE and the M-MMSE. A comparative study performance of these classical precoders such as regulated zero forcing (RZF), ZF (Zero Forcing) and MR (Minimum Ratio) encountered in multi-antenna processing shows an improvement of nearly 51% in terms of system gain and spectral efficiency.