MIMO channel modeling

For the practical simulation and performance evaluation of mobile‐to‐mobile (M2M) communication systems, it is desirable to develop accurate M2M channel simulation models for more realistic scenarios of non‐isotropic scattering. In this paper, by using a ‘double‐ring’ concept to describe M2M non‐isotropic scattering environments, we propose new deterministic and stochastic sum‐of‐sinusoids (SoS) based simulation models. The proposed simulation models extensively consider the distributions of the angle of arrival (AoA) and the angle of departure (AoD), and thus provide a good approximation to the desired statistical properties of the reference model. Copyright © 2009 John Wiley & Sons, Ltd.

We consider a bidirectional time division duplex (TDD) multiple-input multiple-output (MIMO) communication system with time-varying channel and additive white Gaussian noise (AWGN). A blind bidirectional channel tracking algorithm, based on the projection approximation subspace tracking (PAST) algorithm, is applied in both terminals. The resulting singular value decomposition (SVD) of the channel matrix is then used to approximately diagonalize the channel. The proposed method is applied to an orthogonal frequency-division multiplexing-(OFDM-)MIMO setting with a typical indoor time-domain reflection model. The computational cost of the proposed algorithm, compared with other state-of-the-art algorithms, is relatively small. The Kalman filter is utilized for establishing a benchmark for the obtained performance of the proposed tracking algorithm. The performance degradation relative to a full channel state information (CSI) due to the application of the tracking algorithm is evaluated in terms of average effective rate and the outage probability and compared with alternative tracking algorithms. The obtained results are also compared with a benchmark obtained by the Kalman filter with known input signal and channel characteristics. It is shown that the expected degradation in performance of frequency-domain algorithms (which do not exploit the smooth frequency response of the channel) is only minor compared with time-domain algorithms in a range of reasonable signal-to-noise ratio (SNR) levels. The proposed bidirectional frequency-domain tracking algorithm, proposed in this paper, is shown to attain communication rates close to the benchmark and to outperform a competing algorithm. The paper is concluded by evaluating the proposed blind tracking method in terms of the outage probability and the symbol error rate (SER) versus. SNR for binary phase shift keying (BPSK) and 4-Quadrature amplitude modulation (QAM) constellations.

Video monitoring applications for underground rely on wireless train-to-wayside communication systems which require high data rate as well as high Quality of Service (QoS) level. In order to satisfy both constraints we propose a combined source and channel coding approach in the context of MIMO (Multiple Input Multiple Output) video transmission. In the present case, MIMO transmission is based on the PHY layer of IEEE 802.11n Wi-Fi standard currently deployed in a railway tunnels. Two different strategies are studied: first, the association between Multiple Description Coding (MDC) and a STBC (Space Time Block Code) MIMO scheme is considered when no channel information is available at transmitter side. In the case when perfect channel information is available at transmitter side (CSIT), a Singular Value Decomposition of the MIMO channel is possible. This transmission scheme is then associated with scalable video coding, which consists here in the separation of the scene into different Regions Of Interest (ROI). The creation of the regions of interest is based on the Flexible Macroblock Ordering (FMO) technique introduced in the new H.264/AVC compression standard. The stream associated to the area with the maximal perceptual relevance is transmitted on the eigen-channel with the higher gain. Consequently, this strategy which provides unequal protection against channel errors, allows guaranteeing better robustness and acceptable reconstructed video quality at the control-centre. The two different strategies of transmission have been evaluated thanks to realistic simulations. Two antenna configurations representative of real cases encountered in railway tunnels are considered. The channel model is generated by using the correlation based Kronecker model obtained by computing the channel matrix with a 3D ray tracing tool. Simulation results show that the two proposed solutions allow enhancing the reconstructed video quality

A mobile 4x4 multiple input multiple output (MIMO) system containing dual polarized patch antennas is used to perform wireless measurement campaigns on nonline of sight (NLOS) channels in an indoor office setting. The MIMO system uses orthogonal 500 kchips/s Walsh coded signals that are filtered and simultaneously transmitted at 916 MHz. The patch antennas have high isolation between their two ports and are used at both transmitter and receiver stations. The complex MIMO channel matrices obtained in the campaigns were used to calculate the theoretical capacity for a 4x4 system. Our mean measured capacity for the campaign is 21.3 bits/use and the 90 th percentile channel capacity is 19.5 bits/use. These values are slightly higher than those (mean of 21.1 bits/use and 90 th percentile channel capacity of 18.9 bits/use) obtained for an identical campaign which used single polarized whip antennas.

A computationally efficient space-time turbo equalization algorithm is derived for frequency selective multiple-input multiple-output (MIMO) channels. The algorithm is an extension of the iterative equalization algorithm by Reynolds and Wang (2001) for frequency selective fading channels. This paper's proposed equalizer performs MIMO channel estimation, multiple user signal detection, and decoding, jointly all in an iterative manner. The iterative channel estimator

In this paper a performance analysis of the turbo MIMO equalizer concept for broadband MIMO channel is presented. The channel modeling in the link-level simulation is based on MIMO measurement in different scenarios. The results reveal the strong relationships between the propagation conditions in terms of the available spatial and temporal multipath diversity, the antenna configurations, and the achievable bit

This paper investigates iterative frequency domain technieques for the reception of spatially multiplexed single carrier signals transmitted over frequency-selective multiple input multiple output (MIMO) channels. The investigated equalizers are based on the softcancellation (SC) and minimum mean square error (MMSE) filtering technique for turbo-coded single carrier point-to-point MIMO systems. We consider two different transmit antenna separation techniques in the frequency domain: (1) antenna-by-antenna (AA) and (2) joint over antenna techniques (JA). (1) aims to separate signals in different layers by MMSE filtering antenna-by-antenna, whereas (2) aims to detect the composite signal comprised of the signals transmitted from the multiple antennas. In (2) the composite received signal is decomposed by using the spatial maximum aposteriori probability (MAP) algorithm. We evaluate performances in terms of frame error rate (FER) and troughput in pointto-point MIMO frequency-selective fading channels. Impacts of spatial correlation on performance of the two detectors are also investigated in this paper.

The inherent limitations in RF spectrum availability and susceptibility to interference make it difficult to meet the reliability required for automotive safety applications. To address this challenge, this work explores an alternative communication system called Visual MIMO that uses light emitting arrays as transmitters and cameras as receivers. Visual MIMO applied to vehicular communication proposes to reuse existing LED rear and headlights as transmitters and existing cameras (e.g. those used for parking assistance, rear-view cameras) as receivers. In this work we show a proof of concept based demonstration of the Visual MIMO system consisting of an LED transmitter array and a highspeed camera.

In this paper we propose a turbo-detected multiantenna-multi-carrier receiver scheme. Following the philosophy of the turbo processing, our turbo MIMO-OFDM receiver comprises a succession of detection modules, namely the channel estimator, the space-time detector and the decoder, which iteratively exchange soft bit-related information and thus facilitate a substantial improvement of the overall system performance. In this paper we analyse the achievable performance of the iterative system proposed with the aim of documenting the various design trade-offs, such as the achievable error-rate performance, the attainable data-rate as well as the associated computational complexity. Specifically, we report a virtually error-free performance for a rate-1 2 turbo-coded 8x8-QPSK-OFDM system, exhibiting an effective throughput of 8•2• 1 2 =8 bits/sec/Hz and having a pilot overhead of only 10%, at SNR of 7.5dB and normalized Doppler frequency of 0.003, which corresponds to a mobile terminal speed of about 65 km/h.

The terahertz (THz) band offers a vast amount of still unallocated bandwidth, which makes it a promising enabler for future sixth generation wireless systems. The high frequencies of the THz band lead to a signi icantly reduced multipath richness of the propagating THz signals. However, there are still paths that can carry a signi icant amount of power. As a result, the THz band small-scale fading characterization is of particular interest and the appropriate stochastic distributions that best it the empirical channels need to be identi ied. This work investigates the suitability of the 𝛼-𝜇, Rice and Nakagami-m distributions to adequately model the small-scale fading statistics of the channel gain measurements of various Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) indoor THz wireless links. The itting accuracy of the examined analytical distributions is validated by means of the Kolmogorov-Smirnov test, the Kullback-Leibler divergence, the logarithmic Kolmogorov-Smirnov test and the root-mean-square-error. Also, the ergodic capacity based on the channel gain measurements as well as on the 𝛼-𝜇, Rice and Nakagami-m distributions is presented. Based on the itting accuracy metrics the Rice and 𝛼-𝜇 yield the best it for the LoS and NLoS links, respectively. The Nakagami-m does not it the empirical distributions for any of the presented links. Furthermore, insights are provided for the ranges of the extracted values of the analytical distributions in LoS and NLoS transmission conditions.

The problem of optimizing the training signal for the estimation of multiple-input multiple-output (MIMO) fading channels has been of a great interest in the last few years, due to its central role in combining accuracy in estimation with bandwidth efficiency. The general case of correlated channels and colored interference was recently addressed . It was shown that the optimal training for the Linear Minimum Mean Squared Error (LMMSE) estimator, in the sense of minimizing the channel estimation MSE subject to a constraint on the total transmit power, consists of a joint water-filling along the eigenmodes of the desired channel and interference covariance matrices. Thus, the resulting scheme relies on the assumption of the availability of these matrices at the transmitter, which is, in practice, realized via a feedback path. In this paper, that scheme is revisited, with the aim of reducing its requirements for side information and transmit beamforming. Inspired by an interpretation of the LMMSE estimator as a two-step procedure, we investigate possible gains (and tradeoffs) from an alternative scheme, in which the processing of the received data is performed on both of its dimensions, temporal and spatial. The simulation results demonstrate that this approach provides a good trade-off between estimation performance and low feedback communication and beamforming overheads.

A nonlinear loaded differential equation with a parameter on a finite interval is studied. The interval is partitioned by the load points, at which the values of the solution to the equation are set as additional parameters. A nonlinear boundary value problem for the considered equation is reduced to a nonlinear multipoint boundary value problem for the system of nonlinear ordinary differential equations with parameters. For fixed parameters, we obtain the Cauchy problems for ordinary differential equations on the subintervals. Substituting the values of the solutions to these problems into the boundary condition and continuity conditions at the partition points, we compose a system of nonlinear algebraic equations in parameters. A method of solving the boundary value problem with a parameter is proposed. The method is based on finding the solution to the system of nonlinear algebraic equations composed.

Orthogonal Frequency Division Multiplexing (OFDM) is a popular method for high data rate wireless transmission. Combining OFDM with antenna arrays at the transmitter and receiver enhances the capacity gain. This paper proposes a novel transmit power allocation for Coded Multi-Input Multi-Output Multi-Carrier Modulation (MIMO-MCM). This technique consists of adapting the power allocation in the spatial domain (i.e. through the different transmit antennas) in term of the channel conditions by the optimization process based on the optimality of the global Bit Error Rate (BER). Simulation results show significant gains for several antenna configurations.

We develop a computationally efficient approximation of the maximum likelihood (ML) detector for 16 quadrature amplitude modulation (16-QAM) in multiple-input multiple-output (MIMO) systems. The detector is based on solving a convex relaxation of the ML problem by an affinescaling cyclic Barzila-Borwein method for box constrained optimization. Simulation results in a random MIMO system show that this proposed approach outperforms the conventional decorrelator detector and is similar to the semidefinite relaxation (SDR) detector. However, we should note that the complexity of the proposed approach is less than that of those detectors. In the case of 8 antennas and 4 users, about 99% fewer computations are required than the SDR and ML detectors.

In this contribution, the capacity-achieving input covariance matrices for coherent blockfading correlated MIMO Rician channels are determined. In contrast with the Rayleigh and uncorrelated Rician cases, no closed-form expressions for the eigenvectors of the optimum input covariance matrix are available. Classically, both the eigenvectors and eigenvalues are computed by numerical techniques. As the corresponding optimization algorithms are not very attractive, an approximation of the average mutual information is evaluated in this paper in the asymptotic regime where the number of transmit and receive antennas converge to +∞ at the same rate. New results related to the accuracy of the corresponding large system approximation are provided. An attractive optimization algorithm of this approximation is proposed and we establish that it yields an effective way to compute the capacity achieving covariance matrix for the average mutual information. Finally, numerical simulation results show that, even for a moderate number of transmit and receive antennas, the new approach provides the same results as direct maximization approaches of the average mutual information, while being much more computationally attractive.

In this paper, we show how to address the asymptotic behavior of the mutual information of correlated MIMO Ricean channels when the number of transmit and receive antennas converge to +∞ at the same rate. Our approach is based on the extensive use of the Stieljès transform of the Gram matrix of the channel matrix, and is inspired by previous works of Girko in the context of simpler models. We give a closed form expression of the first order approximation of the average mutual information, and evaluate the convergence rate of the corresponding error.

The problem of linear tracking of fast Rayleigh-faded MIMO channels is considered. Due to computational complexity limitations, Kalman filters based on low-order autoregressive (AR) models are often used although realistic Rayleigh-faded channels cannot be properly approximated by low-order AR models. By increasing the order of the AR model, the channel tracking performance is considerably improved, but the resulting computational complexity may become large. In this paper, the filters used to track the channel are restricted to have a recursive structure, based on the previous estimate and new observation. The knowledge of the autocorrelation function of the channel taps is used to derive filters that are significantly less complex than high-order AR model based Kalman filters at the price of only a minor degradation in performance.

An efficient semidefinite programming relaxation (SDPR) based virtually antipodal (VA) detection approach is proposed for Gray coded 16-QAM signalling over multiple-input-multiple-output (MIMO) channels. The existing index-bit-based VA-SDPR (IVA-SDPR) method is incapable of making direct binary decisions concerning the individual information bits without making symbol decisions first, except for the linear natural-mapping aided rectangular QAM constellations. By contrast, our new method is capable of directly deciding on the information bits of the ubiquitous Gray-mapping aided 16-QAM by employing a strikingly simple linear matrix representation (LMR) of 4-QAM. As an appealing benefit, the conventional "signal-to-symbol-to-bits" decision process is substituted by a simpler "signal-to-bits" decision process for the classic Gray-mapping aided rectangular 16-QAM. Furthermore, when combined with low-complexity bit-flipping based "hill climbing", the proposed direct-bit-based VA-SDPR (DVA-SDPR) detector achieves the best biterror-ratio (BER) performance among the known SDPR-based MIMO detectors in the context considered, while still maintaining a worst-case complexity order as low as O (4N T + 1) 3.5 .

In this paper, we use a novel MIMO channel model to characterize the dependence of ergodic capacity and diversity order on the joint statistics of the angular power density. The scattering environment of a MIMO channel is characterized by a double directional angular power distribution, describing the power transferred in each direction from transmitter aperture to receiver aperture. Angular power, which is typically separable Kronecker-modelled, is here generalized to include joint distribution properties using well-known bivariate probability density functions. We show that the joint properties of the power density, namely the shape and the orientation of power distribution contours, have significant impact on capacity and diversity of non-line-of-sight (NLOS) channels.

In this paper, we develop a MIMO channel model for generating the channel gains between arbitrary arrays of transmitter and receiver antennas, for a general class of non-lineof-sight (NLOS) channels. The channel scattering environment is defined by a double directional angular distribution describing the power transferred from transmitter aperture to receiver aperture in each direction. We propose several parametrized bivariate distributions that are consistent with univariate scatterer distributions separately observed at the transmitter and receiver. We derive the second order statistics of the channel gains in terms of the double directional power distribution and characterize a sample system performance as a function of distribution parameters.

This paper presents an analytical model for the fading channel correlation in general scattering environments. In contrast to the existing correlation models, our new approach treats the scattering environment as non-separable and it is modeled using a bi-angular power distribution. The bi-angular power distribution is parameterized by the mean departure and arrival angles, angular spreads of the univariate angular power distributions at the transmitter and receiver apertures, and a third parameter, the covariance between transmit and receive angles which captures the statistical interdependency between angular power distributions at the transmitter and receiver apertures. When this third parameter is zero, this new model reduces to the well known "Kronecker" model. Using the proposed model, we show that Kronecker model is a good approximation to the actual channel when the scattering channel consists of a single scattering cluster. In the presence of multiple remote scattering clusters we show that Kronecker model over estimates the performance by artificially increasing the number of multipaths in the channel. Wireless channel modelling has received much attention in recent years since space-time processing using multiple antennas is becoming one of the most promising areas for improvements in performance of mobile communication sys- tems [1], [2]. In channel modelling research, the effects of fading channel correlation due to insufficient antenna spacing and sparse scattering environments are of primary concern as they impact the performance of multiple-input multiple-output (MIMO) communication systems. A popular channel model that has been used in MIMO performance analysis is the "Kronecker" model [3]-[5]. In this model, the correlation properties of the MIMO channel are modeled at the transmitter and receiver separately, neglecting the statistical interdependency between scattering distributions at the transmitter and receiver antenna apertures. Measurement and analytical results presented in [6], suggest that the Kronecker model does not accurately model the underlying scattering channel, therefore it does not provide accurate performance results. In this paper, using a recently proposed spatial channel model [8], we develop an alternate to the Kronecker model

In this paper, we introduce the novel use of linear spatial precoding based on fixed and known parameters of multiple-input multiple-output (MIMO) channels to improve the performance of space-time coded MIMO systems. We derive linear spatial precoding schemes for both coherent (channel is known at the receiver) and non-coherent (channel is unknown at the receiver) space-time coded MIMO systems. Antenna spacing and antenna placement (geometry) are considered as fixed parameters of MIMO channels, which are readily known at the transmitter. These precoding schemes exploit the antenna placement information at both ends of the MIMO channel to ameliorate the effect of non-ideal antenna placement on the performance of space-time coded systems. In these schemes, the precoder is fixed for given transmit and receive antenna configurations and transmitter does not require any feedback of channel state information (partial or full) from the receiver. Closed form solutions for both precoding schemes are presented for systems with up to three receiver antennas. A generalized method is proposed for more than three receiver antennas. We use the coherent space-time block codes (STBC) and differential space-time block codes to analyze the performance of proposed precoding schemes. Simulation results show that at low SNRs, both precoders give significant performance improvement over a non-precoded system for small antenna aperture sizes.

In this paper, we exploit the symmetry properties of fourthorder cumulants to develop a new blind identification algorithm for multiple-input multiple-output (MIMO) instantaneous channels. The proposed algorithm utilizes the Parallel Factor (Parafac) decomposition of the 4th-order cumulant tensor by solving a single-step (SS) least squares (LS) problem. This approach is shown to hold for chan- nels with more sources than sensors. A simplified approach using a reduced-order tensor is also discussed. Computer simulations are provided to illustrate the performance of the proposed identification algorithms.

An experimental multimodal disputation system, Mr.Bengo, is a knowledge based system with multimodal user interfaces such as face recognition, face generation, speech recognition, speech generation and a WWW browser. Mr. Bengo deals with three agents -a prosecution, an defense attorney and the judge. The prosecution and the attorney dispute about the interpretation of legal rules. The user controls the defense attorney to dispute with the prosecution. After the disputation nishes, the judge decides the winner. As the disputation is a two agent game, to predict the opponent's move is important to win the game. During disputation of the Mr. Bengo, the face of each agent c hanges according to the status of disputation. Using the face information, the user knows if the prosecution nds the counter argument or not, which helps him to search the good next move.

We investigate MIMO eigenmode transmission using statistical channel state information at the transmitter. We consider a general jointly-correlated MIMO channel model, which does not require separable spatial correlations at the transmitter and receiver. For this model, we first derive a closed-form tight upper bound for the ergodic capacity, which reveals a simple and interesting relationship in terms of the matrix permanent of the eigenmode channel coupling matrix and embraces many existing results in the literature as special cases. Based on this closed-form and tractable upper bound expression, we then employ convex optimization techniques to develop low-complexity power allocation solutions involving only the channel statistics. Necessary and sufficient optimality conditions are derived, from which we develop an iterative water-filling algorithm with guaranteed convergence. Simulations demonstrate the tightness of the capacity upper bound and the near-optimal performance of the proposed lowcomplexity transmitter optimization approach.

With mean or covariance channel feedback, the input covariance matrix can be designed to achieve the ergodic capacity of a MIMO fading channel. It is known that the eigenvectors of the optimal input covariance matrix are the same as the eigen-vectors of the channel mean or covariance matrix. However, the optimal power allocation across the eigen-vectors is much less understood. In this paper, two scenarios are investigated: 1) Rician MIMO channels with mean channel feedback, and 2) Rayleigh MIMO channels with covariance channel feedback. We first derive a suboptimal power allocation algorithms in the spatial domain for expected mutual information maximization for two transmit antennas systems, based on an upper bound for the ergodic capacity of a MIMO channel with either channel mean or covariance information at the transmitter. Then, we extend heuristically the results to systems with multiple antennas at both the transmitter and receiver side. The proposed power allocation solution permits a closed-form expression and has a water-filling interpretation. Simulation results reveal that the proposed method performs nearly the same as the optimal solution (which requires highly complex optimization routines over random processes) with inappreciable difference.

In this paper, a novel unified channel model framework is proposed for cooperative multiple-input multiple-output (MIMO) wireless channels. The proposed model framework is generic and adaptable to multiple cooperative MIMO scenarios by simply adjusting key model parameters. Based on the proposed model framework and using a typical cooperative MIMO communication environment as an example, we derive a novel geometry-based stochastic model (GBSM) applicable to multiple wireless propagation scenarios. The proposed GBSM is the first cooperative MIMO channel model that has the ability to investigate the impact of the local scattering density (LSD) on channel characteristics. From the derived GBSM, the corresponding multi-link spatial correlation functions are derived and numerically analyzed in detail.

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In the mobile radio environment, signals are usually impaired by fading and multipath delay phenomenon. In such channels, severe fading of the signal amplitude and inter-symbol-interference (ISI) due to the frequency selectivity of the channel cause an unacceptable degradation of error performance. Orthogonal frequency division multiplexing (OFDM) is an efficient scheme to mitigate the effect of multipath channel. Since it eliminates ISI by inserting guard interval (GI) longer than the delay spread of the channel. In general, the GI is usually designed to be longer than the delay spread of the channel, and is decided after channel measurements in the desired implementation scenario. However, the maximum delay spread is longer than GI, the system performance is significantly degraded. The conventional time-frequency interferometry (TFI) for OFDM does not consider timevariant channel with large delay spread. In this paper, we focus on the large delay spread channel and propose the ISI and inter-carrier-interference (ICI) compensation method for TFI-OFDM.

Some limitations on the performance of parameter estimation algorithms are imposed by the random phenomena contained in channel-sounding data and typically characterized by the Cramer-Rao-Lower-Bound. There are, however, additional limitations stemming from the achievable accuracy of the model of the measurement system that is used for estimation purposes. More specifically, inaccuracies of the antenna array model will reduce dynamic range of the measurement data that can be reliably characterized. In this paper, effects of antenna array model distortion on the highresolution estimation of the specular component parameters are illustrated. Artifacts resulting from this distortion are discussed in the context of channel characterization/modeling.

Among the mobile ad hoc networks appealing characteristics there are network reconfigurability and flexibility. In this context a smart antenna capable of self-configuring multiple high-directivity beams provides a major advantage in terms of power saving, increased range, and spatial reuse of channels. In this paper a smart antenna made of a cylindrical array of patches suitable for MANETs is presented.

Minimising the energy consumption of railway vehicles is nowadays a real economic and technological challenge. Weight reduction using composites for the vehicle structure is one of the solutions currently considered. Indeed, composite materials offer high mechanical characteristics for an overall lighter structure. These composites are however not designed for an optimal electromagnetic (EM) characteristics and their impact on the performance of on-board wireless communication systems requiring a high level of reliability is not negligible. Equipping railway vehicles with wireless communication system is thus getting more complicated. Another constraint is the increasing number of wireless systems onboard and antenna integration on such platforms is becoming a real challenge. Recent research results in the field of wave-matter interaction and antenna technology suggest the design of wireless systems antennas accounting for the EM characteristic of the surrounding environment by usin...

The spatial correlation properties of multiple in- put multiple output (MIMO) channels can greatly affect the performance of MIMO systems. It is therefore important to develop MIMO channel models which can provide accurate spatial correlation characteristics. This paper starts with the brief review of MIMO channel models and then concentrates on investigating the spatial correlation characteristics of the Spatial Channel Model (SCM) in the Third Generation Partnership Project (3GPP) and the Kronecker Based Stochastic Model (KBSM) at three levels, namely the cluster level, link level, and system level. The KBSM has the spatial separability at all the three levels. The SCM shows the spatial separability at the link and system levels, but not at the cluster level since its spatial correlation is related to the joint distribution of the angle of arrival (AoA) and angle of departure (AoD). The KBSM with the Gaussian shaped Power Azimuth Spectrum (PAS) is found to fit best the 3GPP SCM in terms of the spatial correlations. Despite of its simplicity and analytical tractability, the KBSM is restricted to model only the average spatial behavior of MIMO channels. The SCM provides more insights of the variations of different MIMO channel realizations but the implementation complexity is relatively high.

We study the problem of constructing coded modulation schemes over multidimensional signal sets in Nakagami-m block-fading channels. In particular, we consider the optimal diversity reliability exponent of the error probability when the multidimensional constellation is obtained as the rotation of classical complex-plane signal constellations. We show that multidimensional rotations of full dimension achieve the optimal diversity reliability exponent, also achieved by Gaussian constellations. Multidimensional rotations of full dimension induce a large decoding complexity, and in some cases it might be beneficial to use multiple rotations of smaller dimension. We also study the diversity reliability exponent in this case, which yields the optimal rate-diversity-complexity tradeoff in block-fading channels with discrete inputs.

In this paper, we introduce the class of lattice space-time (LAST) codes. We show that these codes achieve the optimal diversity-vs-multiplexing tradeoff defined by Zheng and Tse under generalized minimum Euclidean distance lattice decoding. Our scheme is based on a generalization of Erez and Zamir mod-Λ scheme to the MIMO case. This result settles the open problem posed by Zheng and Tse on the construction of explicit coding and decoding schemes that achieve the optimal diversity-vs-multiplexing tradeoff. Moreover, our results shed more light on the structure of optimal coding/decoding techniques in delay limited MIMO channels. In particular: 1) we show that MMSE-GDFE plays a fundamental role in approaching the limits of delay limited MIMO channels in the high SNR regime, unlike the AWGN channel case and 2) our random coding arguments represent a major departure from traditional space-time code designs based on the rank and/or mutual information design criteria.

Massive Multiple-Input Multiple-Output (MIMO) answers the exponentially increasing demand for comprehensive fixed broadband and broadcast wireless communication services. Massive MIMO is a pivotal technology in the 5G and beyond (5GaB) wireless communication systems. This paper compares linear precoding algorithms such as Minimum Mean Square Error (MMSE), Neumann Series Approximation (NSA) with nonlinear precoders such as Tomlinson-Harashima Precoder (THP), and Lattice Reduction (LR) algorithm, the Lenstra-Lenstra-Lov'asz (LLL) precoder. The comparison was conducted using Bit-Error Rate (BER) and signal-to-noise ratio (SNR) performance measures. Simulated results prove that nonlinear precoders outperform linear precoding in high SNR regions.

Modelling of the environment is an important factor in electromagnetic wave propagation simulation, performed by a 3D raytracing method. The aim of this work is to study the effect of indoor environment modelling accuracy on MIMO (Multiple Input Multiple Output) channel characterisation. The first of the two environments investigated is the hall of our building, while the second one is a more confined environment and represents the floor of our laboratory. For these two indoor environments, three description levels are proposed in order to establish geometrical and electrical modelling impact on MIMO channel characterisation. Results are obtained by analysing the capacity and variation in correlation in relation to the polarisation, the presence of LOS (Line of sight) or NLOS configurations, the spacing between antennae and the number of transmitter and receiver antennae. To cite this article: C.

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The next paper brings the development of an improve implementation of Activity-based costing using Wireless Mesh Networks with MIMO channels. The study of the efficiency of this implementation in MIMO channels, is simulated with Space-Time Block Coding with convolutional codification and BPSK modulation, to show that this way of manages costing and the correct exploitation of wireless technologies in communications, generate new value to enterprises. Also the monetary differences of the costs, the impact of the use of better channels are presented.

In this contribution, the capacity-achieving input covariance matrices for coherent blockfading correlated MIMO Rician channels are determined. In contrast with the Rayleigh and uncorrelated Rician cases, no closed-form expressions for the eigenvectors of the optimum input covariance matrix are available. Classically, both the eigenvectors and eigenvalues are computed numerically and the corresponding optimization algorithms remain computationally very demanding. In the asymptotic regime where the number of transmit and receive antennas converge to infinity at the same rate, new results related to the accuracy of the approximation of the average mutual information are provided. Based on the accuracy of this approximation, an attractive optimization algorithm is proposed and analysed. This algorithm is shown to yield an effective way to compute the capacity achieving matrix for the average mutual information and numerical simulation results show that, even for a moderate number of transmit and receive antennas, the new approach provides the same results as direct maximization approaches of the average mutual information.

This paper investigates the comparison between the link performance and the characteristic channel parameters (e.g. delay spread, spatial correlation). The link performance is described by the bit error rate and the channel capacity. It is shown that there exists strong relation between the performance of the link and properly chosen characteristic channel parameters. The investigations presented within the article are based on measurements performed by a real-time MIMO channel sounder.

Multiple-input multiple-output (MIMO) spatial multiplexing that needs to separate and detect transmitted signal streams by using processing at the receiver end can increase the data rates of transmissions on independent and identically distributed (i.i.d.) channels. Such channels have been considered to exist in nonline-of-sight (NLOS) environments. However, actual communications may also be conducted in line-of-sight (LOS) environments. While an LOS component can increase the received power level, it may also cause correlated channels that make it difficult to detect the transmitted streams. In this paper, we describe the performance of 4 4 MIMO spatial multiplexing based on LOS and NLOS channel measurements in an indoor environment. For eight configurations of uniform linear arrays (four antenna spacings and two array orientations), we evaluated the cumulative distribution function (CDF) of the channel capacity and bit error rate performance versus transmit power, and we analyzed them in terms of antenna pattern, fading correlation, CDFs of MIMO channel elements, and CDFs of eigenvalues. Results show that, despite higher fading correlations and non i.i.d. channel characteristics, the performance of MIMO spatial multiplexing in the LOS environment is better than that in the NLOS one. However, the performance in the measured LOS environment largely depends on the MIMO configuration.

Causal networks are directed graphs that generalize Ishikawa diagrams to encompass multiple responses. Emphasizing tolerance design applications, this work presents an optimal design algorithm when the variables are organized as a causal network. The causal network is first transformed into a causal map, which represents all factors and responses as points in a common D-dimensional metric space. The design approach is algorithmic, optimizing Wynn's entropy criterion. This criterion maximizes dispersion among predicted multivatiate responses, using a distance-inspace coefficients (DiSCo) model. A key constraint is block self-containment-the blocks are analyzable without reference to one another; these analyses are to be complemented by a unified all-block analysis. Also explored is the benefit of skewing blocks by setting a few factors off-target. In 1943, Ishikawa introduced cause-effect (CE) diagrams for quality control. CE diagrams render causal relationships hierarchally, with the single response as the root node, factor groups the primary branches and finer branches from there. Causal networks are directed graphs that generalize CE diagrams in three ways: (1) the number of responses can be more than one, (2) responses can point causally to other responses, and (3) each factor is represented as one and only one node, yet may contribute to more than one response and/or higher-level factor. Regarding (3), causal networks are only fully hierarchal for assembly manufacturing processes, but many are directed acyclic graphs. Here causal networks are used to design experiments. The application area is roughly that of CE diagrams-manufacturing processes with many (10-50) input factors, all of which contribute to response variation. The number of factors F is large enough to suggest a systematic approach. Factor interactions are also important, but F is too large for a resolution V design. A new semiconductor manufacturing process has three phases: (i) Development, where alternatives are evaluated and targets tuned. (ii) Pre-production, where process targets are set, and tolerances tested. (iii) Production, which fixes targets and tolerances, commits manufacturing resources to volume. Pre-production manages risk, providing time for finding critical steps, checking manufacturability, and testing reliability. Competitive pressures offer strong incentives for a brief pre-production phase. Better than nominal runs at characterizing processes are designed experiments, the application of which Taguchi terms tolerance design.