Antenna Spacing Effect on Indoor MIMO Channel Capacity

Visible Light Communication (VLC) has emerged as a powerful technique for wireless communication systems. Providing high data rate and increasing capacity are the major problems in VLC. Recent evidence suggests that Multiple Input Multiple Output (MIMO) technique can offers improved data rates and increased link range. This paper describes the design and implementation of visible light communication system in indoor environment exploring the benefits of MIMO. The specific objective of this research was to implement a 4×4 Multiple Input (LEDs) Multiple Output (photodetectors)-VLC communication system, where a 16 white power LEDs in four arrays are setting up at transmitter and four RX modules are setting up at receiver side without the need for additional power or bandwidth as well as analyze a mathematical model for a VLC- 4×4 MIMO with different modes such as the suitable angles to cover the entire room. The research designs practically an electronic circuit for the transmitter and receiver with inexpensive components. The power distribution of different propagation modes is calculated for Single Input Single Output (SISO) and MIMO channels in typical room dimensions. The results in this paper indicate that the power is distributed uniformly for entire room when implemented VLC-MIMO as compared to VLC-SISO. Furthermore, a 4×4 MIMO implementing VLC is compared in term of BER vs. SINR with SISO communication system for both Line of Sight (LOS) and Non Line of Sight (NLOS) propagation modes. Comparing the two results of LOS and NLOS, it can be seen that a 4×4 MIMO implementing VLC for LOS perform better than the same system with NLOS due to decreasing in the received power resulted from the multipath effect.

In this paper, the influence of the frequency band on the multiple-input multiple-output (MIMO) channel capacity in an office environment is researched. This study is based on an extensive measurement campaign between 2 and 12 GHz in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions.

Precise modelling of the Direction of Arrival (DoA) and Direction of Departure (DoD) of multipath components (MPCs) in a Multiple-Input Multiple-Output (MIMO) channel based on correlation properties and joint distributions have not been investigated properly. This paper examines the correlation between DoAs and DoDs, and presents a modelling approach particularly for the `general trend' of these dual-spatial domains, based on statistical results obtained from several indoor MIMO measurement campaigns. We show that the Laplacian distribution provides better fits for the directional parameters than the normal and Von Mises distributions. The `general trend' of DoA/DoD joint distribution and power density are modelled using a modified Gumbel's bivariate exponential approach. Results show that the proposed bivariate distribution provides a good match to the real data, and offers an alternative approach to generate realistic directional-based stochastic MIMO channel responses in different environments.

This paper describes a broadband multiple input, multiple output (MIMO) channel characterization platform and capacity measurement results in indoor office environment. The MIMO testbed has been designed for broadband MIMO channel sounding, capacity measurements and for characterizing the directional-multipaths of the radio propagation channel. The MIMO channel data have been collected in 5 GHz band inside a modern office environment. Capacity results from these experiments are discussed for different propagation conditions, including non line-of-sight (NLOS) and LOS propagations with various spacings between array elements. Spatial correlations are analyzed from the measured data and a frequency selective MIMO channel model based on the correlation statistics is validated. Post-processing of the measured data with a sequential ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) algorithm is utilized to extract the directions of departures and arrivals of multipath components at the transmitting and receiving arrays, respectively. A method of obtaining broadband MIMO capacity, indirectly, from a single directional measurement of MIMO propagation channel is also proposed and verified from the measurement results.

This paper presents outdoor wideband 8×8 multiple-input multiple-output (MIMO) channel measurements using dual-polarized antennas at 11 GHz. The measurements were conducted in the urban and residential areas of Ishigaki City, Okinawa, Japan. The differences in polarization for MIMO capacity and efficiency are discussed with respect to the dependency on local propagation environments. The MIMO capacity increases in proportion to the number of antennas at least for 8×8 configuration in non-line-of-sight area. Finally, MIMO channel capacity comparison between ultra high frequency and super high frequency band cellular system is simulated using 11 GHz measurement data.

Herein, results from measurements conducted by the University of Bristol are presented. The channel characteristics of multiple input multiple output (MIMO) indoor systems at 5.2 GHz are studied. Our investigation shows that the envelope of the channel for non-line-of-sight (NLOS) indoor situations are approximately Rayleigh distributed and consequently we focus on a statistical description of the first and second order moments of the narrowband MIMO channel. Furthermore, it is shown that for NLOS indoor scenarios, the MIMO channel covariance matrix can be well approximated by a Kronecker product of the covariance matrices describing the correlation at the transmitter and receiver side respectively. A statistical narrowband model for the NLOS indoor MIMO channel based on this covariance structure is presented.

Motivated by the study of the optimization of the quality of service for multiple input multiple output (MIMO) technique in a wireless communication system. The capacity of MIMO channel in independent Rayleigh channels grows linearly as the number of antennas. However, some limitations on the MIMO capacity is due to the correlation between individual sub-channels of the matrix channel. In this paper, we investigate the MIMO channel capacity in correlated channels using a new correlation matrix model. After some mathematical recalls we derive the general upper bound on the MIMO channel capacity. We give the analytical results, which measure the effect of correlation on the MIMO capacity for a proposal model. Then, we use the correlation matrix approach to compute the capacity of MIMO with respect to signal-to-noise (snr) and then predict the numbers of antennas. By fixing the snr variable to a specific value, we extract informations on the optimal numbers of MIMO antennas. Finally, we have given the variation of the MIMO capacity in the limiting case of N rarr infin for our proposal model and the exponential correlation matrix model.

Finite impulse response (FIR) precoders are efficient precoding framework for frequency-selective multiple-input multiple-output (MIMO) channels. In this paper, the optimal linear FIR precoder is investigated to maximize the capacity of frequency-selective MIMO channel under average transmit power constraint. We use Kronecker product to regenerate the nonconvex optimization problem and propose a semidefinite programming (SDP) relaxation scheme with Gaussian randomization to provide approximate solutions. Numerical results indicate that the proposed algorithm outperforms the existing algorithms in the high signal to noise ratio (SNR) regime, especially when the FIR precoder has large filter taps.

This paper researches the capacity of multiple-input multiple-output (MIMO) channels jointly-correlated spatially on both sides in the low signal-to-noise (SNR) regime. We shall derive two key low SNR metrics: the minimum required $E_b/N_0$ and the wideband slope, assuming two types of channel state information (CSI) at the transmitter. We obtain closed-from solutions of these low SNR parameters for Gaussian MIMO channels, which embrace many existing results in the literature as special cases. Monte-Carlo simulations are performed to validate our analytical results.

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

Recently, Multiple Input Multiple Output (MIMO) has become popular research topic among researchers for development of a new wireless communications technology. The system capacity can be increase with deployment of MIMO technique in the communications system. Thus, the used of high frequency bandwidth can be avoid since this method required high cost implementation. High transmitted power also is not required because all transmitted branch will transmit same power in MIMO system. There are three major studies in MIMO which are research on array antenna and adaptive signal processing, research on information theory and coding algorithm and research on MIMO channel propagation (Nirmal et al., 2004). MIMO channel capacity can be increase with the increase of number of transmitter and receiver. When the number of the antennas used is fixed, the channel capacity is related to the spatial correlation and the diversity gain from antenna spacing configuration at 17

A novel training-based channel estimation scheme is presented for an uncoded layered space-time system that operates in a wideband frequency selective fading environment of a time-varying multiple-input multiple-output (MIMO) channel. The method uses a pilot matrix consisting of pilot symbols derived from the Paley-Hadamard matrix to estimate the MIMO channel impulse response. The orthogonality of the pilot matrix resolves both the intersymbol interference and the cochannel interference of the wideband MIMO channel. The Toeplitz-like structure of the Paley-Hadamard matrix is exploited to minimise the length of the pilot sequence, reducing pilot symbol overhead. A decision feedback algorithm is used to track the time-varying MIMO channel. Results are presented which demonstrate the performance of the proposed MIMO channel estimator in a layered space-time system for different mobile velocities and system configurations.

Different multiple input - multiple output (MIMO) channel models are analyzed with regard to the indoor line-of-sight (LOS) MIMO channel. In the literature this channel is proven to achieve high capacities while being highly correlated. The modeling results from physical as well as analytical models are compared with measured channels, showing that none of the established models is exactly suitable for the considered indoor LOS MIMO channel. However, there is a promising approach that applies an extended Saleh model in conjunction with the physically correct spherical wave propagation. Although in its current state this basic approach cannot be regarded as a complete MIMO channel model, at least it provides an adequate origin for the correct prediction of MIMO capacities in different types of LOS channels. This is due to the fact, that the crucial prerequisites for an appropriate LOS MIMO channel model, which are by name the application of the physically correct spherical wave propagation, the correct modeling of the high correlation in LOS channels and the inclusion of the geometrical antenna setup, are regarded correctly. Contrarily, these prerequisites are included in none of the current models that are widely accepted in MIMO radio communications nowadays. The proof of this statement using measured data is a major objective of this paper.

This paper presents a statistical study of the multiple-input multiple-output (MIMO) radio channel in indoor environments in relation to the capacity of multi-antenna wireless systems. Indoor measurements of the transmission matrix, H, at 2.45 GHz are performed using a 16/spl times/16 multi-antenna system in the case of line-of-sight (LOS) and in the case when it is completely obstructed (off-axis). This allows us to study the statistics of the capacity of systems with a large number of antennas N for both Rician and Rayleigh fading channels. In addition, we examine the statistics of the eigenvalues of the transmission matrix, which provide subtle details about the MIMO channel in the case of LOS and off-axis that could be of significant importance for practical applications. Relevant theoretical simulations are performed, which show good agreement with the results obtained from measurements.

Multicanonical Monte Carlo (MMC) is an adaptive importance sampling technique which employs a blind adaptation algorithm to converge to the optimal biasing distribution. In this paper, we propose an improved MMC algorithm for fast estimation of outage probabilities in Multiple Input Multiple Output (MIMO) channels. The algorithm uses an improved estimator which can provide smooth estimates with high reliability at very low error probabilities. The proposed estimator uses moving average filtering to smooth the visits histograms at each iteration thereby reducing the stochastic fluctuations between iterations. We compare the proposed estimator with the well known Berg's update and the simulation results show that the new estimator can accurately estimate lower error probabilities with the same number of samples.