A continuous representation of multi-antenna fading channels and implications for capacity scaling and optimum array design (original) (raw)
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It has been shown that the capacity of a multiple-input multiple-output system increases linearly with the number of antennas, provided that the environment is rich scattering. However, this increase in the capacity is substantially degraded if the multiple input multiple output channels are correlated. In this paper, the capacity of correlated multiple input multiple output fading channel is investigated for laplacian and uniform angular energy distributions that implicitly represent two different scatterer distributions. The effect of the spatial correlation of a uniform circular antenna array is considered. Optimal power allocation is implemented to maximize the capacity. Through extensive Monte Carlo simulations, the results show that multiple input multiple output channel capacity is a function of the angle spread and antenna spacing. These two parameters play a major role in dictating the spatial correlation which in turn affects on the capacity. Large angle spread leads to lower correlation between the antenna elements and consequently higher capacity. The degradation in the capacity can be reduced by increasing the spacing between the antenna elements.
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IEEE Transactions on Information Theory, 2005
The capacity of the multiple-input multiple-output (MIMO) wireless channel with uniform linear arrays (ULAs) of antennas at the transmitter and receiver is investigated. It is assumed that the receiver knows the channel perfectly but that the transmitter knows only the channel statistics. The analysis is carried out using an equivalent virtual representation of the channel that is obtained via a spatial discrete Fourier transform. A key property of the virtual representation that is exploited is that the components of virtual channel matrix are approximately independent. With this approximation, the virtual representation allows for a general capacity analysis without the common simplifying assumptions of Gaussian statistics and product-form correlation (Kronecker model) for the channel matrix elements. A deterministic line-of-sight (LOS) component in the channel is also easily incorporated in much of the analysis. It is shown that in the virtual domain, the capacity-achieving input vector consists of independent zero-mean proper-complex Gaussian entries, whose variances can be computed numerically using standard convex programming algorithms based on the channel statistics. Furthermore, in the asymptotic regime of low signal-to-noise ratio (SNR), it is shown that beamforming along one virtual transmit angle is asymptotically optimal. Necessary and sufficient conditions for the optimality of beamforming, and the value of the corresponding optimal virtual angle, are also derived based on only the second moments of the virtual channel coefficients. Numerical results indicate that beamforming may be close to optimum even at moderate values of SNR for sparse scattering environments. Finally, the capacity is investigated in the asymptotic regime where the numbers of receive and transmit antennas go to infinity, with their ratio being kept constant. Using a result of Girko, an expression for the asymptotic capacity scaling with the number of antennas is obtained in terms of the two-dimensional spatial scattering function of the channel. This asymptotic formula for the capacity is shown to be accurate even for small numbers of transmit and receive antennas in numerical examples.
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The effect of spatial correlation on the capacity of multiple-input multiple-output (MIMO) fading channel with uniform linear arrays (ULAs) at the transmitter and receiver is investigated. The situations when the channel is known and unknown at the transmitter are considered. The effects of antenna spacing, angle spread, and number of antennas on the capacity are also investigated. Extensive Monte Carlo computer simulations are carried out. The results showed that the spatial correlation may severely degrade the capacity of MIMO systems. The degradation in the capacity can be reduced by increasing the separation distance between the antenna elements. However, increasing the antenna spacing beyond ≈ 2λ has no effect on the capacity. Small angle spread leads to higher correlation between the antenna elements and consequently lower capacity.
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Channel capacity of multi-element communication systems in independent Rayleigh channels has been shown to scale linearly with the number of antennas. In reality, the signals received by different receiver antennas can be correlated with each other due to the non-uniform scattering environment and limited aperture of the antenna system. In this paper, the effect of spatial correlation between receiver antennas on capacity is investigated for various scattering environments. The physics of signal propagation is combined with statistics of the scattering environment to derive a capacity expression in terms of spatial correlation, antenna spacings/placement, aperture size, and power distribution of scatters. This result is used to show that for a given aperture size, one can increase the capacity approximately linearly up to a certain value by increasing the number of antennas but further increase will not give any significant capacity gain.
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In this chapter we present a realistic new model for wireless multipleinput multiple-output (MIMO) channels which is more general than previous models. A novel spatial decomposition of the channel is developed to provide insights into the spatial aspects of multiple antenna communication systems. By exploiting the underlying physics of free-space wave propagation we characterize the fundamental communication modes of a physical aperture and develop an intrinsic capacity which is independent of antenna array geometries and array signal processing. We show there exists a maximum achievable capacity for communication between spatial regions of space, which depends on the size of the regions and the statistics of the scattering environment.
Realization of MIMO Channel Model for Spatial Diversity with Capacity and SNR Multiplexing Gains
International Journal of Computer Information Systems and Industrial Management Applications, 2020
Multiple input multiple output (MIMO) system transmission is a popular diversity technique to improve the reliability of a communication system where transmitter, communication channel and receiver are the important elements. Data transmission reliability can be ensured when the bit error rate is very low. Normally, multiple antenna elements are used at both the transmitting and receiving section in MIMO Systems. MIMO system utilizes antenna diversity or spatial diversity coding system in wireless channels because wireless channels severely suffer from multipath fading in which the transmitted signal is reflected along various multiple paths before reaching to the destination or receiving section. Overwhelmingly, diversity coding drives multiple copies through multiple transmitting antennas (if one of the transmitting antenna becomes unsuccessful to receive, other antennas are used in order to decode the data) for improving the reliability of the data reception. In this paper, the MIMO channel model has been illustrated. Moreover, the vector for transmitting signal has been considered by implementing least square minimization as well as linear minimum mean square estimation. Parallel transmission of MIMO system has also been implemented where both the real part and imaginary part of the original, detected and the corresponding received data sequence has been described graphically. One of the important qualities of MIMO is a substantial increase in the capacity of communication channel that immediately translates to comparatively higher signal throughputs. The MIMO communication channels have a limited higher capacity considering the distortions for various deterministic channel recognitions and SNR. The MIMO channel average capacity is achieved more than 80% for dissimilar levels of impairments in transceiver when the value of kappa (Level of impairments in transmitter hardware) reduces from 0.02 to 0.005. The finite-SNR multiplexing gain (Proportion of MIMO system capacity to SISO system capacity) has been observed for deterministic and uncorrelated Rayleigh fading channels correspondingly. The core difference is in the high SNR level. It may occur for two reasons: (a) there is a quicker convergence to the limits under transceiver impairments (b) deterministic channels that are built on digital architectural plans or topographical maps of the propagation environment acquire an asymptotic gain superior than multiplexing gain when the number of transmitting antenna is greater than the number of receiving antenna.
In this paper, we investigate the effect of antenna spacing within the transmitter and receiver arrays as well as the distance between the transmitter and receiver arrays on the capacity of a measured multiple-input multiple-output (MIMO) channel. During the measurement, the transmitter was mounted on a vehicle moving at a constant speed towards the receiver which was mounted on a bridge. The capacity, as expected, was found to increase with increased antenna element spacing within the transmitter or receiver arrays. However, the resulting capacity curves versus distance were found to fluctuate as the transmitter approached the receiver. Reflections from the street surface are assumed to be the cause of those fluctuations. In order to test this theory, we simulated a simple two-path channel model.