We investigate the dynamic behavior of the stationary random process defined by a central complex Wishart matrix @math as it varies along a certain dimension @math . We characterize the second-order joint cumulative distribution function (cdf) of the largest eigenvalue, and the second-order joint cdf of the smallest eigenvalue of this matrix. We show that both cdfs can be expressed in exact closed-form in terms of a finite number of well-known special functions in the context of communication theory. As a direct application, we investigate the dynamic behavior of the parallel channels associated with multiple-input multiple-output (MIMO) systems in the presence of Rayleigh fading. Studying the complex random matrix that defines the MIMO channel, we characterize the second-order joint cdf of the signal-to-noise ratio (SNR) for the best and worst channels. We use these results to study the rate of change of MIMO parallel channels, using different performance metrics. For a given value of the MIMO channel correlation coefficient, we observe how the SNR associated with the best parallel channel changes slower than the SNR of the worst channel. This different dynamic behavior is much more appreciable when the number of transmit ( @math ) and receive ( @math ) antennas is similar. However, as @math is increased while keeping @math fixed, we see how the best and worst channels tend to have a similar rate of change.
This problem was addressed in @cite_10 when studying the mutual information distribution in orthogonal frequency division multiplexing (OFDM) systems operating under frequency-selective MIMO channels; specifically, a closed-form expression for the joint second-order pdf was given for arbitrarily-selected eigenvalues of the equivalent frequency-domain Wishart matrix. However, in order to obtain the joint bivariate cdf or the correlation coefficient for a particular eigenvalue, a two-fold numerical integration with infinite limits was required. In @cite_22 , an expression for this bivariate cdf was derived for the extreme eigenvalues (i.e. the largest and the smallest) in terms of the determinant of a matrix whose entries are expressed as infinite series of products of incomplete gamma functions; hence, its evaluation is highly impractical as the number of antennas is increased.
@cite_10 @cite_22
2153637161 2135963243
This communication considers the distribution of the mutual information of frequency-selective spatially uncorrelated Rayleigh fading multiple-input-multiple-output (MIMO) channels. Results are presented for orthogonal frequency-division multiplexing (OFDM)-based spatial multiplexing. New exact closed-form expressions are derived for the variance of the mutual information. In contrast to previous results, our new expressions apply for systems with both arbitrary numbers of antennas and arbitrary-length channels. Simplified expressions are also presented for high and low signal-to-noise ratio (SNR) regimes. The analytical variance results are used to provide accurate analytical approximations for the distribution of the mutual information, and the outage capacity. In this paper, we consider an adaptive modulation system with multiple-input-multiple-output (MIMO) antennas in conjunction with orthogonal frequency-division multiplexing (OFDM) operating over frequency-selective Rayleigh fading environments. In particular, we consider a type of beamforming with a maximum ratio transmission maximum ratio combining (MRT-MRC) transceiver structure. For this system, we derive a central limit theorem for various block-based performance metrics. This motivates an accurate Gaussian approximation to the system data rate and the number of outages per OFDM block. In addition to the data rate and outage distributions, we also consider the subcarrier signal-to-noise ratio (SNR) as a process in the frequency domain and compute level crossing rates (LCRs) and average fade bandwidths (AFBs). Hence, we provide fundamental but novel results for the MIMO OFDM channel. The accuracy of these results is verified by Monte Carlo simulations, and applications to performance analysis and system design are discussed.