Level-crossing rate and average duration of fades of non-stationary multipath fading channels

The level-crossing rate (LCR) and average duration of fades (ADF) are important statistical quantities describing the fading behaviour of mobile radio channels. To date, these quantities have only been analysed under the assumption that the mobile radio channel is wide-sense stationary, which is generally not the case in practice. In this paper, we propose a concept for the analysis of the LCR and ADF of non-stationary channels. Rice's standard formula for the derivation of the LCR of wide-sense stationary processes is extended to a more general formula enabling the computation of the instantaneous LCR of non-stationary processes. The application of the new concept results in closed-form expressions for the instantaneous LCR and ADF of non-stationary multipath flat fading channels. The contribution of this paper is of central importance for the statistical characterization of non-stationary mobile radio channels.acceptedVersionnivå

Eigenvalue Dynamics of a Central Wishart Matrix with Application to MIMO Systems

We investigate the dynamic behavior of the stationary random process defined by a central complex Wishart (CW) matrix ${\bf{W}}(t)$ as it varies along a certain dimension $t$. We characterize the second-order joint 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 ($N_T$) and receive ($N_R$) antennas is similar. However, as $N_T$ is increased while keeping $N_R$ fixed, we see how the best and worst channels tend to have a similar rate of change.Comment: 15 pages, 9 figures and 1 table. This work has been accepted for publication at IEEE Trans. Inf. Theory. Copyright (c) 2014 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to [email protected]