Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Channel capacity of adaptive transmission with maximal ratio combining in correlated Rayleigh fading
IEEE Transactions on Wireless Communications
Capacity of MIMO Rician channels
IEEE Transactions on Wireless Communications
On the capacity of doubly correlated MIMO channels
IEEE Transactions on Wireless Communications
Impact of Correlation on the Capacity of Multiple Access and Broadcast Channels with MIMO-MRC
IEEE Transactions on Wireless Communications
Capacity of fading channels with channel side information
IEEE Transactions on Information Theory
Fading channels: information-theoretic and communications aspects
IEEE Transactions on Information Theory
On the capacity of spatially correlated MIMO Rayleigh-fading channels
IEEE Transactions on Information Theory
Capacity of multiple-antenna systems with both receiver and transmitter channel state information
IEEE Transactions on Information Theory
Cooperative diversity in wireless networks: Efficient protocols and outage behavior
IEEE Transactions on Information Theory
Capacity of a class of relay channels with orthogonal components
IEEE Transactions on Information Theory
Outage Capacity of the Fading Relay Channel in the Low-SNR Regime
IEEE Transactions on Information Theory
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In this paper, we propose a novel unified analytical framework for the analysis of Channel Capacity over fading channels, which exploits the flexibility and generality of the well-known Moment Generating Function (MGF-) based approach, instead of the usual Probability Density Function (PDF-) based one. In particular, we will introduce a novel transform operator called Ei-Transform, and show that Channel Capacity with side information at the receiver and side information at the transmitter and receiver can be readily expressed as the Ei-Transform of the first derivative of the MGF of the received Signal-to-Noise Ratio (SNR) in the former case, and the Ei-Transform of a linear combination of MGF and truncated MGF (along with their derivatives) of the received SNR in the latter case. The proposed framework turns out to be a useful technique to the analysis and computation of Channel Capacity in all those application scenarios in which estimating the MGF of the received SNR is much simpler than estimating the PDF. Finally, a simple numerical method, which is based on the Gauss-Chebyshev Quadrature (GCQ) rule, for computing Ei-Transform integrals will be described, and numerical results will be shown to validate the accuracy of the proposed MGF-based approach.