Matrix analysis
On Limits of Wireless Communications in a Fading Environment when UsingMultiple Antennas
Wireless Personal Communications: An International Journal
Convex Optimization
Space-Time Wireless Systems: From Array Processing to MIMO Communications
Space-Time Wireless Systems: From Array Processing to MIMO Communications
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Wireless Communications
IEEE Transactions on Wireless Communications
Space-time transmit precoding with imperfect feedback
IEEE Transactions on Information Theory
Spectral efficiency in the wideband regime
IEEE Transactions on Information Theory
Multiple-antenna capacity in the low-power regime
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Impact of antenna correlation on the capacity of multiantenna channels
IEEE Transactions on Information Theory
On the Outage Capacity of Correlated Multiple-Path MIMO Channels
IEEE Transactions on Information Theory
Optimizing MIMO antenna systems with channel covariance feedback
IEEE Journal on Selected Areas in Communications
Capacity limits of MIMO channels
IEEE Journal on Selected Areas in Communications
On the Capacity of MIMO Wireless Channels with Dynamic CSIT
IEEE Journal on Selected Areas in Communications
Collaborative null-steering beamforming for uniformly distributed wireless sensor networks
IEEE Transactions on Signal Processing
Power Control and Allocation for MIMO Broadcast Channels in Cognitive Radio Networks
Wireless Personal Communications: An International Journal
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We consider a single-user multiple-input multiple-output (MIMO) communication system in which the transmitter has access to both the channel covariance and the channel mean. For this scenario, we provide an explicit second-order approximation of the ergodic capacity of the channel, and we use this approximation to show that when the channel has a non-zero mean, the basis of the optimal input covariance matrix depends on the input signal power. (This basis is independent of the signal power in the zero-mean case.) The second-order approximation also provides insight into the way in which the low-signal-to-noise-ratio (SNR) optimal input covariance matrix is related to the optimal input covariance matrix at arbitrary SNRs. Furthermore, we show that the design of the input covariance matrix that optimizes the second-order approximation can be cast as a convex optimization problem for which the Karush-Kuhn-Tucker (KKT) conditions completely characterize the optimal solution. Using these conditions, we provide an efficient algorithm for obtaining second-order optimal input covariance matrices. The resulting covariances confirm our theoretical observation that, in general, the low-SNR optimal signal basis does not coincide with the optimal basis at higher SNRs. Finally, we show how our second-order design algorithm can be used to efficiently obtain input covariance matrices that provide ergodic rates that approach the ergodic capacity of the system.