Linear stochastic systems
Information capacity of channels with partially unknown noise I: finite-dimensional channels
SIAM Journal on Applied Mathematics
Information Capacity of Channels with Partially Unknown Noise. II. Infinite-Dimensional Channels
SIAM Journal on Control and Optimization
Optimization by Vector Space Methods
Optimization by Vector Space Methods
Feedback Control Theory
Information Theory and Reliable Communication
Information Theory and Reliable Communication
Convex Optimization
Min-capacity of a multiple-antenna wireless channel in a static Ricean fading environment
IEEE Transactions on Wireless Communications
Reliable communication under channel uncertainty
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
The worst additive noise under a covariance constraint
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
How much training is needed in multiple-antenna wireless links?
IEEE Transactions on Information Theory
Uniform power allocation in MIMO channels: a game-theoretic approach
IEEE Transactions on Information Theory
Capacity bounds via duality with applications to multiple-antenna systems on flat-fading channels
IEEE Transactions on Information Theory
Correlated jamming on MIMO Gaussian fading channels
IEEE Transactions on Information Theory
Capacity and power allocation for fading MIMO channels with channel estimation error
IEEE Transactions on Information Theory
Capacity of MIMO systems based on measured wireless channels
IEEE Journal on Selected Areas in Communications
Hi-index | 754.84 |
In this paper, achievable rates for compound Gaussian multiple-input-multiple-output (MIMO) channels are derived. Two types of channels, modeled in the frequency domain, are considered when: 1) the channel frequency response matrix H belongs to a subset of H∞ normed linear space, and 2) the power spectral density (PSD) matrix of the Gaussian noise belongs to a subset of L1 space. The achievable rates of these two compound channels are related to the maximin of the mutual information rate. The minimum is with respect to the set of all possible H matrices or all possible PSD matrices of the noise. The maximum is with respect to all possible PSD matrices of the transmitted signal with bounded power. For the compound channel modeled by the set of H matrices, it is shown, under certain conditions, that the code for the worst case channel can be used for the whole class of channels. For the same model, the water-filling argument implies that the larger the set of matrices H, the smaller the bandwidth of the transmitted signal will be. For the second compound channel, the explicit relation between the maximizing PSD matrix of the transmitted signal and the minimizing PSD matrix of the noise is found. Two PSD matrices are related through a Riccati equation, which is always present in Kalman filtering and liner-quadratic Gaussian control problems.