Topics in matrix analysis
Elements of information theory
Elements of information theory
Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices
Journal of Multivariate Analysis
IEEE Transactions on Information Theory
Asymptotic performance of MIMO wireless channels with limited feedback
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
Limited feedback unitary precoding for orthogonal space-time block codes
IEEE Transactions on Signal Processing
Quantifying the power loss when transmit beamforming relies on finite-rate feedback
IEEE Transactions on Wireless Communications
Spectral efficiency of CDMA with random spreading
IEEE Transactions on Information Theory
Systematic design of unitary space-time constellations
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Bounds on packings of spheres in the Grassmann manifold
IEEE Transactions on Information Theory
On beamforming with finite rate feedback in multiple-antenna systems
IEEE Transactions on Information Theory
Grassmannian beamforming for multiple-input multiple-output wireless systems
IEEE Transactions on Information Theory
Outage mutual information of space-time MIMO channels
IEEE Transactions on Information Theory
Multiple-antenna channel hardening and its implications for rate feedback and scheduling
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Limited feedback unitary precoding for spatial multiplexing systems
IEEE Transactions on Information Theory
High-SNR power offset in multiantenna communication
IEEE Transactions on Information Theory
The asymptotic capacity of multiple-antenna Rayleigh-fading channels
IEEE Transactions on Information Theory
Quantization Bounds on Grassmann Manifolds and Applications to MIMO Communications
IEEE Transactions on Information Theory
IEEE Transactions on Signal Processing
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It is well known that multiple-input multiple-output (MIMO) systems have high spectral efficiency, especially when channel state information at the transmitter (CSIT) is available. In many practical systems, it is reasonable to assume that the CSIT is obtained by a limited (i.e., finite rate) feedback and is therefore imperfect. We consider the design problem of how to use the limited feedback resource to maximize the achievable information rate. In particular, we develop a low complexity power on/off strategy with beamforming (or Grassmann precoding), and analytically characterize its performance. Given the eigenvalue decomposition of the covariance matrix of the transmitted signal, refer to the eigenvectors as beams, and to the corresponding eigenvalues as the beam's power. A power on/off strategy means that a beam is either turned on with a constant power, or turned off. We will first assume that the beams match the channel perfectly and show that the ratio between the optimal number of beams turned on and the number of antennas converges to a constant when the numbers of transmit and receive antennas approach infinity proportionally. This motivates our power on/off strategy where the number of beams turned on is independent of channel realizations but is a function of the signal-to-noise ratio (SNR). When the feedback rate is finite, beamforming cannot be perfect, and we characterize the effect of imperfect beamforming by quantization bounds on the Grassmann manifold. By combining the results for power on/off and beamforming, a good approximation to the achievable information rate is derived. Simulations show that the proposed strategy is near optimal and the performance approximation is accurate for all experimented SNRs.