Approximation Bounds for Quadratic Optimization with Homogeneous Quadratic Constraints
SIAM Journal on Optimization
IEEE Transactions on Signal Processing
Quality of Service and Max-Min Fair Transmit Beamforming to Multiple Cochannel Multicast Groups
IEEE Transactions on Signal Processing
Far-Field Multicast Beamforming for Uniform Linear Antenna Arrays
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Transmit beamforming for physical-layer multicasting
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Wireless Communications
Minimum outage probability transmission with imperfect feedback for MISO fading channels
IEEE Transactions on Wireless Communications
Robust Downlink Beamforming Based on Outage Probability Specifications
IEEE Transactions on Wireless Communications
Robust multiuser detection for multicarrier CDMA systems
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Ergodic stochastic optimization algorithms for wireless communication and networking
IEEE Transactions on Signal Processing
Optimized layered multicast with superposition coding in cellular systems
Wireless Communications & Mobile Computing
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The multicast beamforming problem is considered from the viewpoint of minimizing outage probability subject to a transmit power constraint. The main difference with the point-to-point transmit beamforming problem is that in multicast beamforming the channel is naturally modeled as a Gaussian mixture, as opposed to a single Gaussian distribution. The Gaussian components in the mixture model user clusters of different means (locations) and variances (spreads). It is shown that minimizing outage probability subject to a transmit power constraint is an NP-hard problem when the number of Gaussian kernels, J, is greater than or equal to the number of transmit antennas, N. Through dimensionality reduction, it is also shown that the problem is practically tractable for 2 - 3 Gaussian kernels. An approximate solution based on the Markov inequality is also proposed. This is simple to compute for any J and N, and often works well in practice.