On downlink beamforming with greedy user selection: performance analysis and a simple new algorithm
IEEE Transactions on Signal Processing - Part I
Multiple Antenna Broadcast Channels With Shape Feedback and Limited Feedback
IEEE Transactions on Signal Processing - Part I
Zero-Forcing Precoding and Generalized Inverses
IEEE Transactions on Signal Processing
Fading channels: information-theoretic and communications aspects
IEEE Transactions on Information Theory
On the achievable throughput of a multiantenna Gaussian broadcast channel
IEEE Transactions on Information Theory
On the capacity of MIMO broadcast channels with partial side information
IEEE Transactions on Information Theory
Sum-capacity computation for the Gaussian vector broadcast channel via dual decomposition
IEEE Transactions on Information Theory
The Capacity Region of the Gaussian Multiple-Input Multiple-Output Broadcast Channel
IEEE Transactions on Information Theory
MIMO Broadcast Channels With Finite-Rate Feedback
IEEE Transactions on Information Theory
On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming
IEEE Journal on Selected Areas in Communications
Multiuser MISO transmitter optimization for intercell interference mitigation
IEEE Transactions on Signal Processing
| Hi-index | 0.00 |
We reconsider the role of user selection in multiuser MIMO broadcast channels (downlink), in the relevant regime where the number of users K is linear in the number of transmitter (base station) antennas M. User selection is known to achieve mutually quasi-orthogonal user channels and, at the same time, a multiuser diversity effect in terms of receiver SNR. These goals are achieved in the regime of fixed number of transmit antennas, and very large number of users. In contrast, we show that when K = O(M) these effects cannot be achieved, and the role of user selection is marginal. In terms of system design, our results suggest that only a small number K ≈ M of users should feedback their channel state information at each point in time. This greatly alleviates the burden of the channel state information feedback, while achieving essentially optimal performance.