Unified Performance Analysis of Orthogonal Transmit Beamforming Methods with User Selection

TL;DR

This paper derives unified analytical expressions for the joint probability distributions of user SINRs in multiuser orthogonal beamforming with user selection, enabling performance evaluation of these methods.

Contribution

It provides the first unified derivation of joint SINR distributions for orthogonal beamforming with user scheduling, applicable to similar algorithms.

Findings

Analytical joint SINR distributions are verified by simulations.

Unified solutions simplify performance analysis of orthogonal beamforming methods.

Results facilitate comparison of different beamforming and user selection strategies.

Abstract

Simultaneous multiuser beamforming in multiantenna downlink channels can entail dirty paper (DP) precoding (optimal and high complexity) or linear precoding (suboptimal and low complexity) approaches. The system performance is typically characterized by the sum capacity with homogenous users with perfect channel state information at the transmitter. The sum capacity performance analysis requires the exact probability distributions of the user signal-to-noise ratios (SNRs) or signal-to-interference plus noise ratios (SINRs). The standard techniques from order statistics can be sufficient to obtain the probability distributions of SNRs for DP precoding due to the removal of known interference at the transmitter. Derivation of such probability distributions for linear precoding techniques on the other hand is much more challenging. For example, orthogonal beamforming techniques do not completely cancel the interference at the user locations, thereby requiring the analysis with SINRs. In this paper, we derive the joint probability distributions of the user SINRs for two orthogonal beamforming methods combined with user scheduling: adaptive orthogonal beamforming and orthogonal linear beamforming. We obtain compact and unified solutions for the joint probability distributions of the scheduled users' SINRs. Our analytical results can be applied for similar algorithms and are verified by computer simulations.