The Effect of Channel Uncertainty on Max-Min Goodput

TL;DR

This paper investigates how channel uncertainty impacts the reliable data rates (goodput) in wireless systems, proposing a method to optimize the minimum goodput through robust beamforming and outage probability approximation.

Contribution

It introduces a novel approach to approximate outage probability using quadratic form PDF approximation and adapts robust beamforming to maximize minimum goodput under uncertainty.

Findings

The proposed method effectively improves minimum goodput in simulations.

Approximation of outage probability enables better rate selection under uncertainty.

Optimizing goodput yields significant performance gains compared to traditional methods.

Abstract

In this paper, we consider the effect of channel uncertainty on the rates reliably delivered to the users; i.e., the goodput. After the base station (BS) designs a set of beamformers for a specific objective, the BS must select the operating or transmission data rate for each user. However, under channel uncertainty, higher transmission rates cause higher outage probability, and the delivered rate drops. Since lower rates are not desirable, one must balance between the transmission rate and outage. In this paper, we first explain how approximating the PDF of a quadratic form with a positive definite matrix can be used to obtain the outage probability for any set of beamfomers and transmission rate. Then we focus on the specific case of maximizing the minimum delivered rate, where we modify a robust beamforming approach to maximize the resulting goodput. We then derive iterative closed-form expressions for this case. The simulation results illustrate the efficacy of our analysis and the significant gains that can be obtained by optimizing the goodput metric.