Robust Linear Precoder Design for Multi-cell Downlink Transmission

TL;DR

This paper proposes robust linear precoder designs for multi-cell downlink transmission that optimize worst-case QoS measures under imperfect channel state information, ensuring reliable communication despite uncertainties.

Contribution

It introduces novel robust precoding algorithms for multi-cell networks that handle channel estimation errors within bounded regions, applicable in both centralized and distributed settings.

Findings

Maximized worst-case weighted sum-rate.

Achieved guaranteed minimum worst-case rate.

Developed algorithms with varying complexity and cooperation levels.

Abstract

Coordinated information processing by the base stations of multi-cell wireless networks enhances the overall quality of communication in the network. Such coordinations for optimizing any desired network-wide quality of service (QoS) necessitate the base stations to acquire and share some channel state information (CSI). With perfect knowledge of channel states, the base stations can adjust their transmissions for achieving a network-wise QoS optimality. In practice, however, the CSI can be obtained only imperfectly. As a result, due to the uncertainties involved, the network is not guaranteed to benefit from a globally optimal QoS. Nevertheless, if the channel estimation perturbations are confined within bounded regions, the QoS measure will also lie within a bounded region. Therefore, by exploiting the notion of robustness in the worst-case sense some worst-case QoS guarantees for the network can be asserted. We adopt a popular model for noisy channel estimates that assumes that estimation noise terms lie within known hyper-spheres. We aim to design linear transceivers that optimize a worst-case QoS measure in downlink transmissions. In particular, we focus on maximizing the worst-case weighted sum-rate of the network and the minimum worst-case rate of the network. For obtaining such transceiver designs, we offer several centralized (fully cooperative) and distributed (limited cooperation) algorithms which entail different levels of complexity and information exchange among the base stations.