Extreme Value Theory-based Robust Minimum-Power Precoding for URLLC

TL;DR

This paper introduces an EVT-based robust precoding method for URLLC that optimizes power allocation under imperfect CSI, enhancing reliability and reducing worst-case errors in wireless communications.

Contribution

It proposes a novel EVT-based algorithm for robust precoding that improves reliability in URLLC by accounting for channel estimation errors.

Findings

Outperforms worst-case robust precoding benchmarks.

Effectively compensates for CSI estimation errors.

Enhances reliability and power efficiency in URLLC.

Abstract

Channel state information (CSI) is crucial for achieving ultra-reliable low-latency communication (URLLC) in wireless networks. The main associated problems are the CSI acquisition time, which impacts the delay requirements of time-critical applications, and the estimation accuracy, which degrades the signal-to-interference-plus-noise ratio (SINR), thus, reducing reliability. In this work, we formulate and solve a minimum-power precoding design problem simultaneously serving multiple URLLC users in the downlink with imperfect CSI availability. Specifically, we develop an algorithm that exploits state-of-the-art precoding schemes such as the maximal ratio transmission (MRT) and zero-forcing (ZF), and adjust the power of the precoders to compensate for the channel estimation error uncertainty based on the extreme value theory (EVT) framework. Finally, we evaluate the performance of our method and show its superiority concerning worst-case robust precoding, which is used as a benchmark.