Extreme Value Theory-Based Robust Minimum-Power Precoding for 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 latency requirements of timecritical applications, and the estimation accuracy, which degrades the signal-to-interference-plus-noise ratio, thus, reducing communication 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. Specifically, we develop an algorithm that exploits state-ofthe-art precoding schemes such as maximal ratio transmission and zero-forcing, and adjust the power of the precoders to compensate for the channel estimation error uncertainty based on the extreme value theory framework. Finally, we evaluate the performance of our method and show its superiority with respect to a worst-case robust precoding benchmark.