Power Control for a URLLC-Enabled UAV System Incorporated with DNN-Based Channel Estimation

Peng Yang, Xing Xi, Tony Q.S. Quek, Jingxuan Chen, Xianbin Cao

Research output: Contribution to journalArticlepeer-review

Abstract

This letter is concerned with power control for an ultra-reliable and low-latency communications (URLLC) enabled unmanned aerial vehicle (UAV) system incorporated with a deep neural network (DNN) based channel estimation. Particularly, a power control problem for the UAV system is formulated to accommodate the URLLC requirement of uplink control and non-payload signal delivery while ensuring the downlink high-speed payload transmission. Solving this problem is challenging due to the requirement of analytically tractable channel models and its non-convexity. To address the challenges, we propose a novel power control algorithm, which builds analytically tractable channel models based on DNN estimation results and explores semidefinite relaxation (SDR) with provable performance guarantees to tackle the non-convexity. Simulation results demonstrate the accuracy of the DNN estimation and verify the effectiveness of the proposed algorithm.

Original languageEnglish
JournalIEEE Wireless Communications Letters
DOIs
Publication statusAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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