Power Control for a URLLC-enabled UAV system incorporated with DNN-Based Channel Estimation

• URL: http://arxiv.org/abs/2012.00546v1
• Date: Sat, 14 Nov 2020 02:31:04 GMT
• Title: Power Control for a URLLC-enabled UAV system incorporated with DNN-Based Channel Estimation
• Authors: Peng Yang, Xing Xi, Tony Q. S. Quek, Xianbin Cao, Jingxuan Chen
• Abstract summary: This letter is concerned with power control for ultra-reliable low-latency communications (URLLC) enabled unmanned aerial vehicle (UAV) system incorporated with deep neural network (DNN) based channel estimation.
• Score: 82.16169603954663
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This letter is concerned with power control for a ultra-reliable and low-latency communications (URLLC) enabled unmanned aerial vehicle (UAV) system incorporated with deep neural network (DNN) based channel estimation. Particularly, we formulate the power control problem for the UAV system as an optimization problem to accommodate the URLLC requirement of uplink control and non-payload signal delivery while ensuring the downlink high-speed payload transmission. This problem is challenging to be solved due to the requirement of analytically tractable channel models and the non-convex characteristic as well. To address the challenges, we propose a novel power control algorithm, which constructs analytically tractable channel models based on DNN estimation results and explores a semidefinite relaxation (SDR) scheme to tackle the non-convexity. Simulation results demonstrate the accuracy of the DNN estimation and verify the effectiveness of the proposed algorithm.

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