TY - JOUR
T1 - Power Control for a URLLC-Enabled UAV System Incorporated with DNN-Based Channel Estimation
AU - Yang, Peng
AU - Xi, Xing
AU - Quek, Tony Q.S.
AU - Chen, Jingxuan
AU - Cao, Xianbin
N1 - Funding Information:
Manuscript received November 15, 2020; revised December 31, 2020; accepted January 29, 2021. Date of publication February 2, 2021; date of current version May 10, 2021. This work was supported in part by the MOE ARF Tier 2 under Grant T2EP20120-0006. The associate editor coordinating the review of this article and approving it for publication was J. Lee. (Corresponding author: Peng Yang.) Peng Yang and Tony Q. S. Quek are with the Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore 487372 (e-mail: peng_yang@sutd.edu.sg).
Publisher Copyright:
© 2012 IEEE.
PY - 2021/5
Y1 - 2021/5
N2 - 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.
AB - 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.
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U2 - 10.1109/LWC.2021.3056446
DO - 10.1109/LWC.2021.3056446
M3 - Article
AN - SCOPUS:85100797297
SN - 2162-2337
VL - 10
SP - 1018
EP - 1022
JO - IEEE Wireless Communications Letters
JF - IEEE Wireless Communications Letters
IS - 5
M1 - 9344658
ER -