论文标题
基于DNN的频道估计，启用URLLC的无人机系统的电源控制
Power Control for a URLLC-enabled UAV system incorporated with DNN-Based Channel Estimation
论文作者
论文摘要
这封信与基于深神经网络（DNN）基于深神经网络（DNN）的通道估计的超级可靠和低延迟通信（URLLC）的功率控制有关。特别是，我们将无人机系统的功率控制问题作为优化问题，以适应上行链路控制和非付费信号交付的URLLC要求，同时确保下行链路高速有效载荷传输。由于需要进行分析障碍的通道模型和非凸特性，因此解决了这个问题。为了应对挑战，我们提出了一种新型的功率控制算法，该算法基于DNN估计结果构建了可分析的可触及通道模型，并探索了一种半决赛松弛（SDR）方案，以应对非凸性。仿真结果证明了DNN估计的准确性，并验证了所提出算法的有效性。
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.
