Deep Reinforcement Learning for IRS-Assisted UAV Covert Communications

Covert communications can hide the existence of a transmission from the transmitter to receiver.This paper considers an intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)covert communication system.It was inspired by the high-dimensional data processing and decisionmaking capabilities of the deep reinforcement learning(DRL)algorithm.In order to improve the covert communication performance,an UAV 3D trajectory and IRS phase optimization algorithm based on double deep Q network(TAP-DDQN)is proposed.The simulations show that TAP-DDQN can significantly improve the covert performance of the IRS-assisted UAV covert communication system,compared with benchmark solutions.

Theoretical Study of the Energies of the Oscillating System with a Well-Distributed Mass of the Spring

The energy of a spring with a well-distributed mass ms is theoretically studied in this paper.The solution of the wave equation is derived in detail,and then the kinetic energy and potential energy of the spring are studied with the wave equation,as well as the kinetic energy of the oscillating mass M.The kinetic energy and potential energy of the spring,and total energy are numerically simulated for different ratios ms/M with considering the spring’s mass,which makes the property of energy of the oscillating system understood easily.