Deep Reinforcement Learning-Based Content Placement and Trajectory Design in Urban Cache-Enabled UAV Networks

Table 1

List of notations.

Notations	Descriptions
	Number of UAVs
	Total time slots
	Maximum speed of UAVs
	Coordinate of users
	Total number of files
	Caching probability
	LoS and NLoS probability
	Instantaneous rate of user
	Total bandwidth of each UAV
	Additional path loss for LoS, NLoS
	Propulsion power relevant parameters
	UAV set with link to user
	Number of users
	Time slot length
	Altitude of UAVs
	Zipf exponent
	Content popularity
	Storage capacity
	Elevation angle
	Noise power spectral density
	Environmental parameters (urban)
	Path loss exponent
	Battery capacity
	State, action, and reward in RL