Deep Q-Network based coverage hole detection for future wireless networks

In this chapter, we suggest an effective way of discovering a coverage hole with the help of UAV and ML. The main purpose is to take different parameters from the radio environment and detect the coverage hole efficiently and autonomously. The simulation results show that the proposed method is successful in detecting the coverage hole. Further research for this proposed method can be extended in to many directions. For example, the UAV has detected only a single objective or only one coverage hole in this simulation. If there are more than one coverage hole in a complex radio environment then we have to consider multi-objective RL and consider additional constraints such as UAV charging stations and obstacles, e.g., MBS, trees, buildings. Also, the simulation of such complex radio environment needs to urban scenarios with multi obstacles avoidance techniques considering the speed of the UAV. Apart from these, we can also consider an on-demand UAV base station (tethered or untethered UAV) to provide coverage and capacity to a coverage hole or poor network service area. Based on the traffic requirement and available wireless backhaul, UAVs can act as a base station at the same time while flying to the coverage hole area in a shortest distance.