1. A method of managing interference and handover in a broadband, UAVassisted heterogeneous network (BAHN), the BAHN including a plurality of base station devices and a master macrocell base station (MBS) device that is a base station control node of the BAHN, the method comprising: determining, by the master MBS device, a candidate BAHN model from state data received from the base station devices of the BAHN, wherein the candidate BAHN model satisfies the factors including an interference metric, a load-balancing metric, a handover failure metric, a ping-pong probability metric, and a network throughput metric, wherein the base station devices include unmanned aerial base station (UABS) devices, macrocell base station (MBS) devices, small cell base station (SCBS) devices, and Cloud-RAN (C-RAN) base station devices; depending on the candidate BAHN model, selecting, by the master MBS device, a model adjustment from a group consisting of: modifying a duty cycle or pattern of reduced-power resources; modifying a power reduction factor at the reduced-power resources; altering a range-expansion bias at one or more SCBS and UABS; modifying a scheduling threshold for transitioning user equipment devices to a different base station; modifying a three-dimensional location or a velocity of at least one UABS; modifying a mobility management parameter at a selected user equipment; modifying an operating state of UABS features; and aggregating a plurality of component carrier bands using a carrier aggregation feature of LTE; and performing, by the master MBS device, the at least one model adjustment, selecting the model adjustment comprises an autonomous adjustment determined by a reinforcement learning component of the base station devices, and the reinforcement learning component is trained using one or more of a Q-learning approach, a multi-armed-bandit technique, a combinatorial multi-armed-bandit technique, a satisfaction-based learning technique, and a Bayesian approach.