# Intelligent Interference Management in UAV-Based HetNets +
## 1 Introduction  
An unmanned aerial vehicle (UAV) heterogeneous network (HetNet) consists of conventional stationary ground macro base stations (MBSs), supplemented by mobile UAV base stations (UABSs) and cells on wheels [1]. The agility of UAVs coupled with their ability to carry radios and communicate wirelessly has led their adoption in various applications to address network congestion and in public safety communications as a temporary substitute for damaged communication infrastructure. In the aftermath of hurricane Maria in 2017, ground base stations were destroyed and AT&T used UAVs to temporarily restore wireless voice, text, data, and multimedia services [2].  
UABSs can complement existing MBSs in a UAV HetNet by providing wireless coverage to user equipment (UE) that are near cell borders, coverage holes, or far away from the MBS as shown in Figure 1. In this scenario, UABS 1 serves UE 5, while UABS 2 serves UE 6. These are the UEs that are far away from MBS 1, and thus these UABSs extends the coverage area of the network. On the other hand, though UABSs transmit at a lower power than MBSs, UABSs may cause interference to MBSs and vice-versa. Thus, the transmit powers of MBSs and UABSs needs to be adjusted accordingly to ensure sufficient signal to noise ratio (SINR) for all UEs.  
As UABSs are generally powered by battery and have limited computational capabilities, any network optimization algorithms that are developed need to have low computational complexity. Additionally, the solutions should also have low time complexity so that the UABS can respond quickly to changes in cellular demand. Taking intoaccount these constraints, we attempt to reduce the computational and time complexity of our proposed algorithms.  
In this work, we develop computationally efficient algorithms to maximize the mean and median spectral efficiency (SE) in a UAV HetNet and evaluate the performance of these algorithms. This works extends a previous work written by the same authors [3]. In [3], we optimized the fifth percentile of spectral efficiency (5 pSE) in a UAV-HetNet by employing a greedy algorithm and an artificial intelligence (AI) approach based on deep Q learning (DQN). In [3], it was observed that an AI approach, somewhat surprisingly, failed to find the optimum solution and was always out-performed by the greedy approach. In this work, we present an alternate AI solution, which is based on a double deep Q learning algorithm (DDQN) and uses a single AI agent to model all MBSs and UABSs in the UAV HetNet.  
This single AI agent chooses the values of all relevant network parameters, unlike in [3], where each macro base station (MBS) and UAV base station (UABS) was modeled as a separate AI agent. Its performance is evaluated against the optimal exhaustive search and the computationally efficient sequential algorithm introduced in [3], which optimizes BS and UAV parameters in a greedy manner. Compared to [3], the performance of the AI approach is now closer to the optimal. Specifically, the AI algorithm can achieve 93.46% of the optimal, when maximizing the median SE and 95.83% of the optimal, when maximizing the mean SE.