# Intelligent Interference Management in UAV-Based HetNets +
## 2 Literature Review  
Various studies in the literature have explored how UAVs can be used to supplement cellular networks. Approaches to calculate the best UABS position and trajectory have been studied in [4, 5, 6, 7, 8, 9, 10]. The authors in [4] calculated the best trajectory for a single UABS, assumed that the MBS and UABS operated at orthogonal frequencies, and thus the effect of interference was ignored. Greedy and unsupervised learning-based algorithms to position a fleet of UAVs to maximize aggregate user received power were presented in [5], while a sequential spiral algorithm was presented in [6]. However, none of these works considered interference between UAVs and BSs. Interference between UAVs and BSs was taken into account in [7, 8]. Interference was minimized by using a statistical interference map and orthogonal frequency assignment in [7] to select optimal UAV locations, while multi-antenna beam-forming techniques were used in [8]. None of the studies leveraged LTE 3 GPP Release-\(10/11\) interference management techniques, nor any machine learning techniques. Machine learning techniques were used to predict potential network congestion and deploy UAVs accordingly in [10]. Figure 1: A UAV-assisted HetNet composed of UABSs and an MBS where certain UEs are offloaded to UABSs to improve the coverage and fairness. Interference between UAVs and BSs was assumed to be minimized using orthogonal frequency assignments and beamforming techniques rather than with LTE 3 GPP Release-10/11 techniques. The authors in [11] followed the intuition that UAVs should be placed at coverage holes to maximize coverage, and used machine learning to locate coverage holes. A greedy approach to position UAVs was used in [12] to maximize the number of users covered, while ensuring that the QoS requirements of the users are satisfied. Game-theory-based approaches were used in [13] The use of a UAV to provide wireless service to an Internet of Things (IoT) network was studied in [13], wherein IoT nodes harvest energy from a UAV before transmitting data on the uplink to the UAV. The nodes form coalitions with one node acting as the coalition head. The optimal trajectory of the UAV was calculated to maximize the energy available to the IoT coalition heads. Novel approaches to optimally partition a geographical area into UABS and MBS cells were proposed in [14, 15]. While [14] used optimal transport theory with the aim of minimizing the transmission delay for all users in the area, ref. [15] used a neural-based cost function in which user demand patterns are used to assign a cost and density to each area. Both studies, however, did not tackle interference mitigation challenges explicitly. Interference mitigation using 3 GPP Release-10 enhanced inter-cell interference coordination techniques (eICIC) and Release-11 further enhanced inter-cell interference coordination (FeICIC) techniques in HetNets were studied in [16, 17, 18, 19]. While [16] jointly optimized the ICIC parameters, the UE cell association rules, and the spectrum resource sharing between the macro and pico cells, it did not use 3 GPP Release-11 FeICIC or cell range expansion (CRE) techniques. Moreover, ref. [16] only studied LTE HetNets and not UAV HetNets. LTE UAV HetNets were also evaluated in [20], which optimized the allocation of LTE physical resource blocks in addition to the UAV position in order to maximize coverage. However, LTE interference management techniques were not utilized. In [17], the authors developed a stochastic-geometry-based framework to study and compare the effectiveness of 3 GPP FeICIC techniques and eICIC techniques, but [17] also did not study UAV HetNets. The use of 3 GPP Release-10/11 techniques along with UABS mobility in UAV HetNets was evaluated in [19]. However, this study did not individually optimize the 3 GPP ICIC parameters, but rather applied the same ICIC parameter values to each MBS and UABS, which is sub-optimal, as we demonstrate.