# Intelligent Interference Management in UAV-Based HetNets +
## 3 System Model  
We consider a HetNet with MBSs and UABSs operation in two tiers, within a simulation area of \(l\times l\) square meters as shown in Figure 2. MBSs and UEs are randomlydistributed in this area, according to a Poisson point process with the intensities \(\lambda_{\text{mbs}}\) and \(\lambda_{\text{ue}}\), respectively. The number of MBSs and UEs in the simulation can be calculated as \(N_{\text{mbs}}=\lambda_{\text{mbs}}\times l^{2}\) and \(N_{\text{ue}}=\lambda_{\text{ue}}\times l^{2}\), respectively. The 3D locations of all the MBSs, UABSs, and UEs are represented by the matrices \(\mathbf{X}_{\text{mbs}}\in\mathbb{R}^{N_{\text{mbs}}\times 3}\), \(\mathbf{X}_{\text{uabs}}\in\mathbb{R}^{N_{\text{uabs}}\times 3}\), and \(\mathbf{X}_{\text{ue}}\in\mathbb{R}^{N_{\text{ue}}\times 3}\), respectively. The transmission power of each MBSs is \(P_{\text{mbs}}\), while that of UABSs is \(P_{\text{uabs}}\). Given the antenna gains of MBS and UABS as \(K\) and \(K^{\prime}\), respectively, the effective MBS and UABS transmission power is calculated as \(P^{\prime}_{\text{mbs}}=KP_{\text{mbs}}\) and \(P^{\prime}_{\text{uabs}}=K^{\prime}P_{\text{uabs}}\), respectively. We assume that the UABSs and MBSs exchange information over the X2 interfaces. We also consider that the downlink bandwidth available to an MBS or a UABS is shared equally among their served UEs. We assume that downlink data is always available for any UE--i.e., the downlink UE traffic buffers are always full. For an arbitrary UE \(n\), where \(n\in\{1,2,\ldots,N_{\text{ue}}\}\), we define the macro-cell of interest (MOI) as the nearest MBS, and the UAV-cell of interest (UOI) as the nearest UABS. For example, in the specific scenario shown in Figure 2, MBS 1 is the MOI for UEs 1 and 2, MBS 2 is the MOI for UEs 5 and 6, UABS 1 is the UOI of UE 2, and UABS 2 is the UOI of UE 3. We denote the reference symbol received power (RSRP) of the \(n\)th UE from the MOI and the UOI as \(S_{\text{mbs}}(d_{mn})\) and \(S_{\text{uabs}}(d_{un})\), respectively, where \(d_{mn}\) is the distance from the nearest MOI, and \(d_{un}\) is the distance from the nearest UOI for the \(n\)th UE. We use the Okumura Hata suburban propagation model without any Rayleigh or Rician fading. An arbitrary UE \(n\) is always assumed to connect to the nearest MBS or UABS, where \(n\in\,1,2,\ldots,N_{\text{ue}}\). Then, for the \(n\)th UE, the reference symbol received power (RSRP) from the macro-cell of interest (MOI) and the UAV-cell of interest (UOI) are given by  
\[S_{\text{mbs}}(d_{mn})=\frac{P^{\prime}_{\text{mbs}}}{10\
icefrac{{\sigma}}{ {10}}},\;S_{\text{uabs}}(d_{un})=\frac{P^{\prime}_{\text{uabs}}}{10\
icefrac{{ \sigma}}{{10}}}, \tag{1}\]  
where \(\varphi\) is the path-loss observed from MBS in dB, \(\varphi^{\prime}\) is the path-loss observed from UABS in dB, \(d_{mn}\) is the distance from the nearest MOI, and \(d_{un}\) is the distance from the nearest UOI. Figure 2: Illustration of a UAV HetNet where the ICIC parameters of all MBSs and UABSs, and the locations of UABSs are optimized individually. This approach is in contrast to some existing work, such as [22], which, while optimizing locations for each UABS individually, assigns the same ICIC parameters to all UABSs and MBSs, Reprinted with permission from Ref. [3]. Copyright 2018 IEEE. The Okumura Hata path loss is a function of the carrier frequency, distance between the UE, serving cell, base station height, and UE antenna height. The path-loss (in dB) observed by the \(n\)th UE from MOI and UOI is given by:  
\[\varphi=A+B\text{log}(d_{mn})+C, \tag{2}\] \[\varphi^{\prime}=A+B\text{log}(d_{nn})+C, \tag{3}\]  
where the distances \(d_{mn}\) and \(d_{nn}\) are in km, and the factors \(A\), \(B\), and \(C\) depend on the carrier frequency and antenna height.