# NFV/SDN as an Enabler for Dynamic Placement Method of mmWave Embedded UAV Access Base Stations
## 4 Proposed Method
### Frequency Division for Each UAV  
In this and the next section, we will introduce an interference-avoidance frequency division method after the placement of UAVs. We will also determine the association between user and UAV, i.e., which users are covered by which UAV.  
As a previous study, [43] described a technique for frequency division and large bandwidth allocation for areas with increasing traffic in a terrestrial/satellite shared cell phone system. Unlike previous studies such as [43] and those for a conventional ground base stations system, the UAV network studied in this research can have a flexible cell configuration, so the coverage situation will change. Therefore, in this and the following section, we propose an algorithm for bandwidth allocation that is suitable for UAV networks.  
Unlike macro base stations on the ground, the spacing between base stations in a UAV network is sparse at different times, and the overlap in coverage can be large. Therefore, it is necessary to reduce the interference power from neighboring UAVs. In this paper, we use frequency division as a method to reduce interference.  
In this section, we divide the frequency for each UAV to reduce the interference between neighboring UAVs. Although completely dividing the frequency among all UAVs is the best way to reduce interference, it is not practical because it sacrifices the frequency utilization efficiency. Therefore, we consider using frequency division only for UAVs that contain users suffering from co-channel interference, i.e., UAVs whose coverages overlap with the others. However, in the construction of a UAV network, the overlapping coverage areas of these UAVs dynamically change in both time and space due to the mobility of both UAVs and users, so a clear frequency division rule is necessary. The proposed rules in this paper are shown in Algorithm 1. The final objective of this algorithm is to efficiently distribute the available bandwidth to each UAV. To achieve this, bandwidth is distributed according to the number of users, starting with the UAV with the largest number of overlapping UAVs, and then the bandwidth used by each UAV is determined. By distributing the data in such a way, the overall data rate provided can be improved.  
Figure 3: Coverage circle without (**left**) or with (**right**) adaptation to the smallest-circle problem.  
Here, \(O_{k}\) is the number of UAVs overlapping the coverage of the \(k\)-th UAV and \(\{B_{\overline{k}}\}\) represents the set of bandwidths of UAVs that overlap with the \(k\)-th UAV's coverage and whose bandwidths have already been determined in prior steps. In addition, \(\varnothing\) represents an empty set, card() represents the number of elements in the set, and sort() performs the process of sorting in a descending manner. As an example, consider the case of \(k=3\) in Figure 4. For instance, let us assume the bandwidths of cell 1 and cell 2 have been determined, but the bandwidth of cell 6 is yet to be determined, so \(\{B_{\overline{k}=3}\}=\) [20 M,30 M]. In this case, since the condition in Process 5 is invalid, Process 8 is applied. Moreover, \(\{N_{\overline{k}}\}\) represents the set of the number of users accommodated by the UAVs that overlap with the \(k\)-th UAV's coverage and whose bandwidths are yet to be determined.  
Process 6 of Algorithm 1 allocates all the remaining bandwidth to the \(k\)-th UAV when the bandwidths of all the UAVs overlapping the \(k\)-th UAV have already been allocated to improve the frequency utilization efficiency. In Process 8, the remaining bandwidth is divided by the number of users of the overlapping UAVs that have not yet been allocated.