# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Unmanned Arial Vehicle (UAV)
#### 4.4.4 Related Work in UAVs
The simulation results stated that the noise and interference ratio between UAV and IoV is calculated under various steering angles between interference and interfered antenna and various altitude angles from UAV to geosynchronous orbit (GSO) satellite. Yet, the interference probability and duration that have a massive effect on the system gain were not considered in the calculation process of the proposed algorithm. In [170], the authors investigated the interference management in uplink wireless UAV-enabled information collection from a dispersed set of sensors in the scenario of IoT by using the mobility of several UAVs operating in the same band of frequency for the supporting network. According to the simulation results, the proposed algorithm with RA, and track optimization took at least 25% less time than previous dynamic orthogonal benchmark algorithms when deployed with four UAVs. Finally, a perceptible metric and associated concept for assessing the appropriateness of the suggested algorithm were presented, which can aid in the creation of a strategy for calculating the maximum number of UAVs that can be used in practice. However, this work did not take into consideration the altitude freedom of UAVs that can help to minimize interference and optimize the performance of the proposed system. Mutual Interference Schemes  
In [171], the idea of associating SCs with network flying platforms (NFPs) in HetNet, eliminating total interference and maintaining a minimum data rate requirement was formulated. The simulation results stated that the proposed algorithms return sub-optimal solutions with less complexity and minimum overall interference. However, in this study, the mobility and power consumption of NFPs, which affect the system's power consumption, were not considered. In [172], the authors investigated a transmission method based on TDMA in both up and downlinks scenarios to maximize the data rate between a BS and UE by using multiple relaying UAVs. A joint optimum strategy for 3D track design and power allocation was proposed to maximize the network's data rate while meeting the interference restriction. The simulation results demonstrated the effectiveness of the proposed strategy in optimizing the maximum flow and mitigating interference. Yet, in this strategy, the effect of the non-LoS path that has a massive effect on the system's data rate was neglected. In [173], a power optimization based on CoMP and clustering strategy was designed in a UAV-assisted network. The strategies of power allocation were then updated frequently until convergence is achieved, and the ultimate optimum PA result is acquired. The results demonstrated that the cluster size regularity, cluster number, and the limitation of minimum distance have large effects on interference mitigation. Nonetheless, when the number of ground users increased, the inter-cluster interference also increased, and this caused system data rate degradation. Furthermore, the mobility of ground users and UAVs that affect the power allocation of the proposed system was not taken into consideration. The summary of previous studies of UAVs is shown in Table 5