# 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 proposed strategy outperforms the peak side-lobe mitigation strategy in terms of the total side-lobe level and the performance capacity. The proposed side-lobe mitigation strategy was an excellent candidate for implementation in CBF in feasible wireless sensor networks based on 3D-UAVs. However, the mobility of sensor nodes that affect the system power consumption was neglected in this strategy. In [165], the authors investigated a distribution algorithm for interference management in UAV HetNets two-tier downlink scenarios that influence the mobility of UAV, optimized ICIC and cell range expansion (CRE) methods. The simulation results demonstrated that a simple heuristic-based ICIC strategy beats the deep Q-learning-based ICIC strategy. Taking advantage of various optimization factors for interference coordination, the ICIC strategy based on heuristic can realize 5 pSE values that are relatively close to those obtained with comprehensive brute force search strategies, with significantly less complexity. Yet, the impact of Rician or Rayleigh fading that causes a decrease in the SE of the proposed scenario was neglected. In [166], the authors investigated interference management based on an artificial intelligence (AI) solution and used a single AI agent to model all MBSs and UABSs in the UAV HetNet two-tier downlink scenario. A greedy algorithm and an algorithm based on double deep Q-learning (DDQN) were proposed to compute the optimal FelCIC and eICIC criteria independently for all MBSs and UABSs, and the positions of UABSs, to optimize the mean and median SE. In comparison to traditional optimization methods, for the suggested algorithms, the greedy algorithm was able to obtain better performance in terms of mean and median SE, while the AI approach achieved 95.83 % and 93.46 % of the optimum mean and median SE respectively. However, the effect of Rician or Rayleigh fading that minimizes the SE of the proposed model was not taken into consideration. Co-channel Interference Schemes  
In [167], the co-channel interference in the industrial, scientific, and medical (ISM) frequency band 2.4 GHz between UAV and connected WLAN vehicles system was investigated according to the disturbing system's received SNR. The simulation results stated that if the height separation between the UAV and the WLAN-connected vehicles system is more than 6.2 km, the UAV elevation angle can guarantee that there is no co-channel interference with the WLAN-connected vehicles system in all statuses. Moreover, it was found that increasing the UAV elevation angle can mitigate the UAV interference with the WLAN-connected vehicles system. Nonetheless, this study neglected the variable altitude and elevation of the UAV that affect the received SNR of the proposed system. In [168], the authors investigated the joint unmanned aerial vehicles-ground user (UAV-GU) association, sub-channel allocation, and UAV track control issue for wireless networks based on UAVs with spectrum re-utilization and interference management to enhance the fairness of resource participation among ground users concerning the requests of their data transition and spectrum re-utilization. Furthermore, it was demonstrated that the deployed number of UAVs, the number of subchannels, and the maximum velocity of the UAV all have significant effects on the realized maximum-minimum average rate. Nevertheless, when the number of ground users and UAVs increased, the co-channel interference also increases, and this resulted in a decrease in the average data rate of the proposed method. In [169], the authors investigated the downlink interference problem of the UAV and internet of vehicles (IoVs) cooperative network that exists in the same region. In which the compatibility of frequency between UAVs and IoVs operating in the Ka-band is studied to quantify the separation distance necessary to avoid co-channel interference between UAVs and IoVs.