# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 2 Background and Related Works  
The utilization of UAVs for meeting the growing communication needs has been widely advocated. Despite this, operation of UAVs at high altitude would enable LoS links to be significantly pervasive within the viable communication channels; as a result, a UAV has a high likelihood of communicating with both associated gNBs and interfering gNBs simultaneously. In the case of ground users, who are served by the primary lobe of the gNB antenna, an escalation in pathloss typically correlates with a reduction in interfering signals. However, when UAVs are served by the side lobes of the gNB antenna, the relationship between increasing pathloss and interference becomes less straightforward. This complexity arises because side lobes have the potential to introduce unintended coverage areas and additional propagation paths, thereby giving rise to interference with other systems or gNBs. Consequently, even when the main lobe signal strength is weakened due to pathloss, these side lobes can still generate interference. This scenario increases the likelihood of interference as compared to ground-based users, posing a significant challenge for interference management in cellular networks that include UAVs. Several techniques have been proposed in extant literature to mitigate interference in terrestrial networks, including inter-cell interference coordination (ICIC) [13; 14] and coordinated multi-point (CoMP) transmission [15; 16]. However, these techniques prove inadequate in handling new interference challenges arising from UAVs and the emergence of LoS-dominated air-ground links at high altitudes. ICIC is a radio resource management technique used to enhance spectral efficiency. This is achieved by imposing constraints on the management block to improve favourable channel conditions for a subset of users significantly affected by interference. The coordination of resources may be achieved through fixed, adaptive, or real-time approaches, supported by additional inter-cell signalling, with the frequency adjusted based on specific requirements. A cooperative interference cancellation technique is proposed in [17] to minimise the sum rate to available gNBs and eliminate co-channel interference at each occupied gNB for multi-beam UAV uplink communication. Although similar techniques have been extensively studied in terrestrial cellular networks [18], they are not effective in mitigating strong UAV interference, leading to limited frequency reuse in the network and lower spectral efficiency for both UAVs and ground users [19]. In [20], the interference in directional UAV networks is characterised using stochastic geometry, considering UAVs equipped with directional antennas and situated in a 3D environment. This work provides a comprehensive analysis of the interference in directional UAV networks, but no strategies are presented for mitigating it. The approach of employing a path planning or trajectory design to avoid or minimise interference is a widely adopted strategy for interference management. In the paper [21], the authors consider a relay-assisted UAV network overlaid with an existing network and propose a joint power and 3D trajectory design approach to reposition the UAVs in 3D space to circumvent interference. They propose a joint optimisation solution based on spectral graph theory and convex optimisation to address the problem of 3D trajectory design and power allocation. The simulation results demonstrate that the proposed algorithm improves the maximum flow and reduces undesirable interference to the coexisting network [22]. The authors of [23] explore the joint trajectory and power control (TPC) design problem for multi-user UAV-enabled interference channels (UAV-IC). As the TPC problem is NP-hard and involves a significant number of optimisation variables, the authors propose effective sub-optimal algorithms.