# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 4 5G, UAV, and gNB Functionality
### gNB Functionality  
The primary component of the 5G-RAN architecture is the gNB, which stands for \"Next Generation NodeB\". The gNB provides the air interface for user devices to connect to the 5G network and manages the radio resources used for wireless communication. The flight controller unit reports SINR values, while the Global Positioning System (GPS) module reports coordinates. Ground controls primarily perform tasks such as controlling the UAV's attitude, displaying and manipulating payload data, planning missions, tracking the UAV's position, displaying routes on maps, positioning navigational targets, and maintaining communication links with other subsystems. According to the DQL algorithm proposed in  
Figure 3: 5G-software-based architecture.  
this work, the gNB receives coordinates and receives SINR values from the UAV to reduce interference. Figure 4 represents the system model used in this work, which shows the RAN network linked to the MEC architecture and core network.  
MEC is a key technology in the 5G network architecture that enables the computation of traffic, processes, and services to be moved from centralised data centers to the edge of the network, closer to the end-users. By processing data and running applications closer to the RAN, MEC can significantly reduce latency, improve network performance, and enable new high-bandwidth, low-latency applications that were not possible with earlier mobile network technologies. Figure 5 depicts the MEC architecture as per the 3 GPP standard [41]. It consists of a MEC orchestrator and MEC host. The MEC orchestrator interacts with the NEF, while the MEC host is deployed in the data network of the 5G system. The NEF can also be deployed at the edge to enable low-latency and high-throughput service access. The proposed DQL algorithm is implemented in the MEC host along with other applications on the application platform.  
Figure 4: Interference mitigation system model.  
Figure 5: MEC cloud architecture.  
Figure 6 shows the functional blocks of the system model from Figure 4. This is divided into three sections: (i) RAN: within the RAN, a UAV is being served by 1 gNB and receives an interfering signal from a non-associated gNB. The UAVs are primarily served by the side lobes of gNB antennas as the main lobes are down-tilted for optimal support to mobile devices. This also shows the structure of the gNB and its different components. (ii) Cloud location: the gNBs in RAN are linked to the cloud architecture, where the proposed DQL is deployed. The MEC architecture block was shown in Figure 5. It receives the SINR and location parameters from the gNB and computes the optimal power control commands and sends them back to the gNB. The gNB, in turn, sends these to the UAVs and thus interference is mitigated. Further, the cloud is linked to the core network. (iii) Core network: the core network is linked to the RAN via the cloud architecture and provides support through the virtualised functions as discussed in Section 4.1.  
This paper proposes a solution to the issue of interference in 5G networks caused by non-associated gNB for UAVs. The solution involves the implementation of a DQL algorithm within a cloud architecture. The algorithm is designed to enhance the SINR of UAVs and reduce interference by modifying the functions of the gNB. The decision making process takes place at a central location, where data from UAVs are transmitted over the backhaul to the central hub, which is then connected to the 5G core network.