# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 2 Background and Related Works
Although a multi-UAV setup is considered in both [22] and [23], the trajectory optimisation approach may not be optimal in situations where the path or trajectory of the UAV is unknown beforehand or changes during the mission. In [24], an alternative solution to mitigate UAV-ground interference is proposed by utilizing intelligent reflective surfaces (IRSs) near gNBs for both downlink and uplink communications. The authors suggest an optimal passive beamforming design using IRSs for the downlink to minimise terrestrial interference with UAVs, and a hybrid linear and nonlinear interference coordination (IC) scheme in the uplink to handle the strong interference from UAVs. The authors further investigate the optimal performance of this scheme. However, it should be noted that IRSs are unsuitable for digital applications as they are designed based on the concept of analog beamforming. In [25], an interference-aware energy-efficient scheme is proposed to allow cellular-connected aerial users to reduce the interference they impose on terrestrial users while maximizing their energy efficiency and spectral efficiency. The optimization problem is formulated considering the key performance indicator (KPI) requirements for both aerial and terrestrial users, including energy efficiency, spectral efficiency, and interference. The problem is solved through the use of a deep Q-learning approach. However, it is critical to note the energy efficiency problem cannot be universally solved as it is dependent on various factors, such as mission design, UAV capabilities, and UAV size. In recent years, artificial intelligence (AI) has emerged as a critical technology for 5G-and-beyond wireless networks that include UAVs, as demonstrated in studies like [26]. This is primarily due to AI's capacity to handle complex problems and large amounts of data in system design and optimisation, making it a crucial tool for creating highly dynamic UAV communication networks. Conventional offline and model-driven trajectory design methods are limited in practical scenarios with variable traffic and open operating environments due to the requirement for accurate communication models and complete understanding of system parameters. However, with the use of deep reinforcement learning, UAVs can predict future network states in real time and adjust communication resource allocation and UAV trajectories accordingly. Additionally, AI-embedded UAVs can play a significant role in edge computing applications, where multiple UAVs can collaborate as aerial edge servers or edge devices for efficient data and computation offloading. Several research studies have investigated the application of DRL in the field of communications in recent years, as evidenced by publications such as [8; 27; 28; 29]. In particular, ref. [29] employs DRL for power regulation in millimeter-wave (mm-wave) transmission as an alternative to beamforming for enhancing non-line-of-sight (NLoS) performance. The authors employ DRL to tackle the power allocation problem with the objective of maximizing the aggregate rate of the user equipment (UE) while meeting quality and transmission power constraints. The proposed approach involves estimating the Q-function of the DRL problem using a convolutional neural network. In another work, ref. [30] introduces a policy framework that employs DQL to maximise the number of transmissions in a multi-channel access network. In [31], the authors propose a joint optimization approach that employs a Q-learning algorithm to optimise power control, interference coordination, and communication performance for end-users in a 5G wireless network, enhancing the downlink SINR in a multi-access Orthogonal Frequency Division Multiplexing (OFDM) network from a gNB with multiple antennas to UEs with a single antenna. Finally, ref. [32] uses DQL to develop a policy to maximise transmissions in a dynamic correlated multi-channel access scenario. It is important to note, however, that these studies only address interference in the context of mobile devices.