Interference Mitigation for 5G-Connected UAV using Deep Q-Learning Framework

Abstract

To boost large-scale deployment of unmanned aerial vehicles (UAVs) in the future, a new wireless communication paradigm namely cellular-connected UAVs has recently received an upsurge of interest in both academia and industry. Fifth generation (5G) networks are expected to support this largescale deployment with high reliability and low latency. Due to the high mobility, speed, and altitude of the UAVs there are numerous challenges that hinder its integration with the 5G architecture. Interference is one of the major roadblocks to ensuring the efficient co-existence between UAVs and terrestrial users in 5G networks. Conventional interference mitigation schemes for terrestrial networks are insufficient to deal with the more severe air-ground interference, which thus motivates this paper to propose a new algorithm to mitigate interference. A deep Q-learning (DQL) based algorithm is developed to mitigate interference intelligently through power control. The proposed algorithm formulates a non-convex optimization problem to maximize the Signal to Interference and Noise Ratio (SINR) and solves it using DQL. Its performance is measured as effective SINR against the complement cumulative distribution function. Further, it is compared with an adaptive link technique: Fixed Power Allocation (FPA), a standard power control scheme and tabular Q-learning(TQL). It is seen that the FPA has the worst performance while the TQL performs slightly better. This is since power control and interference coordination are introduced but not as effectively in the TQL method. It is observed that DQL algorithm outperforms the TQL implementation. To solve the severe air-ground interference experienced by the UAVs in 5G networks, this paper proposes a DQL algorithm. The algorithm effectively mitigates interference by optimizing SINR of the air-ground link and outperforms the existing methods. This paper therefore, proposes an effective algorithm to resolve the interference challenge in air-ground links for 5G-connected UAVs.

Reinforcement Learning-based Joint Interference Mitigation and Resource Allocation in Dense Beyond 5G Heterogeneous Networks

“…However, While the method mitigated interference, it primarily did in a heuristic manner, with its primary focus on energy conservation as the main objective. The authors of [17] presented an algorithm in their study that leverages DQL for intelligent interference mitigation. This approach focused on power control and aimed to solve a non-convex optimization problem to maximize the SINR using DQL techniques without the need for knowledge of CSI.…”

“…The above literature showed that some existing approaches [1,9,10,12,14,16,18] tend to propose heuristic methods that lack a strong mathematical foundation. Some studies [1,9,10,17] have overlooked the crucial aspect of ensuring the minimum QoS for CEUEs. While certain researchers [15] have delved into complex MIMO systems and tackled the intricate problem of beamforming, there is still room for investigations in scenarios involving single-antenna systems.…”

Future 6G Communications Powering Vertical Handover in Non-Terrestrial Networks

“…To achieve a vertical seamless HO for a network as shown in figure 1, efficient techniques and algorithms must be devised. As detailed in further sections, reliable solutions exist for ground based vertical HO for UAVs [3], [4], [5]. However, air-to-space remains unexplored for UAV networks.…”

“…There are 5 elements that enable a seamless HO between the future next generation NodeB (gnB) and satellite in a future 6G network: the UAV, source future gNB, target satellite, the Access and Mobility function (AMF) and the User Plane Function (UPF). The functions of the AMF and UPF are described in [3]. preparing the different elements to carry out the process.…”

A Systematic Review of Interference Mitigation Techniques in Current and Future UAV-Assisted Wireless Networks

“…A power control and interference mitigation method based on DRL was created in [78]. This method was deployed in a cloud location and gets UAV measurements via the backhaul using the cloud-based architecture of 5G platforms.…”