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1. Ensuring reliable connectivity to cellular-connected UAVs with up-tilted antennas and interference coordination

   https://doi.org/10.52953/LEFT7402  

   Uddin, Md Moin; Guvenc, Ismail; Saad, Walid; Bhuyan, Arupjyoti (January 2021, ITU Journal on Future and Evolving Technologies)

   To integrate unmanned aerial vehicles (UAVs) in future large-scale deployments, a new wireless communication paradigm, namely, the cellular-connected UAV has recently attracted interest. However, the line-of-sight dominant air-to-ground channels along with the antenna pattern of the cellular ground base stations (GBSs) introduce critical interference issues in cellular-connected UAV communications. In particular, the complex antenna pattern and the ground reflection (GR) from the down-tilted antennas create both coverage holes and patchy coverage for the UAVs in the sky, which leads to unreliable connectivity from the underlying cellular network. To overcome these challenges, in this paper, we propose a new cellular architecture that employs an extra set of co-channel antennas oriented towards the sky to support UAVs on top of the existing down-tilted antennas for ground user equipment (GUE). To model the GR stemming from the down-tilted antennas, we propose a path-loss model, which takes both antenna radiation pattern and configuration into account. Next, we formulate an optimization problem to maximize the minimum signal-to-interference ratio (SIR) of the UAVs by tuning the up-tilt (UT) angles of the up-tilted antennas. Since this is an NP-hard problem, we propose a genetic algorithm (GA) based heuristic method to optimize the UT angles of these antennas. After obtaining the optimal UT angles, we integrate the 3GPP Release-10 specified enhanced inter-cell interference coordination (eICIC) to reduce the interference stemming from the down-tilted antennas. Our simulation results based on the hexagonal cell layout show that the proposed interference mitigation method can ensure higher minimum SIRs for the UAVs over baseline methods while creating minimal impact on the SIR of GUEs. 

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2. Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learning

   https://doi.org/10.1109/CCNC.2019.8651796  

   Shamsoshoara, Alireza; Khaledi, Mehrdad; Afghah, Fatemeh; Razi, Abolfazl; Ashdown, Jonathan (February 2019, 16th IEEE Annual Consumer Communications & Networking Conference (CCNC))

   In this paper, we develop a distributed mechanism for spectrum sharing among a network of unmanned aerial vehicles (UAV) and licensed terrestrial networks. This method can provide a practical solution for situations where the UAV network may need external spectrum when dealing with congested spectrum or need to change its operational frequency due to security threats. Here we study a scenario where the UAV network performs a remote sensing mission. In this model, the UAVs are categorized to two clusters of relaying and sensing UAVs. The relay UAVs provide a relaying service for a licensed network to obtain spectrum access for the rest of UAVs that perform the sensing task. We develop a distributed mechanism in which the UAVs locally decide whether they need to participate in relaying or sensing considering the fact that communications among UAVs may not be feasible or reliable. The UAVs learn the optimal task allocation using a distributed reinforcement learning algorithm. Convergence of the algorithm is discussed and simulation results are presented for different scenarios to verify the convergence. 

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3. Interference Analysis and Mitigation for UAV Communications in Drone Corridors

   https://doi.org/10.1109/WPMC59531.2023.10338970  

   Ellis, Cameron; Zhang, Minglong; Marojevic, Vuk (November 2023, IEEE)

   Unmanned aerial vehicles (UAVs) have witnessed widespread adoption in the modern world, with their development set to continue into the future. As UAV technology and applications advance, it becomes imperative to understand their communication capabilities. UAVs experience distinct radio propagation conditions compared to ground-based radio nodes, necessitating a critical investigation into aerial radio node performance. This paper analyzes interference in UAV-to-UAV (U2U) communications within drone corridors and proposes an interference mitigation strategy utilizing millimeter wave (mmWave) beamforming. Employing a semi-persistent scheduling approach from the Third Generation Partnership Project (3GPP) sidelink communications for low altitude aerial nodes in drone corridors, the study primarily examines interference from drone clusters within designated air corridors. To assess U2U communication performance, a 3GPP standard-compliant cross-layer simulator is developed. Simulation results demonstrate that employing mmWave beamforming instead of isotropic transmission substantially reduces interference, leading to higher communications reliability and enabling more UAVs to occupy and communicate in the airspace. 

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4. Deep Reinforcement Learning for Interference Management in UAV-Based 3D Networks: Potentials and Challenges

   https://doi.org/10.1109/MCOM.001.2200973  

   Vaezi, Mojtaba; Lin, Xingqin; Zhang, Hongliang; Saad, Walid; Poor, H Vincent (February 2024, IEEE Communications Magazine)

   Modern cellular networks are multi-cell and use universal frequency reuse to maximize spectral efficiency. This results in high inter-cell interference. This challenge is growing as cellular networks become three-dimensional with the adoption of unmanned aerial vehicles (UAVs). This is because the strength and number of interference links rapidly increase due to the line-of-sight channels in UAV communications. Existing interference management solutions require each transmitter to know the channel information of interfering signals, rendering them impractical due to excessive signaling overhead. In this article, we propose leveraging deep reinforcement learning for interference management to tackle this shortcoming. In particular, we show that interference can still be effectively mitigated even without knowing its channel information. We then discuss novel approaches to scale the algorithms with linear/sublinear complexity and decentralize them using multi-agent reinforcement learning. By harnessing interference, the proposed solutions enable the continued growth of civilian UAVs. 

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5. Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach

   https://doi.org/10.1109/WCNC51071.2022.9771893  

   Abdalla, Aly Sabri; Behfarnia, Ali; Marojevic, Vuk (April 2022, 2022 IEEE Wireless Communications and Networking Conference (WCNC))

   The unmanned aerial vehicle (UAV) is one of the technological breakthroughs that supports a variety of services, including communications. UAVs can also enhance the security of wireless networks. This paper defines the problem of eavesdropping on the link between the ground user and the UAV, which serves as an aerial base station (ABS). The reinforcement learning algorithms Q-learning and deep Q-network (DQN) are proposed for optimizing the position of the ABS and the transmission power to enhance the data rate of the ground user. This increases the secrecy capacity without the system knowing the location of the eavesdropper. Simulation results show fast convergence and the highest secrecy capacity of the proposed DQN compared to Q-learning and two baseline approaches. 

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