UAV-to-UAV Communications in Cellular Networks
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Abstract (arXiv)
We consider a cellular network deployment where UAV-to-UAV (U2U) transmit-receive pairs share the same spectrum with the uplink (UL) of cellular ground users (GUEs). For this setup, we focus on analyzing and comparing the performance of two spectrum sharing mechanisms: (i) underlay, where the same time-frequency resources may be accessed by both UAVs and GUEs, resulting in mutual interference, and (ii)overlay, where the available resources are divided into orthogonal portions for U2U and GUE communications. We evaluate the coverage probability and rate of both link types and their interplay to identify the best spectrum sharing strategy. We do so through an analytical framework that embraces realistic height-dependent channel models, antenna patterns, and practical power control mechanisms. For the underlay, we find that although the presence of U2U direct communications may worsen the uplink performance of GUEs, such effect is limited as base stations receive the power-constrained UAV signals through their antenna sidelobes. In spite of this, our results lead us to conclude that in urban scenarios with a large number of UAV pairs, adopting an overlay spectrum sharing seems the most suitable approach for maintaining a minimum guaranteed rate for UAVs and a high GUE UL performance.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Dec 2019.
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Citing papers per year: 2022: 1; 2023: 3; 2024: 2.
6 papers.
- 2024Large Language Models for UAVs: Current State and Pathways to the Futurecited by 33
- 2022Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Controlcited by 3
- 2023Energy-Efficient Cellular-Connected UAV Swarm Control Optimizationcited by 1
- 2023Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy Environments Based on Deep Reinforcement Learningcited by 1
- 2023Decentralized federated learning methods for reducing communication cost and energy consumption in UAV networkscited by 0
- 2024The Future of Aerial Communications: A Survey of IRS-Enhanced UAV Communication Technologiescited by 0
- 2024The Future of Aerial Communications: A Survey of IRS-Enhanced UAV Communication Technologies2 Jun 2024
- 2024Large Language Models for UAVs: Current State and Pathways to the Future2 May 2024
- 2023Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy Environments Based on Deep Reinforcement Learning31 Aug 2023
- 2023Decentralized federated learning methods for reducing communication cost and energy consumption in UAV networks13 Apr 2023
- 2023Energy-Efficient Cellular-Connected UAV Swarm Control Optimization18 Mar 2023
- 2022Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control4 Nov 2022