AirBeam: Experimental Demonstration of Distributed Beamforming by a Swarm of UAVs

Mohanti, Subhramoy; Bocanegra, Carlos; Meyer, J.; Secinti, Gokhan; Diddi, Mithun; Singh, Hanumant; Chowdhury, Kaushik R.

doi:10.1109/mass.2019.00028

Cited by 36 publications

(18 citation statements)

References 11 publications

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“…Distributed beamforming is an important enabler for leveraging high throughput and long range communications through flying UAVs, given their high probability of LoS links due to their altitude. Drawbacks in these scenarios such as inaccurate GPS signals, unpredictable UAV hovering, etc., create the need for accurate transmission synchronization between multiple UAVs through external sensor data input [132], in order to realize a practical distributed beamforming implementation for multi-UAV to ground [133] and UAV-UAV communications. [134] and [135] highlight the use of mmWave links for UAV-UAV and UAV-ground communication.…”

Section: Uav Communicationmentioning

confidence: 99%

Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard

Roy¹,

Salehi²,

Banou³

et al. 2022

Preprint

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Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguration. However, the effort so far has purely focused on learning from radio frequency (RF) signals. Future standards and next-generation (nextG) networks beyond 5G will have two significant evolutions over the state-of-the-art 5G implementations: (i) massive number of antenna elements, scaling up to hundreds-tothousands in number, and (ii) inclusion of AI/ML in the critical path of the network reconfiguration process that can access sensor feeds from a variety of RF and non-RF sources. While the former allows unprecedented flexibility in 'beamforming', where signals combine constructively at a target receiver, the latter enables the network with enhanced situation awareness not captured by a single and isolated data modality. This survey presents a thorough analysis of the different approaches used for beamforming today, focusing on mmWave bands, and then proceeds to make a compelling case for considering non-RF sensor data from multiple modalities, such as LiDAR, Radar, GPS for increasing beamforming directional accuracy and reducing processing time. This so called idea of multimodal beamforming will require deep learning based fusion techniques, which will serve to augment the current RF-only and classical signal processing methods that do not scale well for massive antenna arrays. The survey describes relevant deep learning architectures for multimodal beamforming, identifies computational challenges and the role of edge computing in this process, dataset generation tools, and finally, lists open challenges that the community should tackle to realize this transformative vision of the future of beamforming.

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Section: Uav Communicationmentioning

confidence: 99%

Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard

Roy¹,

Salehi²,

Banou³

et al. 2022

Preprint

Self Cite

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“…However, several research results have proved the advantages of DBF for distributed wireless networks in terrestrial communications. Results such as the increment of the transmission range [218] and the SNR [219], as well as the experimental demonstration of DBF by a swarm of UAV [220] could be extrapolated to DSS. Future research in the field should focus on the synchronization algorithms to perform DBF with small satellites systems.…”

Section: Distributed Beamforming As An Enabler For Distributed Satell...mentioning

confidence: 99%

Architectures and Synchronization Techniques for Distributed Satellite Systems: A Survey

Marrero¹,

Duncan²,

Querol³

et al. 2022

Preprint

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CDSS is a key enabling technology for the future of remote sensing and communication missions. However, they have to meet strict synchronization requirements before their use is generalized. When clock or local oscillator signals are generated locally at each of the distributed nodes, achieving exact synchronization in absolute phase, frequency, and time is a complex problem. In addition, satellite systems have significant resource constraints, especially for small satellites, which are envisioned to be part of the future CDSS. Thus, the development of precise, robust, and resource-efficient synchronization techniques is essential for the advancement of future CDSS. In this context, this survey aims to summarize and categorize the most relevant results on synchronization techniques for DSS. First, some important architecture and system concepts are defined. Then, the synchronization methods reported in the literature are reviewed and categorized. This article also provides an extensive list of applications and examples of synchronization techniques for DSS in addition to the most significant advances in other operations closely related to synchronization, such as inter-satellite ranging and relative position. The survey also provides a discussion on emerging data-driven synchronization techniques based on ML. Finally, a compilation of current research activities and potential research topics is proposed, identifying problems and open challenges that can be useful for researchers in the field.

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“…The growing commercialization of UAVs, along with their greater affordability and increasing popularity, has recently spawned the interest of telecom operators and equipment providers toward the use of drones for networking applications. Examples of such applications include the use of UAVs to create swarm of flying networks [3,29], to promptly aid in the aftermath of disaster scenarios [30][31][32], and to bring connectivity to remote areas [33,34]. However, security concerns, strict flying regulations, and the cost of the equipment itself impose additional challenges to the development and testing of solutions at scale.…”

Section: Unmanned Aerial Vehiclesmentioning

confidence: 99%

Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation

Bonati¹,

Johari²,

Polese³

et al. 2021

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Colosseum is an open-access and publicly-available large-scale wireless testbed for experimental research via virtualized and softwarized waveforms and protocol stacks on a fully programmable, "white-box" platform. Through 256 state-of-theart Software-defined Radios and a Massive Channel Emulator core, Colosseum can model virtually any scenario, enabling the design, development and testing of solutions at scale in a variety of deployments and channel conditions. These Colosseum radiofrequency scenarios are reproduced through high-fidelity FPGAbased emulation with finite-impulse response filters. Filters model the taps of desired wireless channels and apply them to the signals generated by the radio nodes, faithfully mimicking the conditions of real-world wireless environments. In this paper we describe the architecture of Colosseum and its experimentation and emulation capabilities. We then demonstrate the effectiveness of Colosseum for experimental research at scale through exemplary use cases including prevailing wireless technologies (e.g., cellular and Wi-Fi) in spectrum sharing and unmanned aerial vehicle scenarios. A roadmap for Colosseum future updates concludes the paper.

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AirBeam: Experimental Demonstration of Distributed Beamforming by a Swarm of UAVs

Cited by 36 publications

References 11 publications

Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard

Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard

Architectures and Synchronization Techniques for Distributed Satellite Systems: A Survey

Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation

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