Energy Optimal Distributed Beamforming Using Unmanned Vehicles

In this paper, we consider a team of unmanned vehicles that are tasked with distributed transmit beamforming (virtual antenna array placement and design), in order to co-operatively transmit information to a remote station in realistic communication environments. We are interested in the energy-awar...

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Bibliographic Details
Published in:IEEE transactions on control of network systems 2018-12, Vol.5 (4), p.1529-1540
Main Authors: Muralidharan, Arjun, Mostofi, Yasamin
Format: Article
Language:English
Subjects:
Antenna arrays
Array signal processing
Beamforming
Communication-aware robotics
cooperative communication
Decision making
Distributed management
distributed robotic beamforming
energy aware
Energy consumption
Energy management
Energy transmission
mobile robots
multiagent systems
Optimization
Path planning
Robots
Signal to noise ratio
Transmitters
Unmanned vehicles
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Summary:In this paper, we consider a team of unmanned vehicles that are tasked with distributed transmit beamforming (virtual antenna array placement and design), in order to co-operatively transmit information to a remote station in realistic communication environments. We are interested in the energy-aware (both motion and communication energy) co-optimization of robotic paths and transmission powers for cooperative transmit beamforming under a reception quality requirement. We first consider the case where the channel is known. For this case, we propose an efficient approach for getting arbitrarily close to the optimum solution, which involves solving a series of multiple-choice knapsack problems. We then extend our analysis and methodology to the case where the channel is not known. The robots then probabilistically predict the channel at unvisited locations and integrate it with path planning and decision making for energy-aware distributed transmit beamforming. Finally, we extensively confirm our proposed approach with several simulation results with real channel parameters. Our results highlight the underlying trends of the optimum strategy and indicate considerable energy savings.
ISSN:2325-5870
2325-5870
2372-2533
DOI:10.1109/TCNS.2017.2732160