Self-organizing flying drones with massive MIMO networking

This article studies distributed algorithms to con-trol self-organizing swarm drone hotspots with massive MIMO networking capabilities - a network scenario referred to as OrgSwarm. We attempt to answer the following fundamental question: what is the optimal way to provide spectrally-efficient wirele...

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Bibliographic Details
Published in:2018 17th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net) pp. 1 - 8
Main Authors: Guan, Zhangyu, Cen, Nan, Melodia, Tommaso, Pudlewski, Scott
Format: Conference Proceeding
Language:English
Published: IFIP 01.06.2018
Subjects:
Antennas
Channel estimation
Drones
Interference
MIMO communication
Signal to noise ratio
Wireless communication
Online Access:Get full text
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Summary:This article studies distributed algorithms to con-trol self-organizing swarm drone hotspots with massive MIMO networking capabilities - a network scenario referred to as OrgSwarm. We attempt to answer the following fundamental question: what is the optimal way to provide spectrally-efficient wireless access to a multitude of ground nodes with mobile base stations/aerial relays mounted on a swarm of drones and endowed with a large number of antennas; when we can control the position of many-antenna-enabled drones, access association of ground nodes to drones, and the transmit power of ground nodes? The article first derives a mathematical formulation of the problem of spectral efficiency maximization through joint control of the movement of many-antenna-enabled aerial drones, access association of single-antenna ground nodes to many-antenna drones, and transmit power of ground nodes. It is shown that the resulting network control problem is a mixed integer nonlinear nonconvex programming problem (MINLP). We then first design a distributed solution algorithm with polynomial computational complexity. Then, a centralized but globally optimal solution algorithm is designed based on a combination of the branch and bound framework and convex relaxation techniques to provide a performance benchmark for the distributed algorithm. Results indicate that the distributed algorithm achieves a network spectral efficiency very close (over 95% on average) to the global optimum.
DOI:10.23919/MedHocNet.2018.8407088