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Beam selection assisted UAV-BS deployment and trajectory for beamspace MmWave systems

Beam selection assisted UAV-BS deployment and trajectory for beamspace MmWave systems
Beam selection assisted UAV-BS deployment and trajectory for beamspace MmWave systems
Exploiting unmanned aerial vehicles (UAVs) as base stations (UAV-BS) can enhance capacity, coverage, and energy efficiency of wireless communication networks. To fully realize this potential, millimeter wave (mmWave) technology can be exploited with UAV-BS to form mmWave UAV-BS. The major difficulty of mmWave UAV-BS, however, lies in the limited energy of UAV-BS and the multiuser interference (MUI). Beam division multiple access with orthogonal beams can be employed to alleviate the MUI. Since each user has dominant beams around the line of sight direction, beam selection can reduce the power consumption of radio frequency chain. In this paper, we formulate the problem of maximizing the sum rate of all users by optimizing the beam selection for beamspace and UAV-BS deployment in mmWave UAV-BS system. This nonconvex problem is solved in two steps. First, we propose a signal to interference plus noise ratio based greedy beam selection scheme to ensure that all the ground users in the given area can be served by the UAV-BS, where a zero-forcing precoding scheme is used to eliminate the MUI. Then, we utilize the continuous genetic algorithm to find the optimal UAV-BS deployment and beam pattern to maximize the sum rate of all users. Moreover, considering the mobility of the UAV-BS, the UAV-BS trajectory and beam selection for beamspace are optimized in the mmWave UAV-BS system. The simulation results demonstrate the effectiveness of the proposed design for the mmWave UAV-BS system.
1-21
Zuo, Xingxuan
2ee08871-e945-4d01-b8d5-02c83360dd58
Zhang, Jiankang
a6180135-e2c5-47e5-84e6-8945f73a4c90
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Mu, Xiaomin
fe1a8744-0002-4ba2-8604-da4d8b1d63f7
Zuo, Xingxuan
2ee08871-e945-4d01-b8d5-02c83360dd58
Zhang, Jiankang
a6180135-e2c5-47e5-84e6-8945f73a4c90
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Mu, Xiaomin
fe1a8744-0002-4ba2-8604-da4d8b1d63f7

Zuo, Xingxuan, Zhang, Jiankang, Chen, Sheng and Mu, Xiaomin (2021) Beam selection assisted UAV-BS deployment and trajectory for beamspace MmWave systems. Wireless Communications and Mobile Computing Journal, 2021, 1-21, [1363586]. (doi:10.1155/2021/1363586).

Record type: Article

Abstract

Exploiting unmanned aerial vehicles (UAVs) as base stations (UAV-BS) can enhance capacity, coverage, and energy efficiency of wireless communication networks. To fully realize this potential, millimeter wave (mmWave) technology can be exploited with UAV-BS to form mmWave UAV-BS. The major difficulty of mmWave UAV-BS, however, lies in the limited energy of UAV-BS and the multiuser interference (MUI). Beam division multiple access with orthogonal beams can be employed to alleviate the MUI. Since each user has dominant beams around the line of sight direction, beam selection can reduce the power consumption of radio frequency chain. In this paper, we formulate the problem of maximizing the sum rate of all users by optimizing the beam selection for beamspace and UAV-BS deployment in mmWave UAV-BS system. This nonconvex problem is solved in two steps. First, we propose a signal to interference plus noise ratio based greedy beam selection scheme to ensure that all the ground users in the given area can be served by the UAV-BS, where a zero-forcing precoding scheme is used to eliminate the MUI. Then, we utilize the continuous genetic algorithm to find the optimal UAV-BS deployment and beam pattern to maximize the sum rate of all users. Moreover, considering the mobility of the UAV-BS, the UAV-BS trajectory and beam selection for beamspace are optimized in the mmWave UAV-BS system. The simulation results demonstrate the effectiveness of the proposed design for the mmWave UAV-BS system.

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Accepted/In Press date: 17 September 2021
Published date: 30 September 2021
Additional Information: Publisher Copyright: © 2021 Xingxuan Zuo et al.

Identifiers

Local EPrints ID: 451484
URI: http://eprints.soton.ac.uk/id/eprint/451484
PURE UUID: 2858d8e6-4a5a-466e-917d-13469a01c49c

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Date deposited: 30 Sep 2021 16:34
Last modified: 23 Jul 2022 02:22

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Contributors

Author: Xingxuan Zuo
Author: Jiankang Zhang
Author: Sheng Chen
Author: Xiaomin Mu

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