Millimeter-Wave Beamforming of UAV
Communications for Small Cell Coverage
Weizhi Zhong
1(&) , Lei Wang
1 , Qiuming Zhu
2(&) , Xiaomin Chen
2 ,
and Jianjiang Zhou
2
1 College of Astronautics, Nanjing University of Aeronautics and Astronautics,
Nanjing 210016, China
zhongwz@nuaa.edu.cn
2 College of Electronic and Information Engineering, NUAA,
Nanjing 210016, China
zhuqiuming@nuaa.edu.cn
Abstract. The unmanned aerial vehicle (UAV) communications in the
millimeter-wave (mmWave) band have found a wide range of concerns recently.
In order to improve the communication capacity of UAV cellular networks, a
low-complexity beam optimization method for the hybrid beamforming system
with uniform planar arrays (UPAs) is proposed in this paper. First, the target
cellular cell is quantified in spatial domain and the equivalent channel model of
quantified region is established. Then, the data rate is formulated and an ideal
precoding vector is achieved in terms of the expected beam gain. At last, a
hybrid precoding method based on dynamic dictionary learning orthogonal
matching pursuit (DDL-OMP) algorithm is introduced for producing an optimized beam. Simulation results demonstrate that our proposed method outperforms the traditional ones in achieving considerable capacity with faster
convergence speed.
Keywords: UAV Á mmWave communications Á UPA Á Beamforming Á Beam
coverage
1 Introduction
UAVs have found a wide range of applications during recent years, i.e., environmental
monitoring, post-disaster rescue, and communication relay [1, 2]. Under these scenarios, a UAV acts as an aerial base station (BS) and serves multiple ground users. Due
to the mobility of UAVs, the service quality of cellular cells can be improved and the
business of hotspot communities can be diverted [3].
The mmWave band has been adopted in UAV communications to achieve larger
capacity, but the severe path loss restricts its practical applications [4]. In order to
overcome this shortcoming, mmWave communication systems are usually equipped
with large-scale array antennas for beamforming [5].
Different from traditional terrestrial communications, UAV-based communication
systems face some new challenges. For example, more efficient beamforming training
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 281–288, 2020.
https://doi.org/10.1007/978-981-15-0187-6_32
Communications for Small Cell Coverage
Weizhi Zhong
1(&) , Lei Wang
1 , Qiuming Zhu
2(&) , Xiaomin Chen
2 ,
and Jianjiang Zhou
2
1 College of Astronautics, Nanjing University of Aeronautics and Astronautics,
Nanjing 210016, China
zhongwz@nuaa.edu.cn
2 College of Electronic and Information Engineering, NUAA,
Nanjing 210016, China
zhuqiuming@nuaa.edu.cn
Abstract. The unmanned aerial vehicle (UAV) communications in the
millimeter-wave (mmWave) band have found a wide range of concerns recently.
In order to improve the communication capacity of UAV cellular networks, a
low-complexity beam optimization method for the hybrid beamforming system
with uniform planar arrays (UPAs) is proposed in this paper. First, the target
cellular cell is quantified in spatial domain and the equivalent channel model of
quantified region is established. Then, the data rate is formulated and an ideal
precoding vector is achieved in terms of the expected beam gain. At last, a
hybrid precoding method based on dynamic dictionary learning orthogonal
matching pursuit (DDL-OMP) algorithm is introduced for producing an optimized beam. Simulation results demonstrate that our proposed method outperforms the traditional ones in achieving considerable capacity with faster
convergence speed.
Keywords: UAV Á mmWave communications Á UPA Á Beamforming Á Beam
coverage
1 Introduction
UAVs have found a wide range of applications during recent years, i.e., environmental
monitoring, post-disaster rescue, and communication relay [1, 2]. Under these scenarios, a UAV acts as an aerial base station (BS) and serves multiple ground users. Due
to the mobility of UAVs, the service quality of cellular cells can be improved and the
business of hotspot communities can be diverted [3].
The mmWave band has been adopted in UAV communications to achieve larger
capacity, but the severe path loss restricts its practical applications [4]. In order to
overcome this shortcoming, mmWave communication systems are usually equipped
with large-scale array antennas for beamforming [5].
Different from traditional terrestrial communications, UAV-based communication
systems face some new challenges. For example, more efficient beamforming training
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 281–288, 2020.
https://doi.org/10.1007/978-981-15-0187-6_32