Deep double-pilot-based hybrid precoding in UAV-enabled mmWave massive MIMO

EURASIP Journal on Wireless Communications and Networking

Ziyao Hong¹  Ting Li¹  Fei Li¹ 

[1] College of Telecommunication and Information Engineering, Nanjing University of Post and Telecommunication, Xinmofan Road No. 66, 210003, Nanjing, People’s Republic of China;

关键词: UAV;    Time-varying;    Massive MIMO;    Millimeter wave;    Lens model;    Deep learning;    Long- and short-term memory (LSTM);   

DOI  :  10.1186/s13638-020-01854-7

来源: Springer

【 摘 要 】

Unmanned aerial vehicle (UAV)-enabled communication system provides flexibility and reliability compared to conventional ones. Millimeter wave (mmWave) and massive multiple-input–multiple-output (MIMO) have widely been researched since recent years, which are promising techniques for the next and even the later generation communication system. Hybrid precoding, as a method to reduce the high cost in hardware and power brought by massive antenna array, develops fiercely and is often combined to deep learning, a kind of popular optimization tool, which brings an overwhelming performance. On the other hand, there are not so many attentions about the hybrid precoding in time-varying mmWave massive MIMO, which is necessary to be considered in a UAV-enabled communication scenario because the performance will degrade seriously if the channel changed while the transmitter and receiver use the precoding matrix corresponding to the expired channel, yet. In this paper, we propose a double-pilot-based hybrid precoding system, which completes analog precoding and digital precoding separately—predicting the previous one using deep learning structure and updating equivalent channel frequently for the post one by enhancing the frequency of equivalent channel estimation.

【 授权许可】

CC BY