Deep Learning Based Robust Beamforming for UAV Communication System

Machine learning (ML) has been broadly connected to the upper layers of communication systems for different purposes, for example, arrangement of cognitive radio and communication network. Nevertheless, its application to the physical layer is hindered by complex channel conditions and constrained learning capacity of regular ML algorithms. Deep learning (DL) has been as of late connected for some fields, for example, computer vision and normal dialect preparing, given its expressive limit and advantageous enhancement ability. This paper describes about a novel use of DL for the physical layer. By deciphering a communication system as an auto encoder, we build up an essential better approach to consider communication system outline as a conclusion to-end reproduction undertaking that tries to together enhance transmitter and receiver in a solitary procedure. This DL based technique demonstrates promising execution change than traditional communication system.

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Deep Learning Network for Multiuser Detection in Satellite Mobile Communication System

Computational Intelligence and Neuroscience ◽

10.1155/2019/8613639 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Guan Qing yang ◽

Wu Shuang ◽

He Ya-Ru

Keyword(s):

Deep Learning ◽

Multiuser Detection ◽

Mobile Communication ◽

Communication System ◽

Interference Cancellation ◽

Multipath Fading ◽

Mobile Communication System ◽

Multipath Fading Channel ◽

Learning Network ◽

Deep Learning Network

A multiuser detection (MUD) algorithm based on deep learning network is proposed for the satellite mobile communication system. Due to relative motion between the satellite and users, multiple access interference (MUI) introduced by multipath fading channel reduces system performance. The proposed MUD algorithm based on deep learning network firstly establishes the CINR optimal loss function according to the multiuser access mode and then obtains the best multiuser detection weight through the steepest gradient iteration. Multilayer nonlinear learning obtains interference cancellation sharing weights to achieve maximum signal-to-noise ratio through gradient iteration, which is superior than the traditional serial interference cancellation algorithm and parallel interference cancellation algorithm. Then, the weights with multiuser detection through multilayer network forward learning iteration are obtained with traditional multiuser detecting quality characteristics. The proposed multiuser access detection based on deep learning network algorithm improves the MUD accuracy and reduces the number of traditional multiusers. The performance of the satellite multifading uplink system shows that the proposed deep learning network can provide high precision and better iteration times.

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A Design of a Developable Automatic Avoidance System of UAV Based on ADS-B

Wireless Communications and Mobile Computing ◽

10.1155/2021/3072606 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Xuzheng Zhang ◽

Yifei Meng ◽

Chenxiao Mao ◽

Yaohua Xu ◽

Na Bai

Keyword(s):

Deep Learning ◽

Communication System ◽

Short Distance ◽

Learning Algorithm ◽

Route Planning ◽

Base Station ◽

Long Distance ◽

Deep Learning Algorithm ◽

Satellite Platform ◽

Matching Mechanism

There are two primary defects in the existing UAV avoidance systems: the system is memoryless; airborne radars are used to detect long-distance barriers, which are unreliable and expensive. The paper adopts the deep learning algorithm and ADS-B communication system based on a satellite base station to solve the above problems. It divides the avoidance problem into two parts: short-distance obstacle avoidance and long-distance route planning. On the one hand, the system establishes the knowledge base storing the previous avoidance experience and the matching mechanism, realizing the correspondence between input and experience through a deep learning algorithm. They can dramatically improve the reaction speed and safety of UAVs. On the other hand, the system realizes the interconnection between UAV and the satellite base station through the ADS-B communication system to replace the radars, putting the task of route planning on the satellite platform. Therefore, the satellite can achieve large-scale and all-weather detection to improve the overall safety of UAVs depending on its high and long-range characteristics. The paper also illustrates the design elements of the RF baseband integrated ADS-B transceiver and the simulation performance of the short-distance avoidance system in the end, whose results show that the system can be applied to dense obstacle environments and significantly improve the security of UAVs in a complex domain.

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Study on Deep Learning Political Culture Communication System in Universities under the Perspective of Postmodern Media

2017 International Conference on Robots & Intelligent System (ICRIS) ◽

10.1109/icris.2017.85 ◽

2017 ◽

Author(s):

Yan Luo ◽

Jing Chen ◽

Shuhui Ren ◽

Lan Luo ◽

Tianlin Chen

Keyword(s):

Deep Learning ◽

Political Culture ◽

Communication System

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Deep Learning Driven 3D Robust Beamforming for Secure Communication of UAV Systems

IEEE Wireless Communications Letters ◽

10.1109/lwc.2021.3075996 ◽

2021 ◽

pp. 1-1

Author(s):

Runze Dong ◽

Buhong Wang ◽

Kunrui Cao

Keyword(s):

Deep Learning ◽

Secure Communication ◽

Robust Beamforming

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

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01854-7 ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Ziyao Hong ◽

Ting Li ◽

Fei Li

Keyword(s):

Deep Learning ◽

Communication System ◽

Massive Mimo ◽

Multiple Input Multiple Output ◽

The Other ◽

Time Varying ◽

Hybrid Precoding ◽

Multiple Input ◽

Input Multiple Output ◽

Aerial Vehicle

Abstract 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.

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