Sensing and Communication Integrated Fast Neighbor Discovery for UAV Networks

doi:10.12142/ZTECOM.202403009

ZTE Communications ›› 2024, Vol. 22 ›› Issue (3): 69-82.DOI: 10.12142/ZTECOM.202403009

• Special Topic • 上一篇下一篇

收稿日期:2024-08-06 出版日期:2024-09-25 发布日期:2024-09-29

Sensing and Communication Integrated Fast Neighbor Discovery for UAV Networks

WEI Zhiqing(), ZHANG Yongji, JI Danna, LI Chenfei

Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2024-08-06 Online:2024-09-25 Published:2024-09-29
About author:WEI Zhiqing ( weizhiqing@bupt.edu.cn) received his BE and PhD degrees from Beijing University of Posts and Telecommunications (BUPT), China in 2010 and 2015, respectively. He is an associate professor with BUPT. He has authored one book, three book chapters, and more than 50 papers. His research interest is the performance analysis and optimization of intelligent machine networks. He was granted the Exemplary Reviewer of IEEE Wireless Communications Letters in 2017, the Best Paper Award of WCSP 2018. He was the Registration Co-Chair of IEEE/CIC ICCC 2018 and the Publication Co-Chairs of IEEE/CIC ICCC 2019 and IEEE/CIC ICCC 2020.
ZHANG Yongji is an undergraduate student studying at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), China. His research interests include integrated perception, communication, computing, machine learning, etc.
JI Danna received her BE degree from University of Science and Technology Beijing (USTB), China and ME degree from the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), China. Her research interests include wireless communication networking, joint radar and communication, etc.
LI Chenfei received her BE degree from Nankai University (NKU), China in 2021 and master's degree from Beijing University of Posts and Telecommunications (BUPT), China in 2024. Her research interests include integrated sensing and communication neighbor discovery and positioning.
Supported by:
the Fundamental Research Funds for the Central Universities(2024ZCJH01);the National Natural Science Foundation of China (NSFC)(62271081);the National Key Research and Development Program of China(2020YFA0711302)

摘要/Abstract

Abstract:

In unmanned aerial vehicle (UAV) networks, the high mobility of nodes leads to frequent changes in network topology, which brings challenges to the neighbor discovery (ND) for UAV networks. Integrated sensing and communication (ISAC), as an emerging technology in 6G mobile networks, has shown great potential in improving communication performance with the assistance of sensing information. ISAC obtains the prior information about node distribution, reducing the ND time. However, the prior information obtained through ISAC may be imperfect. Hence, an ND algorithm based on reinforcement learning is proposed. The learning automaton (LA) is applied to interact with the environment and continuously adjust the probability of selecting beams to accelerate the convergence speed of ND algorithms. Besides, an efficient ND algorithm in the neighbor maintenance phase is designed, which applies the Kalman filter to predict node movement. Simulation results show that the LA-based ND algorithm reduces the ND time by up to 32% compared with the Scan-Based Algorithm (SBA), which proves the efficiency of the proposed ND algorithms.

Key words: unmanned aerial vehicle networks, neighbor discovery, integrated sensing and communication, reinforcement learning, Kalman filter

. [J]. ZTE Communications, 2024, 22(3): 69-82.

WEI Zhiqing, ZHANG Yongji, JI Danna, LI Chenfei. Sensing and Communication Integrated Fast Neighbor Discovery for UAV Networks[J]. ZTE Communications, 2024, 22(3): 69-82.

图/表 17

Table 1 Parameter value setting

$η$	$ϖ$
$η 1 = 0.3$	$ϖ 1 = (0.2, 0.3, 0.2)$
$η 2 = 0.5$	$ϖ 2 = 0.1, 0.4, 0.1$
$η 3 = 0.7$	$ϖ 3 = 0.1, 0.2, 0.05$
$η 4 = 0.8$	$ϖ 4 = 0.1, 0.2, 0.1$
$η 5 = 0.9$	$ϖ 5 = 0.1, 0.3, 0.2$

Table 1 Parameter value setting

$η$	$ϖ$
$η 1 = 0.3$	$ϖ 1 = (0.2, 0.3, 0.2)$
$η 2 = 0.5$	$ϖ 2 = 0.1, 0.4, 0.1$
$η 3 = 0.7$	$ϖ 3 = 0.1, 0.2, 0.05$
$η 4 = 0.8$	$ϖ 4 = 0.1, 0.2, 0.1$
$η 5 = 0.9$	$ϖ 5 = 0.1, 0.3, 0.2$

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