Optimal Pilot and Data Power Allocation for Joint Communication-Radar Air-to-Ground Networks

被引:7
作者
Park, Ji Min [1 ]
Cho, Juphil [2 ]
Noh, Song [3 ]
Yu, Heejung [1 ]
机构
[1] Korea Univ, Dept Elect & Informat Engn, Sejong 30019, South Korea
[2] Kunsan Natl Univ, Dept Integrated IT & Commun Engn, Kunsan 54150, South Korea
[3] Incheon Natl Univ, Dept Informat & Telecommun Engn, Incheon 22012, South Korea
基金
新加坡国家研究基金会;
关键词
Radar; Channel estimation; Estimation; Radar cross-sections; Resource management; Receivers; Bistatic radar; Joint communication-radar; achievable rate; channel estimation; CRLB; optimization; FRONTHAUL COMPRESSION; OPTIMIZATION; DESIGN;
D O I
10.1109/ACCESS.2022.3174869
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Integration of communication and radar functions with a single waveform has been actively investigated in various wireless communication applications, including unmanned aerial vehicles (UAVs). This means that communication frames consisting of a pilot part and subsequent data part can be utilized to transmit information and detect the surrounding objects simultaneously. Because a predetermined waveform is required for the radar function, the pilot part in the communication frame can be utilized for radar purposes. Specifically, the pilot is used for both channel estimation, which is required for data decoding, and a radar waveform. Assuming that the length of the pilot and data parts is given and the transmit energy for each frame is limited, the optimal transmit power for the pilot and data parts can be analytically obtained by considering both radar and communication performance metrics. The optimality of the proposed analytical solution was verified through numerical simulations.
引用
收藏
页码:52336 / 52342
页数:7
相关论文
共 16 条
[1]   Internet of Radars: Sensing versus Sending with Joint Radar-Communications [J].
Akan, Ozgur B. ;
Arik, Muharrem .
IEEE COMMUNICATIONS MAGAZINE, 2020, 58 (09) :13-19
[2]   Radar Sensing-Throughput Tradeoff for Radar Assisted Cognitive Radio Enabled Vehicular Ad-Hoc Networks [J].
Huang, Sai ;
Jiang, Nan ;
Gao, Yue ;
Xu, Wenjun ;
Feng, Zhiyong ;
Zhu, Fusheng .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (07) :7483-7492
[3]   IEEE 802.11ad-Based Radar: An Approach to Joint Vehicular Communication-Radar System [J].
Kumari, Preeti ;
Choi, Junil ;
Gonzalez-Prelcic, Nuria ;
Heath, Robert W., Jr. .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2018, 67 (04) :3012-3027
[4]  
Kumari P, 2017, INT CONF ACOUST SPEE, P4281, DOI 10.1109/ICASSP.2017.7952964
[5]   Multiagent Q-Learning-Based Multi-UAV Wireless Networks for Maximizing Energy Efficiency: Deployment and Power Control Strategy Design [J].
Lee, Seungmin ;
Yu, Heejung ;
Lee, Howon .
IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (09) :6434-6442
[6]   Optimal Tethered-UAV Deployment in A2G Communication Networks: Multi-Agent Q-Learning Approach [J].
Lim, Suhyeon ;
Yu, Heejung ;
Lee, Howon .
IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (19) :18539-18549
[7]   Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead [J].
Liu, Fan ;
Masouros, Christos ;
Petropulu, Athina P. ;
Griffiths, Hugh ;
Hanzo, Lajos .
IEEE TRANSACTIONS ON COMMUNICATIONS, 2020, 68 (06) :3834-3862
[8]  
Richards Mark A., 2010, Fundamentals of Radar Signal Processing
[9]  
Thomä R, 2021, PROC EUR CONF ANTENN
[10]   Unmanned Aerial Vehicle Base Station (UAV-BS) Deployment With Millimeter-Wave Beamforming [J].
Xiao, Zhenyu ;
Dong, Hang ;
Bai, Lin ;
Wu, Dapeng Oliver ;
Xia, Xiang-Gen .
IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (02) :1336-1349