Co-Design for Overlaid MIMO Radar and Downlink MISO Communication Systems via Cramér-Rao Bound Minimization

This paper considers the problem of co-existence of collocated multiple-input multiple-output (MIMO) radar and downlink multiple-input single-output (MISO) communication systems, which share the same frequency band. As the interference caused by the communication systems would degrade the accuracy o...

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Published inIEEE transactions on signal processing Vol. 67; no. 24; pp. 6227 - 6240
Main Authors Cheng, Ziyang, Liao, Bin, Shi, Shengnan, He, Zishu, Li, Jun
Format Journal Article
LanguageEnglish
Published New York IEEE 15.12.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
alternating direction method of multipliers (ADMM)
Co-design
Communication
Communications systems
Computer simulation
Cramer-Rao bounds
Cramér-Rao Bound (CRB) minimization
decent-ralized block successive upper-bound minimization (D-BSUM)
Direction of arrival
downlink MISO system
Frequencies
Interference
Iterative methods
MIMO radar
Minimization methods
MISO (control systems)
MISO communication
Optimization
Radar targets
semidefinite programs (SDP)
Signal to noise ratio
Upper bounds
Waveforms
Online AccessGet full text
ISSN1053-587X
1941-0476
DOI10.1109/TSP.2019.2952048

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Abstract This paper considers the problem of co-existence of collocated multiple-input multiple-output (MIMO) radar and downlink multiple-input single-output (MISO) communication systems, which share the same frequency band. As the interference caused by the communication systems would degrade the accuracy of radar target localization, we formulate the co-design of radar waveform and communication transmit weights by minimizing the Cramér-Rao Bound (CRB) of direction-of-arrival (DOA) estimation, subject to a set of constraints accounting for the worst-case signal-to-noise-plus-interference ratio (SINR), similarity and energy. However, the objective is nonconvex and all variables are coupled together in the SINR constraints, the formulated problem is thus NP-hard. Towards that end, a decentralized block successive upper-bound minimization (D-BSUM) method based on the decomposition theory is developed. More specifically, at each iteration of this algorithm, the radar waveform is obtained with the aid of the alternating direction method of multipliers (ADMM) algorithm, and communication weights are obtained by exploiting the semidefinite programs (SDP). It is also proved that the proposed SDP-based method gives a rank-one solution. Numerical simulations are conducted to evaluate the effectiveness of the proposed algorithm.
AbstractList This paper considers the problem of co-existence of collocated multiple-input multiple-output (MIMO) radar and downlink multiple-input single-output (MISO) communication systems, which share the same frequency band. As the interference caused by the communication systems would degrade the accuracy of radar target localization, we formulate the co-design of radar waveform and communication transmit weights by minimizing the Cramér-Rao Bound (CRB) of direction-of-arrival (DOA) estimation, subject to a set of constraints accounting for the worst-case signal-to-noise-plus-interference ratio (SINR), similarity and energy. However, the objective is nonconvex and all variables are coupled together in the SINR constraints, the formulated problem is thus NP-hard. Towards that end, a decentralized block successive upper-bound minimization (D-BSUM) method based on the decomposition theory is developed. More specifically, at each iteration of this algorithm, the radar waveform is obtained with the aid of the alternating direction method of multipliers (ADMM) algorithm, and communication weights are obtained by exploiting the semidefinite programs (SDP). It is also proved that the proposed SDP-based method gives a rank-one solution. Numerical simulations are conducted to evaluate the effectiveness of the proposed algorithm.
This paper considers the problem of co-existence of collocated multiple-input multiple-output (MIMO) radar and downlink multiple-input single-output (MISO) communication systems, which share the same frequency band. As the interference caused by the communication systems would degrade the accuracy of radar target localization, we formulate the co-design of radar waveform and communication transmit weights by minimizing the Cramér-Rao Bound (CRB) of direction-of-arrival (DOA) estimation, subject to a set of constraints accounting for the worst-case signal-to-noise-plus-interference ratio (SINR), similarity and energy. However, the objective is nonconvex and all variables are coupled together in the SINR constraints, the formulated problem is thus NP-hard. Towards that end, a decentralized block successive upper-bound minimization (D-BSUM) method based on the decomposition theory is developed. More specifically, at each iteration of this algorithm, the radar waveform is obtained with the aid of the alternating direction method of multipliers (ADMM) algorithm, and communication weights are obtained by exploiting the semidefinite programs (SDP). It is also proved that the proposed SDP-based method gives a rank-one solution. Numerical simulations are conducted to evaluate the effectiveness of the proposed algorithm.
Author Li, Jun
Shi, Shengnan
He, Zishu
Cheng, Ziyang
Liao, Bin
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Snippet This paper considers the problem of co-existence of collocated multiple-input multiple-output (MIMO) radar and downlink multiple-input single-output (MISO)...
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SubjectTerms Algorithms
alternating direction method of multipliers (ADMM)
Co-design
Communication
Communications systems
Computer simulation
Cramer-Rao bounds
Cramér-Rao Bound (CRB) minimization
decent-ralized block successive upper-bound minimization (D-BSUM)
Direction of arrival
downlink MISO system
Frequencies
Interference
Iterative methods
MIMO radar
Minimization methods
MISO (control systems)
MISO communication
Optimization
Radar targets
semidefinite programs (SDP)
Signal to noise ratio
Upper bounds
Waveforms
Title Co-Design for Overlaid MIMO Radar and Downlink MISO Communication Systems via Cramér-Rao Bound Minimization
URI https://ieeexplore.ieee.org/document/8892631
https://www.proquest.com/docview/2325190239
Volume 67
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