Optimal Precoder Design for MIMO-OFDM-based Joint Automotive Radar-Communication Networks

Large-scale deployment of connected vehicles with cooperative awareness technologies increases the demand for vehicle-to-everything (V2X) communication spectrum in 5.9 GHz that is mainly allocated for the exchange of safety messages. To supplement V2X communication and support the high data rates ne...

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Veröffentlicht in:2021 19th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt) S. 1 - 8
Hauptverfasser: Ozkaptan, Ceyhun D., Ekici, Eylem, Wang, Chang-Heng, Altintas, Onur
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IFIP 18.10.2021
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Abstract Large-scale deployment of connected vehicles with cooperative awareness technologies increases the demand for vehicle-to-everything (V2X) communication spectrum in 5.9 GHz that is mainly allocated for the exchange of safety messages. To supplement V2X communication and support the high data rates needed by broadband applications, the millimeter-wave (mmWave) automotive radar spectrum at 76-81 GHz can be utilized. For this purpose, joint radar-communication systems have been proposed in the literature to perform both functions using the same waveform and hardware. While multiple-input and multiple-output (MIMO) communication with multiple users enables independent data streaming for high throughput, MIMO radar processing provides high-resolution imaging that is crucial for safety-critical systems. However, employing conventional precoding methods designed for communication generates directional beams that impair MIMO radar imaging and target tracking capabilities during data streaming. In this paper, we propose a MIMO joint automotive radar-communication (JARC) framework based on orthogonal frequency division multiplexing (OFDM) waveform. First, we show that the MIMO-OFDM preamble can be exploited for both MIMO radar processing and estimation of the communication channel. Then, we propose an optimal precoder design method that enables high accuracy target tracking while transmitting independent data streams to multiple receivers. The proposed methods provide high-resolution radar imaging and high throughput capabilities for MIMO JARC networks. Finally, we evaluate the efficacy of the proposed methods through numerical simulations.
AbstractList Large-scale deployment of connected vehicles with cooperative awareness technologies increases the demand for vehicle-to-everything (V2X) communication spectrum in 5.9 GHz that is mainly allocated for the exchange of safety messages. To supplement V2X communication and support the high data rates needed by broadband applications, the millimeter-wave (mmWave) automotive radar spectrum at 76-81 GHz can be utilized. For this purpose, joint radar-communication systems have been proposed in the literature to perform both functions using the same waveform and hardware. While multiple-input and multiple-output (MIMO) communication with multiple users enables independent data streaming for high throughput, MIMO radar processing provides high-resolution imaging that is crucial for safety-critical systems. However, employing conventional precoding methods designed for communication generates directional beams that impair MIMO radar imaging and target tracking capabilities during data streaming. In this paper, we propose a MIMO joint automotive radar-communication (JARC) framework based on orthogonal frequency division multiplexing (OFDM) waveform. First, we show that the MIMO-OFDM preamble can be exploited for both MIMO radar processing and estimation of the communication channel. Then, we propose an optimal precoder design method that enables high accuracy target tracking while transmitting independent data streams to multiple receivers. The proposed methods provide high-resolution radar imaging and high throughput capabilities for MIMO JARC networks. Finally, we evaluate the efficacy of the proposed methods through numerical simulations.
Author Wang, Chang-Heng
Altintas, Onur
Ozkaptan, Ceyhun D.
Ekici, Eylem
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  givenname: Ceyhun D.
  surname: Ozkaptan
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  givenname: Eylem
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  organization: The Ohio State University,Dept. of Electrical and Computer Engineering,Columbus,OH,USA
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  givenname: Chang-Heng
  surname: Wang
  fullname: Wang, Chang-Heng
  email: chang-heng.wang@toyota.com
  organization: Toyota Motor North America,InfoTech Labs,Mountain View,CA,USA
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  givenname: Onur
  surname: Altintas
  fullname: Altintas, Onur
  email: onur.altintas@toyota.com
  organization: Toyota Motor North America,InfoTech Labs,Mountain View,CA,USA
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SubjectTerms Imaging
Measurement
MIMO radar
OFDM
Radar imaging
Streaming media
Target tracking
Title Optimal Precoder Design for MIMO-OFDM-based Joint Automotive Radar-Communication Networks
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