OFDM-based automotive joint radar-communication system

Dokhanchi, Sayed Hossein; Shankar, M. R. Bhavani; Stifter, Thomas; Ottersten, Björn

doi:10.1109/radar.2018.8378680

Cited by 37 publications

(34 citation statements)

References 12 publications

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“…Another interesting approach has been proposed recently where a multi-carrier system allocates subcarriers to either communication or radar subsystems according to a mutual information (MI)-based objective function (Bicȃ and Koivunen, 2019). In (Dokhanchi et al, 2019(Dokhanchi et al, , 2018, phase modulated continuous wave (PMCW) and orthogonal frequency division multiple access (OFDMA) waveforms were proposed as integrated JCR/JRC waveforms. In these work, a bistatic system is proposed, which extends the sensing data to vehicles located at non-line-of-sight positions, thus providing spatial diversity to the target scene.…”

Section: Integrated Waveformsmentioning

confidence: 99%

A Survey on Joint Communication-Radar Systems

2021

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Concerns for spectrum congestion have spurred extensive research efforts on efficient spectrum management. Therefore, devising schemes for spectrum sharing between radar and wireless communication systems has become an important area of research. Joint communications-radar (JCR) systems are among the several approaches proposed to achieve this objective. In JCR systems, additional components and processes are added to an existing standardized communication platform to enable radar functions. Moreover, the communication waveform is used as an integrated JCR waveform, i.e., the same signal is used to communicate information to a receiver and to perform radar detection and estimation operations for a nearby target. The most common application of JCR systems is found in vehicle-to-vehicle (V2V) communication scenarios. In this article, an overview of the spectrum sharing methods is presented, with a focus on JCR systems in automotive and other applications. We first review the recent works on IEEE 802.11p- and IEEE 802.11ad-based radars. A basic description of the modeling of a JCR system and channels is presented, followed by discussions on the main components and processes employed in various JCR systems. We are mainly interested in how radar detection and estimation functions are performed in conjunction with the communication receiver functions with minimal alterations to the existing system. At the end of the paper, some performance trade-offs between the communication and radar sub-systems are also discussed.

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Section: Integrated Waveformsmentioning

confidence: 99%

A Survey on Joint Communication-Radar Systems

2021

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“…Ts , T r is the time duration for sensing as defined before, h d is the complex channel coefficient from the transmitter to the radar receiver in the direct channel, h s is the complex channel coefficient from the transmitter to the radar receiver via the target in the surveillance channel, w ci is the transmitted signal for radar detection, n di and n si are the complex AWGN with mean zero and variance σ 2 . We have used the same signal model in (4) and 5, as in [20] - [24] that ignore Doppler, angle or resolution detection. Detailed discussion can be found in [20] - [24].…”

Section: A Time Divisionmentioning

confidence: 99%

“…This is further motivated by emerging applications in intelligent transportation, where the system topology and surroundings are time-variant so that the intelligent vehicles will not only sense the driving environment but also need to exchange information with each other for efficient maneuvers [1] - [3]. For example, in [4], [5], radar signals were sent to determine the channel parameters, based on which vehicle-to-vehicle communications data were exchanged. In [6], vehicles performed collaborative positioning, where features were communicated between vehicles during collaboration.…”

Section: Introductionmentioning

confidence: 99%

Joint Bi-Static Radar and Communications Designs for Intelligent Transportation

Cao

Chen

et al. 2020

IEEE Trans. Veh. Technol.

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“…Due to high-frequency efficiency and ease of interference management, orthogonal frequency division multiplexing (OFDM) radar has been extensively investigated [ 4 ]. In [ 5 , 6 ] (and references therein), symbol-based signal processing is proposed to estimate range profile without correlation based baseband signal processing, which also motivates the joint radar and communication (RadCom) systems [ 7 , 8 , 9 ]. In [ 8 ], the OFDM waveforms carrying the modulated data in each subcarrier are exploited to obtain the range-Doppler map, where the payload data is canceled out in the received OFDM baseband signal at RadCom platform.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing-Based Radar Imaging and Subcarrier Allocation for Joint MIMO OFDM Radar and Communication System

Hwang

Seo

Park

et al. 2021

Sensors

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In this paper, a joint multiple-input multiple-output (MIMO OFDM) radar and communication (RadCom) system is proposed, in which orthogonal frequency division multiplexing (OFDM) waveforms carrying data to be transmitted to the information receiver are exploited to get high-resolution radar images at the RadCom platform. Specifically, to get two-dimensional (i.e., range and azimuth angle) radar images with high resolution, a compressive sensing-based imaging algorithm is proposed that is applicable to the signal received through multiple receive antennas. Because both the radar imaging performance (i.e., the mean square error of the radar image) and the communication performance (i.e., the achievable rate) are affected by the subcarrier allocation across multiple transmit antennas, by analyzing both radar imaging and communication performances, we also propose a subcarrier allocation strategy such that a high achievable rate is obtained without sacrificing the radar imaging performance.

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OFDM-based automotive joint radar-communication system

Cited by 37 publications

References 12 publications

A Survey on Joint Communication-Radar Systems

A Survey on Joint Communication-Radar Systems

Joint Bi-Static Radar and Communications Designs for Intelligent Transportation

Compressive Sensing-Based Radar Imaging and Subcarrier Allocation for Joint MIMO OFDM Radar and Communication System

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