Deep Neural Network Detection for Pulsed Radar-Embedded M-PSK Communications

Liu, Christopher Y.; Romero, Ric A.

doi:10.1109/eurad48048.2021.00068

Cited by 8 publications

(1 citation statement)

References 4 publications

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“…They propose cooperative spectrum sharing, in which information is shared between the communication and the radar system in near real time, which implies the co-existence of two separate systems. In contrast, to achieve communication and sensing via a single, integrated system, several authors have proposed communication that is embedded in signals transmitted by radar devices (radar-embedded communications) [16], [17], [18], [19]. For instance, [3] propose a dual-use radar-communication multicarrier waveform, in which different subcarriers are assigned to different subsystems to achieve RF-convergence.…”

Section: Related Workmentioning

confidence: 99%

Integrating Sensing and Communication in Cellular Networks via NR Sidelink

Salami,

Hasibi,

Savazzi

et al. 2021

Preprint

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RF-sensing, the analysis and interpretation of movement or environment-induced patterns in received electromagnetic signals, has been actively investigated for more than a decade. Since electromagnetic signals, through cellular communication systems, are omnipresent, RF sensing has the potential to become a universal sensing mechanism with applications in smart home, retail, localization, gesture recognition, intrusion detection, etc. Specifically, existing cellular network installations might be dual-used for both communication and sensing. Such communications and sensing convergence is envisioned for future communication networks. We propose the use of NR-sidelink direct device-to-device communication to achieve device-initiated, flexible sensing capabilities in beyond 5G cellular communication systems. In this article, we specifically investigate a common issue related to sidelink-based RF-sensing, which is its angle and rotation dependence. In particular, we discuss transformations of mmWave point-cloud data which achieve rotational invariance, as well as distributed processing based on such rotational invariant inputs, at angle and distance diverse devices. To process the distributed data, we propose a graph based encoder to capture spatio-temporal features of the data and propose four approaches for multi-angle learning. The approaches are compared on a newly recorded and openly available dataset comprising 15 subjects, performing 21 gestures which are recorded from 8 angles.

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