FRaC: FMCW-Based Joint Radar-Communications System via Index Modulation
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Abstract (arXiv)
Dual function radar communications (DFRC) systems are attractive technologies for autonomous vehicles, which utilize electromagnetic waves to constantly sense the environment while simultaneously communicating with neighbouring devices. An emerging approach to implement DFRC systems is to embed information in radar waveforms via index modulation (IM). Implementation of DFRC schemes in vehicular systems gives rise to strict constraints in terms of cost, power efficiency, and hardware complexity. In this paper, we extend IM-based DFRC systems to utilize sparse arrays and frequency modulated continuous waveforms (FMCWs), which are popular in automotive radar for their simplicity and low hardware complexity. The proposed FMCW-based radar-communications system (FRaC) operates at reduced cost and complexity by transmitting with a reduced number of radio frequency modules, combined with narrowband FMCW signalling. This is achieved via array sparsification in transmission, formulating a virtual multiple-input multiple-output array by combining the signals in one coherent processing interval, in which the narrowband waveforms are transmitted in a randomized manner. Performance analysis and numerical results show that the proposed radar scheme achieves similar resolution performance compared with a wideband radar system operating with a large receive aperture, while requiring less hardware overhead. For the communications subsystem, FRaC achieves higher rates and improved error rates compared to dual-function signalling based on conventional phase modulation.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jun 2021.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 1.
4 papers.
- 2022Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AIcited by 31
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- 2023ISAC-NET: Model-driven Deep Learning for Integrated Passive Sensing and Communicationcited by 3
- 2024Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamicscited by 2
- 2024Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics24 Aug 2024
- 2023ISAC-NET: Model-driven Deep Learning for Integrated Passive Sensing and Communication14 Jul 2023
- 2022Task-Oriented Over-the-Air Computation for Multi-Device Edge AI2 Nov 2022
- 2022Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI3 Jul 2022