Cross-Domain Dual-Functional OFDM Waveform Design for Accurate Sensing/Positioning

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Abstract
Orthogonal frequency division multiplexing (OFDM) has been widely recognized as the representative waveform for 5G wireless networks, which can directly support sensing/positioning with existing infrastructure. To guarantee superior sensing/positioning accuracy while supporting high-speed communication simultaneously, the dual functions tend to be assigned with different resource elements (REs) due to their diverse design requirements. This motivates optimization of resource allocation/waveform design across time, frequency, power and delay-Doppler domains. Therefore, this article proposes two cross-domain waveform optimization strategies for effective convergence of OFDM-based communication and sensing/positioning, following communication- and sensing-centric criteria, respectively. For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communication according to prior knowledge of the communication channel. The remaining REs are then employed for sensing/positioning, where the sidelobe level and peak-to-average power ratio are suppressed by optimizing its power-frequency and phase-frequency characteristics for sensing performance improvement. For the sensing-centric design, a ‘locally’ perfect auto-correlation property is ensured for accurate sensing and positioning by adjusting the unit cells of the ambiguity function within its region of interest (RoI). Afterwards, the irrelevant cells beyond RoI, which can readily determine the sensing power allocation, are optimized with the communication power allocation to enhance the achievable data rate. Numerical results demonstrate the superiority of the proposed waveform designs.
Funding Information
  • National Key Research and Development Program of China (2021YFA0716600)
  • National Natural Science Foundation of China (62088101)
  • Young Elite Scientists Sponsorship Program by China Association for Science and Technology (2022QNRC001)
  • NSF (CNS-2107216, CNS-2128368, CMMI-2222810, ECCS-2302469)
  • Japan Science and Technology Agency (JST) Adopting Sustainable Partnerships for Innovative Research Ecosystem (JPMJAP2326)
  • U.S. Department of Transportation, Toyota, and Amazon

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