4.7 Article

Reconfigurable Intelligent Surface Assisted Spatial Modulation for Symbiotic Radio

Journal

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 70, Issue 12, Pages 12918-12931

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2021.3121698

Keywords

Modulation; Backscatter; Symbiosis; Wireless communication; Radio frequency; Transceivers; Internet of Things; Reconfigurable intelligent surface; symbiotic radio; symbiotic spatial modulation; star-QAM; Internet-of-Things

Funding

  1. National Key R&D Program of China [2019YFB1803400]
  2. National Natural Science Foundation of China [61971360]
  3. Fundamental Research Funds for the Central Universities [XJ2021KJZK007]
  4. Open Research Fund of National Mobile Communications Research Laboratory, Southeast University [2021D05]

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This paper investigates an RIS-assisted symbiotic radio (SR) system with symbiotic spatial modulation (SSM) schemes, which enable simultaneous detection of cellular and IoT data transmissions. The proposed modulation design shows good detection performance for both types of data.
Reconfigurable intelligent surface (RIS) is a key enabler for future paradigm-shifting communication technologies. In a cellular network, an RIS can be used to simultaneously assist the communication of a cellular link and transmit additional data for various Internet of Things (IoT) applications. This coincides with the concept of symbiotic radio (SR). In this paper, we investigate this RIS-assisted SR system. We introduce the new idea of symbiotic modulation: the modulation constellation design allows a receiver to simultaneously detect the data transmitted in the RIS-assisted cellular link and the additional IoT data transmitted by the RIS. We propose three specific modulation schemes all having the feature of receive-antenna index modulation, and hence, called symbiotic spatial modulation (SSM). The first scheme is a coherent SSM scheme with a star-QAM (quadrature amplitude modulation) constellation. Its detection performance is analyzed using either an optimal maximum likelihood detector or a suboptimal successive greedy detector. We then extend the design to two noncoherent SSM schemes and analytically derive their detection performances as well. Simulation results corroborate the analysis and illustrate good detection performance of the proposed SSM schemes for both the cellular and IoT data transmissions in the considered SR system.

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