LAUSR

Symbiotic radio (SR) is a promising technology for energy- and spectrum-efficient wireless communication, which exploits passive communication for Internet-of-Things (IoT) transmission and achieves a mutualistic spectrum sharing between the passive and active transmissions. In this paper, we study an reconfigurable intelligent surface (RIS) empowered symbiotic radio over a broadcasting system, i.e., a base station (BS) broadcasts signals to multiple primary receivers (PRs) under the assistance of an RIS, while the RIS also transmits information to an IoT receiver (IR) by riding over the broadcasting signals. We formulate a problem to minimize the BS’s transmit power by jointly optimizing the BS’s active precoding and the RIS’s passive beamforming, under the signal-to-noise-ratio constraints of the primary and IoT transmissions. However, the problem is challenging to be solved optimally, since the variables are coupled and the constraints are non-convex. An iterative algorithm based on block coordinated descent (BCD) and semidefinite relaxation (SDR) techniques is first proposed, and its convergence together with complexity are analyzed. Then, to tackle the problem of high computational complexity caused by SDR technique, we further propose an alternative algorithm based on generalized power method (GPM) technique. Simulation results validate that the proposed system outperforms the traditional broadcasting system without RIS. The GPM-based algorithm achieves nearly the same transmit power performance as SDR-based algorithm, with a significantly reduced computational complexity.