Robust beamforming for IRS-aided SWIPT in cognitive radio networks

Intelligent reflecting surface (IRS) is widely recognized as a promising technique to enhance the system performance, and thus is a hot research topic in future wireless communications. In this context, this paper proposes a robust BF scheme to improve the spectrum and energy harvesting efficiencies for the IRS-aided simultaneous wireless information and power transfer (SWIPT) in a cognitive radio network (CRN). Here, the base station (BS) utilizes spectrum assigned to the primary users (PUs) to simultaneously serve multiple energy receivers (ERs) and information receivers (IRs) through IRS-aided multicast technology. In particular, by assuming that only the imperfect channel state information (CSI) is available, we first formulate a constrained problem to maximize the minimal achievable rate of IRs, while satisfying the harvesting energy threshold of ERs, the quality-of-service requirement of IRs, the interference threshold of PUs and transmit power budget of BS. To address the nonconvex problem, we then adopt triangle inequality to deal with the channel uncertainty, and propose a low-complexity algorithm combining alternating direction method of multipliers (ADMM) with alternating optimization (AO) to jointly optimize the active and passive beamformers for the BS and IRS, respectively. Finally, our simulation results confirm the effectiveness of the proposed BF scheme and also provide useful insights into the importance of introducing IRS into the CRN with SWIPT.