TY - GEN
T1 - Robust Beamforming Optimization for Intelligent Reflecting Surface Aided Cognitive Radio Networks
AU - Zhang, Lei
AU - Pan, Cunhua
AU - Wang, Yu
AU - Ren, Hong
AU - Wang, Kezhi
AU - Nallanathan, Arumugam
N1 - Funding Information:
This work is supported by National Natural Science Foundation of China (No. 61701202 and No. 61901196), the open research fund of National Mobile Communications Research LaboratorySoutheast University (No. 2019D17) and Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (No. 19KJB510026).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - Intelligent reflecting surface (IRS) has been proved to be an efficient technology to improve the spectrum and energy efficiency in cognitive radio (CR) networks. Unfortunately, due to the fact that the primary users (PUs) and the secondary users (SUs) are non-cooperative, it is challenging to obtain the perfect PUs-related channel sate information (CSI). In this paper, we investigate the robust beamforming design based on the statistical CSI error model for PU-related cascaded channels in IRS-aided CR systems. We jointly optimize the transmit precoding (TPC) matrix and phase shifts to minimize the SU's total transmit power, meanwhile subject to the quality of service (QoS) of SUs, the interference imposed on the PU and unit-modulus of the reflective beamforming. The non-convex optimization problems are transformed into two second-order cone programming (SOCP) subproblems and efficient algorithms are proposed for solving these subproblems. Simulation results verify the efficiency of the proposed algorithms and reveal the impacts of CSI uncertainties on ST's transmit power and feasibility rate of the optimization problem.
AB - Intelligent reflecting surface (IRS) has been proved to be an efficient technology to improve the spectrum and energy efficiency in cognitive radio (CR) networks. Unfortunately, due to the fact that the primary users (PUs) and the secondary users (SUs) are non-cooperative, it is challenging to obtain the perfect PUs-related channel sate information (CSI). In this paper, we investigate the robust beamforming design based on the statistical CSI error model for PU-related cascaded channels in IRS-aided CR systems. We jointly optimize the transmit precoding (TPC) matrix and phase shifts to minimize the SU's total transmit power, meanwhile subject to the quality of service (QoS) of SUs, the interference imposed on the PU and unit-modulus of the reflective beamforming. The non-convex optimization problems are transformed into two second-order cone programming (SOCP) subproblems and efficient algorithms are proposed for solving these subproblems. Simulation results verify the efficiency of the proposed algorithms and reveal the impacts of CSI uncertainties on ST's transmit power and feasibility rate of the optimization problem.
KW - cognitive radio
KW - intelligent reflecting surface
KW - Reconfigurable intelligent surfaces
KW - robust beamforming optimization
UR - http://www.scopus.com/inward/record.url?scp=85100383244&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM42002.2020.9322371
DO - 10.1109/GLOBECOM42002.2020.9322371
M3 - Conference contribution
AN - SCOPUS:85100383244
T3 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
BT - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
PB - IEEE
T2 - 2020 IEEE Global Communications Conference, GLOBECOM 2020
Y2 - 7 December 2020 through 11 December 2020
ER -
