Low-Complexity Robust Transmission Algorithm for IRS-Enhanced Cognitive Satellite-Aerial Networks

• Published in: IEEE International Conference on Communications (2003) pp. 6145 - 6150
• Main Authors: Zhao, Bai, Lin, Min, Xiao, Shengjie, Cheng, Ming, Wang, Jun-Bo, Cheng, Julian
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.05.2023
• Subjects: Cognitive-satellite-aerial-network; Intelligent reflecting surface; Low complexity algorithm; Minimization; NOMA; Non-orthogonal multiple access; Quality of service; Robust beam-forming; Satellites; Simulation; System performance; Taylor series
• ISSN: 1938-1883
• DOI: 10.1109/ICC45041.2023.10278798

This paper proposes a downlink transmission scheme for intelligent reflecting surface (IRS) enhanced cognitive-satellite-aerial-network to support massive access of Internet-of-Things devices (IoTDs). By sharing the same frequency band with satellite network, the aerial network offers services for IoTDs having line-of-sight links through space division multiple access, and for IoTDs locating in blocked area via IRS-enhanced non-orthogonal multiple access. Assuming that only the imperfect channel state information is available, we formulate a transmit power minimization problem subject to the probabilistic constraints of the quality-of-service requirements for IoTDs, the co-channel interference power limitation, and unit-modulus requirement for IRS. To tackle this mathematically intractable problem, we propose a generalized zero-forcing based low-complexity robust transmission algorithm, integrating the second-order Taylor expansion and Bernstein-type inequality, to obtain a satisfactory performance while reducing the computational load. Finally, simulation results validate the effectiveness and superiority of the proposed robust algorithms compared to existing algorithms.