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

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 I...

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Veröffentlicht in:IEEE International Conference on Communications (2003) S. 6145 - 6150
Hauptverfasser: Zhao, Bai, Lin, Min, Xiao, Shengjie, Cheng, Ming, Wang, Jun-Bo, Cheng, Julian
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 28.05.2023
Schlagworte:
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
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Zusammenfassung: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.
ISSN:1938-1883
DOI:10.1109/ICC45041.2023.10278798