Robust transmission algorithm for IRS-assisted NOMA network with hardware impairments and imperfect CSI
To improve the robustness and reduce the energy consumption of non-orthogonal multiple access (NOMA) networks, based on hardware impairments (HWI) of transceiver and non-perfect channel state information (CSI), an intelligent reflecting surface (IRS) assisted transmission power minimization algorith...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
China InfoCom Media Group
2024-03-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00370/ |
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| Summary: | To improve the robustness and reduce the energy consumption of non-orthogonal multiple access (NOMA) networks, based on hardware impairments (HWI) of transceiver and non-perfect channel state information (CSI), an intelligent reflecting surface (IRS) assisted transmission power minimization algorithm for NOMA networks was proposed.The joint optimization problem of active beam assignment at the base station and passive beam assignment at the IRS was modeled based on HWI and non-perfect CSI.The system considered the user quality of service (QoS) constraint, the serial interference cancellation constraint and the reflection phase shift constraint of the IRS.To solve this nonconvex optimization problem, the QoS constraints were firstly transformed using linear approximation and S-Procedure methods.Then the optimization problem was decomposed into two subproblems.The active beam assignment subproblem was solved using the successive convex approximation (SCA) method.The passive beam assignment subproblem was solved using the penalized convex-concave process algorithm.Finally, the final solution was obtained by iterating the subproblems alternately using alternating optimization.The simulation results show that the proposed algorithm reduces 17.05% compared to the or thogonal multiple access robust algorithm in terms of transmitted power.In terms of system robustness, the proposed algorithm improves by 20.69% and 31.14% compared to the HWI robust algorithm and the CSI robust algorithm, respectively. |
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| ISSN: | 2096-3750 |