Exploiting Multi-Layer Refracting RIS-Assisted Receiver for HAP-SWIPT Networks

计算机科学 衰退 发射机功率输出 信道状态信息 数学优化 传输(电信) 频道(广播) 无线 电子工程 发射机 电信 数学 工程类
作者
Kang An,Yifu Sun,Zhi Lin,Yonggang Zhu,Wanli Ni,Naofal Al‐Dhahir,Kai‐Kit Wong,Dusit Niyato
出处
期刊:IEEE Transactions on Wireless Communications [Institute of Electrical and Electronics Engineers]
卷期号:23 (10): 12638-12657 被引量:22
标识
DOI:10.1109/twc.2024.3394214
摘要

Aiming to circumvent the severe large-scale fading and the energy scarcity dilemma in high-altitude platform (HAP) networks, this paper investigates the benefits of the reconfigurable intelligent surface (RIS) and simultaneous wireless information and power transfer (SWIPT) on HAP communications. Specifically, we propose a concept of multi-layer refracting RIS-assisted receiver to achieve concurrent transmission of the information and energy, which is conducive to overcoming the severe fading effect induced by extreme long-distance HAP links and fully exploits RIS's degrees-of-freedom (DoFs) for the SWIPT design. Based on the RIS-enhanced receiver, we then formulate a worst-case sum-rate maximization problem by considering the channel state information (CSI) error, the information rate requirements, and the energy harvesting constraint. To handle the intractable non-convex problem, a scalable robust optimization framework is proposed to obtain semi-closed-form solutions. Specifically, a discretization method is adopted to convert the imperfect CSI into a robust one. Then, by utilizing the LogSumExp inequality to smooth the objective and constraints, we develop a dual method to obtain the optimal solution for the HAP transmit precoder. In addition, a modified cyclic coordinate descent (M-CCD) is adopted to update the block-wise RIS coefficients. Moreover, closed-form solutions for power splitting (PS) ratios and the receive decoder are derived. Finally, the asymptotic performance of our proposed RIS-enhanced receiver is provided to reveal the substantial capacity gain for HAP communications. Numerical simulations demonstrate that the proposed architecture and optimization framework are capable of achieving superior performance with low complexity compared to state-of-the-art schemes in HAP networks.
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