联合天线选择与智能反射面辅助MISO下行网络

Shang Xia, Peichang Zhang, Zheng-Ming Jiang, Li Li, Xiaojun Wang, Shuo Feng
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引用次数: 1

摘要

智能反射面(IRS)是提高多输入单输出(MISO)下行通信系统性能的有效方法之一。为了在整个MISO系统中降低能耗并进一步提高系统性能,我们将发射机天线选择(AS)技术纳入irs辅助MISO通信系统中。我们的目标是通过联合优化天线子集、接入点(AP)的发射波束形成和IRS的无源相移矩阵来最大化用户瞬时接收信噪比(SNR)。我们提出了一种新的离散布谷鸟算法结合优化理论(NDCOT)来解决这个问题。NDCOT由AS和优化两个步骤组成。在AS步骤中,利用一种新颖的离散布谷鸟算法来降低AS的复杂度。在优化步骤中,采用半定松弛(SDR)算法得到发射机处的最优发射波束形成矢量和IRS处的最优相位矩阵。仿真结果验证了AS在降低红外光谱辅助MISO通信系统能耗和提高系统性能方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint Antenna Selection and Intelligent Reflecting Surface Aided MISO Downlink Networks
Intelligent Reflecting Surface (IRS) has been recognized as one of the effective ways to enhance the system performance of multiple input single output (MISO) downlink communication system. In order to reduce energy consumption and further improve system performance in the entire MISO system, we incorporate the transmitter antenna selection (AS) technology into the IRS-aided MISO communication system. We aim to maximize the user instantaneous receiver signal-to-noise ratio (SNR) by jointly optimizing antenna subset, transmit beamforming of the access point (AP) and passive phase-shift matrix of the IRS. We propose to solve the problem using a novel discrete cuckoo algorithm combined with optimization theory (NDCOT). NDCOT consists of AS and optimization steps. In AS steps, a novel discrete cuckoo algorithm is exploited to reduce the complexity of AS. In the optimization step, the semidefinite relaxation (SDR) algorithm is employed to obtain the optimal transmit beamforming vector at the transmitter and phase matrix at the IRS. The simulation results validate the effectiveness of AS in reducing energy consumption and improving the performance of the IRS-aided MISO communication system.
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