基于毫米波混合波束成形优化的5G异构网络跨层多用户选择

Ahmad Fadel, Ahmad Nimr, Hsiao-Lan Chiang, Marwa Chafii, Bernard A. Cousin
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引用次数: 1

摘要

网络层之间缺乏协调限制了大多数提出的解决方案的性能,以应对无线网络带来的新挑战。为了克服这些限制,提出了异构网络中多输入多输出正交频分多址系统的跨层物理和介质接入(PHY-MAC)设计。在本文中,我们提出了一个多用户HetNET场景下混合波束形成的优化问题,旨在最大化系统的总吞吐量。此外,模拟波束形成是从包含有限数量的候选转向矢量的码本中选择的。所提出的问题是非凸的,难以求解。因此,通过将其转化为两个凸函数的减法形式来松弛它。然后,我们应用了一组著名的元启发式算法来计算归一化混合波束形成向量。采用穷举搜索(ES)算法获得最优解,该算法提供了理想解,但复杂度较高。此外,采用基于零强迫的方法(ZFA)、匹配滤波器(MF)和基于QR的方法(QR)来快速获得次优解。因此,我们使用吞吐量度量来分析系统的性能。仿真结果表明,QR算法在中低信噪比下优于ZFA和MF算法,而ZFA算法在高信噪比下优于QR和MF算法。且QR与最优解ES较为接近。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cross-Layer Multi-User Selection in 5G Heterogeneous Networks Based on Hybrid Beamforming Optimization for Millimeter-Wave
Lack of coordination between network layers limits the performance of most proposed solution for new challenges posed by wireless networks. To overcome such limitations, cross-layer physical and medium access (PHY-MAC) design for multi-input-multi-output orthogonal frequency division multiple access system in heterogeneous networks (HetNETs) is proposed. In this paper, we formulate an optimization problem for hybrid beamforming, in a multi-user HetNET scenario aiming to maximize the total system throughput. Furthermore, analog beamforming is selected from a codebook containing a limited number of candidates for steering vectors. The proposed problem is non-convex and hard to solve. Thus it is relaxed by transforming it into a subtraction form of two convex funcions. Afterward we apply a group of well-known metaheuristic algorithms to calculate the normalized hybrid beamforming vectors. The optimal solution is obtained using an exhaustive search (ES) algorithm that provides an ideal solution, but with high complexity. In addition, zero-forcing-based approach (ZFA), matched filter (MF), and QR-based approach (QR) are applied to get quick sub-optimal solutions. Hence, we analyze the performance of our systems using the throughput metric. The simulation results show that QR algorithm outperforms ZFA and MF in low and middle signal-tonoise ratio (SNR) regime, while ZFA outperforms QR and MF at higher SNRs. Moreover, QR is close to the optimal solution ES.
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