论文标题
基于GMD的混合边界,用于大型可重构智能表面辅助毫米波大量MIMO
GMD-Based Hybrid Beamforming for Large Reconfigurable Intelligent Surface Assisted Millimeter-Wave Massive MIMO
论文作者
论文摘要
可重新配置的智能表面(RIS)被认为是一种节能方法,可重塑无线环境以改善吞吐量。它的被动特征大大降低了能源消耗,这使得RIS成为实现未来智慧城市的有前途的技术。现有的波束形成设计主要是专注于优化单载体系统的光谱效率。为了避免在MIMO系统中不同空间结构域亚渠道上的复杂位分配,在本文中,我们提出了基于RIS辅助毫米波(MMWave)混合MIMO系统的基于几何平均分解的光束形成,因此可以将空间域中的多个并行数据流视为相同的通道收益。具体而言,通过利用MMWave大量MIMO通道的常见角域的稀疏性在不同的子载波上,同时正交匹配匹配算法可用于从过度采样2D-DFT Code Book中获得最佳的多光束。此外,通过仅利用与视线(LOS)通道相关的到达角度和出发角度,我们通过最大化LOS通道的阵列增益来进一步设计用于RIS的相变。仿真结果表明,所提出的方案比常规方法可以实现更好的BER性能。我们的工作是为了讨论RIS辅助MMWave混合MIMO系统的宽带混合波束的初步尝试。
Reconfigurable intelligent surface (RIS) is considered to be an energy-efficient approach to reshape the wireless environment for improved throughput. Its passive feature greatly reduces the energy consumption, which makes RIS a promising technique for enabling the future smart city. Existing beamforming designs for RIS mainly focus on optimizing the spectral efficiency for single carrier systems. To avoid the complicated bit allocation on different spatial domain subchannels in MIMO systems, in this paper, we propose a geometric mean decomposition-based beamforming for RIS-assisted millimeter wave (mmWave) hybrid MIMO systems so that multiple parallel data streams in the spatial domain can be considered to have the same channel gain. Specifically, by exploiting the common angular-domain sparsity of mmWave massive MIMO channels over different subcarriers, a simultaneous orthogonal match pursuit algorithm is utilized to obtain the optimal multiple beams from an oversampling 2D-DFT codebook. Moreover, by only leveraging the angle of arrival and angle of departure associated with the line of sight (LoS) channels, we further design the phase shifters for RIS by maximizing the array gain for LoS channel. Simulation results show that the proposed scheme can achieve better BER performance than conventional approaches. Our work is an initial attempt to discuss the broadband hybrid beamforming for RIS-assisted mmWave hybrid MIMO systems.