Heuristic Inspired Precoding for Millimeter-Wave MIMO Systems with Lens Antenna Subarrays
Heuristic Inspired Precoding for Millimeter-Wave MIMO Systems with Lens Antenna Subarrays
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DOI：
10.1109/vtc2022-spring54318.2022.9861021
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Sinasi Cetinkaya;Liza Afeef;G. Mumcu;H. Arslan
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文献类型：
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作者：
Sinasi Cetinkaya;Liza Afeef;G. Mumcu;H. Arslan
具有混合波束成形的毫米波中的传统阵列（TA）多输入多输出（MIMO）架构遭受高功耗和硬件开销。因此，透镜天线子阵列（LAS）-MIMO架构最近已经被提出作为用于功率高效系统并且降低硬件成本和复杂度的有前景的技术。此外，LAS-MIMO可以提供接近TA-MIMO的频谱效率（SE）性能，并且高于单透镜天线阵列（SLA）-MIMO。在本文中，我们提出了一种混合预编码算法的LAS-MIMO毫米波有效地控制LAS设计。由于毫米波信道的稀疏特性，预编码问题被公式化为稀疏重构问题。该算法是一个迭代的过程，联合使用人工蜂群（ABC）优化与正交匹配追踪（OMP）算法。在每次迭代中，该算法首先使用ABC随机选择每个透镜的开关，然后使用OMP近似最优无约束预编码器。这个过程一直持续到达到最大SE。仿真结果表明，与SLA相比，LAS的SE增加了约30%，同时为单射频（RF）链和多RF链场景提供了显著的能效（EE）增益。
A traditional array (TA) multiple-input multiple-output (MIMO) architecture in mmWave with hybrid beamforming suffers from high power consumption and hardware overhead. Therefore, a lens antenna subarray (LAS)-MIMO architecture has been recently proposed as a promising technology for a power-efficient system and reducing hardware cost and complexity. Additionally, the LAS-MIMO can offer spectral efficiency (SE) performance close to TA-MIMO and higher than single-lens antenna array (SLA)-MIMO. In this paper, we propose a hybrid precoding algorithm for the LAS-MIMO in mmWave to efficiently control the LAS design. The precoding problem is formulated as a sparse reconstruction problem due to the sparse behavior of mmWave channel. The proposed algorithm is an iterative process developed jointly using artificial bee colony (ABC) optimization with orthogonal matching pursuit (OMP) algorithms. In each iteration, the algorithm first selects the switches for each lens randomly using ABC and then uses OMP to approximate optimal unconstrained precoders. This process continues until achieving maximum SE. The simulation results show that LAS has around a 30% increase in SE compared to SLA while providing a significant gain in energy efficiency (EE) for single radio-frequency (RF) chain and multi RF chain scenarios.