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首页> 外文期刊>International journal of speech technology >Hybrid optimization algorithm to estimate azimuth angle for millimeter wave massive MIMO system
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Hybrid optimization algorithm to estimate azimuth angle for millimeter wave massive MIMO system

机译：混合优化算法来估算毫米波大规模MIMO系统方位角的估计方位角

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摘要

Further enhancement of 4G (or 4G-LTE advanced) spectral efficiency is possible only by allocating more bandwidth i.e., broadcasting on millimeter wave (mmWave) spectrum, by using the concept of small cells and also by increasing the number of spatial channels which is referred to as MIMO systems. However, recognizing the mmWave in MIMO remains a complex task that faces the issues like increased propagation loss. A new optimization based estimation algorithm is proposed to estimate the mm wave channel and to improve the performance of hybrid precoding. Azimuth angle is considered as objective function and is optimized without deviating from the actual characteristics. In this paper, Dragonfly Evaluated Grey Wolf Optimization (DE-GWO) model is introduced that hybridizes the concepts of DA and GWO respectively. The proposed algorithm is validated by comparing with Deer Hunting Optimization Algorithm (DHOA), Lion Algorithm (LA) and LDHOA (combination of LA and DHOA). Finally, the performance of proposed work is compared and validated over other state-of-the-art models with respect to CSI and error measures.
机译：仅通过使用小单元的概念来分配更多的带宽即可通过增加毫米波（MMWAVE）频谱，并且还通过增加所指的空间通道的数量来进一步通过分配更多的带宽而进一步增强4G（或4G-LTE高级）光谱效率。作为MIMO系统。但是，识别MIMO中的MMWave仍然是一个复杂的任务，面临着增加的传播损失等问题。提出了一种新的基于优化的估计算法来估计MM波通道并提高混合预编码的性能。方位角被认为是目标函数，并且在不偏离实际特征的情况下进行优化。本文介绍了蜻蜓评估灰狼优化（DE-GWO）模型，分别杂交DA和GWO的概念。通过与鹿狩猎优化算法（DHOA），狮子算法（LA）和LDHOA（La和DHOA的组合）进行比较，验证了所提出的算法。最后，比较了所提出的工作的表现，并在其他最先进的模型上验证了CSI和误差措施。

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