不同晶格约简辅助多用户海量MIMO检测技术的误码率分析

Annu Singh, S. Joshi
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引用次数: 0

摘要

对更高数据速率和同时向多用户提供服务的需求日益增长,对频谱的高效利用提出了挑战。5G所需的两项重要技术是毫米波和大规模MIMO,以利用毫米波更宽的带宽优势,并使用大规模MIMO技术减轻干扰。本文比较了28GHz频率下不同的LR (Lattice Reduction)辅助大规模MIMO检测技术的误码率(BER)。我们将QR分解作为整个MIMO检测器的预处理步骤。LRA (Lattice Reduction Aided)探测器性能复杂度权衡因子δ, (δ=3/4)。散射通道模型采用100个散射体。我们采用不同的MIMO配置、调制技术、多个用户数量、每个用户的单数据流和多个数据流来比较和分析误码率(BER)结果。对brown算法、Lenstra Lenstra Lovasz (LLL)算法、Complex LLL(CLLL)算法、Fixed Complexity LLL(fc-LLL)算法、Efficient LLL(ell)算法、Possible Swap LLL算法(PSLLL)、Greedy LLL算法和ll - mmse算法进行了分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BER Analysis of Different Lattice Reduction Aided Multi User-Massive MIMO Detection Techniques
The escalating demand for higher data rate and to provide service to multiple users simultaneously has increased the challenge of efficient spectrum utilization. The two important technologies required in 5G is Mmwave and Massive MIMO to use the benefit of wider bandwidth of mmwave and to mitigate interference using Massive MIMO Technology. In this paper we compare the BER (Bit Error Rate) of different LR (Lattice Reduction) aided Massive MIMO detection technique at 28GHz frequency. We use the QR decomposition as a preprocessing step in the entire MIMO detector. LRA (Lattice Reduction Aided) detectors performance-complexity tradeoff factor δ, (δ=3/4). Scattering Channel Model is used with 100 numbers of scatterers. We employ the different MIMO configuration, modulation techniques, multiple number of users, single and multiple data stream per user to compare and analyze the BER (Bit Error Rate) results. The results are analyzed for Brun's algorithm, Lenstra Lenstra Lovasz (LLL) algorithm, Complex LLL(CLLL) algorithm, Fixed Complexity LLL (fc-LLL) Algorithm, Efficient LLL(ELLL) Algorithm, Possible Swap LLL Algorithm (PSLLL), Greedy LLL algorithm, and LLL-MMSE algorithm.
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