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An MIMO Configuration Mode and MCS Level Selection Scheme by Fuzzy Q-Learning for HSPA⁺ Systems

机译:基于模糊Q学习的HSPA⁺系统MIMO配置模式和MCS等级选择方案

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摘要

In this paper, we propose a fuzzy Q-learning-based MIMO configuration mode and MCS level (FQL-MOMS) selection scheme for high speed packet access evolution ({rm HSPA}^{+}) systems. The FQL-MOMS selection scheme intends to enhance the system throughput under the block error rate (BLER) requirement guarantee. It will determine an appropriate MIMO configuration mode and MCS (modulation and coding scheme) level for packet data transmission in {rm HSPA}^{+} systems, under the situations that the channel status is varying and the channel quality indication (CQI) has report delay. The FQL-MOMS scheme considers not only the reported CQI and the last transmission result but also the BLER performance metric and the transmission efficiency. Moreover, it is effectively configured, where the fuzzy rules and the reinforcement signals for the Q-learning algorithm are sophisticatedly designed. Simulation results show that the proposed FQL-MOMS scheme increases the system throughput by up to 49.3 and 35.9 percent, compared to the conventional adaptive threshold selection (ATS) scheme [CHECK END OF SENTENCE] and the Q-HARQ scheme [CHECK END OF SENTENCE], respectively, under the BLER requirement fulfillment.
机译:在本文中,我们针对高速分组接入演进({rm HSPA} ^ {+})系统提出了一种基于模糊Q学习的MIMO配置模式和MCS级(FQL-MOMS)选择方案。 FQL-MOMS选择方案旨在在块错误率(BLER)要求保证下提高系统吞吐量。在{rm HSPA} ^ {+}系统中,在信道状态变化且信道质量指示(CQI)具有以下情况的情况下,它将确定适当的MIMO配置模式和MCS(调制和编码方案)级别,用于分组数据传输报告延迟。 FQL-MOMS方案不仅考虑报告的CQI和最后的传输结果,还考虑BLER性能指标和传输效率。此外,它是有效配置的,其中精心设计了用于Q学习算法的模糊规则和增强信号。仿真结果表明,与传统的自适应阈值选择(ATS)方案[CHECK END SENTENCE]和Q-HARQ方案[CHECK END SENTENCE]相比,该FQL-MOMS方案可将系统吞吐量提高多达49.3%和35.9%。 ],分别在BLER要求下实现。

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