稀疏MLD解码器用于1位ADC MIMO恒定包络调制

Hany S. Hussein
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引用次数: 2

摘要

由于MIMO- ofdm组件(即线性功率放大器和高分辨率ADC)的功率低效率,引入了带有1位ADC的MIMO恒定包络调制(MIMO- cem)作为低功耗通信系统。然而,由于在MIMO-CEM接收端(RX)使用1位ADC,接收到的MIMO-CEM信号遭受巨大的失真。因此,一个非常复杂的基于中频的最大似然解码器(IF- mld)与MIMO-CEM RX一起使用,以抵消1位ADC量化效应。在IF-MLD中,整个MIMO-CEM在每个MLD状态下都在中频波段复制,这太复杂而不实用。为此,提出了一种高效的低复核稀疏MIMO-CEM MLD。其中,基于1位ADC的简单而有效的近似,提出了线性基带MLD。然后,提出了一种基于相关熵最大化的核稀疏选择器(KSS),从上述线性基带MLD空间中指定候选数量。其中,利用高阶统计量(HOS)熵来补偿MIMO-CEM接收信号与所提出的线性近似基带MLD之间的不匹配。最后,将基于中频的MLD应用于候选信号,以准确估计传输序列。在不同的场景下,采用不同的调制技术对稀疏MLD的有效性进行了测试和验证。平均而言,与传统的(IF-MLD)和最近的MIMO-CEM解码器算法相比,所提出的核稀疏MLD算法的复杂度分别降低了99%和91%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sparse MLD Decoder for 1-Bit ADC MIMO Constant Envelope Modulation
Due to the power inefficiency of the MIMO-OFDM components (i.e. linear power amplifier and high resolution ADC), the MIMO constant envelope modulation (MIMO-CEM) with 1-bit ADC was introduced as a low power communication system. However, the received MIMO-CEM signal suffers from an enormous distortion as a result of using the 1-bit ADC at MIMO-CEM receiver side (RX). Therefore, a very high complex IF based maximum likelihood decoder (IF-MLD) is used with the MIMO-CEM RX to neutralize the 1-bit ADC quantization effect. In the IF-MLD the whole MIMO-CEM is replicated in the IF band in each MLD state, which is too complex to be practical. Therefore, an efficient low complex kernel sparse MIMO-CEM MLD is proposed. Where, a linear baseband MLD is proposed based on simple yet efficient approximation for the 1-bit ADC. Then, a kernel sparse selector (KSS) based on the correntropy maximization is proposed to nominate number of candidates from the space of aforementioned linear baseband MLD. In which, the higher order statistics (HOS) correntropy is exploited to compensate the mismatch between the MIMO-CEM received signal and the proposed linear approximation baseband MLD. Finally, the IF based MLD is applied on the nominated candidates to accurately estimate the transmitted sequence. The effectiveness of the proposed sparse MLD is tested and verified under different scenarios with different modulation techniques. On average, the proposed kernel sparse MLD achieves a complexity reduction by 99 % and 91 % compared to the conventional (IF-MLD) and recently MIMO-CEM decoder algorithm respectively.
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