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A complexity efficient equalization technique for MIMO-constant envelope modulation

机译:用于MIMO恒定包络调制的复杂性高效均衡技术

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MIMO-Constant Envelope Modulation, MIMO-CEM, with Low Resolution ADC receiver (1-bit ADC in the default operation) is considered as one of the alternative candidates to the well-known MIMO-OFDM, suitable for wireless backhaul networks. Because MIMO-CEM receiver is based upon low resolution ADC sampled at the Intermediate Frequency (IF) band, the currently used MIMO-CEM equalizer is too complicated. The equalizer high complexity comes from its design; in which IF based Maximum Likelihood Sequence Estimator (MLSE) is utilized. In order to efficiently estimate the transmitted sequence, the MLSE equalizer replicates the whole IF MIMO-CEM transceiver, which results in too complicated MIMO-CEM equalizer. In this paper, low complexity equalization technique for MIMO-CEM is proposed. In the proposed method, a baseband approximation model for the 1-bit ADC MIMO-CEM is investigated. Through using this model, a baseband based MIMO-CEM MLSE equalizer can be constructed. This baseband equalizer will nominate number of candidate sequences, which are further refined by the IF based MLSE equalizer to accurately estimate the transmitted sequence. Hence, the number of equalization processes required by the high complexity IF based MLSE are greatly reduced, which powerfully contributes in reducing the complexity of MIMO-CEM equalization. The effectiveness of the proposed estimator is proven under different scenarios using Minimum Shift Keying (MSK) and Gaussian MSK (GMSK) modulations.
机译:MIMO-恒定包络调制,MIMO-CEM,与低分辨率ADC接收器(在默认操作1位ADC)被认为是替代候选公知的MIMO-OFDM,适用于无线回程网络之一。因为MIMO-CEM接收器基于在中频(IF)频带处采样的低分辨率ADC,所以当前使用的MIMO-CEM均衡器太复杂。均衡器高复杂性来自其设计;其中,如果使用基于最大似然序列估计器(MLSE)。为了有效估计传输的序列,MLSE均衡器复制整个如果MIMO-CEM收发器,这导致MIMO-CEM均衡器太复杂。在本文中,提出了MIMO-CEM的低复杂性均衡技术。在所提出的方法中,研究了1位ADC MIMO-CEM的基带近似模型。通过使用该模型,可以构建基于基带的MIMO-CEM MLSE均衡器。该基带均衡器将提名候选序列的数量,该候选序列由IF基于MLSE均衡器进一步改进,以准确估计发送的序列。因此,如果基于MLSE的MLSE大大降低,则高复杂性所需的均衡过程的数量有力有助于降低MIMO-CEM均衡的复杂性。使用最小移位键控(MSK)和高斯MSK(GMSK)调制,在不同场景下证明了所提出的估计器的有效性。

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