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首页> 外文期刊>IEEE Transactions on Vehicular Technology >Joint Estimation of Transmitter and Receiver IQ Imbalance With ML Detection for Alamouti OFDM Systems
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Joint Estimation of Transmitter and Receiver IQ Imbalance With ML Detection for Alamouti OFDM Systems

机译:带有ML检测的Alamouti OFDM系统的发射机和接收机IQ不平衡联合估计

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摘要

The application of multiple transmit antennas in orthogonal frequency-division multiplexing (OFDM) systems provides better spectral efficiency with high performance gain. However, these systems are very sensitive to channel estimation errors and analog front-end impairments, such as in-phase/quadrature-phase (IQ) imbalance. To ensure a reliable transmission, the channel impulse response (CIR) and IQ imbalance have to be accurately estimated. Unlike previous work, we show that the unwanted IQ imbalance can be exploited to achieve a diversity gain. This paper develops a maximum-likelihood (ML) detector by exploiting the diversity gain resulting from the IQ imbalance for Alamouti space-time block code OFDM systems. Moreover, we propose a novel iterative algorithm to jointly estimate the CIR and IQ imbalance occurring at both the transmitter and the receiver. We initially employ a pilot sequence for estimation and detection. Then, we exploit the soft information provided by the detector via an expectation-maximization (EM) algorithm to improve the estimation efficiency iteratively. To reduce the computational complexity of the estimation process, a suboptimal algorithm is also developed. Simulation results indicate that the bit-error-rate (BER) performance of the proposed detector in conjunction with the proposed estimation algorithms is very close to the BER of the perfectly known parameters case with a diversity gain resulting from the IQ imbalance.
机译:多个发射天线在正交频分复用(OFDM)系统中的应用提供了更高的频谱效率和高性能增益。但是,这些系统对信道估计误差和模拟前端损害(例如同相/正交相位(IQ)不平衡)非常敏感。为了确保可靠的传输,必须准确估计信道脉冲响应(CIR)和IQ不平衡。与以前的工作不同,我们表明可以利用不需要的IQ不平衡来获得多样性。本文利用Alamouti空时分组码OFDM系统因IQ不平衡而产生的分集增益,开发了最大似然(ML)检测器。此外,我们提出了一种新颖的迭代算法来共同估计在发送器和接收器处出现的CIR和IQ不平衡。我们最初采用导频序列进行估计和检测。然后,我们通过期望最大化(EM)算法利用检测器提供的软信息来迭代地提高估计效率。为了降低估计过程的计算复杂性,还开发了次优算法。仿真结果表明,所提出的检测器与所提出的估计算法相结合的误码率(BER)性能非常接近于完全已知的参数情况的BER,并且具有IQ失衡导致的分集增益。

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