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Joint CFO and channel estimation in OFDM-based massive MIMO systems

机译:基于OFDM的大规模MIMO系统中的联合CFO和信道估计

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Estimation of carrier frequency offset (CFO) is a challenging task in practical systems specifically in the uplink of multiuser systems where multiple CFOs are present in the received signal. Massive MIMO as a multiuser technique has recently attracted a great deal of attention among researchers. However, to the best of our knowledge, there is no study looking into the joint estimation of CFOs and wireless channel in orthogonal frequency division multiplexing (OFDM) based massive MIMO systems. Therefore, in this paper, we propose joint estimation of multiple CFOs and the users' channel responses based on the maximum likelihood (ML) criteria in such systems. We propose to use the zadoff-chu (ZC) training sequences to reduce the implementation complexity. Additionally, utilization of ZC sequences for training simplifies the multidimensional grid search problem of estimating multiple CFOs and converts it into a set of line search problems, i.e., one line search problem per user. Also this sequence has a low peak to average ratio (PAPR). Finally, we show the efficacy of our proposed algorithm through numerical simulations.
机译:在实际系统中,尤其是在接收信号中存在多个CFO的多用户系统的上行链路中,载波频率偏移(CFO)的估计是一项艰巨的任务。大规模MIMO作为一种多用户技术最近引起了研究人员的广泛关注。然而,据我们所知,没有研究研究基于正交频分复用(OFDM)的大规模MIMO系统中CFO和无线信道的联合估计。因此,在本文中,我们建议在此类系统中基于最大似然(ML)标准对多个CFO和用户的信道响应进行联合估计。我们建议使用zadoff-chu(ZC)训练序列来降低实现的复杂性。另外,利用ZC序列进行训练简化了估计多个CFO的多维网格搜索问题,并将其转换为一组线搜索问题,即每个用户一个线搜索问题。同样,该序列具有较低的峰均比(PAPR)。最后,我们通过数值仿真证明了所提出算法的有效性。

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