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Mixture PLL-based loop for joint CFO and channel estimation in slow time-varying OFDM environment

机译:混合基于PLL的回路,用于关节CFO和频率慢时改的OFDM环境中的信道估计

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This work deals with joint carrier recovery and channel estimation for orthogonal frequency-division-multiplexing (OFDM) systems over slow time-varying Rayleigh channel. In this paper, we propose a less complex algorithm based on a mixture PLL-based loop for estimating and tracking param-eters in non-linear model. The algorithm can work with both the physical channel model (assuming time delays information) and the equivalent discrete-time channel model. In the algorithm, we combinate two techniques the PLL-based loop and the Sequential Monte Carlo Sampling in order to track the channel complex gains and unknown carrier frequency offset (CFO). Afterwards, the channel matrix can be simply constructed, and then the data symbol can be estimated with free intercarrier interference (ICI) by using MMSE equalizer. It is shown that our algorithm has a good performance in terms of MSE and BER and approaches the BER of the ideal case for which the channel response and CFO are known. Moreover, the proposed mixture has less complexity compared to the mixture with kalman filter.
机译:该工作涉及在慢速时变瑞利信道上对正交频分复用(OFDM)系统的联合载波回收和信道估计。在本文中,我们提出了一种基于混合PLL的循环的算法,用于估计和跟踪非线性模型中的参数。该算法可以与物理信道模型(假设时间延迟信息)和等效离散时间信道模型一起使用。在算法中,我们将两个技术组合到基于PLL的循环和顺序蒙特卡罗采样,以便跟踪信道复合增益和未知的载波频率偏移(CFO)。然后,可以简单地构造信道矩阵,然后通过使用MMSE均衡器可以通过自由的互载干扰(ICI)来估计数据符号。结果表明,我们的算法在MSE和BER方面具有良好的性能,并接近频道响应和CFO的理想情况的BER。此外,与用Kalman过滤器的混合物相比,所提出的混合物的复杂性较差。

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