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ANN-based channel estimation algorithm of IM/DD-OFDM/OQAM-PON systems with mobile fronthaul network in 5G

机译:IM / DD-OFDM / OQAM-PON系统的基于ANN的信道估计算法5G的移动Fronthaul网络

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摘要

5G mobile fronthaul (MFH) network requires data transmission with large bandwidth and ultra-low latency. Orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) passive optical network (PON) system is the 5G MFH solution with low spectrum efficiency and large bandwidth. The OFDM/OQAM-PON transmission performance will be undermined by intrinsic imaginary interference (IMI) induced by chromatic dispersion (CD) and polarization mode dispersion (PMD). In this paper, we proposed an artificial neural network (ANN) based channel estimation (CE) algorithm, which can effectively reduce IMI by estimation of channel transfer function (TF). Simulation results show that the proposed algorithm can optimize the system performance, compared with the conventional LS method, the proposed algorithm can improve bit error rate optimization capability by an order of magnitude.
机译:5G移动Fronthaul(MFH)网络需要具有大带宽和超低延迟的数据传输。 正交频分复用/偏移正交调制(OFDM / OQAM)无源光网络(PON)系统是具有低频谱效率和大带宽的5G MFH溶液。 OFDM / OQAM-PON传输性能将受到色散(CD)和偏振模式色散(PMD)引起的内在虚拟干扰(IMI)的破坏。 在本文中,我们提出了一种基于人工神经网络(ANN)的信道估计(CE)算法,其可以通过估计信道传递函数(TF)有效地减少IMI。 仿真结果表明,该算法可以优化系统性能,与传统的LS方法相比,所提出的算法可以通过幅度提高误码率优化能力。

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