A novel intercarrier interference (ICI)-aware orthogonal frequency division multiplexing (OFDM) channel estimation network ICINet is presented for rapidly time-varying channels. ICINet consists of two components: a preprocessing deep neural subnetwork (PreDNN) and a cascaded residual learning-based neural subnetwork (CasResNet). By fully taking into account the impact of ICI, the proposed PreDNN first refines the initial channel estimates in a subcarrier-wise fashion. In addition, the CasResNet is designed to further enhance the estimation accuracy. The proposed cascaded network is compatible with any pilot patterns and robust against mismatched system configurations. Simulation results verify the superiority of ICINet over existing networks in terms of better performance and much less complexity.
翻译:在快速时间变化的频道中,出现了一种新的载体间干扰(ICI)对正方位频率分解多路估计网络(ICDM) ICINet,由两个部分组成:预处理深神经子网络(PERDNN)和一个级联的残余学习神经子网络(CasResNet),充分考虑到ICI的影响,拟议的PreDN首次以亚载分解方式完善最初的频道估计。此外,CasResNet旨在进一步提高估计的准确性。拟议的级联网络与任何试点模式兼容,对不匹配的系统配置具有很强的力度。模拟结果可以验证ICNet在业绩更好、复杂性更小方面优于现有网络的优势。