ID 原文 译文
1993 然后提出一种归一化广义内积统计量修正杂波的分布， Then a normal-ized generalized inner product statistic (N-GIP)is proposed to modify the clutter distribution parameters.
1994 最后利用 PCE 方法估计 CCM 并进行 STAP 杂波抑制。 Finally, the PCEmethod is utilized to estimate the CCM and the STAP is used to suppress clutter.
1995 通过分析舰载高频地波雷达仿真和实测数据处理结果表明:所提方法的稳健性大幅提升， The simulation experiments and measureddata processing results show that the robustness of the proposed method is greatly improved.
1996 相比稀疏恢复 STAP 方法和前后向空时平滑 STAP 方法滤波器凹口更加准确且更深，在有效抑制杂波的同时更利于慢速目标的检测。 Compared with the sparse re-covery STAP (SR STAP)and forward/backward smoothing STAP (F/B STAP), the filter notches are more accurate and deeper. This benefits the detection of slow targets.
1997 针对高速移动环境下信道快时变、非平稳特性导致下行链路信道估计性能受限的问题，本文提出一种基于深度学习的信道估计网络，即 ChanEstNet。 Aiming at the problem that the downlink channel estimation performance is limited due to the fast time-va-rying and non-stationary characteristics in the high-speed mobile environment, this paper proposes a channel estimation net-work based on deep learning, called ChanEstNet.
1998 ChanEstNet 使用卷积神经网络(Convolutional Neural Network，CNN)提取信道响应特征矢量和循环神经网络(Recurrent Neural Network，RNN)进行信道估计。 ChanEstNet uses the convolutional neural network (CNN)to extract chan-nel response feature vectors and recurrent neural network (RNN)for channel estimation.
1999 我们利用标准的高速信道数据对学习网络进行离线训练，充分挖掘训练样本中的信道信息，使其学习到高速移动环境下信道快时变和非平稳的特点，更好的跟踪高速环境下信道的变化特征。 We use the standard high-speed channel data to conduct offline training for the learning network, fully excavate the channel information in the training sam-ple, make it learn the characteristics of fast time-varying and non-stationary channels in high-speed mobile environments, and better track the characteristics of channel changing in high-speed environment.
2000 仿真结果表明，在高速移动环境下，与传统方法相比，所提信道估计方法计算复杂度低，性能提升明显。 The simulation results show that in the high-speed mobile environment, compared with the traditional methods, the proposed channel estimation method has low computa-tional complexity and significant performance improvement.
2001 无线电资源交易发生在 MTC 网关(MTC Gateway MTCG)和 LTE 用户之间。 The radio resources trading happened between the MTCGs and the LTE users.
2002 根据基于联盟区块链的空闲无线电资源交易来建立 MTCG 之间的信用度。 The credit degrees are built among the MTCGs according to the free radio resources trading based on the consortium blockchain.