ID 原文 译文
23455 该方法将 OFDM-IM 技术应用于从用户发送端，将从用户的传输空间划分为信号空间和索引空间。 In the proposed scheme, OFDM-IM technique is used at the Secondary User (SU) to split the transmission space into the signal constellation domain and the index domain.
23456 从用户在其信号空间上转发主用户的信息，同时在索引空间传输从用户信息。 Specifically, the Secondary Transmitter (ST) of SU acts as a Decode-and-Forward (DF) relay to transmit the information of the Primary User (PU) in the signal constellation domain, while the information bits of SU are carried by the index domain.
23457 通过这种方式，主、从用户之间不存在相互干扰。 Through this design concept, the mutual interference between the PU and SU can be avoided.
23458 针对此模型，该文分析了主、从用户的误码率，理论推导了相关表达式，并讨论了从用户的位置对主、从用户性能的影响。 Upper bounds on the Bit Error Probabilities (BEPs) of the PU and SU are analytically derived. The influence of ST's location to the BER performance of PU and SU is also analysed.
23459 仿真结果表明，该文所提设计方法能够同时提升主、从用户的误码率性能，且均优于传统的基于OFDM 的协作中继方式。 Several numerical results and discussions are provided to substantiate the theoretical analysis, and it is shown that the proposed protocol is a viable candidate for OFDM-based CR networks since it can enhance the BER performances of both PU and SU.
23460 该文针对概率主成分分析(PPCA)模型用于 1 维高分辨距离像(HRRP)识别对噪声敏感的问题，对经典PPCA 模型进行修正。 In order to improve the sensitivity problem of using Probabilistic Principal Component Analysis (PPCA) model for HRRP recognition, a modified method is proposed.
23461 该方法将基于高斯分布的 PPCA 模型扩展为基于 t 分布的 PPCA 模型，能够综合利用 t 分布对噪声稳健和 PPCA 模型自由参数少的特性。 T-distribution is adopted as the basis of PPCA model rather than Gaussian distribution in this method, which utilizes not only the t-distribution's robustness, but also less free parameters of PPCA characteristic.
23462 同时为了减少目标方位敏感性对 HRRP 统计建模的影响，进一步将 t 分布模型扩展为混合概率 t 分布模型，能够以分布趋同的原则将不同方位帧内具有相同统计特性的 HRRP 数据进行聚类，减少模型的失配，改善识别性能。 Further, to eliminate the targets' azimuth sensitivity, the mixture t-distribution is substituted for single t-distribution. This modification offers a potential to model the similar density of HRRP in different azimuth range adequately for clustering and reduces the mismatch between models, thus improves the recognition performance.
23463 模型参数通过期望最大值(EM)算法估计，可提高计算效率。 Estimation of parameters is achieved by EM algorithm to avoid the drawbacks of maximum-likelihood estimation and improve the estimation efficiency.
23464 最后，通过贝叶斯规则，以获取的统计特征识别测试数据，仿真结果表明该方法能够提高低信噪比条件下 PPCA 模型的稳健性。 Finally, in the simulation experiment Bayesian rule and the estimation statistical features are adopted together to test new HRRPs, the results show this method can improve the robustness of PPCA model in low SNR conditions.