ID 原文 译文
16585 在频率为30 GHz、幅度为4 dBm的LO信号驱动下，电路的变频增益为–8.5～–5.5 dB。 A measured CG of –8.5～–5.5 dB is achievedwithin such a wide IF band at a LO power (PLO) of 4 dBm and a LO frequency (fLO) of 30 GHz.
16586 当固定IF为0.5 GHz、LO幅度为4 dBm时，变频增益随25～45 GHz的RF信号在–7.9～–5.9 dB范围内变化，波动幅度为2 dB。 The proposed mixer also achieves a CG with a ripple of 2 dB from –7.9 to –5.9 dB in a wide RF band (fRF) from 25 to 45GHz at a PLO of 4 dBm and a fixed IF frequency (fIF) of 0.5 GHz.
16587 LO-IF, LO-RF, RF-IF的隔离度测试结果分别优于42, 50, 43 dB。 The measured LO-to-IF, LO-to-RF and RF-to-IF isolations are better than 42, 50 and 43 dB, respectively.
16588 该下变频器芯片采用TSMC 90 nm CMOS工艺设计，芯片面积为0.4 mm2。 The chip is fabricated in TSMC 90 nm CMOSprocess with an area of 0.4 mm2.
16589 针对部分传输序列(PTS)算法在交错正交幅度调制的滤波器组多载波(FBMC-OQAM)系统中受符号重叠的影响，造成峰值再生，从而导致系统峰值功率比(PAPR)较高、计算复杂度较大等问题，该文提出一种基于双层优化的PTS算法(DO-PTS)。 The Partial Transmission Sequences (PTS) algorithm is affected by symbol overlap in the FilterBank MultiCarrier with Offset Quadrature Amplitude Modulation (FBMC-OQAM), resulting in peak valueregeneration, which leads to high Peak-to-Average Power Ratio (PAPR) and computational complexity.
16590 该算法对信号数据块进行两层相位因子搜索以获得更好的PAPR抑制性能， In this paper, a PTS algorithm based on Double Optimization (DO-PTS) is proposed, which searches two-layer phase factor for signal data blocks to obtain better PAPR suppression performance.
16591 第1层充分考虑重叠特性，结合前面重叠数据块进行初步优化， The first layer takes full account of the overlap characteristics and combines the previous overlapping data blocks for initial optimization.
16592 第2层对数据块进行分组，在每组选择对峰值影响最大的数据块进行优化，来减少进行相位因子搜索的数据块数量，并且在第1层优化中缩小相位因子的搜索范围，以降低系统的计算复杂度。 The second layer groups the signals, and in each group, the data blocks that have the greatest impact on the peak value are optimized to reduce the number of data blocks for phase factor search. The search range of phasefactor is reduced in the first layer optimization to reduce the calculation complexity.
16593 通过对计算复杂度和仿真结果的分析表明，同其它主流PTS优化算法相比，所提算法不仅能取得很好的 PAPR抑制性能，还具有较低的计算复杂度，同时也保证了系统的传输数据率。 Through the analysis of computational complexity and simulation results, it is shown that compared with other mainstream PTS optimization methods, this algorithm can not only offer good PAPR reduction performance, but also have low computational complexity, and also ensure the transmission data rate of the system.
16594 轻量级神经网络部署在低功耗平台上的解决方案可有效用于无人机(UAV)检测、自动驾驶等人工智能(AI)、物联网(IOT)领域， Lightweight neural networks deployed on low-power platforms have proven to be effective solutionsfor Artificial Intelligence (AI) and Internet Of Things (IOT) domains such as Unmanned Aerial Vehicle (UAV)detection and unmanned driving.