ID 原文 译文
57738 对可见光通信系统进行了理论分析、模拟仿真和实验验证，实验结果表明，在有限的 LED 带宽约束下，当误码率达到 10 － 3时，传输距离可达 120 cm. Simulation verification of the VLC system is carried out and shows that the transmission distance can achieve 120 cm for the bit error rate of 10 － 3 with limited bandwidth of LEDs.
57739 为了提高易受视距( LOS) 和非视距( NLOS) 传输影响无线应用的性能，对大规模三维多输入多输出( 3D MI- MO) 系统中的 LOS /NLOS 识别进行了研究，针对实际场景，采用实际信道而非通常假设的理想准确信道，提出了一种改进的时－空－频信道相关识别算法 TSFCI-1. To improve the performance of some wireless technologies，which are susceptible to line of sight ( LOS) and non line of sight ( NLOS) ，LOS /NLOS identification in 3D massive multi-input multi- output ( MIMO) system is studied. Based on channel correlation，an improved identification algorithm， TSFCI-1，is proposed，which uses actual channel information instead of the normally assumed ideal accu- rate channel.
57740 识别过程包括 3 个阶段: The process includes:
57741 根据 LOS /NLOS 用户不同的时－空－频特性定义测量;针对大规模 3D MIMO 中信道空间相关性不平稳的特点，对评价指标在空间间隔上求期望;使用时域信道信息进行建模和识别. defining measurement based on time-space-frequency properties of LOS /NLOS; in view of the unsteady spatial channel correlation for 3D massive MIMO systems，finding the expectation of measurement on the spatial interval; using channel information to construct the statisti- cal identification model.
57742 在此基础上，考虑到天线双极化的影响，改进评价指标，并提出算法 TSFCI-2.仿真结果表明，TSFCI-1 和 TSFCI-2 的算法性能均优于对比算法 6% 以上，错误率分别低至 1.92% 和 1.72% . Considering the influence of antenna dual-polarization，TSFCI-2 with better e- valuation index is proposed. It is shown that the identification error of TSFCI-1 and TSFCI-2 is as low as 1. 92% and 1. 72% ，with over 6% better than a previous study.
57743 此外，讨论了信噪比和时域径数对表现最好的 TSFCI-2 性能的影响. Besides，the effects of signal to noise ratio and the taps number on TSFCI-2 with the best performance is discussed.
57744 为提高非平稳噪声下远场非相干窄带信号波达方向( DOA) 的估计精度，提出了一种基于稀疏重构的 DOA估计算法. In order to improve the direction of arrival ( DOA) estimation accuracy of the far-field non-co- herent narrow-band signal in non-stationary noise environment，an improved DOA estimation algorithm based on sparse reconstruction is proposed.
57745 采用类协方差差分算法构造差分矩阵，抑制非平稳噪声的影响; Firstly，the class differential covariance algorithm is used to construct the difference matrix to suppress the influence of non-stationary noise.
57746 基于类旋转不变子空间参数估计算法基本原理构造稀疏表示模型与权函数; Then the sparse repre- sentation model and the weight function is constructed based on the basic principle of estimation of signal parameters via rotational invariance technique algorithm.
57747 利用加权 l1 范数对模型求解，实现 DOA 估计. Finally，the DOA estimation is realized by solving the model with weighted l1 norm.