相对强度噪声作为光源噪声之一,当光源光功率达到一定程度时,在量级上将取代散粒噪声和热噪声成为光源噪声的主要部分。为解决此问题,作者提出一种采用饱和半导体光放大器抑制相对强度噪声的全新方案。在传统光源结构的基础上,将光源与...相对强度噪声作为光源噪声之一,当光源光功率达到一定程度时,在量级上将取代散粒噪声和热噪声成为光源噪声的主要部分。为解决此问题,作者提出一种采用饱和半导体光放大器抑制相对强度噪声的全新方案。在传统光源结构的基础上,将光源与耦合器之间加入一级半导体光放大器,使光源出光功率达到半导体光放大器深饱和区,从而实现对相对强度噪声的抑制。通过对采用半导体光放大器的光源结构进行相对强度噪声测量发现,在各个频率处相比原有光源结构,均有10 d B/Hz的下降。实验结果表明,采用半导体光放大器的光源方案实现过程简单,可靠性高,在提高光功率的基础上降低了相对强度噪声。展开更多
Blind identification-blind equalization for Finite Impulse Response (FIR) Multiple Input-Multiple Output (MIMO) channels can be reformulated as the problem of blind sources separation. It has been shown that blind ide...Blind identification-blind equalization for Finite Impulse Response (FIR) Multiple Input-Multiple Output (MIMO) channels can be reformulated as the problem of blind sources separation. It has been shown that blind identification via decorrelating sub-channels method could recover the input sources. The Blind Identification via Decorrelating Sub-channels(BIDS)algorithm first constructs a set of decorrelators, which decorrelate the output signals of subchannels, and then estimates the channel matrix using the transfer functions of the decorrelators and finally recovers the input signal using the estimated channel matrix. In this paper, a new approximation of the input source for FIR-MIMO channels based on the maximum likelihood source separation method is proposed. The proposed method outperforms BIDS in the presence of additive white Gaussian noise.展开更多
文摘相对强度噪声作为光源噪声之一,当光源光功率达到一定程度时,在量级上将取代散粒噪声和热噪声成为光源噪声的主要部分。为解决此问题,作者提出一种采用饱和半导体光放大器抑制相对强度噪声的全新方案。在传统光源结构的基础上,将光源与耦合器之间加入一级半导体光放大器,使光源出光功率达到半导体光放大器深饱和区,从而实现对相对强度噪声的抑制。通过对采用半导体光放大器的光源结构进行相对强度噪声测量发现,在各个频率处相比原有光源结构,均有10 d B/Hz的下降。实验结果表明,采用半导体光放大器的光源方案实现过程简单,可靠性高,在提高光功率的基础上降低了相对强度噪声。
基金Supported by the National Natural Science Foundation of China (No.60172048)
文摘Blind identification-blind equalization for Finite Impulse Response (FIR) Multiple Input-Multiple Output (MIMO) channels can be reformulated as the problem of blind sources separation. It has been shown that blind identification via decorrelating sub-channels method could recover the input sources. The Blind Identification via Decorrelating Sub-channels(BIDS)algorithm first constructs a set of decorrelators, which decorrelate the output signals of subchannels, and then estimates the channel matrix using the transfer functions of the decorrelators and finally recovers the input signal using the estimated channel matrix. In this paper, a new approximation of the input source for FIR-MIMO channels based on the maximum likelihood source separation method is proposed. The proposed method outperforms BIDS in the presence of additive white Gaussian noise.
