Tolerance of laser frequency offset in optical minimum-shift keying transmission systems

2009 ◽  
Vol 282 (14) ◽  
pp. 2774-2779 ◽  
Author(s):  
Han Chen ◽  
Yi Dong ◽  
Hao He ◽  
Weisheng Hu ◽  
Lemin Li
2008 ◽  
Author(s):  
Han Chen ◽  
Yi Dong ◽  
Hao He ◽  
Weisheng Hu ◽  
Lemin Li

2014 ◽  
Vol 53 (12) ◽  
pp. 2632 ◽  
Author(s):  
Kang Ying ◽  
Yueping Niu ◽  
Dijun Chen ◽  
Haiwen Cai ◽  
Ronghui Qu ◽  
...  

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Qi An ◽  
Zi-shu He ◽  
Hui-yong Li ◽  
Yong-hua Li

Promptitude and accuracy of signals’ non-data-aided (NDA) identification is one of the key technology demands in noncooperative wireless communication network, especially in information monitoring and other electronic warfare. Based on this background, this paper proposes a new signal classifier for phase shift keying (PSK) signals. The periodicity of signal’s phase is utilized as the assorted character, with which a fractional function is constituted for phase clustering. Classification and the modulation order of intercepted signals can be achieved through its Fast Fourier Transform (FFT) of the phase clustering function. Frequency offset is also considered for practical conditions. The accuracy of frequency offset estimation has a direct impact on its correction. Thus, a feasible solution is supplied. In this paper, an advanced estimator is proposed for estimating the frequency offset and balancing estimation accuracy and range under low signal-to-noise ratio (SNR) conditions. The influence on estimation range brought by the maximum correlation interval is removed through the differential operation of the autocorrelation of the normalized baseband signal raised to the power ofQ. Then, a weighted summation is adopted for an effective frequency estimation. Details of equations and relevant simulations are subsequently presented. The estimator proposed can reach an estimation accuracy of10-4even when the SNR is as low as-15 dB. Analytical formulas are expressed, and the corresponding simulations illustrate that the classifier proposed is more efficient than its counterparts even at low SNRs.


2014 ◽  
Vol 12 (4) ◽  
pp. 040604-40608 ◽  
Author(s):  
Dingxin Xie Dingxin Xie ◽  
Jing He Jing He ◽  
Lin Chen Lin Chen ◽  
Jin Tang Jin Tang ◽  
Ming Chen Ming Chen

2010 ◽  
Vol 38 (4) ◽  
pp. 915-922 ◽  
Author(s):  
V Semenov ◽  
M Buyanova ◽  
D Anderson ◽  
M Lisak ◽  
R Udiljak ◽  
...  

2007 ◽  
Vol 90 (17) ◽  
pp. 171120 ◽  
Author(s):  
S. C. Bell ◽  
D. M. Heywood ◽  
J. D. White ◽  
J. D. Close ◽  
R. E. Scholten

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