Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (1): 17-22, 29.doi: 10.6040/j.issn.1672-3961.0.2018.196

• Machine Learning & Data Mining • Previous Articles     Next Articles

Improvement of bandwidth model for high speed optical communicationlaser and its optimization by parallel computing

Si YANG1(),Sitong LI2,Jindong ZHANG3,Yu BAI4,*()   

  1. 1. School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
    2. School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
    3. Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing 100083, China
    4. Beijing Key Laboratory of Functional Materials for Building Structure and Environment Remediation, School of Science, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
  • Received:2018-05-25 Online:2019-02-20 Published:2019-03-01
  • Contact: Yu BAI E-mail:1262421800@qq.com;baiyu@bucea.edu.cn
  • Supported by:
    国家自然基金项目(21576023);国家自然基金项目(51406008);“十三五”重点研发计划项目(2016YFC0700601);北京建筑大学市属高校科研业务基本经费项目(X18122);北京建筑大学市属高校科研业务基本经费项目(X18087)

Abstract:

In order to adapt to high-speed network, features of optical fiber communication laser need to be optimized. Laser thermal sensing model was improved by measuring the data exported by vertical cavity surface emitting laser (VCSEL). In the modified model, the quantum rate equation was developed to describe features of laser VCSEL. Considering the relationship between the gain constant, the transparent current carriers and the temperature, the high order fitting method was introduced to optimize the laser bandwidth system, which maked it closer to the measured data. Meanwhile, based on the parallel data mining method, the buffer helped to speed up the solving process to satisfy the demand of the next-generation high-speed network. The results showed that the output light power appeared to decrease in thermal saturation under the high temperature and large injection current. The model after optimization was closer to the actual measured data, genetic algorithm had a strong adaptability towards scale change of system, and the route planned by algorithm was reliable. The calculation speed was increased by 9.15% based on parallel data processing method. This modified model met the demand of the new-generation high-speed fiber optic communication, and considered the thermal limitation.

Key words: VCSEL, thermal analysis, bandwidth model, parallel computation, data mining

CLC Number: 

  • TP391.9

Fig.1

Schematic diagram of VCSEL laser structure"

Fig.2

Figure for variation of maximum optical poweralong with temperature"

Fig.3

Amplitude-frequency response curve of laser"

Table 1

The interating results of parameters"

类型 ηi β/10-6 τn/ns k/μW G0/Ms-1 N0/106 τp/ps ε/10-8
初值 0.700 0 10.00 9.6 0.015 1.8 0.497 3.80 4.7
迭代计算后 0.564 2 8.38 17.9 7.526 1 751.0 3.193 5.91 9.3

Fig.4

The optimization process of optical power amplitude ofthe small-signal drving model"

Fig.5

Comparison of measured curve and simulation curve"

Fig.6

Bandwidth response curves of 7.5 mA currentbias under different ambient temperatures"

Fig.7

Bandwidth response curves of different current biasunder the temperature of 20 ℃"

Table 2

The optimizating results of parameters"

参数 a0 a1 a2 b0 b1 b2
优化结果 46.402 7 88.627 8 17.591 0 85.025 7 99.982 5 65.045 8

Fig.8

Comparation of simulation curves betweengain constant and temperature"

Fig.9

Genetic algorithm process based on MapReduce"

Fig.10

Reusable parameter state machine"

Table 3

Real-time performance comparison incluster environment"

系统运行次序 并行计算时延/s 串联计算时延/s
1 0.525 0.575
2 0.535 0.557
3 0.554 0.657
4 0.527 0.574
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