Abstract:
A method for selecting a light source for optical communication system comprises the steps of: measuring time division chirping characteristics and optical response waveforms of the light source responding to a fixed strength random pulse signal; performing a simulation of a transmission process based on measured data; computing a selection parameter as an index for determining a dispersion tolerance quality of the light source according to a computed post-transmission waveform of an optical signal that propagated through an optical fiber path; and deciding the dispersion tolerance quality of the light source based on values of the selection parameter. There is no need for providing the usual facilities required for dispersion tolerance evaluation such as EDFA, optical fibers, wavelength filter, receiving disk and error rate detector and the like and the time required for selection is significantly reduced.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to a method for selecting a light source, such as semiconductor lasers and integrated light source for optical modulator, used in optical communication systems, and relates in particular to a selection method to determine dispersion tolerance quality of the light source. 
     2. Description of Related Art 
     One of indexes showing performance of semiconductor lasers used as a light source in optical communication systems is transmission capability, i.e., dispersion tolerance, and a light source having a superior dispersion tolerance is selected and used to operate a communication system. A conventional method for selecting a semiconductor laser having a superior dispersion tolerance utilizes a device shown in FIG. 10 to measure the post-transmission power penalty of an optical fiber path to determine its quality. 
     FIG. 10 is a schematic diagram of a conventional evaluation system used to select the dispersion tolerance quality. As shown in FIG. 10, a dispersion tolerance evaluation device is composed of: an NRZ (non-return-to-zero) signal generator  51  for supplying NRZ signals to a semiconductor laser (referred to as the element hereinafter)  53  to be evaluated through an electric amplifier  52 ; optical fiber  45 ; EDFAs (Erbium doped fiber amplifier)  44 ; a wavelength filter  43 ; a receiver disk  42 ; an error rate detector  57 ; a sampling oscilloscope  54  for post-transmission waveform observation; and a computer  56  for controlling the error rate detector. 
     NRZ-modulated light output from the element  53  propagates through the optical fiber  45  while receiving loss compensation by the EDFA  44 , and after ASE noise is eliminated by the wavelength filter  43 , arrives in the receiver disk  42 . The error rate of signals detected by the receiver disk  42  is evaluated in the error rate detector  57 , and a bit error rate curve is measured in real-time. Further, the bit-error rate of the optical signal just after emission from the element  53 , that is, the bit-error rate of the optical signal before it propagates through the optical fiber  45  is separately measured in real-time. From the measured data of bit error rates before or after transmission, the power penalty is determined, and an element that produces results lower than a predetermined power penalty value is selected as an acceptable product. In FIG. 10, the arrangement shown for dispersion tolerance evaluation is for a 600 km transmission path, but in practice, the fiber length is varied according to the dispersion tolerance quality of the element. 
     However, according to the conventional method for evaluating the dispersion tolerance, actual transmission experiments must be carried out, thus it is necessary to provide incidental facilities such as optical fibers, EDFAs, wavelength filter, receiving disk (RX) and the like. Also, depending on the dispersion tolerance of an element to be required, the fiber length must be varied for each test. Furthermore, to measure the bit error rate (BER), it is necessary to devote about 15 minutes for each element. Therefore, the conventional method for evaluating the dispersion tolerance presents problems of excessive facility cost and lengthy selection process. 
     BRIEF SUMMARY OF THE INVENTION 
     It is an object of the present invention to eliminate the need for facilities such as EDFAs, optical fibers, wavelength filter, receiving disk, error rate detector, and the like for measuring the dispersion tolerance of a light source, and to significantly shorten the selection time required in evaluating the light source. 
     A first aspect of the present invention provides a method for selecting a light source for optical communication system comprising the steps of: measuring time division chirping characteristics and optical response waveforms of the light source responding to a fixed strength random pulse signal; performing a simulation of a transmission process based on measured data; computing a selection parameter as an index for determining a dispersion tolerance quality of the light source according to a computed post-transmission waveform of an optical signal that propagated through an optical fiber path; and deciding the dispersion tolerance quality of the light source based on values of the selection parameter. 
     A second aspect of the present invention provides a device for selecting a light source for optical communication system comprising: a measuring section for measuring time division chirping characteristics and optical response waveforms of the light source responding to a fixed strength random pulse signal; and a simulation section for computing a post-transmission waveform of an optical signal according to measured data, and computing a selection parameter as an index for determining a dispersion tolerance quality of the light source; and determining the dispersion tolerance quality of the light source by comparing the selection parameter with a predetermined selection criterion. 
     In the above aspects, the selection parameter is a value of an eye opening penalty P eye  computed according to an equation: 
     
       
           P   eye =10·log ( Q/Q   B.B )  
       
     
     (Notice: Q refers to a Q-factor computed from a post-transmission waveform of an optical signal resulting from a transmission simulation process, and Q B.B  refers to a Q-factor computed from a pre-transmission waveform of the optical signal.) or a Q-factor computed from a post-transmission waveform resulting by a transmission simulation process. 
     The present invention not only reduces the number of selection steps but is able to simulate the transmission process through the optical fiber itself so that it offers not only a freedom to choose transmission distance and dispersion characteristics through the fiber path but also an advantage that lesser incidental facilities such as optical fibers and EDFAs are needed for the selection process. 
     According to the above aspects, the present invention enables to replace actual experimentation of signal transmission through an optical fiber path with a simulation process, so that the present invention not only enables to freely select the transmission distance and dispersion characteristics of the fiber path, but also eliminates the necessity for items of experimental facility, such as EDFAs, optical fibers, wavelength filter, receiving device, error detector and the like. Also, a selection parameter for indexing the dispersion tolerance can be computed readily by simply changing the values of transmission distance (fiber length L) and the secondary group velocity dispersion β, according to the dispersion tolerance required, so that a dispersion tolerance quality required for an application can be easily and speedily determined. Furthermore, because a simulation process itself is completed in short time, the selection time can be significantly reduced compared with an actual experimental evaluation process. 
    
    
     BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING 
     FIG. 1 is a flowchart for a selection process according to the present invention. 
     FIG. 2 is a flowchart for a simulation process in a first embodiment according to the present invention. 
     FIG. 3 is a graph of measured data used for selection method in the present invention. 
     FIGS. 4A and 4B are diagrams of an eye pattern computed in the selection method in the present invention. 
     FIG. 5 is a flowchart for the simulation process in a second embodiment according to the present invention. 
     FIG. 6 is a graph showing the relation between actual measurements of power penalty and Q-factors computed by simulation. 
     FIG. 7 is a block diagram of a selection device according to the present invention. 
     FIG. 8 is a block diagram of the simulator used in the selection device according to the present invention. 
     FIG. 9 is a block diagram of an embodiment of a selection device according to the present invention. 
     FIG. 10 is a block diagram of a conventional dispersion tolerance selection evaluation system. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     First Embodiment 
     FIG. 1 shows a flowchart of a selection method of the present invention, and FIG. 2 is a flowchart of the computational steps carried out in step  2  shown in FIG. 1 for simulating the transmission process. 
     FIG. 3 is a diagram of an example of actual measurements of time division chirp data and others, and represent optical response signal data when the element is modulated with fixed voltage random NRZ signals. The data are comprised by time-dependent light intensity data (f in  (t)) of the launching signals, and frequency variation data of the carrier light, i.e., chirp data (f chirp  (t)). Here, in the diagram, the horizontal axis relates to time (in seconds), and the left vertical axis relates to frequency variation (in Hz) while the right vertical axis relates to relative light intensity (suitable scale). 
     FIG. 4A shows eye patterns obtained by simulation and FIG. 4B shows light intensity distribution curves in the eye patterns obtained at light-on and light-off levels, where μ 1 , μ 0  refer to average values of the light intensity distribution at light-on and light-off conditions, respectively, and σ 1 , σ 0  refer to respective standard deviations. 
     The selection method of the present invention, as shown in FIG. 1, is comprised of the following steps: first, the element to be selected is driven by modulating with random fixed voltage pulses in step S 1 , and the optical response waveform and the time division chirp characteristics generated in response to the drive signal are measured to obtain light intensity launching signal data  6  (f in  (t)) and chirp data  7  (f chirp  (t)) shown in FIG.  3 . In step S 2 , after constructing a light launching signal on the basis of light intensity launching signal data  6  (f in  (t)) and chirp data  7  (f chirp (t)), that have been measured and collected in the first step S 1 , values of the optical fiber parameters, such as fiber length L, secondary group velocity dispersion β are input into the simulation equations so as to simulate post-transmission waveforms resulting from the launching signal transmitting through the optical fiber path, and based on the results of simulation of post-transmission waveforms of the launching signal, the eye opening penalty P eye  is computed as a selection parameter that functions as a index for deciding the dispersion tolerance quality. The details of the process is shown in FIG.  2 . Lastly, in step S 3 , the computed selection parameter is compared with the pre-determined selection criterion to decide for quality of the element so as to select and divide into an acceptable product and a substandard product. 
     The computational steps of transmission simulation carried out in step S 2  are outlined in the flowchart shown in FIG.  2 . 
     First, chirp data  7  (f chirp  (t)) are extracted from the measured data shown in FIG. 3, and, after computing the phase Φ(t) of the electric field component of the sending light according to equation (1) shown below (step S 21 ), using the phase Φ(t) and launching signal light intensity data  6  (f in  (t)) extracted from the measured data shown in FIG. 3, the sending light electric field component E in  (t) is computed according to equation (2) shown below (step S 22 ). 
     
       
         Φ( t )=2π∫ 0   t   f   chirp ( t ) dt    (1)  
       
     
     
       
           E   in ( t )={ f   in ( t )} ½ ·exp{ i Φ( t )}  (2)  
       
     
     Subsequently, according to equation (3) shown below, Fourier transform is applied to convert the sending light electric field component E in  (t) to the frequency space so as to carry out spectrum analysis and compute the sending light electric field component in the frequency space, and the computed sending light electric field component in the frequency space is used as the light launching signal input in the optical fiber path (step S 23 ). 
     
       
           {tilde over (E)}   in (ω)= FFT ( E   in ( t ))   (3)  
       
     
     Next, the secondary group velocity dispersion coefficient β of the optical fiber path and the fiber length L are chosen, and the transfer function during the transmission through the optical fiber is computed according to equation (4) shown below, and based on the computed transfer function, the electric field component of the post-transmission optical signal (receiving light) is computed according to equation (5) shown below (step S 24 ).                  H   ~          (   ω   )       =     exp        {       -               β   ·   L     2            (     ω   -     ω   c       )     2       }               (   4   )                                
     where ω c  is the carrier frequency. 
     
       
           {tilde over (E)}   out (ω)= {tilde over (H)} (ω)· {tilde over (E)}   in (ω)   (5)  
       
     
     After performing inverse Fourier transform of the electric field component of the receiving light in the frequency space, computed according to equation (5), to return to the receiving light electric field component e out  (t) in the time space according to equation (6) shown below (step S 25 ), receiving light intensity I out  (t) is computed according to equation (7) shown below (step S 26 ), and based on the computed receiving light intensity I out  (t), the eye patterns  17  shown in FIG. 4A are computed (step S 27 ). Here, the eye patterns  17  are obtained by plotting the receiving light intensity I out  (t) for each value of time t. At this time, similar to the steps for computing the receiving light intensity I out  (t), the pre-transmission light intensity I in  of the light launching signal is also computed from the sending light electric field component E in  (t) to obtain eye patterns before the launching signal is transmitted through the optical fiber path (pre-transmission eye patterns may be computed in step S 22 ). 
     
       
           e   out ( t )= IFFT ( {tilde over (E)}   out (ω))   (6)  
       
     
     
       
           I   out ( t )=| e   out ( t )| 2    (7)  
       
     
     Using the computed eye patterns  17  (for example, 2.5 Gb/s NRZ modulation), and from the data points  18  that are centered about the maximum opening section  20  within a region of 20 ps time-width, the light intensity distribution curve  19   a  at the light-on level, and the light intensity distribution curve  19   b  at the light-off level are obtained, which are shown in FIG.  4 B. Using the light intensity distribution curves  19   a,    19   b,  the average values μ 1 , μ 0 , the standard deviation values σ 1 , σ 0  at light-on and light-off levels, respectively, are computed, and the Q-factor of the receiving light is computed according to equation (8) shown below. At this time, the Q-factor (Q B.B ) of pre-transmission optical launching signal (launching light) is similarly computed from the pre-transmission eye patterns (step S 28 ). 
     
       
           Q =(μ 1 −μ 0 )/(σ 1 +σ 0 )   (8)  
       
     
     The Q-factor is a parameter to show the degree of opening of the eye pattern quantitatively, and changes in the pre- and post-transmission Q-factors are proportional to the power penalty. Therefore, by inputting the value of the computed Q-factor (Q B.B ) of the sending light and the Q-factor (Q) of the receiving light in the eye opening penalty P eye , as defined in equation (9) shown below, the result can be used as a parameter for selecting the transmission capability, i.e., the selection parameter of dispersion tolerance quality of an element. Therefore, a value of the eye opening penalty P eye  is computed as the selection parameter from equation (9) in step S 29 , and a decision of quality for the element is made according to a magnitude of the eye penalty opening (step S 3 ). 
     
       
           P   eye =10·log( Q/Q   B.B )   (9)  
       
     
     As explained above, the selection method of the present invention enables to select a light source having a superior dispersion tolerance quality, without actually carrying out measurements (i.e., power penalty) of optical transmission characteristics for each element in each application of the element. 
     Second Embodiment 
     This embodiment relates to an example of using the post-transmission Q-factor (i.e., Q-factor of the receiving light, denoted by Q) for the selection parameter. In general, Q&lt;&lt;Q B.B , and minute changes δQ, δQ B.B  of the Q-factors (Q and Q B.B ) relative to minute changes δP eye  in the eye penalty P eye  is given by total differential equation (9) as follows: 
     
       
         δ P   eye =10{(1 /Q )δ Q −(1 /Q   B.B )δ Q   B.B }∝(1 /Q )δ Q −(1 /Q   B.B )δ Q   B.B ≈(1 /Q )δ Q    
       
     
     so that the variation in the selection parameter due to the transmission capability of the element, i.e., the variation in the eye opening penalty P eye  may be considered to be governed mainly by the Q-factor of the receiving light. Therefore, instead of using the eye opening penalty P eye , selection parameter may be based on a Q-factor itself computed according to equation (8). 
     FIG. 5 shows a flowchart of the process in the second embodiment based on using the Q-factor of the receiving light as the selection parameter. The processing steps to step S 25  are the same as those in the first embodiment, but slightly different steps are taken after step S 26 . In the first embodiment, pre- and post-transmission signal light intensity I in  (t), I out  (t), eye patterns, Q-factors (Q B.B , Q) are computed in steps S 26  to S 28 , and in step S 29 , the eye opening penalty P eye  is computed to be used as the selection parameter, but in the second embodiment, post-transmission signal light intensity I out  (t), eye patterns, one Q-factor (Q) are computed in steps S 26 B to S 28 B, and pre-transmission parameters are not computed. The eye opening penalty P eye  is also not computed. Therefore, the computational process in the second embodiment is simpler than that in the first embodiment, so that an advantage is that the simulation time can be shortened even more. 
     First, similar to the first embodiment, the electric field component of the receiving light e out  (t) is computed according to the procedure described in steps S 21  to S 25 . Next, in step S 26 B, the post-transmission signal light intensity (receiving light intensity) I out  (t) is computed according to equation (7), and the eye patterns shown in FIG. 4A are computed from the receiving light intensity I out  (t) in step S 27 B. Subsequently, in step S 28 B, based on data points  18  (FIG. 4A) that are centered about the maximum opening section  20  within a region of 20 ps time-width, the light intensity distribution curve  19   a  at the light-on level, and the light intensity distribution curve  19   b  at the light-off level are obtained, which are shown in FIG.  4 B. Using the light intensity distribution curves  19   a,    19   b,  average values μ 1 , μ 0 , standard deviation values σ 1 , σ 0  at light-on and light-off levels, respectively, are computed, and the Q-factor of the receiving light is computed according to equation (8) mentioned earlier. Lastly, the Q-factor computed in step S 28  is used as the selection parameter, and in step S 3 , this Q-factor is compared against a reference value to reach a decision of quality for the element. 
     FIG. 6 shows the results of comparing actual measured values of power penalty for several elements operated at 2.5 Gb/s modulation rate through a 480 km single mode fiber, and the resulting Q-factors computed according to the simulation method in the second embodiment. When Q&gt;8, the condition of power penalty &lt;3 dB is achieved. Therefore, it is possible to select those elements having less than 3 dB power penalty, by using the Q-factor (Q) obtained by equation (8) as the selection parameter, instead of the eye penalty opening P eye , and using Q&gt;8 as the reference value for selection of acceptable or substandard products. 
     Third Embodiment 
     FIG. 7 shows a block diagram of a selection device for carrying out the selection method of the present invention. This selection device is comprised by: a data collection section  1  for measuring data such as time division chirp data of an element; and a simulation section  2  that, based on the data obtained by the data collection section  1 , computes a post-transmission waveform of an optical signal, computes a selection parameter from a computed optical signal waveform, and compares the selection parameter and a pre-determined selection criterion to reach a decision of quality for the element. 
     The data collection section  1  is comprised by an element drive section  32  for impressing a high frequency modulation signal on the element  53 , and a chirp measure section  33  for measuring the chirp characteristics and output waveform (optical response waveform) of the element  53 . 
     The simulation section  2  is comprised by a simulator  34  and an input/output (i/o) section  35 . The i/o section  35 , under the control of the simulator  34 , inputs and sets the parameters of the optical fiber path in the simulator  34 , and displays the results of simulation by the simulator  34 . The simulator  34 , as shown in FIG. 8, is comprised by: a light launching signal construction section  21  that includes a phase computation section  8  to compute the phase Φ(t) of the sending light electric field component from the chirp data  7  measured by the data collection section  1 , a sending light electric field component computation section  9  to compute the sending light electric field component E in  (t) based on the computed phase Φ(t) and the launching signal light intensity data (f in  (t)) extracted from the optical response waveform, and a Fourier conversion section  10  for spectrum analysis of the sending light electric field component E in  (t) in the frequency space; a transmission characteristics computation section  22  that includes a transfer function computation section  11  for inputting the secondary group velocity coefficient β and the fiber length L to compute a transfer function during transmission through the fiber, and an optical signal waveform computation section  12  for computing post-transmission optical signal waveform through the optical fiber by adding the transfer function to the sending light electric field component in the frequency space; a receiving signal waveform computation section  23  that includes a reverse Fourier transform section  13  for reversing the electric field component of the optical signal waveform in the frequency space computed by the transmission characteristics computation section  22  to an electric field component e out  (t) in the time space, and a receiving signal light intensity computation section  14  for computing the receiving signal light intensity I out  (t) from the electric field component e out  (t); an eye pattern computation section  24  for computing an eye pattern from the receiving signal light intensity I out  (t); a Q-factor computation section  25  for computing a Q-factor from the eye pattern; a selection parameter computation section  26  for computing a selection parameter on the basis of the computed Q-factor as an index of dispersion tolerance; a dispersion tolerance evaluation section  27  for reaching a decision of quality for the element on the basis of the selection parameter; and a control section (not shown). Simulation process is carried out under the simulator  34  controlling the operation of the data collection section  1  and the i/o section  35 , according to the steps outlined in the flowchart shown in FIG. 2 or FIG.  5 . 
     FIG. 9 shows a schematic diagram of the details of the selection device shown in FIG.  7 . The element driving section  32  includes an NRZ signal generator  51  and an electrical amplifier  52  for amplifying the signal from the NRZ signal generator  51  to drive the element  53 . The chirp measure section  33  includes a chirp measuring device  55  necessary for determining chirping, and a sampling oscilloscope  54  for determining of a waveform of output light. The simulation section  2  includes a computer  56 . The computer  56  includes a computation device and i/o keyboard, printer and display among others. 
     In the selection device, the element  53  is driven by a fixed voltage pulse train of NRZ signals output from the NRZ signal generator  51  and amplified in the electrical amplifier  52 . Modulated output light  50  output from the element  53  is input into the chirp measure device  55 , and a portion of the light is diverted inside the chirp measuring device  55  and input into the sampling oscilloscope  54 . The sequences of the chirp measuring device  55  and the sampling oscilloscope  54  are controlled by the computer  56 , and time division chirp data and the launching signal light intensity data shown in FIG. 3 are measured and extracted from the received modulated output light  50 . 
     The computer  56  not only control sequencing of the chirp measuring device  55  and the sampling oscilloscope  54 , but also processes the measured data obtained by the chirp measuring device  55  and the sampling oscilloscope  54  through the simulated transmission process by following the steps indicated in the flowchart in FIG. 2 or  5 , and reaches a decision of quality for the target element. Simulation results are output by displaying the results on an associated display device or by printing the results by a printer. When the required dispersion tolerance or transmission conditions are changed, the parameters such as transmission distance (fiber length L), secondary group velocity dispersion β of the optical fiber and selection criterion are changed (by inputting from a keyboard the parameters such as transmission conditions and selection criterion, or reading new set of transmission data and selection criterion from the memory), and re-start the simulation process. Accordingly, the method and device of the present invention enable to select a light source that provides superior dispersion tolerance characteristics for optical communication applications, without actually carrying out measurements of optical transmission quality (i.e., power penalty) for each element for each application of the element.