Bit error rate estimation device and bit error rate estimation method

Abstract

This bit error rate estimation device provided to an optical transmission system using a direct-detection receiver comprises: a transmission path model creation unit that creates a physical model of a transmission path for each candidate path for communication between user devices; a propagated waveform calculation unit that generates an electric field signal waveform to be output from a transmitter assumed in the physical model of the transmission path and uses linear fiber propagation simulation to generate a received signal waveform at the time of the direct detection; a nonlinear noise calculation unit that calculates a nonlinear noise light intensity of light on the basis of the physical model of the transmission path; a noise intensity conversion unit that converts the calculated nonlinear noise light intensity of light to a noise at an electrical stage; a received signal waveform calculation unit that calculates a Gaussian distribution of each symbol or each sample in the received signal waveform at the time of the direct detection on the basis of the received signal waveform at the time of the direct detection and the noise at an electrical stage obtained by conversion; and a bit error rate calculation unit that calculates a bit error rate on the basis of the Gaussian distribution of each symbol or each sample.　

Description

[Technical Field]

[0001] The present invention relates to a bit error rate estimation device and a bit error rate estimation method. [Background technology]

[0002] Conventionally, in optical communication networks, a configuration has been proposed in which each node in the network is configured using an optical switch, and optimal wavelengths and routes are dynamically assigned according to the connection requests of user terminals, thereby connecting user terminals without photoelectric conversion (see, for example, Non-Patent Documents 1 to 3).

[0003] Fig. 11 is a diagram showing an example of the configuration of a conventional optical communication network system. In the conventional optical communication network system shown in Fig. 11, a plurality of nodes 200-1 to 200-5 form a mesh network, and a user terminal 300-1 and a user terminal 300-2 are connected to each other. The plurality of nodes 200-1 to 200-5 are optical switches. The user terminal 300 and the node 200, and the plurality of nodes 200 are connected to each other using optical fibers.

[0004] Now, consider a case where user terminal 300-1 is newly connected to the optical communication network system and a connection request is made to user terminal 300-2. In this case, possible routes for connecting user terminal 300-1 and user terminal 300-2 include a first route and a second route. The first route is a route from user terminal 300-1 to user terminal 300-2 via nodes 200-1, 200-2, and 200-3. The second route is a route from user terminal 300-1 to user terminal 300-2 via nodes 200-1, 200-4, and 200-3.

[0005] Generally, in optical fiber transmission, the longer the transmission distance, the greater the fiber loss. This reduces the optical signal strength at the fiber output, resulting in an increase in the bit error rate. The bit error rate also deteriorates due to the effects of waveform distortion caused by chromatic dispersion and nonlinear optical effects that occur during fiber propagation. Communication becomes impossible on routes where the bit error rate exceeds a specified value.

[0006] To determine the route to be assigned between user terminal 300-1 and user terminal 300-2, it is necessary to measure the bit error rate for each candidate route and select a route that can communicate from the candidate routes based on the measurement results. However, actually sending a signal through each route to measure the bit error rate increases the route assignment time, which is not practical. Therefore, a method of estimating the bit error rate when a signal is sent through each route is being studied.

[0007] It is well known from Non-Patent Document 4 that when there is no waveform degradation in the received signal, the bit error rate can be calculated based on the probability density of marks and spaces. Mark and space symbols containing noise are expressed by the Gaussian distribution g(x) shown in the following equation (1).

[0008]

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[0009] Furthermore, the variance σ in the Gaussian distribution g(x) 2 is expressed by the following equation (2).

[0010]

number

[0011] In equation (2), σ th 2 represents the thermal noise, and σ sig-ASE 2 represents the signal-ASE (Amplified Spontaneous Emission) beat noise, and σshot 2 represents the shot noise. Note that the thermal noise σ th 2 is expressed by the following equation (3), and the signal-ASE beat noise σ sig-ASE 2 is expressed by the following equation (4), and the shot noise σ shot 2 is expressed by the following equation (5).

[0012]

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[0013]

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[0014]

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[0015] In equation (3), R represents the electrical resistance of the photodiode, k represents the Boltzmann coefficient, T represents the absolute temperature, and Δf represents the receiving bandwidth of the receiver. In equation (4), h represents Planck's constant, ν represents the frequency of light, e represents the amount of charge of an electron, η represents the quantum efficiency of the photodiode, P0 represents the light intensity, and P ASE represents the received ASE intensity contained in the band Δf. In equation (5), I represents the current.

[0016] The probability density function when there is no waveform degradation in the received signal is shown in Figure 12. The bit error rate can be calculated based on the following equation (6).

[0017]

number

[0018] Bit error rate estimation methods based on probability density are effective when waveform degradation due to propagation is small. However, they cannot be applied to long-distance fiber propagation because the symbol intensity changes due to waveform degradation caused by chromatic dispersion and nonlinear optical effects. One possible method for considering the effects of these waveform degradations during propagation is to use optical fiber transmission simulations. Generally, waveform changes due to optical fiber transmission are described by the nonlinear Schrödinger equation. By applying an algorithm called the split-step Fourier method to the nonlinear Schrödinger equation, it is possible to calculate with high accuracy the changes in waveform after propagation due to linear effects such as chromatic dispersion and nonlinear optical effects.

[0019] The split-step Fourier transform method divides an optical fiber into short sections and simulates propagation through the fiber by repeatedly performing calculations in the time and frequency domains for each section. However, to ensure high calculation accuracy, the fiber section must be divided into small sections for calculation, which requires a huge amount of calculation time, making it impossible to estimate the bit error rate in real time.

[0020] Therefore, a method using a Gaussian noise model has been proposed as a bit error rate estimation method that takes into account the influence of nonlinear optical effects while reducing calculation time (see, for example, Non-Patent Documents 5 to 7). In the method using the Gaussian noise model, waveform distortion due to nonlinear optical effects is treated as random noise σ with a Gaussian distribution, such as thermal noise, shot noise, and signal-ASE beat noise. NLI 2 The bit error rate is calculated assuming that

[0021] This method can calculate the effects of nonlinear optical effects in a short time, but it cannot take linear waveform degradation such as chromatic dispersion into account. Therefore, it is currently envisioned that it will be applied to digital coherent transmission systems that can compensate for waveform degradation due to chromatic dispersion. On the other hand, the Intensity Modulation-Direct Detection (IM-DD) system, a cheaper communication system, cannot fully compensate for linear waveform changes such as chromatic dispersion, so methods using the Gaussian noise model cannot be applied as is. [Prior art documents] [Non-patent literature]

[0022] [Non-Patent Document 1] Hiroki Kawahara et al., “Optical Full-mesh Network Technologies Supporting the All-Photonics Network”, NTT Technical Review, vol. 18, no. 5, pp.24-29, May 2020. [Non-patent document 2] M. Birk et al., “The OpenROADM initiative [Invited]”, J. Opt. Commun. Netw., vol. 12, no. 6, pp. C58-67, June 2020. [Non-patent document 3] M. Bouda et al., “Accurate Prediction of Quality of Transmission with Dynamically Configurable Optical Impairment Model”, Th1J.4, OFC2017. [Non-patent document 4] W. Freude et al., “Quality Metrics for Optical Signals: Eye Diagram, Q-factor, OSNR, EVM and BER”, Mo.B1.5, ICTON 2012. [Non-patent document 5] A. Ferrari et al., “GNPy: an open source application for physical layer aware open optical networks”, J. Opt. Commun. Netw., vol. 12, no. 6 , pp. C31-C40, June 2020. [Non-patent document 6] P. Poggiolini et al., “A Detailed Analytical Derivation of the GN Model of Non-Linear Interference in Coherent Optical Transmission Systems”. [Non-Patent Document 7] P. Poggiolini et al., ”The GN Model of Non-Linear Propagation in Uncompensated Coherent Optical Systems”, J. Lightw. Technol., vol. 30, no. 24, pp. 3857-3879, DECEMBER 2012. Summary of the Invention [Problem to be solved by the invention]

[0023] As described above, conventionally, optical transmission systems using direct detection receivers have had the problem that they are unable to estimate the bit error rate with high accuracy and in a short time, taking into account both nonlinear optical effects and linear waveform changes.

[0024] In view of the above circumstances, an object of the present invention is to provide a technique that can estimate a bit error rate with high accuracy and in a short time in an optical transmission system that uses a direct detection receiver. [Means for solving the problem]

[0025] One aspect of the present invention is a bit error rate estimation device provided in an optical transmission system using a direct detection receiver, the bit error rate estimation device including: a transmission path model creation unit that creates a physical model of a transmission path for each candidate path for communication between user devices; a propagation waveform calculation unit that generates an electric field signal waveform output from a transmitter assumed in the physical model of the transmission path created by the transmission path model creation unit, and generates a received signal waveform at the time of direct detection by linear fiber propagation simulation; and a nonlinear noise calculation unit that calculates the nonlinear noise light intensity of light based on the physical model of the transmission path created by the transmission path model creation unit. a noise intensity conversion unit that converts the nonlinear noise light intensity of the light calculated by the nonlinear noise calculation unit into noise of an electrical stage; a received signal waveform calculation unit that calculates a Gaussian distribution of each symbol or each sample in the received signal waveform at the time of direct detection based on the received signal waveform at the time of direct detection obtained by the propagation waveform calculation unit and the noise of the electrical stage converted by the noise intensity conversion unit; and a code error rate calculation unit that calculates a code error rate based on the Gaussian distribution of each symbol or each sample calculated by the received signal waveform calculation unit.

[0026] One aspect of the present invention is a bit error rate estimation method performed by a bit error rate estimation device provided in an optical transmission system using a direct detection receiver, the bit error rate estimation method comprising the steps of: creating a physical model of the transmission path for each candidate path for communication between communicating user devices; generating an electric field signal waveform output from an assumed transmitter in the created physical model of the transmission path; generating a received signal waveform at the time of direct detection by linear fiber propagation simulation; calculating the nonlinear noise light intensity of light based on the created physical model of the transmission path; converting the calculated nonlinear noise light intensity of light into noise of an electrical stage; calculating a Gaussian distribution of each symbol or each sample in the received signal waveform at the time of direct detection based on the obtained received signal waveform at the time of direct detection and the converted noise of the electrical stage; and calculating a bit error rate based on the Gaussian distribution of each symbol or each sample. [Effects of the Invention]

[0027] According to the present invention, it is possible to estimate the bit error rate highly accurately and in a short time in an optical transmission system using a direct detection receiver. [Brief explanation of the drawings]

[0028] [Figure 1] 1 is a diagram illustrating an example of the configuration of an optical transmission system according to the present invention. [Figure 2] FIG. 2 is a diagram illustrating an example of the configuration of a code error rate estimation unit in the first embodiment. [Figure 3] 5A and 5B are diagrams for explaining specific processing by a received signal waveform calculation unit and a bit error rate calculation unit in the first embodiment. [Figure 4] 3 is a diagram illustrating an example of the configuration of a propagation waveform calculation unit and a received signal waveform calculation unit in the first embodiment. FIG. [Figure 5] 4 is a flowchart showing the flow of a route determination process performed by an optical route control device in the first embodiment. [Figure 6] 4 is a flowchart showing the flow of a code error estimation process performed by the optical route control device in the first embodiment. [Figure 7]FIG. 10 is a diagram illustrating an example of the configuration of a code error rate estimation unit in the second embodiment. [Figure 8] FIG. 10 is a diagram illustrating an example of the configuration of a received signal waveform calculation unit in the second embodiment. [Figure 9] 10A and 10B are diagrams illustrating changes in a signal waveform after propagation due to processing in the second embodiment. [Figure 10] FIG. 11 is a diagram illustrating an example of the configuration of a propagation waveform calculation unit and a received signal waveform calculation unit in the third embodiment. [Figure 11] FIG. 1 is a diagram illustrating an example of the configuration of a conventional optical communication network system. [Figure 12] FIG. 10 is a diagram showing a probability density function when there is no waveform distortion in the received signal. DETAILED DESCRIPTION OF THE INVENTION

[0029] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a diagram illustrating an example of the configuration of an optical transmission system 100 according to the present invention. The optical transmission system 100 includes an optical route control device 10 and a plurality of nodes 20. In FIG. 1, five nodes 20-1 to 20-5 are shown as the plurality of nodes 20. Note that the number of nodes 20 is merely an example, and any number of nodes may be used. A user terminal 30-1 is connected to node 20-1, and a user terminal 30-2 is connected to node 20-3. In the following description, an example will be given in which the user terminals 30-1 and 30-2 included in the optical transmission system 100 according to the present invention communicate using the IM-DD method. Note that the optical transmission system 100 according to the present invention is applicable to a system that performs direct detection, and the modulation method is not limited to direct modulation and may be other methods. Note that one or more optical amplifiers may be provided in the paths between the nodes 20 and between the node 20 and the user terminal 30.

[0030] The optical route control device 10 determines candidate routes (hereinafter referred to as "candidate routes") for communication between user terminals 30, and estimates the bit error rate for each determined candidate route. Based on the estimated bit error rate for each candidate route, the optical route control device 10 determines the optimal route for communication between user terminals 30. The optical route control device 10 controls the nodes 20 to set the route so that communication can be performed via the determined optimal route.

[0031] 1, a first route and a second route are shown as candidate routes. The first route is a route from the user terminal 30-1 that passes through nodes 20-1, 20-2, and 20-3 to the user terminal 30-2. The second route is a route from the user terminal 30-1 that passes through nodes 20-1, 20-4, and 20-3 to the user terminal 30-2. The optical route control device 10 determines the optimal route based on, for example, the results of estimating the bit error rates for the first route and the second route.

[0032] The node 20 connects the user terminal 30-1 and the user terminal 30-2 so that they can communicate with each other by switching the connection route under the control of the optical route control device 10. The node 20 is, for example, an optical switch.

[0033] The user terminal 30 is a terminal operated by a user who uses the optical transmission system 100. When connecting to the optical transmission system 100, the user terminal transmits user information (user authentication information, user location information (information indicating which node 20 and which optical fiber the user terminal 30 is connected to), modulation method and modulation speed (baud rate), etc.) to the optical route control device 10. The user terminal 30 includes a transmitter that performs predetermined modulation (for example, intensity modulation) and a receiver that performs direct detection.

[0034] Next, we will explain the configuration of the optical route control device 10. The optical route control device 10 includes a communication unit 11, a connection terminal detection unit 12, a route database 13, a candidate route determination unit 14, a bit error rate estimation unit 15, and an optimal route determination unit 16.

[0035] The communication unit 11 communicates with the nodes 20 and the user terminals 30. For example, the communication unit 11 receives user information from the user terminals 30. For example, the communication unit 11 transmits information on the route determined by the optimal route determination unit 16 to each node 20. This allows the communication unit 11 to control the connection relationship of each node 20.

[0036] The connected terminal detection unit 12 identifies the user terminal 30 connected to the optical transmission system 100 based on the user information received by the communication unit 11. Here, identifying the user terminal 30 means detecting that the user terminal 30 has been connected to the optical transmission system 100 and performing user authentication. Hereinafter, the user terminal 30 for which user authentication has been performed will also be referred to as the identified user terminal 30.

[0037] The route database 13 stores information about the nodes 20 that make up the entire network (for example, the location information of each node 20, the state of each node, etc.).

[0038] The candidate route determination unit 14 determines candidate routes based on the information of the nodes 20 registered in the route database 13 and the user information of the user terminal 30 identified by the connected terminal detection unit 12 .

[0039] The bit error rate estimation unit 15 estimates a bit error rate for each candidate route calculated by the candidate route determination unit 14. Specifically, the bit error rate estimation unit 15 estimates a bit error rate that is expected to be obtained when a signal is passed through each candidate route. That is, the bit error rate estimation unit 15 estimates the bit error rate of each candidate route without actually passing a signal through each candidate route. Hereinafter, the process of estimating a bit error rate performed by the bit error rate estimation unit 15 will be referred to as a bit error estimation process.

[0040] The optimum route determination unit 16 determines the optimum route based on the bit error rate of each candidate route estimated by the bit error rate estimation unit 15. Specifically, the optimum route determination unit 16 first selects a candidate route whose bit error rate is less than a specified value. Then, the optimum route determination unit 16 determines the optimum route from the selected candidate routes based on a network design policy stored in advance.

[0041] Next, we will explain the outline of a method for estimating a bit error rate with high accuracy and in a short time in the optical transmission system 100. This processing is performed in the bit error rate estimator 15. The bit error rate estimator 15 separates the influence of linear distortion and the influence of nonlinear distortion that occur during fiber transmission, calculates the influence of linear distortion and the influence of nonlinear distortion independently, and then combines them to estimate a bit error rate in a short time while taking both into account.

[0042] Specifically, the bit error rate estimation unit 15 generates an electric field signal waveform for an arbitrary modulation method, and calculates the received signal waveform during direct detection in a short time by fiber propagation simulation that takes linear distortion into account (for example, based on the linear term of the nonlinear Schrodinger equation).On the other hand, assuming that nonlinear distortion generates random noise in the receiver, the bit error rate estimation unit 15 calculates the optical nonlinear noise power based on the methods described in Non-Patent Documents 5 to 7, and converts the optical nonlinear noise power into an electrical signal to calculate a random probability density distribution.

[0043] The bit error rate estimating unit 15 adds noise containing nonlinear distortion components to the received waveform after direct detection following the linear propagation simulation. There are two methods for adding noise containing nonlinear distortion components.

[0044] The first method of adding noise is to add together the probability density functions for each received symbol after linear propagation simulation.

[0045] The second method of adding noise is to add random errors according to the distribution of the noise to the received waveform after linear propagation simulation.

[0046] By performing the above processing, the bit error rate estimation unit 15 can calculate the bit error rate in a short time while taking into account both linear and nonlinear effects. A specific configuration for realizing the above processing will be described in detail below.

[0047] (First embodiment) In the first embodiment, a configuration will be described in which noise including a nonlinear distortion component is added to a received waveform after direct detection following a linear propagation simulation by a first noise addition method.

[0048] 2 is a diagram showing an example of the configuration of the bit error rate estimator 15 in the first embodiment. The bit error rate estimator 15 includes a transmission path model generator 151, a reception intensity calculator 152, an ASE intensity calculator 153, a thermal noise calculator 154, a nonlinear noise calculator 155, a noise intensity converter 156, a propagation waveform calculator 157, a received signal waveform calculator 158, and a bit error rate calculator 159.

[0049] The transmission path model creation unit 151 creates a physical model of the transmission path between the user terminals 30 (hereinafter referred to as the "transmission path model") for each candidate route. The transmission path model includes, for example, the type of optical fiber used in the transmission path, the length of the fiber (fiber length), the type and position of the optical amplifier, and the presence or absence of signals from other existing users. In the following explanation, we consider a transmission path model in which an optical amplifier for loss compensation is inserted after propagation through an optical fiber with a fiber length L.

[0050] The reception intensity calculation unit 152 calculates the reception light intensity based on at least the fiber loss and the gain of the optical amplifier by referring to the transmission path model created by the transmission path model creation unit 151. The reception intensity calculation unit 152 outputs a gain coefficient G corresponding to the calculated reception light intensity to the propagation waveform calculation unit 157.

[0051] The ASE intensity calculation unit 153 calculates the intensity P of the ASE input to the receiver assumed in the transmission path model created by the transmission path model creation unit 151. ASE Calculate.

[0052] The thermal noise calculation unit 154 calculates the thermal noise σ based on the characteristics of the receiver assumed in the transmission path model created by the transmission path model creation unit 151. th 2 Calculate.

[0053] The nonlinear noise calculation unit 155 calculates the nonlinear noise light intensity P NLI Calculate the nonlinear noise intensity of the light P NLI is calculated based on the methods shown in Non-Patent Documents 5 to 7, for example.

[0054] The noise intensity conversion unit 156 converts the nonlinear noise light intensity P NLI is converted into noise in the electrical stage. As a result, the noise intensity conversion unit 156 converts the nonlinear noise σ NLI 2 Obtain the nonlinear noise σ NLI 2 is noise including nonlinear distortion components.

[0055] The propagation waveform calculation unit 157 generates an electric field signal waveform output from a transmitter assumed in the transmission path model created by the transmission path model creation unit 151, and calculates the waveform after propagation through a fiber of length L based on the Schrödinger equation. In this case, only linear effects (such as chromatic dispersion) are taken into account in fiber propagation, making it possible to calculate the waveform after transmission in a short time. In this way, the propagation waveform calculation unit 157 generates an electric field signal waveform for an arbitrary modulation method, and calculates the received signal waveform during direct detection in a short time by linear fiber propagation simulation (based on the linear term of the nonlinear Schrödinger equation) that takes linear distortion into account. In this way, the propagation waveform calculation unit 157 generates the signal electric field waveform after fiber propagation.

[0056] The propagation waveform calculation unit 157 multiplies the electric field signal waveform output from the transmitter assumed in the transmission path model by the coefficient G calculated by the reception intensity calculation unit 152, thereby matching the signal intensity to the reception light intensity calculated by the reception intensity calculation unit 152.

[0057] The received signal waveform calculation unit 158 ​​calculates the intensity P of each symbol in the signal field waveform after fiber propagation. symbol and the ASE intensity P calculated by the ASE intensity calculation unit 153. ASE and the thermal noise σ calculated by the thermal noise calculation unit 154. th 2 and the nonlinear noise σ converted by the noise intensity conversion unit 156. NLI 2 and the intensity of each symbol P based on symbol Calculate the probability density of

[0058] The received symbols obtained at the receiver have the intensity P symbol It is considered that the distribution is a Gaussian distribution g(x) centered on μ=P symbol ) where the nonlinear noise is assumed to be Gaussian noise and its variance is σ NLI Then, the variance of the Gaussian distribution including other noise components is σ 2 can be expressed as the following equation (7).

[0059]

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[0060] In equation (7), the shot noise σ shot is the intensity of each symbol P symbol The signal-ASE beat noise σ is calculated based on sig-ASE 2 is the intensity of each symbol P symbol and the ASE intensity P ASE It is calculated based on the following.

[0061] The received signal waveform calculation unit 158 ​​divides the received symbols into marks and spaces, and calculates a Gaussian distribution g for each symbol, with the symbol intensity at the center. i Calculate (x). Gaussian distribution g i (x) is the distribution for the i-th symbol. The received signal waveform calculation unit 158 ​​calculates the probability density of each mark and space by adding up the Gaussian distributions calculated for each symbol. In this way, the received signal waveform calculation unit 158 ​​calculates the probability distribution of the entire received symbol sequence by adding up the probability density functions for each received symbol after the linear propagation simulation.

[0062] The bit error rate calculation unit 159 calculates the bit error rate based on a threshold determination of the probability density of marks and spaces calculated by the received signal waveform calculation unit 158. For example, the bit error rate calculation unit 159 estimates the bit error rate based on the probability distribution of the entire received symbol sequence.

[0063] Next, the nonlinear noise σ NLI 2 The method for deriving the nonlinear noise light intensity P of the light derived in Non-Patent Documents 5 to 7 will be specifically described. NLI is a noise in the optical domain similar to ASE. Therefore, the nonlinear noise σ NLI 2 It is necessary to calculate the SNR. Non-Patent Documents 5 to 7 assume coherent reception in which the signal-ASE beat noise is canceled. In this case, the signal-to-noise ratio (SNR) of the optical stage is equal to the SNR of the electrical stage. Therefore, if the amount of optical noise is known, the receiver characteristics can be determined. On the other hand, in a direct detection system in which the signal-ASE beat noise is not canceled, the optical and electrical SNRs may differ. Therefore, optical-to-electrical conversion is necessary. Therefore, existing methods cannot be applied to a direct detection system like the present invention.

[0064] Nonlinear noise intensity of light P NLISince it is an optical noise like ASE, it can be considered to have the same effect on the receiving characteristics as the ASE power. Here, we will explain the relationship between the OSNR (Optical Signal-to-Noise ratio), which is the optical SNR due to ASE, and the electrical SNR. From Reference 1, the gain G amp The optical amplifier has an intensity P in The signal-ASE beat noise σ when an optical signal is input and a receiver is placed after the optical amplifier. sig-ASE 2 is expressed by the following equation (8). (Reference 1: NA OLSSON, “Lightwave Systems With Optical Amplifiers”, JOURNAL OF LIGHTWAVE TECHNOLOGY. VOL. 7. NO. 7. JULY 1989, p.1071-1082)

[0065]

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[0066] Here, for simplicity, the quantum efficiency of the PD (Photo Diode) is set to η=1,2Be (electrical band) = B0 (optical band). sig and signal current I sig If the quantum efficiency of the PD is η, then I sig =(P sig e) / (hν),I ASE =(P ASE e) / (hν), and the signal strength P sig =G×P in Then, equation (8) can be expressed as equation (9).

[0067]

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[0068] From equation (9), the SNR when only ASE is considered is ASE is expressed by the following equation (10).

[0069]

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[0070] As shown in equation (10), it can be seen that it is 1 / 2 of the OSNR (see Reference 2). (Reference 2: Wolfgang Freude et al., “Quality Metrics for Optical Signals: Eye Diagram, Q-factor, OSNR, EVM and BER”, ICTON 2012)

[0071] Here, the nonlinear noise intensity of the light P NLI The optical signal-to-noise ratio when only the optical noise is considered is ONSR', and P sig =I sig Based on (hν / e), equation (11) is obtained.

[0072]

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[0073] From equation (11), the nonlinear noise σ in the electrical stage NLI 2 can be derived as shown in the following equation (12).

[0074]

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[0075] 3 is a diagram for explaining specific processing by the received signal waveform calculation unit 158 ​​and the bit error rate calculation unit 159 in the first embodiment. In FIG. 3, (a) shows the electric field signal waveform before fiber propagation, (b) shows the signal electric field waveform after fiber propagation, and (c) shows the Gaussian distribution g of each symbol calculated by the received signal waveform calculation unit 158. i (x) and (d) are the Gaussian distributions of each symbol g i Represents the result of adding (x).

[0076] As shown in (b) of Fig. 3, there are five symbols in the signal electric field waveform after fiber propagation. Therefore, the received signal waveform calculation unit 158 ​​calculates a Gaussian distribution g i (x) (here, i=1 to 5) is calculated (see (c) of FIG. 3). The bit error rate calculation unit 159 calculates the bit error rate based on a threshold decision for the probability density.

[0077] Fig. 4 is a diagram showing an example of the configuration of the propagation waveform calculation unit 157 and the received signal waveform calculation unit 158 ​​in the first embodiment. Note that in Fig. 4, in order to show the relationship with other functional units, functional units other than the propagation waveform calculation unit 157 and the received signal waveform calculation unit 158 ​​are also shown. The propagation waveform calculation unit 157 includes a PRBS (Pseudo Random Bit Sequence) generation unit 161, a low-pass filter 162, an intensity modulation unit 163, a Fourier transform unit 164, a linear propagation unit 165, an inverse Fourier transform unit 166, a square-law detection unit 167, and a low-pass filter 168.

[0078] The PRBS generating unit 161 generates a PRBS signal. That is, the PRBS generating unit 161 generates a PRBS signal.

[0079] The low-pass filter 162 filters the PRBS signal generated by the PRBS generator 161. The low-pass filter 162 attenuates and blocks signals of frequencies higher than a specific threshold among the PRBS signals, and passes signals of frequencies equal to or lower than the specific threshold. In this way, the low-pass filter 162 limits the bandwidth of the transmitter, thereby reducing the modulation signal E m Here, an NRZ (Non-Return-to-Zero) signal is assumed. The same applies to the following explanation.

[0080] The intensity modulation unit 163 modulates the modulated signal E generated by the low-pass filter 162. mThe intensity modulation unit 163 generates an optical field waveform E(t) modulated by the intensity modulation unit 163 (t). Specifically, the intensity modulation unit 163 generates a complex electric field E(t) having a carrier frequency f0 and an amplitude G·E0(t). Here, G represents the net gain calculated based on the optical fiber loss, the optical amplifier gain, the transmitter output, etc. (G·E0(t)). 2 The value of G is set so that the average of these becomes the received light intensity calculated by the received intensity calculation unit 152. The complex electric field E(t) generated by the intensity modulation unit 163 is expressed by the following equation (13).

[0081]

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[0082] The Fourier transform unit 164 performs a Fourier transform to convert the complex electric field E(t) generated by the intensity modulation unit 163 into a frequency domain (E(f)). The frequency domain (E(f)) converted by the Fourier transform unit 164 is expressed by the following equation (14).

[0083]

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[0084] The linear propagation unit 165 calculates the phase change amount expj(2π 2 β2Lf 2 ) is multiplied by the frequency domain (E(f)). As a result, the linear propagation unit 165 calculates the electric field waveform (EL(f)) after propagation through the fiber for a distance L. This method is a transmission simulation method for linear components derived by solving the linear term of the nonlinear Schrodinger equation. The electric field waveform (E L (f)) is expressed as the following equation (15).

[0085]

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[0086] The inverse Fourier transform unit 166 performs an inverse Fourier transform to convert the electric field waveform (E L By converting the waveform (f) back to the time domain, the electric field waveform (E L The electric field waveform (E L (t)) is expressed as the following equation (16).

[0087]

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[0088] The square-law detector 167 detects the electric field waveform (E L The optical intensity I obtained after PD reception is calculated from the square of (t). The optical intensity I(t) calculated by the square law detector 167 is expressed as in the following equation (16). Note that the asterisk in equation (17) represents the complex conjugate.

[0089]

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[0090] The low-pass filter 168 filters the signal representing the optical intensity I(t) calculated by the square-law detector 167. The low-pass filter 168 attenuates and blocks signals representing the optical intensity I(t) with frequencies higher than a specific threshold, and transmits signals with frequencies equal to or lower than the specific threshold. In this way, the low-pass filter 168 limits the bandwidth of the receiver. This makes it possible to obtain, for example, the signal waveform after fiber propagation shown in FIG. 3(b).

[0091] The received signal waveform calculation unit 158 ​​includes a symbol position detection unit 169 , a symbol intensity calculation unit 170 , and a noise distribution calculation unit 1581 .

[0092] The symbol position detector 169 detects the symbol position in the signal waveform after fiber propagation obtained by the propagation waveform calculator 157 .

[0093] The symbol intensity calculation unit 170 calculates the intensity P of each symbol based on the signal waveform after fiber propagation obtained by the propagation waveform calculation unit 157 and the symbol position detected by the symbol position detection unit 169. symbol Calculate.

[0094] The noise distribution calculation unit 1581 calculates the intensity P of each symbol calculated by the symbol intensity calculation unit 170. symbol and the ASE intensity P calculated by the ASE intensity calculation unit 153. ASE and the thermal noise σ calculated by the thermal noise calculation unit 154. th 2 and the nonlinear noise σ converted by the noise intensity conversion unit 156. NLI 2 and the intensity of each symbol P based on symbol Furthermore, the noise distribution calculation unit 1581 calculates the probability density of the entire received symbol sequence by adding up the probability density functions for the received symbols after the linear propagation simulation.

[0095] Fig. 5 is a flowchart showing the flow of the route determination process performed by the optical route control device 10 in the first embodiment. Note that Fig. 5 describes a case where the connection terminal detection unit 12 of the optical route control device 10 detects connection requests from the user terminal 30-1 and the user terminal 30-2. That is, the description will be given assuming that the communication unit 11 receives user information from each of the user terminal 30-1 and the user terminal 30-2.

[0096] The connected terminal detection unit 12 identifies each user terminal 30 based on the user information of the user terminal 30-1 and the user information of the user terminal 30-2 received by the communication unit 11 (step S101). The connected terminal detection unit 12 outputs the user information of the identified user terminal 30 to the candidate route determination unit 14.

[0097] The candidate route determination unit 14 determines candidate routes based on the route database 13 and the user information of the identified user terminal 30 output from the connected terminal detection unit 12 (step S102). The candidate route determination unit 14 outputs information about each determined candidate route to the bit error rate estimation unit 15.

[0098] Here, the information on each candidate route output by the candidate route determination unit 14 includes, for each candidate route, information indicating which node 20 is connected to user terminal 30-1 or 30-2 and which node 20 the route passes through. For example, if the candidate routes are the first route and the second route shown in FIG. 1 , the information includes information indicating that user terminal 30-1 connects to node 20-1, user terminal 30-2 connects to node 20-3, and the route passes through nodes 20-1, 20-2, and 20-3. The information includes information indicating that user terminal 30-1 connects to node 20-1, user terminal 30-2 connects to node 20-3, and the route passes through nodes 20-1, 20-4, and 20-3.

[0099] The bit error rate estimation unit 15 performs bit error estimation processing for each candidate route based on the information about each candidate route output from the candidate route determination unit 14 (step S103). Specific processing of the bit error estimation processing will be described later (FIG. 6). The bit error rate estimation unit 15 outputs the result of the bit error estimation processing for each candidate route to the optimal route determination unit 16. Based on the result of the bit error estimation processing for each candidate route output from the bit error rate estimation unit 15, the optimal route determination unit 16 determines the optimal route for communication between the user terminal 30-1 and the user terminal 30-2 (step S104).

[0100] Based on the determined optimal route, the optimal route determination unit 16 generates control information for controlling each node 20 included in the optimal route to perform communication via the optimal route. The control information includes switching information such that the connection relationships of the nodes 20, which are optical switches, form an optimal route. The optimal route determination unit 16 controls the communication unit 11 to transmit the generated control information to the target node 20. The communication unit 11 transmits the control information to the target node 20 under the control of the optimal route determination unit 16 (step S105). As a result, the node 20 that has received the control information switches its connection relationship in accordance with the control information.

[0101] For example, if the optimal route determination unit 16 determines the first route as the optimal route, the optimal route determination unit 16 generates switching information as control information for node 20-1 to switch the route so as to connect user terminal 30-1 and node 20-2. Furthermore, the optimal route determination unit 16 generates switching information as control information for node 20-2 to switch the route so as to connect node 20-1 and node 20-3. Furthermore, the optimal route determination unit 16 generates switching information as control information for node 20-3 to switch the route so as to connect node 20-2 and user terminal 30-2.

[0102] FIG. 6 is a flowchart showing the flow of the bit error estimation process performed by the optical route control device 10 in the first embodiment. The transmission path model creation unit 151 selects one candidate route from among the candidate routes indicated by the information on each candidate route output from the candidate path determination unit 14 (step S201). For example, the transmission path model creation unit 151 selects an unselected candidate route from among the candidate routes output from the candidate path determination unit 14. The transmission path model creation unit 151 creates a transmission path model for the selected candidate route (step S202).

[0103] The transmission path model creation unit 151 outputs information about the created transmission path model to the reception intensity calculation unit 152, the ASE intensity calculation unit 153, the thermal noise calculation unit 154, and the nonlinear noise calculation unit 155. The reception intensity calculation unit 152 calculates the reception light intensity based on the transmission path model created by the transmission path model creation unit 151 (step S203). The reception intensity calculation unit 152 outputs a gain coefficient G corresponding to the calculated reception light intensity to the propagation waveform calculation unit 157.

[0104] The propagation waveform calculation unit 157 creates a signal electric field waveform after fiber propagation based on the transmission path model created by the transmission path model creation unit 151 (step S204). Note that the propagation waveform calculation unit 157 multiplies the electric field signal waveform output from the transmitter assumed in the transmission path model by the coefficient G calculated by the reception intensity calculation unit 152, thereby making the signal intensity coincide with the received light intensity calculated by the reception intensity calculation unit 152. Thereafter, the propagation waveform calculation unit 157 creates a signal electric field waveform after fiber propagation by processing by the Fourier transform unit 164, linear propagation unit 165, inverse Fourier transform unit 166, square-law detection unit 167, and low-pass filter 168.

[0105] The propagation waveform calculation unit 157 outputs information about the created signal field waveform after fiber propagation to the received signal waveform calculation unit 158. The symbol position detection unit 169 of the received signal waveform calculation unit 158 ​​detects the position of a symbol in the signal field waveform after fiber propagation. The symbol position detection unit 169 outputs information about the detected symbol position to the symbol intensity calculation unit 170. The symbol intensity calculation unit 170 of the received signal waveform calculation unit 158 ​​calculates the intensity P of each symbol specified by the symbol position information output from the symbol position detection unit 169. symbol The propagation waveform calculation unit 157 calculates the intensity P symbol The ASE intensity calculation unit 153 outputs the information to the noise distribution calculation unit 1581. The ASE intensity calculation unit 153 calculates the ASE intensity P ASE (Step S205). The ASE intensity calculation unit 153 calculates the calculated ASE intensity P ASE The information is output to the received signal waveform calculation unit 158.

[0106] The thermal noise calculation unit 154 calculates the thermal noise σ based on the characteristics of the receiver assumed in the transmission path model created by the transmission path model creation unit 151. th 2 (Step S206). The thermal noise calculation unit 154 calculates the calculated thermal noise σ th 2 The nonlinear noise calculation unit 155 outputs the information to the received signal waveform calculation unit 158. Based on the transmission path model created by the transmission path model creation unit 151, the nonlinear noise light intensity P NLI (Step S207). The nonlinear noise calculation unit 155 calculates the calculated nonlinear noise light intensity P NLI The information is output to the noise intensity conversion unit 156.

[0107] The noise intensity conversion unit 156 converts the nonlinear noise light intensity P NLI The nonlinear noise of the electrical stage σ NLI 2 (step S208). The noise intensity conversion unit 156 converts the nonlinear noise σ NLI 2 The information is output to the received signal waveform calculation unit 158. The noise distribution calculation unit 1581 of the received signal waveform calculation unit 158 ​​calculates the intensity P symbol and the ASE intensity P calculated by the ASE intensity calculation unit 153. ASE and the thermal noise σ calculated by the thermal noise calculation unit 154. th 2 and the nonlinear noise σ converted by the noise intensity conversion unit 156. NLI 2 Based on the information of and the strength of each symbol P symbol The received signal waveform calculation unit 158 ​​calculates the probability density of each of the marks and spaces by adding up the Gaussian distributions calculated for each symbol (step S209). The received signal waveform calculation unit 158 ​​outputs information on the probability density of each of the marks and spaces to the bit error rate calculation unit 159.

[0108] The bit error rate calculation unit 159 calculates a bit error rate based on a threshold determination for the probability density of marks and spaces calculated by the received signal waveform calculation unit 158 ​​(step S210). The bit error rate calculation unit 159 uses the calculated bit error rate as an estimation result of the bit error rate of the candidate route selected in the processing of step S201. The bit error rate calculation unit 159 outputs the estimation result (the bit error rate of the selected candidate route) together with information on the candidate route to the optimal route determination unit 16. The transmission path model creation unit 151 determines whether or not the bit error rates have been estimated for all candidate routes (step S211).

[0109] The transmission channel model creation unit 151 may determine that the bit error rates have been estimated for all the candidate routes when all the candidate routes indicated in the information on each candidate route output from the candidate route determination unit 14 have been selected. On the other hand, the transmission channel model creation unit 151 may determine that the bit error rates have not been estimated for all the candidate routes when any of the candidate routes indicated in the information on each candidate route output from the candidate route determination unit 14 has not been selected.

[0110] If the transmission channel model creation unit 151 determines that the bit error rates have been estimated for all candidate routes (step S211-YES), the bit error rate estimation unit 15 ends the bit error estimation process. If the transmission channel model creation unit 151 determines that the bit error rates have not been estimated for all candidate routes (step S211-NO), the transmission channel model creation unit 151 selects a candidate route that has not been selected in the process of step S201.

[0111] In the above example, the configuration has been shown in which the bit error rate calculation unit 159 outputs the estimation result to the optimal route determination unit 16 every time it calculates the bit error rate for one candidate route. However, the bit error rate calculation unit 159 may calculate the bit error rates for all candidate routes and then output the estimation results for all candidate routes to the optimal route determination unit 16.

[0112] The optical path control device 10 configured as described above includes a transmission path model creation unit 151 that creates a transmission path model for each candidate path, a propagation waveform calculation unit 157 that generates an electric field signal waveform output from a transmitter assumed in the transmission path model and generates a received signal waveform at the time of direct detection by linear fiber propagation simulation, a nonlinear noise calculation unit 155 that calculates the nonlinear noise light intensity of light based on the transmission path model, a noise intensity conversion unit 156 that converts the nonlinear noise light intensity of light into noise in the electrical stage, a received signal waveform calculation unit 158 ​​that calculates a Gaussian distribution of each symbol in the received signal waveform at the time of direct detection based on the received signal waveform at the time of direct detection and the noise in the electrical stage and calculates the sum of the calculated Gaussian distributions of each symbol, and a bit error rate calculation unit 159 that calculates a bit error rate based on the sum of the Gaussian distributions of each symbol. In this way, the optical path control device 10 separates the influence of linear distortion and the influence of nonlinear distortion that occur during fiber transmission, calculates the influence of linear distortion and the influence of nonlinear distortion independently, and then combines them to estimate the bit error rate in a short time while taking both into account. Therefore, it becomes possible to calculate the bit error rate in a short time while taking into account both linear and nonlinear influences.

[0113] (Second embodiment) In the first embodiment, a configuration was shown in which received symbols were divided into marks and spaces, and the bit error rate was calculated based on the probability density of each of the marks and spaces. Meanwhile, in recent years, a method has been proposed for improving chromatic dispersion tolerance during long-distance IM-DD transmission by using waveform equalization processing in a receiving DSP (Digital Signal Processor). In the second embodiment, a configuration for estimating the bit error rate when waveform equalization processing is performed by a receiving DSP is described. In the second embodiment, a second noise addition method is used to add noise containing nonlinear distortion components to the received waveform after direct detection following a linear propagation simulation. Note that the configuration of the bit error rate estimation unit in the second embodiment differs from that in the first embodiment.

[0114] 7 is a diagram showing an example of the configuration of a bit error rate estimator 15a in the second embodiment. The bit error rate estimator 15a includes a transmission path model generator 151, a reception intensity calculator 152, an ASE intensity calculator 153, a thermal noise calculator 154, a nonlinear noise calculator 155, a noise intensity converter 156, a propagation waveform calculator 157, a received signal waveform calculator 158a, and a bit error rate calculator 159a.

[0115] The bit error rate estimation unit 15a differs in configuration from the bit error rate estimation unit 15 in that it includes a received signal waveform calculation unit 158a and a bit error rate calculation unit 159a instead of the received signal waveform calculation unit 158 ​​and the bit error rate calculation unit 159. Other configurations of the bit error rate estimation unit 15a are the same as those of the bit error rate estimation unit 15. The received signal waveform calculation unit 158a and the bit error rate calculation unit 159a will be described below.

[0116] The received signal waveform calculation unit 158a adds random noise according to a Gaussian distribution to the signal field waveform after fiber propagation for each received signal. Then, the received signal waveform calculation unit 158a performs DSP processing (for example, adaptive equalization processing such as an FIR (Finite Impulse Response) filter) on the noise-added signal and threshold determination at symbol positions to decode the received code sequence.

[0117] The bit error rate calculation unit 159a calculates a bit error rate based on the received code sequence decoded by the received signal waveform calculation unit 158a. For example, the bit error rate calculation unit 159a estimates the bit error rate by comparing the transmitted code sequence with the received code sequence decoded by the received signal waveform calculation unit 158a.

[0118] Fig. 8 is a diagram showing an example of the configuration of the received signal waveform calculation unit 158a in the second embodiment. In Fig. 8, functional units other than the received signal waveform calculation unit 158a are also shown to show the relationship with other functional units. The received signal waveform calculation unit 158a includes a noise distribution calculation unit 1581a, a downsampling unit 171, a noise addition unit 172, a DSP unit 173, and a threshold determination unit 174.

[0119] The received signal waveform calculation unit 158a differs in configuration from the received signal waveform calculation unit 158 ​​in that it includes a noise distribution calculation unit 1581a instead of the noise distribution calculation unit 1581, does not include the symbol position detection unit 169 and the symbol intensity calculation unit 170, and includes a downsampling unit 171, a noise adder 172, a DSP unit 173, and a threshold determination unit 174. The noise distribution calculation unit 1581a, the downsampling unit 171, the noise adder 172, the DSP unit 173, and the threshold determination unit 174 will be described below.

[0120] The downsampling unit 171 downsamples the signal waveform after fiber propagation, obtained by the propagation waveform calculation unit 157 , to a sampling rate that matches the DSP unit 173 .

[0121] The noise distribution calculation unit 1581a calculates the ASE intensity P ASE and the thermal noise σ calculated by the thermal noise calculation unit 154. th 2 and the nonlinear noise σ converted by the noise intensity conversion unit 156. NLI 2 The noise distribution calculation unit 1581a calculates the intensity of each sample based on the calculated intensities of each sample. Then, the noise distribution calculation unit 1581a calculates a Gaussian distribution centered on the calculated intensities of each sample. In this way, the noise distribution calculation unit 1581a calculates a Gaussian distribution for each sample.

[0122] The noise adding unit 172 adds random noise according to the Gaussian distribution of each sample calculated by the noise distribution calculating unit 1581a to the post-propagation signal waveform downsampled by the downsampling unit 171. The noise adding unit 172 outputs the post-propagation signal waveform downsampled with the noise added to the DSP unit 173.

[0123] When the noise added by the noise adder 172 is white noise, noise components are added across a wide band from low to high frequencies. On the other hand, when a demodulation configuration is used in which the downstream DSP unit 173 performs band limiting using a digital filter, high-frequency noise components are removed. As a result, the noise intensity observed after the DSP unit 173 may be smaller than the actual noise intensity. However, if the noise adder 172 is placed downstream of the DSP unit 173, the frequency distribution of noise in an actual receiver may not be reproduced, which may result in a decrease in the accuracy of bit error rate estimation. To avoid this, the noise adder 172 adds appropriately amplified noise to the received signal waveform during direct detection so that the noise intensity after removal by the DSP unit 173 matches the noise intensity calculated by the noise distribution calculation unit 1581a.

[0124] The DSP unit 173 performs DSP processing on the downsampled, noise-added, propagated signal waveform. The DSP unit 173 outputs the DSP-processed, propagated signal waveform to the threshold determination unit 174.

[0125] The threshold decision unit 174 decodes the received code sequence by making a threshold decision at the symbol position of the signal waveform after DSP processing and propagation. The threshold decision unit 174 outputs the decoded received code sequence to the bit error rate calculation unit 159a.

[0126] FIG. 9 is a diagram illustrating changes in a signal waveform after propagation due to processing in the second embodiment. FIG. 9 shows four diagrams. The first diagram from the top shows a signal waveform after propagation after processing (downsampling) by the downsampling unit 171. The second diagram from the top shows a signal waveform after propagation after processing (noise addition) by the noise adding unit 172. The third diagram from the top shows a signal waveform after propagation after processing (waveform equalization by DSP processing) by the DSP unit 173. The fourth diagram from the top shows the result of threshold determination by the threshold determining unit 174. As shown in FIG. 9, the threshold determination result by the threshold determining unit 174 shows that 101101 is obtained as a received code sequence. The bit error rate calculation unit 159a compares the received code sequence 101101 obtained by the threshold determining unit 174 with the transmitted code sequence to estimate a bit error rate.

[0127] According to the bit error rate estimating unit 15a in the second embodiment configured as described above, it is possible to simulate the influence of noise including nonlinear optical effects by adding noise in the noise adding unit 172. Therefore, it is possible to estimate the bit error rate with high accuracy and in a short time while taking into consideration both the nonlinear optical effects and changes in linear waveforms.

[0128] In the above example, an NRZ signal is used, but as long as a direct detection receiver is used, the influence of noise including nonlinear optical effects can be simulated for any modulation method by adding noise in the noise adder 172. Furthermore, by performing decoding processing in accordance with the modulation method in the DSP unit 173, it is possible to estimate the bit error rate even when any modulation method is applied.

[0129] (Modification of the second embodiment) In the above embodiment, the bit error rate calculation unit 159a is configured to estimate the bit error rate by comparing the received code sequence decoded as a result of threshold decision at the symbol position of the signal waveform after DSP processing with the transmitted code sequence. The bit error rate calculation unit 159a may be configured to estimate the bit error rate similarly to the first embodiment based on the probability density of the symbol of the signal waveform after DSP processing. In this configuration, the received signal waveform calculation unit 158a includes a symbol position detection unit 169 and a symbol intensity calculation unit 170 instead of the threshold decision unit 174. The symbol position detection unit 169 detects the symbol position in the signal waveform after propagation after DSP processing by the DSP unit 173. The symbol intensity calculation unit 170 calculates the intensity P of each symbol to which noise has been added based on the signal waveform after propagation after DSP processing by the DSP unit 173 and the symbol position detected by the symbol position detection unit 169. symbol Then, the noise distribution calculation unit 1581a calculates the intensity P symbol Histograms for marks and spaces are created from the received signal waveform calculation unit 158a, and the created histograms are used as the probability densities of marks and spaces. As in the first embodiment, the bit error rate calculation unit 159a calculates the bit error rate based on threshold determination of the probability densities of marks and spaces calculated by the received signal waveform calculation unit 158a.

[0130] (Third embodiment) In the second embodiment, a configuration in which chromatic dispersion equalization is performed by DSP processing on the receiver side has been described, whereas in the third embodiment, a configuration in which chromatic dispersion pre-equalization is performed on the transmitter side will be described.

[0131] The configuration in the third embodiment for performing pre-equalization of chromatic dispersion on the transmitter side can be applied to either the first or second embodiment. First, a configuration in the case where the configuration in the third embodiment for performing pre-equalization of chromatic dispersion on the transmitter side is applied to the first embodiment will be described. When the configuration in the third embodiment for performing pre-equalization of chromatic dispersion on the transmitter side is applied to the first embodiment, it differs from the first embodiment in that a propagation waveform calculation unit 157b is provided in the bit error rate estimation unit 15 instead of the propagation waveform calculation unit 157, as shown in FIG.

[0132] 10 is a diagram showing an example of the configuration of the propagation waveform calculation unit 157b and the received signal waveform calculation unit 158 ​​in the third embodiment. In order to show the relationship with other functional units, functional units other than the propagation waveform calculation unit 157b and the received signal waveform calculation unit 158 ​​are also shown in FIG. 10. The following describes the configuration that differs from the first embodiment. The propagation waveform calculation unit 157b includes a PRBS generation unit 161, an intensity modulation unit 163, a Fourier transform unit 164, a linear propagation unit 165, an inverse Fourier transform unit 166, a square-law detection unit 167, a low-pass filter 168, and a variance pre-equalization unit 175.

[0133] When pre-equalization is performed on the transmitting side by performing appropriate filtering on the modulated signal to cancel out the transmission path characteristics, the propagation waveform calculation unit 157b uses a dispersion pre-equalization unit 175 instead of the low-pass filter 162 used in the first embodiment.

[0134] The dispersion pre-equalization unit 175 performs pre-equalization on the transmitting side by performing appropriate filtering to cancel out the transmission path characteristics.

[0135] Next, a configuration will be described in which the configuration in the third embodiment for performing pre-equalization of chromatic dispersion on the transmitter side is applied to the second embodiment. When the configuration in the third embodiment for performing pre-equalization of chromatic dispersion on the transmitter side is applied to the second embodiment, the propagation waveform calculation unit 157 shown in Fig. 7 can be replaced with the propagation waveform calculation unit 157b shown in Fig. 10.

[0136] According to the bit error rate estimating unit 15 and bit error rate estimating unit 15a equipped with the propagation waveform calculating unit 157b in the third embodiment configured as described above, chromatic dispersion is pre-equalized on the transmitter side. After that, by performing the same processing as in the first or second embodiment, it becomes possible to estimate the bit error rate with high accuracy and in a short time while taking into account both the nonlinear optical effect and the linear waveform change.

[0137] This time, we have explained an example in which an NRZ signal is used as the modulation method, but even if the optical signal electric field E(t) in the intensity modulation unit 163 is created arbitrarily, the propagation waveform calculation units 157b and 157c can calculate linear waveform degradation.

[0138] (Modification 1 of the third embodiment) The propagation waveform calculation unit 157b shown in Figure 10 is configured to use an intensity modulation unit 163 as a modulator. When using the intensity modulation unit 163, there is a limit to the ability to equalize waveform degradation due to chromatic dispersion. On the other hand, an IQ modulator may be used as the modulator. By using an IQ modulator, waveform degradation can be more effectively suppressed when modulating not only the intensity but also the phase. Therefore, if this is simulated using the IQ modulation unit, it becomes possible to estimate the bit error rate in this case.

[0139] (Modification 1 common to the first to third embodiments) The optical path control device 10 may be composed of one or more information processing devices. When the optical path control device 10 is composed of multiple information processing devices, some of the functional units of the optical path control device 10 may be provided in other devices. For example, the bit error rate estimation units 15 and 15a may be implemented in other devices to form a bit error rate estimation device, and the optical path control device 10 may be configured to include a communication unit 11, a connection terminal detection unit 12, a route database 13, a candidate route determination unit 14, and an optimal route determination unit 16. In this configuration, the optical path control device 10 transmits information about candidate routes to the bit error rate estimation device and receives estimation results from the bit error rate estimation device. The optimal route determination unit 16 of the optical path control device 10 then determines the optimal route based on the received estimation results.

[0140] Some or all of the functional units of the optical path control device 10 described above are realized as software by a processor such as a CPU (Central Processing Unit) executing a program stored in a storage device having a non-volatile storage medium (non-transitory storage medium) and a storage unit. The program may be recorded on a computer-readable non-transitory storage medium. Examples of computer-readable non-transitory storage media include portable media such as flexible disks, magneto-optical disks, ROMs (Read Only Memory), and CD-ROMs (Compact Disc Read Only Memory), and storage devices such as hard disks built into computer systems.

[0141] Some or all of the functional units of the optical path control device 10 described above may be realized using hardware including an electronic circuit (electronic circuit or circuitry) using, for example, an LSI (Large Scale Integrated circuit), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), or an FPGA (Field Programmable Gate Array).

[0142] Although an embodiment of the present invention has been described above in detail with reference to the drawings, the specific configuration is not limited to this embodiment, and includes designs within the scope of the gist of the present invention. [Industrial Applicability]

[0143] The present invention is applicable to optical transmission systems that use direct detection receivers. [Explanation of symbols]

[0144] 10...optical route control device, 11...communication unit, 12...connection terminal detection unit, 13...route database, 14...candidate route determination unit, 15, 15a...bit error rate estimation unit, 16...optimal route determination unit, 20, 20-1 to 20-5...nodes, 30, 30-1, 30-2...user terminals, 151...transmission path model creation unit, 152...received signal strength calculation unit, 153...ASE strength calculation unit, 154...thermal noise calculation unit, 155...nonlinear noise calculation unit, 156...noise strength conversion unit, 157, 157b...propagation waveform calculation unit, 158, 158a...received signal waveform calculation unit, 159, 159a...bit error rate calculation unit, 161...PRBS generation unit, 162...low-pass filter, 163...intensity modulation unit, 164...Fourier transform unit 165...linear propagation unit, 166...inverse Fourier transform unit, 167...square-law detection unit, 168...low-pass filter, 169...symbol position detection unit, 170...symbol intensity calculation unit, 171...downsampling unit, 172...noise addition unit, 173...DSP unit, 174...threshold determination unit, 175...variance pre-equalization unit, 1581, 1581a...noise distribution calculation unit

Claims

1. A bit error rate estimation device provided in an optical transmission system using a direct detection receiver, a transmission path model creation unit that creates a physical model of a transmission path for each candidate path for communication between user devices that communicate with each other; a propagation waveform calculation unit that generates an electric field signal waveform output from a transmitter assumed in the physical model of the transmission path created by the transmission path model creation unit, and generates a received signal waveform at the time of direct detection by linear fiber propagation simulation; a nonlinear noise calculation unit that calculates the nonlinear noise light intensity of light based on the physical model of the transmission path created by the transmission path model creation unit; a noise intensity conversion unit that converts the nonlinear noise light intensity of the light calculated by the nonlinear noise calculation unit into noise of an electrical stage; a received signal waveform calculation unit that calculates a Gaussian distribution of each symbol or each sample in the received signal waveform at the time of direct detection, based on the received signal waveform at the time of direct detection obtained by the propagation waveform calculation unit and the noise of the electrical stage converted by the noise intensity conversion unit; a code error rate calculation unit that calculates a code error rate based on the Gaussian distribution of each symbol or each sample calculated by the received signal waveform calculation unit; A code error rate estimation device comprising:

2. the received signal waveform calculation unit divides each symbol in the received signal waveform at the time of direct detection into marks and spaces, calculates a Gaussian distribution of each symbol centered on the intensity of each symbol, and calculates a probability density of each of the marks and spaces by adding up the calculated Gaussian distributions of each symbol; the code error rate calculation unit calculates the code error rate by performing threshold determination on the probability density of the mark and the space.

2. The code error rate estimation device according to claim 1.

3. The received signal waveform calculation unit a noise distribution calculation unit that calculates the intensity of each sample after downsampling the received signal waveform during direct detection, and calculates a Gaussian distribution for each sample based on the intensity calculated for each sample; a noise adding unit that adds random noise according to the Gaussian distribution of each sample calculated by the noise distribution calculating unit to the received signal waveform during direct detection; a digital signal processing unit that performs at least waveform equalization as digital signal processing on the received signal waveform during direct detection to which the random noise has been added, 2. The code error rate estimation device according to claim 1.

4. The received signal waveform calculation unit a threshold value determination unit that decodes a received code sequence by threshold value determination of the received signal waveform at the time of direct detection after digital signal processing by the digital signal processing unit, the bit error rate calculation unit calculates the bit error rate by comparing a transmission code sequence with the reception code sequence decoded by the threshold determination unit.

4. The code error rate estimation device according to claim 3.

5. the received signal waveform calculation unit divides each symbol in the received signal waveform at the time of direct detection after digital signal processing by the digital signal processing unit into marks and spaces, and calculates a probability density of each of the marks and spaces from a histogram of the intensity of each symbol to which noise has been added; the code error rate calculation unit calculates the code error rate by performing threshold determination on the probability density of the mark and the space.

4. The code error rate estimation device according to claim 3.

6. the noise adding unit adds the amplified noise to the received signal waveform during direct detection so that the noise intensity after removal by the digital signal processing unit becomes the intensity calculated by the noise distribution calculating unit. The bit error rate estimation device according to any one of claims 3 to 5.

7. the propagation waveform calculation unit generates a pseudo-random signal and performs pre-equalization on the transmitting side by filtering the generated pseudo-random signal to cancel out the transmission path characteristics; The bit error rate estimation device according to any one of claims 1 to 5.

8. A bit error rate estimation method performed by a bit error rate estimation device provided in an optical transmission system using a direct detection receiver, comprising: creating a physical model of a transmission path for each candidate path for communication between communicating user devices; generating an electric field signal waveform output from an assumed transmitter in the created physical model of the transmission path, and generating a received signal waveform at the time of direct detection by linear fiber propagation simulation; Calculating the nonlinear noise intensity of the light based on the created physical model of the transmission line; converting the calculated nonlinear noise light intensity of the light into noise in an electrical stage; calculating a Gaussian distribution of each symbol or each sample in the received signal waveform at the time of direct detection based on the obtained received signal waveform at the time of direct detection and the converted noise of the electrical stage; A code error rate estimation method for calculating a code error rate based on the Gaussian distribution of each symbol or each sample.

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