Communication quality estimation device, communication device, and communication quality estimation method

Abstract

To provide a communication quality estimation device and a communication quality estimation method, capable of accurately estimating quality of communication that uses an error correction code.SOLUTION: A communication quality estimation device includes a measurement value acquisition section, a determination section and an estimation section, and estimates an error rate of signals after an error correction is performed, the signals being transmitted using an error correction code that can correct K errors. The measurement value acquisition section acquires a frequency measurement value indicating a frequency at which a codeword including m errors is received. The determination section determines a transition probability and a continuation probability, respectively, which are included in each of a plurality of formulae, such that a frequency calculation value that is calculated using the plurality of formulae and indicates a frequency at which a codeword including m errors is received is brought close to the frequency measurement value, the plurality of formulae including a transition probability that a normal state transitions to an error state and a continuation probability that the error state continues, respectively. The estimation section calculates a frequency at which a codeword including more than K errors is received, by using the plurality of formulae each with the transition probability and the determined continuation probability determined by the determination section, and estimates, on the basis of, a result of the calculation, an error rate after an error correction is performed.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention estimates communication quality. Communication quality estimation Device , communication device and Communication quality estimation It concerns the method. [Background technology]

[0002] One of the technologies that has been put into practical use for high-speed optical communications is PAM (Pulse Amplitude Modulation) 4. PAM4 is a modulation method that uses four amplitude levels and can transmit two bits of data per symbol.

[0003] However, PAM4 tends to have poorer signal quality than NRZ (Non-Return to Zero). For this reason, communication devices that transmit PAM4 signals often include a powerful equalizer. For example, a communication device that transmits PAM4 signals includes a decision feedback equalizer (DFE) with a large number of taps. A decision feedback equalizer for use with PAM-modulated signals has been proposed (see, for example, Patent Document 1).

[0004] On the other hand, many communication systems use forward error correction (FEC) codes to correct bit errors. In this case, the communication system may set communication parameters taking into consideration not only bit errors that occur on the transmission path but also the bit error rate of the signal after error correction processing. A method has been proposed for determining a forward error correction scheme so that a required output bit error rate is generated from an input bit error rate (for example, Patent Document 2). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] WO2019 / 167275 [Patent Document 2] Special Table 2012-529847

Summary of the Invention

Problems to be Solved by the Invention

[0006] When a strong equalizer is used to improve the quality of a signal, burst errors are likely to occur. For example, a decision feedback equalizer corrects a new input symbol using one or more past symbols. Therefore, when an error occurs, burst errors are likely to occur. Also, compared with an NRZ signal, it is difficult to reproduce a clock from a received PAM4 signal. Therefore, burst errors are likely to occur even when a phase shift of the clock occurs due to Jitter or Wander.

[0007] However, in the prior art, it is difficult to accurately estimate the bit error rate after error correction processing in cases where burst errors occur. Here, a communication system adjusts communication parameters so that, for example, the bit error rate after error correction processing becomes lower than a predetermined threshold value. Therefore, when the bit error rate after error correction processing cannot be accurately estimated, the communication parameters may not be adjusted appropriately, and the communication quality may deteriorate.

[0008] Note that if bit errors are intentionally generated by controlling communication parameters, the bit error rate after error correction processing may be measurable. However, it is not preferable to intentionally generate bit errors during the operation of a communication service. Also, the above problems are not limited to PAM4 communication, but are related to communication in which burst errors are likely to occur.

[0009] An object related to one aspect of the present invention is to accurately estimate the quality of communication using an error correction code Communication quality estimation device , communication device and Communication quality estimation to provide a method.

Means for Solving the Problems

[0010] A communication quality estimation device according to one aspect of the present invention is a communication quality estimation device that estimates the error rate after error correction processing of a signal transmitted using an error correction code capable of correcting K errors, and includes a measurement value acquisition unit that acquires a frequency measurement value representing the frequency of receiving a codeword including m errors, In communication where there are multiple error factors causing the errors, for each error factor a determination unit that determines a transition probability from a normal state to an error state and a continuation probability of continuing in the error state, respectively, so that a frequency calculation value representing the frequency of receiving a codeword including m errors, which is calculated using a plurality of calculation formulas each including the transition probability and the continuation probability, approaches the frequency measurement value; and an estimation unit that calculates the frequency of receiving a codeword including more than K errors using the plurality of calculation formulas in which the transition probability and the continuation probability determined by the determination unit are respectively set, and estimates the error rate after error correction processing based on the calculation result.

Advantages of the Invention

[0011] According to the above aspect, the quality of communication using an error correction code can be accurately estimated.

Brief Description of the Drawings

[0012]

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DETAILED DESCRIPTION OF THE INVENTION

[0013] FIG. 1 shows an example of a communication system according to an embodiment of the present invention. In this example, the communication system includes a communication device 100A and a communication device 100B. And a signal is transmitted from the communication device 100A to the communication device 100B using an error correction code.

[0014] The communication device 100A includes an encoder 110. The encoder 110 encodes the transmission data to generate a codeword. In this embodiment, the error correction code is not particularly limited, but is a Reed-Solomon code (544, 514, 10, 15). That is, the codeword is composed of 544 symbols and includes 510 information symbols. Each symbol is composed of 10 bits. The number of correctable symbols is 15. And the signal encoded by the encoder 110 is transmitted from the communication device 100A to the communication device 100B. The communication device 100A and the communication device 100B are connected by an optical fiber or an electrical line.

[0015] The communication device 100B includes a decoder 120. The decoder 120 decodes the received signal. At this time, the decoder 120 corrects errors in the symbols within the codeword. Also, the decoder 120 counts the number of corrected symbols. However, the maximum number of correctable symbols per codeword by the decoder 120 is 15.

[0016] The modulation method is not particularly limited, but is PAM4. PAM4 is a modulation method that uses four amplitude levels and transmits data in 2 bits per symbol. Therefore, each symbol of the Reed - Solomon code transmits 5 PAM4 symbols. In the example shown in FIG. 1, a signal is transmitted from the communication device 100A to the communication device 100B, but it is preferable that the communication devices 100A and 100B can transmit signals bidirectionally.

[0017] FIG. 2 shows an example of a communication device according to an embodiment of the present invention. The communication device 100 includes a transceiver 10, a signal processing unit 20, and a control unit 30. Note that the communication device 100 may include other circuits or functions not shown in FIG. 2. Also, the communication device 100 corresponds to the communication device 100B shown in FIG. 1.

[0018] The transceiver 10 is a pluggable module and receives signals transmitted from other communication devices (in FIG. 1, the communication device 100A). In the case where the communication device 100 receives an optical signal, the transceiver 10 includes a coherent receiver that receives the optical signal and generates an electrical signal representing the received optical signal.

[0019] The signal processing unit 20 includes an equalizer 21 and an FEC decoder 22. Note that the signal processing unit 20 is realized by, for example, a DSP (Digital Signal Processor) or an FPGA (Field Programmable Gate Array). Also, the signal processing unit 20 may include other functions not shown in FIG. 2.

[0020] The equalizer 21 is, for example, a decision feedback equalizer (DFE) that equalizes the waveform of the input signal. The decision feedback equalizer corrects a new input symbol using one or more past symbols.

[0021] FIG. 3 shows an example of a decision feedback equalizer. In this example, the decision feedback equalizer includes an adder 501, a comparator 502, and a feedback filter 503.

[0022] The adder 501 removes the output signal of the feedback filter 503 from the input signal Xk to generate a signal Yk. The output signal of the feedback filter 503 is assumed to represent a signal degradation component (e.g., an inter-symbol interference component). Therefore, the signal Yk represents a signal with the signal degradation component compensated. Then, the comparator 502 determines the value of the signal Yk. As a result, an equalized signal Dk is obtained.

[0023] The feedback filter 503 includes a delay element Z, a multiplier, and a summer. Each delay element Z delays the signal Dk by the symbol time in order. Each multiplier multiplies the weight W (W1 to Wn) by the signal Dk output from the corresponding delay element Z. The weight W is controlled by the control unit 30 so as to represent, for example, the signal degradation component. Then, the summer calculates the sum of the output values of each multiplier. As a result, the signal degradation component of the input signal Xk is obtained. Therefore, in the decision feedback equalizer, the signal degradation component is compensated.

[0024] The FEC decoder 22 performs error correction processing on the output signal of the equalizer 21. That is, the FEC decoder 22 decodes each codeword. At this time, the FEC decoder 22 can correct symbol errors in the codeword. And when the FEC decoder 22 corrects a symbol error, it outputs correction number information representing the number of symbols corrected in the codeword. Note that in this embodiment, the maximum number of symbols that the FEC decoder 22 can correct is 15 per codeword.

[0025] The control unit 30 includes a measurement value acquisition unit 31, a quality estimation unit 32, and a parameter control unit 33. Note that the control unit 30 is realized by, for example, a processor system including a processor and a memory. In this case, the processor estimates the quality of the received signal by executing a software program stored in the memory and controls the operation of the signal processing unit 20. Further, the control unit 30 may have other functions not shown in FIG. 2.

[0026] The measurement value acquisition unit 31 receives the correction number information output from the FEC decoder 22. Here, the measurement value acquisition unit 31 receives the correction number information over a predetermined period. Also, if the number of symbol errors in the codeword is less than or equal to the correctable error number, the number of errors corrected in the FEC decoder 22 corresponds to the number of symbol errors in the received codeword. Therefore, the measurement value acquisition unit 31 can calculate the frequency at which a codeword including m (m = 1, 2,...) symbol errors occurs based on the correction number information output from the FEC decoder 22. In the following description, information representing the frequency of receiving a codeword including m (m = 1, 2,...) symbol errors may be referred to as a "frequency measurement value".

[0027] The quality estimation unit 32 estimates the communication quality based on the correction number information or the frequency measurement value. Specifically, the quality estimation unit 32 estimates the bit error rate after the error correction process is performed in the FEC decoder 22. In this embodiment, if the number of symbol errors in one codeword is 15 or less, those errors are corrected in the FEC decoder 22. Therefore, when the number of symbol errors in one codeword exceeds 15, an error remains in the output signal of the FEC decoder 22. In the following description, the bit error rate after the error correction process may be referred to as "Post-FEC BER".

[0028] In the evaluation of the transmission path, the bit error rate before error correction processing may be used. However, in the evaluation of the end-to-end communication quality, the bit error rate after error correction processing is important. Therefore, the quality estimation unit 32 has a function of estimating the Post-FEC BER.

[0029] The parameter control unit 33 controls the signal processing unit 20 based on the Post-FEC BER estimated by the quality estimation unit 32. For example, the parameter control unit 33 adjusts the communication parameters of the equalizer 21 so that the Post-FEC BER becomes lower than a predetermined threshold. In this case, the parameter control unit 33 may control the weight W and / or the number of taps shown in FIG. 3. As a result, the desired communication quality is realized.

[0030] FIG. 4 shows an example of a method for estimating the occurrence of errors. Here, it is assumed that the occurrence of errors is estimated using a two-state error propagation model. In the two-state error propagation model, as shown in FIG. 4(a), two states are defined. The normal state (GOOD) represents a state where no error has occurred, and the error state (Bad) represents a state where an error has occurred. Px represents the probability of transitioning from the normal state to the error state. Therefore, the probability that the normal state continues is represented by 1 - Px. Py represents the probability that the error state continues. Therefore, the probability of transitioning from the error state to the normal state is represented by 1 - Py.

[0031] FIG. 4(b) shows an example of the state of the received symbol. The ○ mark represents a normal symbol, and the × mark represents a symbol in which an error has occurred. Before time T1, no error has occurred.

[0032] At time T1, an error in the received symbol is detected. Here, the probability that an error occurs in a certain symbol (i.e., the probability of transitioning from the normal state to the error state) is Px. Note that the transition probability Px is usually sufficiently small.

[0033] After that, the probability that the symbol next to the symbol in which an error is detected is an error (i.e., the probability that the error state continues) is Py. Here, for example, when the modulation method is PAM4 and the communication device 100 uses a strong equalizer, the continuation probability Py may increase. And when the continuation probability Py is large, symbol errors are likely to continue. In this example, symbol errors continue from time T1 to T2. That is, a burst error has occurred. And at time T2, a normal symbol is detected. Note that the probability of returning from the error state to the normal state is represented by 1 - Py.

[0034] As described above, if the transition probability Px and the continuation probability Py are known in the two-state error propagation model, it is possible to estimate the occurrence of symbol errors. Here, in this example, the FEC decoder 22 can correct symbol errors within the codeword if the number of symbol errors is 15 or less. Therefore, if the probability or frequency of 16 or more symbol errors occurring within the codeword is calculated, the Post-FEC BER can be estimated.

[0035] However, the error propagation model shown in FIG. 4 represents communication in which there is one factor causing symbol errors. However, in actual communication, symbol errors can occur due to a plurality of factors. Therefore, the communication quality estimation device according to the embodiment of the present invention estimates the Post-FEC BER using an error propagation model representing communication in which symbol errors occur due to a plurality of factors.

[0036] FIG. 5 shows an example of an error propagation model used by the quality estimation unit 32 according to the embodiment of the present invention. The error propagation model according to the embodiment of the present invention represents communication in which symbol errors can occur due to a plurality of factors. The error propagation model shown in FIG. 5 represents communication in which symbol errors occur due to n factors. n is an integer of 2 or more. And each error state (Bad(1) to Bad(n)) shown in FIG. 5 corresponds to the factor causing the symbol error, respectively.

[0037] The transition probability Px and the continuation probability Py are substantially the same in FIGS. 4 and 5. However, the transition probability Px and the continuation probability Py are set for each error factor, respectively. For example, Px(1) represents the probability of transitioning from the normal state to the error state due to factor (1). Similarly, Px(n) represents the probability of transitioning from the normal state to the error state due to factor (n). Therefore, the probability that the normal state continues is represented by 1-(Px(1)+···+Px(n)). Py(1) represents the probability that the error state continues due to factor (1). Thus, the probability of transitioning from the error state due to factor (1) to the normal state is represented by 1-Py(1). Similarly, Py(n) represents the probability that the error state due to factor (n) continues. Thus, the probability of transitioning from the error state due to factor (n) to the normal state is represented by 1-Py(n). In this way, the error propagation model represents the transition between one normal state and multiple error states, and includes the probability of transitioning from one normal state to each error state and the continuation probability that each error state continues. Note that the error propagation model is represented by a plurality of calculation formulas.

[0038] FIG. 6 is a flowchart showing an example of a method for estimating the Post-FEC BER. The processing of this flowchart is periodically executed by, for example, the control unit 30. Also, the control unit 30 may execute the processing shown in FIG. 6 in response to an instruction given from the network administrator. Further, the control unit 30 may execute the processing shown in FIG. 6 when a predetermined condition (for example, a temperature change) is satisfied.

[0039] In S1, the measurement value acquisition unit 31 acquires correction number information from the FEC decoder 22. That is, the FEC decoder 22 sequentially decodes the codewords received by the communication device 100, and for each codeword, counts the number of corrected symbols. Then, the FEC decoder 22 outputs correction number information representing the number of corrected symbols. When the number of symbol errors occurring within a codeword is equal to or less than the correction ability of the FEC decoder 22, the FEC decoder 22 can correct all symbol errors. Therefore, in this case, the number of corrected symbols is the same as the number of symbol errors occurring within the codeword received by the communication device 100. Then, the measurement value acquisition unit 31 acquires the occurrence frequencies of the number of symbol errors (1, 2,...) within the codeword, respectively.

[0040] For example, assume that the FEC decoder 22 decodes 1 billion (10 9 ) codewords within a predetermined monitoring period. Also, assume that one symbol error is corrected in each of 100,000 (10 5 ) codewords, two symbol errors are corrected in each of 10,000 (10 4 ) codewords, and three symbol errors are corrected in each of 1,000 (10 3 ) codewords. In this case, the frequency of occurrence of one symbol error is 10 -4 , the frequency of occurrence of two symbol errors is 10 -5 , and the frequency of occurrence of three symbol errors is 10 -6 .

[0041] In this way, the measurement value acquisition unit 31 calculates a frequency measurement value representing the frequency of receiving codewords including m (1, 2,...) symbol errors. However, the frequency of receiving codewords including a large number of symbol errors should be low. That is, a long monitoring period is required to measure the frequency of receiving codewords including a large number of symbol errors. Therefore, the measurement value acquisition unit 31 measures the occurrence frequency of codewords including fewer symbol errors than a predetermined number.

[0042] FIG. 7 shows an example of the frequency measurement values calculated by the measurement value acquisition unit 31. In this embodiment, the frequency of receiving codewords in which the number of correction symbols (i.e., the number of symbol errors) is from "1" to "7" is measured. The ● marks represent the respective measurement values. For example, the frequency of receiving a codeword with 1 correction symbol is about 10 -2 and the frequency of receiving a codeword with 6 correction symbols is about 10 -8 . Then, the measurement value acquisition unit 31 stores the calculated frequency measurement values in a memory (not shown).

[0043] In S2, the quality estimation unit 32 calculates the optimal values of the transition probability and the continuation probability included in the error calculation formula corresponding to each error factor, respectively, using the frequency measurement values obtained by the measurement value acquisition unit 31. The error calculation formula corresponding to each error factor is prepared in advance and is as follows.

[0044]

Equation

[0045]

Equation

[0046] In the above error calculation formula, m represents the number of symbol errors in the codeword. n identifies the error factor. Px represents the probability of transitioning from the normal state to the error state, as described with reference to FIG. 5. Therefore, for example, Px(1) represents the probability of transitioning from the normal state to the error state due to factor (1), and Px(2) represents the probability of transitioning from the normal state to the error state due to factor (2). Py represents the probability of the error state continuing, as described with reference to FIG. 5. Thus, for example, Py(1) represents the probability of the error state continuing due to factor (1), and Py(2) represents the probability of the error state continuing due to factor (2).

[0047] Equation (1) is for the case of m = 1 and represents the frequency at which one symbol error occurs within the coded word due to factor (n). Therefore, the frequency E(1,n) at which one symbol error occurs is the same as the occurrence probability Px(n).

[0048] Equation (2) represents the frequency at which m symbol errors occur within the coded word due to factor (n). Here, the case where m symbol errors occur within the coded word occurs when, after m - 1 symbol errors have occurred within the coded word, an error continues in the next symbol. Therefore, the frequency E(m,n) at which m symbol errors occur is calculated by multiplying the frequency E(m - 1,n) at which m - 1 symbol errors occur by the continuation probability Py.

[0049] Here, the quality estimation unit 32 estimates the Post-FEC BER considering multiple error factors. At this time, the number of error factors is not particularly limited, but may be determined based on, for example, the nature of the communication. As an example, when there are two main factors considered to cause symbol errors, two sets of error calculation formulas are prepared, and when there are three main factors considered to cause symbol errors, three sets of error calculation formulas are prepared.

[0050] Then, the quality estimation unit 32 performs fitting of the error propagation model using the frequency measurement values obtained in S1. In this embodiment, the error propagation model is represented by multiple sets of error calculation formulas. Therefore, the quality estimation unit 32 determines the variables (transition probability and continuation probability) of each error calculation formula so that the frequency calculation value calculated using the error propagation model approaches the frequency measurement value.

[0051] For example, when the error propagation model is represented by two error factors, the following two sets of error calculation formulas (3.1) and (3.2) are prepared. The value of m is from "2" to "7" in the example shown in FIG. 7.

[0052]

Number

[0053] In this case, the quality estimation unit 32 calculates the optimal values of the four variables (Px(1), Py(1), Px(2), Py(2)). At this time, the quality estimation unit 32 calculates the optimal values of the respective variables by, for example, the steepest descent method. That is, while changing the values of the respective variables, the optimal values of the respective variables are searched so that the frequency calculation value representing the calculation results of the plurality of error calculation formulas approaches the frequency measurement value. Note that the quality estimation unit 32 may calculate the optimal values of the respective variables by other methods.

[0054] For example, in the error propagation model, the occurrence frequency of "symbol error number = 1" is the sum of E(1,1) and E(1,2). That is, this frequency is the sum of Px(1) and Px(2). Then, the variables are adjusted so that this calculation result approaches the measurement value of the occurrence frequency of "corrected symbol number = 1".

[0055] In the error propagation model, the occurrence frequency of "symbol error number = 2" is the sum of E(2,1) and E(2,2). That is, this frequency is calculated based on E(1,1), Py(1), E(1,2), and Py(2). Then, the variables are adjusted so that this calculation result approaches the measurement value of the occurrence frequency of "corrected symbol number = 2". The same processing is performed for "symbol error number = 3" to "symbol error number = 7". As a result, the optimal values of the transition probability and the continuation probability in the error calculation formula corresponding to each error factor are obtained.

[0056] In S3, the quality estimation unit 32 calculates the occurrence frequency of each symbol error number using the transition probability and the continuation probability calculated in S2. That is, the frequency of receiving a codeword including m(1, 2,...) symbol errors is calculated. In this embodiment, the frequency of receiving a codeword including m symbol errors is calculated by the following formula (4).

[0057]

Equation

[0058] E ALLE(m) represents the frequency of receiving a codeword containing m symbol errors. Thus, for example, when the error propagation model is represented by two error factors, ALL E(m) represents the sum of the frequency of m symbol errors occurring due to factor (1) and the frequency of m symbol errors occurring due to factor (2). When the error propagation model is represented by three error factors, ALL E(m) represents the sum of the frequency of m symbol errors occurring due to factor (1), the frequency of m symbol errors occurring due to factor (2), and the frequency of m symbol errors occurring due to factor (3).

[0059] FIG. 8 shows an example of the occurrence frequency for each number of symbol errors calculated using the transition probability and the continuation probability. In FIG. 8, the occurrence frequency E ALL (1) to the occurrence frequency E ALL (20) of the case where the number of symbol errors is "20" are represented by ▲ marks. Note that the occurrence frequencies E ALL (1) to E ALL (7) almost coincide with the corresponding measured values (● marks).

[0060] In S4, the quality estimation unit 32 estimates the Post-FEC BER based on the occurrence frequency E for each number of symbol errors calculated in S3. Here, the sum of the occurrence frequencies E for each number of symbol errors calculated in S3 corresponds to the symbol error rate of the received signal. However, the FEC decoder 22 can correct a predetermined number of symbol errors per codeword. In this example, the correction ability of the FEC decoder 22 is 15 symbols. Therefore, if the number of symbol errors in the codeword is 15 or less, the number of errors after error correction is zero. That is, in order to estimate the Post-FEC BER, it is sufficient to consider the occurrence frequency of the case where the number of symbol errors is 16 or more. Therefore, the Post-FEC BER is represented by the following equation (5).

[0061] [Equation]

[0062] That is, the Post-FEC BER is calculated based on the sum of the occurrence frequencies of cases where the number of symbol errors is 16 or more. Note that the "10" shown in equation (5) represents the number of bits transmitted by one symbol of the Reed-Solomon code. Also, since the number of symbol errors occurring within the codeword is 16 or more, "16" is an approximate value of the number of symbol errors occurring within the codeword. Furthermore, since the number of information symbols of the codeword transmitted in this embodiment is 514, the sum of the frequencies corresponding to the values of m from 16 to 514 is calculated.

[0063] FIG. 9 is a diagram for explaining a method of estimating the Post-FEC BER. Here, the error propagation model is represented by two error factors. Also, according to equations (3.1) and (3.2), it is assumed that the frequencies of receiving a codeword including 16 symbol errors with respect to factor (1) and factor (2) are as follows. E(15,1)=10 -15 E(15,2)=10 -16 Furthermore, the continuation probability Py(1) of factor (1) and the continuation probability Py(2) of factor (2) are each 0.1.

[0064] In this case, the frequency of receiving a codeword including 16 symbol errors is as follows. E(16,1)=10 -15 ×0.1 E(16,2)=10 -16 ×0.1 E ALL (16)=E(16,1)+E(16,2)=1.1×10 -16

[0065] Also, the frequency of receiving a codeword including 16 symbol errors is as follows. E(17,1)=10 -16 ×0.1 E(17,2)=10 -17 ×0.1 E ALL (17)=E(17,1)+E(17,2)=1.1×10-17

[0066] Similarly, the frequency of receiving a coded word containing 18 or more symbol errors is also calculated. However, as the number of symbol errors increases, the occurrence frequency decreases exponentially. Therefore, cases where the occurrence frequency can be regarded as substantially zero can be ignored in the estimation of the Post-FEC BER. For example, in a case where the frequency of receiving a coded word containing more than 20 symbol errors can be regarded as substantially zero, the quality estimation unit 32 may estimate the Post-FEC BER based on the sum of the frequencies of receiving coded words containing 16 to 20 symbol errors.

[0067] In this way, the quality estimation unit 32 estimates the Post-FEC BER of the received signal using an approximate formula defined in the error propagation model. Here, the error propagation model of the embodiment of the present invention is created in consideration of a plurality of error factors. Therefore, the error propagation model of the embodiment of the present invention can accurately represent the actual communication state (that is, the measured value of the number of symbol errors for each). As a result, the quality estimation unit 32 can accurately estimate the Post-FEC BER.

[0068] FIG. 10 shows an example of the occurrence frequency calculated by an error propagation model with one error factor. In the error propagation model with one error factor, the occurrence frequency is represented by one exponential function. Therefore, it is difficult to fit the error propagation model to the measured values. In the example shown in FIG. 10, the ● marks represent the frequency measurement values, and the dashed line represents the frequency calculated by the error propagation model with one error factor. Therefore, it is difficult to accurately estimate the Post-FEC BER by this method.

[0069] On the other hand, in the error propagation model of the embodiment of the present invention, the occurrence frequency is represented by a combination of a plurality of exponential functions. Therefore, it is possible to fit the error propagation model to the measured values. Therefore, according to the embodiment of the present invention, the Post-FEC BER can be accurately estimated.

[0070] FIG. 11 is a flowchart showing an example of a method for adjusting communication parameters of the communication device 100. The processing of this flowchart is periodically executed by, for example, the control unit 30. Further, the control unit 30 may execute the processing shown in FIG. 11 in response to an instruction given from a network administrator.

[0071] In S11, the control unit 30 estimates the Post-FEC BER. Note that S11 corresponds to S1 to S4 of the flowchart shown in FIG. 6. That is, the Post-FEC BER is estimated by the quality estimation unit 32.

[0072] In S12, the parameter control unit 33 compares the Post-FEC BER obtained in S11 with a predetermined threshold value. This threshold value is, for example, the maximum bit error rate defined in the communication system in which the communication device 100 operates. When the Post-FEC BER is greater than the threshold value, the processing of the control unit 30 proceeds to S13.

[0073] In S13, the parameter control unit 33 adjusts the communication parameters of the communication device 100. As an example, the parameter control unit 33 controls the parameters representing the operation of the equalizer 21. For example, when the equalizer 21 is the decision feedback equalizer shown in FIG. 3, the coefficients of each tap (that is, the weights W1 to Wn) are adjusted. Alternatively, the number of taps of this equalizer may be adjusted. After that, the processing of the control unit 30 returns to S11.

[0074] That is, the control unit 30 repeatedly executes the processing of S11 to S13 until the Post-FEC BER becomes equal to or less than the threshold value. When the Post-FEC BER becomes equal to or less than the threshold value, the processing of the control unit 30 ends.

[0075] Thus, in the communication device 100, the communication parameters are adjusted so that the Post-FEC BER becomes equal to or less than the threshold value. At this time, the Post-FEC BER is accurately estimated by the quality estimation unit 32. Therefore, the control unit 30 can appropriately adjust the reception quality of the communication device 100. Further, the control unit 30 can estimate the Post-FEC BER without stopping the communication service by the communication device 100 or intentionally generating an error.

[0076] FIG. 12 shows an example of a method of adjusting communication parameters by intentionally generating an error. In this embodiment, while sweeping communication parameter A between P1 and P3 and sweeping communication parameter B between P4 and P6, the presence or absence of an error is detected. Then, it is assumed that no error is detected within region R. In this case, the communication parameters are set to values corresponding to the center of region R. That is, communication parameter A is set to P2 and communication parameter B is set to P5. However, the center of region R is not necessarily the optimum value of the communication parameters. That is, in this method, not only an error occurs when adjusting the communication parameters, but also a suitable communication parameter may not be obtained.

[0077] <Variation> The error rate is considered to change due to the following factors. (1) Temperature of the processing chip (signal processing unit 20) (2) Deterioration of each element on the transmission path (optical fiber, electrical device, power supply, etc.) (3) Resynchronization of SerDes mounted on the transceiver 10 Therefore, it is preferable that the communication device estimates the Post-FEC BER triggered by the occurrence of these factors.

[0078] FIG. 13 shows a variation of the communication device according to an embodiment of the present invention. In addition to the configuration shown in FIG. 2, the communication device 100B includes a temperature sensor 41 and a timer 34. The temperature sensor 41 measures the temperature of the signal processing unit 20. Also, the timer 34 counts the elapsed time from the time when the Post-FEC BER is estimated. Further, a SerDes (serializer / deserializer) (not shown) is implemented in the transceiver 10. The SerDes performs resynchronization processing when recovering from a signal interruption. And when this resynchronization processing is completed, the transceiver 10 sends a recovery message to the control unit 30.

[0079] FIG. 14 is a flowchart showing an example of a method for setting an opportunity to estimate the Post-FEC BER. The processing of this flowchart is executed in the communication device 100B shown in FIG. 13.

[0080] In S21, the control unit 30 estimates the Post-FEC BER. Note that S21 corresponds to S1 to S4 of the flowchart shown in FIG. 6. That is, the Post-FEC BER is estimated by the quality estimation unit 32.

[0081] In S22, the control unit 30 records the time when the Post-FEC BER was estimated and the temperature of the signal processing unit 20 at that time in a memory (not shown). The temperature of the signal processing unit 20 is measured by the temperature sensor 41.

[0082] In S23, the control unit 30 determines whether or not the elapsed time from the time recorded in S22 exceeds a predetermined threshold. That is, it is determined whether or not the elapsed time from the previous estimation exceeds the threshold. The threshold is, for example, 1 hour. And if the elapsed time from the previous estimation does not exceed the threshold, the processing of the control unit 30 proceeds to S24.

[0083] In S24, the control unit 30 determines whether or not the change from the temperature recorded in S22 exceeds a predetermined threshold value. That is, it is determined whether or not the temperature change from the previous estimation exceeds the threshold value. The threshold value is, for example, 5 degrees. If the temperature change from the previous estimation does not exceed the threshold value, the process of the control unit 30 proceeds to S25.

[0084] In S25, the control unit 30 determines whether or not a recovery message has been received from the transceiver 10. That is, it is determined whether or not the SerDes has performed resynchronization. If the SerDes has not performed resynchronization, the process of the control unit 30 returns to S23.

[0085] In this way, the control unit 30 waits for an opportunity in S23 to S25. When any opportunity in S23 to S25 occurs, the process of the control unit 30 returns to S21. That is, when any opportunity in S23 to S25 occurs, the control unit 30 estimates the Post-FEC BER. After this, the communication device 100B executes the process of the flowchart shown in FIG. 11. That is, when a factor that can change the error rate occurs, the communication device 100B can immediately update the communication parameters. Therefore, stable quality is ensured.

[0086] Note that in the configuration shown in FIG. 2, the control unit 30 implemented in the communication device 100 estimates the Post-FEC BER, but the present invention is not limited to this configuration. That is, the Post-FEC BER may be estimated using a computer connected to the communication device 100.

[0087] FIG. 15 shows another variation of the embodiment of the present invention. In the example shown in FIG. 15, a computer 50 is connected to the communication device 100. The computer 50 includes a processor and a memory. Then, by the processor executing a software program stored in the memory, the functions of the measurement value acquisition unit 31, the quality estimation unit 32, and the parameter control unit 33 are provided. Note that the computer 50 does not have to be a dedicated computer for estimating the Post-FEC BER and may provide other functions.

Explanation of Symbols

[0088] 20 Signal processing unit 21 Equalizer 22 FEC decoder 30 Control unit 31 Measurement value acquisition unit 32 Quality estimation unit 33 Parameter control unit 34 Timer 41 Temperature sensor 50 Computer 100, 100B Communication device

Claims

1. A communication quality estimation device that estimates the error rate after error correction processing of a signal transmitted using an error correction code capable of correcting K errors, comprising: a measurement value acquisition unit that acquires a frequency measurement value representing the frequency of receiving a codeword including m errors; in communication where there are a plurality of error factors that cause errors, using a plurality of calculation formulas each including a transition probability of transitioning from a normal state to an error state and a continuation probability of continuing the error state for each error factor, so that the frequency calculation value representing the frequency of receiving a codeword including m errors approaches the frequency measurement value, a determination unit that determines the transition probability and the continuation probability included in each calculation formula respectively; using the plurality of calculation formulas in which the transition probability and the continuation probability determined by the determination unit are respectively set, calculating the frequency of receiving a codeword including more than K errors, and estimating the error rate after error correction processing based on the calculation result, an estimation unit; A communication quality estimation device comprising:

2. The plurality of calculation formulas include a transition probability of transitioning from a normal state to each error state and a continuation probability of each error state continuing. The communication quality estimation device according to claim 1, characterized in that.

3. In each calculation formula, the frequency of receiving a codeword including m errors is calculated by multiplying the frequency of receiving a codeword including m - 1 errors by the continuation probability. The communication quality estimation device according to claim 1, characterized in that.

4. In each calculation formula, the transition probability represents the frequency of receiving a codeword including one error. The communication quality estimation device according to claim 3, characterized in that.

5. The measurement value acquisition unit, the determination unit, and the estimation unit estimate the error rate after error correction processing when the temperature change of a decoder that decodes a received signal exceeds a predetermined threshold. The communication quality estimation device according to any one of claims 1 to 4, characterized in that.

6. A communication device that receives a signal transmitted using an error correction code capable of correcting K errors, comprising: a decoder that decodes a received signal using the error correction code; a measurement value acquisition unit that acquires a frequency measurement value representing the frequency of receiving a codeword including m errors based on the number of errors corrected in the decoder; In communication where there are multiple error factors causing the errors, a frequency calculation value representing the frequency of receiving a codeword containing m errors, which is calculated using a plurality of calculation formulas each including a transition probability of transitioning from a normal state to an error state and a continuation probability of the error state continuing for each error factor, is made to approach the frequency measurement value, and a determination unit that determines the transition probability and the continuation probability included in each calculation formula respectively; An estimation unit that calculates the frequency of receiving a codeword containing more than K errors using the plurality of calculation formulas in which the transition probability and the continuation probability determined by the determination unit are respectively set, and estimates the error rate after error correction processing based on the calculation result; A communication device comprising the above.

7. An equalizer that equalizes a received signal on the input side of the decoder; A parameter control unit that controls a parameter representing the operation of the equalizer based on the error rate estimated by the estimation unit, further comprising: The communication device according to claim 6, characterized in that.

8. A communication quality estimation method for estimating the error rate after error correction processing of a signal transmitted using an error correction code capable of correcting K errors, comprising: Obtaining a frequency measurement value representing the frequency of receiving a codeword containing m errors; In communication where there are multiple error factors causing the errors, a frequency calculation value representing the frequency of receiving a codeword containing m errors, which is calculated using a plurality of calculation formulas each including a transition probability of transitioning from a normal state to an error state and a continuation probability of the error state continuing for each error factor, is made to approach the frequency measurement value, and the transition probability and the continuation probability included in each calculation formula are determined respectively; Calculating the frequency of receiving a codeword containing more than K errors using the plurality of calculation formulas in which the determined transition probability and continuation probability are respectively set, and estimating the error rate after error correction processing based on the calculation result; A communication quality estimation method characterized by the above.

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