Channel noise compensation method based on photon number distinguishable detector

Abstract

The invention relates to the technical field of coherent optical communication, and discloses a channel noise compensation method based on a photon number distinguishable detector. The method comprises the following steps: a sending end generates a signal light pulse with a known coding format and an unknown coding state; a receiving end face is used for preparing local oscillation light pulses with the same coding format and different coding states for signal light pulses which are transmitted and attenuated through a long-distance noisy channel and are partitioned at different time; displacement operation is carried out on the signal light pulses in the same coding state in different time partitions for multiple times in an interference mode; detecting the interfered light pulse by using a photon number distinguishable detector to obtain a photon number distribution result of the interference light pulse; and by matching the photon number distribution result of each time with the coding state of the correspondingly prepared local oscillation light, the coding state preparation of the next local oscillation light is guided in combination with deep learning. By adopting the method, the detection bit error rate of the coherent optical communication system on signal light with weak intensity and unknown coding state after transmission in a long-distance noisy channel is reduced.

Description

Technical Field

[0001] The present application relates to the field of coherent optical communication technology, and more specifically, to a method and device for compensating channel noise based on a photon number resolvable detector. Background Art

[0002] As the transmission distance gradually increases, the coherent light received by the detector as a signal (hereinafter referred to as signal light) continues to attenuate. When it decays to the photon level intensity, the coherent optical communication system using traditional coherent detection technology (homodyne, heterodyne detection) will be limited by the sensitivity and shot noise of commonly used detectors (such as balanced detectors), making it difficult for the detection bit error rate of the entire coherent optical communication system to be lower than the standard quantum limit (SQL) under the corresponding coding format.

[0003] The quantum enhanced detection receiver based on single-photon detectors prepares a local oscillator light pulse with the same frequency, spatial mode, polarization direction, coding format and known coding phase as a weak signal light pulse (weak means that the average number of photons in a single pulse is less than 10, which can be detected and distinguished by a photon number-resolvable detector) as a displacement operator, and completes an interference-based displacement operation with the weak signal light to be measured: when the prepared local oscillator light pulse is completely consistent with the coding state of the signal light pulse, the weak signal light can be moved from the coherent state to the photon number state of 0 (the single-photon detector fails to detect photons); if they are inconsistent, the single-photon detector can detect that there are still photons remaining after the displacement operation. Thus, based on the local oscillator light pulse of known coding state prepared at this time and the detection result (whether there are photons remaining), it can be determined whether the coding state of the signal light detected and received in a single measurement is consistent with the coding state of the local oscillator light pulse. Subsequently, the preparation of local oscillator light pulses of different coding states under the same coding format can be completed through adaptive feedback, and the coding state determination of the signal light of the same coding state can be completed through multiple measurements, achieving a bit error rate lower than that of the coherent optical communication system, and even lower than the standard quantum limit.

[0004] However, the quantum enhanced detection receiver based on single-photon detectors can only form a judgment on the coding state of weak signal light through the detection results of the presence or absence of photons after the shift operation. The noise accumulated in the actual channel under long-distance transmission will affect the detection results (especially the result that there are no photons remaining when the prepared local oscillator light is consistent with the coding state of the weak signal light). The photon number resolvable detector (PNRD, photon-number resolving detector) that can distinguish the number of photons of the detected photon-level weak signal can achieve "photon number resolvability" based on the detection result of "the presence or absence of photon number", obtain more information from the detection results for subsequent multiple detections of adaptive feedback, and achieve a lower bit error rate judgment of the coding state of weak signal light under the actual channel noise background.

[0005] In addition to the traditional estimation algorithm that can model the noise in the transmission channel and adaptively reduce the impact on the effective determination of the coding state of weak signal light through compensation of the prepared coded state local oscillator light, with the increase in data volume and computing power, deep learning has also been applied to noise compensation. The patent named "A channel estimation method based on deep learning and data pilot assistance" and the application number CN202110195357.X has also applied deep learning to communication systems to achieve compensation for phase noise. The data set generated by phase noise is trained using the stochastic gradient descent method, and the weighted function optimization model is calculated to compensate for and improve the performance deterioration of the communication system caused by noise.

[0006] However, in the field of quantum-enhanced coherent optical communication systems, deep learning algorithms have not yet been applied to compensate for the intensity / phase noise in the channel noise, and cannot effectively reduce the impact of channel noise on the detection of weak signal light coding states, and cannot effectively reduce the bit error rate of coherent optical communication systems when detecting weak signal light. Summary of the invention

[0007] In order to solve the above problems, the present invention provides a method and device for compensating channel noise based on a photon number resolvable detector, which will reduce the impact of channel noise on the detection of weak signal light and reduce the bit error rate of detecting weak signal light.

[0008] To achieve the above object, according to a first aspect of the present invention, a method for compensating channel noise of a photon number resolvable detector is provided, the method comprising:

[0009] The transmitting end generates a signal light pulse with a known coding format and an unknown coding state, and time-partitions the signal light pulse within a time period corresponding to each pulse width of the signal light pulse;

[0010] The receiving end prepares local oscillator optical pulses with the same coding format and different coding states for the signal optical pulses in different time partitions after attenuation through a long-distance noisy channel transmission;

[0011] Using the local oscillator light pulse as a displacement operator, within a period corresponding to a pulse width, the signal light pulse of the same coding state is repeatedly displaced in different time partitions by means of interference, and an interference light pulse is output;

[0012] The interference light pulse is detected by using a photon number resolvable detector to obtain the photon number distribution result of the interference light pulse;

[0013] By matching the photon number distribution results each time with the corresponding coded state of the prepared local oscillator light, combined with the deep learning network feedback, the offset of the coded state of the local oscillator light relative to the coded state of the signal light under the influence of channel noise is calculated, and the next local oscillator light is guided to prepare the coded state of the noise using intensity and phase modulation based on the standard coding format.

[0014] Furthermore, by matching the photon number distribution result each time with the corresponding prepared LO light coding state, combined with the deep learning network feedback, the offset of the LO light coding state relative to the signal light coding state under the influence of channel noise is calculated, and the next LO light coding state preparation using intensity and phase modulation to compensate for noise is guided in the standard coding format, including the photon number distribution result. and the corresponding coded state of the prepared local oscillator light (|β k >) Form a data set The dataset A channel noise compensation model is constructed as input data of the deep learning network; the influence of the channel noise on the coding state of the signal light is calculated through the channel noise compensation model, and the intensity and phase modulation compensation of the coding state of the local oscillator light is calculated based on the influence of the channel noise on the coding state of the signal light to form a feedback instruction; according to the feedback instruction, the next coding state preparation of the local oscillator light is compensated until the average value of the photon number distribution result is 0.

[0015] Furthermore, the channel noise compensation method based on the photon number resolvable detector also includes a pulse width of the signal light pulse being T, a time partition of the signal light pulse being M, and a pulse width of the prepared local oscillator light pulse being less than T / M.

[0016] Furthermore, the above-mentioned channel noise compensation method based on the photon number resolvable detector also includes that the photon number distribution result of the interference light pulse conforms to the Gaussian distribution (μ, σ 2 ), μ represents the average value of the photon number distribution, σ 2 Represents the fluctuation of the photon number distribution around the average value.

[0017] Further, the coding state preparation of the next local oscillator light is compensated according to the feedback instruction until the average value of the photon number distribution result is 0, including when μ=0, determining that the coding state of the signal light pulse is consistent with the coding state of the local oscillator light pulse; when μ≠0, according to the photon number distribution result of the interference light pulse (μ, σ 2 ), prepare new coded state local oscillator light pulses under the same coding format, and combine μ and σ 2 The numerical value of and the channel noise compensation model are used to calculate the intensity and phase noise caused to the signal light pulse during channel transmission, and the modulation compensation of the intensity and phase is completed on the basis of preparing a new standard coded state local oscillator light under the coding format.

[0018] According to the second aspect of the present invention, there is also provided a compensation device for channel noise based on a photon number resolvable detector, which includes a signal light generation module for generating a signal light pulse with a known coding format and an unknown coding state; a local oscillator light generation module for generating a local oscillator light pulse with the same coding format as the signal light pulse and an adjustable coding state; an interference module for interfering the signal light pulse with the local oscillator light pulse to form a displacement operation of the local oscillator light pulse on the signal light pulse, and outputting an interference light pulse; a photon number resolvable detection module for detecting the interference light pulse and obtaining a photon number distribution result of the interference light pulse; a data acquisition and adaptive feedback module for calculating, based on the photon number distribution result, the offset of the coding state of the local oscillator light relative to the coding state of the signal light under the influence of the channel noise, and providing feedback guidance for preparing a new coding state of the local oscillator light pulse with the same coding format as the signal light pulse and after considering noise compensation.

[0019] Furthermore, the signal light generation module includes a narrow linewidth continuous light fiber laser, an acousto-optic modulator, an electro-optic phase modulator, an adjustable optical attenuator and a long-distance transmission optical fiber connected in sequence; the local oscillator light generation module includes a narrow linewidth continuous light fiber laser, an acousto-optic modulator, an electro-optic phase modulator, an electro-optic intensity modulator and a compensation adjustment module connected in sequence, and the compensation adjustment module includes an optical fiber polarization controller and an optical fiber delay line, which are used to adjust the local oscillator light pulse and the signal light pulse to maintain the same polarization and consistent pulse front.

[0020] Furthermore, the interference module includes a fiber coupler, and the photon number resolvable detection module includes a photon number resolvable detector.

[0021] Furthermore, the data acquisition and adaptive feedback module includes an FPGA, a host computer communicating with the FPGA, and a digital-to-analog conversion module; the FPGA is used to synchronously complete the recording of the coded state of the prepared local oscillator light and the matching photon number distribution results, form a data set, and upload it to the host computer; the host computer is used to input the data set into the channel noise compensation model to calculate the influence of the channel noise, and output the output result as a feedback instruction to the FPGA; the digital-to-analog conversion module is used to convert the feedback instruction into an electrical signal, complete the modulation of the electro-optical phase modulator and the electro-optical intensity modulator in the local oscillator light generation module, and realize the compensation of the modulation of the coding state of the local oscillator light / channel noise influence for the next time.

[0022] Furthermore, the training method of the channel noise compensation model includes step I, taking the photon number distribution matrix and the intensity and phase data of the local oscillator light of the coded state corresponding to the detected photon number distribution as sample data; step II, establishing a channel noise compensation model based on a simplified U-Net network structure of deep learning, using the simplified U-Net network structure as the main structure, using 32×3×3 convolution layers to sequentially extract shallow features, using 64×3×3 convolution layers to sequentially extract middle-level features, and using 128×3×3 convolution layers to sequentially extract high-level features; the high-level features extracted by the convolutional neural network are input into the simplified U-Net decoding network, and after 64×2×2 and 32×2×2 upsampling convolution layers, the second decoding features and the first decoding features are obtained in turn, and the second decoding features and the middle-level features are spliced ​​during the decoding process. The first decoding network output feature is obtained, and the first decoding feature and the shallow feature are concatenated to obtain the second decoding network output feature; the second decoding network output feature is flattened to obtain one-dimensional feature data and input into the fully connected layer, wherein the size of the fully connected layer is (32, 2), and the output result of the fully connected layer is the intensity and phase data of the coded state local oscillator light; step III, initialize the channel noise compensation algorithm model parameters, optimizer, and learning rate; step IV, input the sample data into the channel noise compensation model for training, and determine the loss function; step V, back propagate the error of the channel noise compensation model, adjust the parameters of the model, and optimize the channel noise compensation model; return to step I, iteratively train the channel noise compensation model until the loss function converges, and obtain the final channel noise compensation model after the training is completed.

[0023] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0024] (1) The present invention provides a channel noise compensation method based on a photon number resolvable detector. In the process of transmission, signal light of unknown coding state is interfered by complex channel noise, and the signal strength becomes weak after long-distance transmission. At the receiving end, a local oscillator light of the same coding format and known coding state is prepared to perform interference displacement operation on the signal light of weak intensity and coding state affected by channel noise at this time, and then the photon number resolvable detector is used to detect the photon number distribution of the light beam after the interference between the signal light and the local oscillator light, and the detected photon number distribution result and the matching coding state of the local oscillator light are collected to establish a data set. Combined with the deep learning method, the complex noise in the channel (mainly considering phase noise and intensity noise) is re-compensated to the preparation of the new coding state of the local oscillator light through adaptive feedback, so that the coding state of the local oscillator light can better adapt to the weak signal light under the influence of channel noise, so as to achieve better and more accurate detection results of the photon number resolvable detector. This method can not only effectively reduce the impact of channel noise on the detection of weak signal light coding states, but also provide more accurate judgment of weak signal light coding states based on the detection results of photon number resolution detectors, thereby achieving lower bit error rate of coherent optical communication systems under quantum enhanced detection, and can surpass the standard quantum limit.

[0025] (2) The present invention provides a channel noise compensation method based on a photon number resolvable detector. Under the condition of weak incident signal light intensity, the compensation algorithm can be based on interference displacement operation, detection by a photon number resolvable detector, and simplified U-Net neural network structure in deep learning. While being closer to actual long-distance channel transmission and introducing more noise analysis, it can achieve intelligent compensation for channel noise and ensure that the detection bit error rate of the coherent optical communication system is lower than the standard quantum limit. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 A schematic flow chart of a method for compensating channel noise of a photon number resolvable detector provided in an embodiment of the present application;

[0028] Figure 2 A schematic diagram of the structure of a channel noise compensation device based on a photon number resolvable detector provided in an embodiment of the present application;

[0029] Figure 3 A schematic diagram of the internal structure of each module of a channel noise compensation device based on a photon number resolvable detector provided in an embodiment of the present application;

[0030] Figure 4 The phase space representation diagram of the standard QPSK format phase coding modulation and the corresponding QPSK coding state under the influence of intensity noise and phase noise after channel transmission provided in the embodiment of the present application;

[0031] Figure 5 Phase space characterization of the local oscillator light displacement operation (interference) on the signal light provided in the embodiment of the present application and the photon number distribution diagram of the light pulse after interference based on PNRD measurement;

[0032] Figure 6 A schematic diagram of a QPSK-coded signal light pulse in the time domain, a signal light pulse after channel transmission, and a local oscillator light preparation / compensation detection test phase corresponding to a signal light pulse partition provided in an embodiment of the present application;

[0033] Figure 7 The coding state prepared for detecting the QPSK coded signal provided in the embodiment of the present application |β k > Matched interference light pulse detection result photon number distribution records will form a data set Schematic diagram of a convolutional neural network that uses a matrix to compensate for channel noise. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0035] The terms "first", "second", "third", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0036] like Figure 1 As shown, a method for compensating channel noise of a photon number resolvable detector is provided, comprising the following steps:

[0037] Step 101: The transmitting end generates a signal light pulse with a known coding format and an unknown coding state, and time-partitions the signal light pulse within a time period corresponding to each pulse width of the signal light pulse.

[0038] The signal light pulse is a pulse form of coherent light as a signal. The transmitting end may include a narrow linewidth continuous light fiber laser, an acousto-optic modulator, an electro-optic phase modulator, and an adjustable optical attenuator connected in sequence.

[0039] Step 102, the receiving end prepares local oscillator optical pulses with the same coding format and different coding states for the signal optical pulses in different time partitions that have been attenuated after being transmitted through a long-distance noisy channel.

[0040] The local oscillator light pulse and the signal light pulse have the same coding format and different coding states, and both the local oscillator light and the signal light are coherent light. The receiving end may include a narrow linewidth continuous light fiber laser, an acousto-optic modulator, an electro-optic phase modulator, an electro-optic intensity modulator, and a compensation adjustment module connected in sequence.

[0041] Step 103, using the local oscillator light pulse as a shift operator, within a period corresponding to a pulse width, performing shift operations on the signal light pulse of the same coding state in different time partitions by interference multiple times, and outputting an interference light pulse.

[0042] Step 104 , detecting the interference light pulses using a photon number resolvable detector to obtain a photon number distribution result of the interference light pulses.

[0043] Step 105, by matching the photon number distribution result each time with the corresponding prepared coding state of the local oscillator light, combined with the deep learning network feedback, calculate the offset of the coding state of the local oscillator light relative to the coding state of the signal light under the influence of channel noise, and guide the next local oscillator light to prepare the coding state of the noise using intensity and phase modulation based on the standard coding format.

[0044] The above-mentioned channel noise compensation method based on the photon number resolvable detector relies on the deep learning algorithm, combined with the influence of the channel noise (mainly including the phase and intensity noise in the transmission process) reflected by the photon number distribution result of the interference light pulse detected by the photon number resolvable detector on the coding state of the signal light, adaptively adjusts the fitting parameters of the noise model, and realizes low bit error rate detection and discrimination of weak signal light by preparing and compensating the coding state of the local oscillator light.

[0045] In one embodiment, Figure 2 As shown, a compensation device for channel noise of a photon number resolvable detector includes:

[0046] A weak coherent signal light generating module (also referred to as a signal light generating module) is used to generate a signal light pulse, the encoding format of the signal light pulse is known (taking the QPSK encoding format as an example) and the encoding state is unknown;

[0047] A local oscillator light generation module, used to generate a local oscillator light pulse having the same coding format as the signal light pulse;

[0048] An interference module is used to interfere the signal light pulse and the local oscillator light pulse, form a displacement operation of the local oscillator light pulse on the signal light pulse, and output an interfered light beam (also called an interference light pulse);

[0049] The photon number resolvable detection module is used to detect the interference light pulse and obtain the detection result of its photon number distribution (referred to as photon number distribution result);

[0050] The data acquisition and adaptive feedback module is used to collect the photon number distribution results of the interference light pulse and the corresponding coding state of the local oscillator light, and upload them to the host computer. The host computer uses a neural network based on deep learning to analyze the data, and controls the local oscillator light generation module to re-encode the local oscillator light pulse and compensate for the channel transmission noise based on the analysis results.

[0051] Specifically, within the time period of a signal light pulse, the host computer continuously modulates the coding state of the prepared local oscillator light, interferes with the signal light, forms a corresponding interference light pulse, and then combines the interference light pulse to form a change in the coding state of the local oscillator light prepared next time, forming a multi-round adaptive feedback cycle.

[0052] The channel noise compensation device based on the photon number resolvable detector provided in this embodiment can, when the incident signal light pulse is in a weak intensity and unknown coding state, be based on the displacement operation of the interference module, the photon number resolvable detection module and the deep learning algorithm. While being closer to actual long-distance channel transmission and introducing more noise analysis, it can achieve intelligent compensation for channel noise and ensure a quantum enhanced detection bit error rate lower than the standard quantum limit.

[0053] In one embodiment, Figure 3As shown, in a channel noise compensation device based on a photon number resolvable detector, the signal light generation module includes a narrow linewidth continuous light fiber laser 1, an acousto-optic modulator 2, an electro-optic phase modulator 3, an adjustable optical attenuator 4 and a long section of optical fiber 12, and the devices and equipment are connected through a flange. Among them, the narrow linewidth continuous light fiber laser 1 is used to generate continuous linear polarized light, the acousto-optic modulator 2 is used to modulate the continuous linear polarized light into an optical pulse with a pulse width of T (the pulse width T is generally tens to hundreds of μs, and is divided into M equal regions in the time domain), the electro-optic phase modulator 3 is used for phase encoding under the modulation format (taking the QPSK encoding format as an example), the adjustable optical attenuator 4 is used to adjust the intensity of the encoded signal light pulse (also called the coded state signal light pulse), and the long section of optical fiber 12 is used to transmit the coded state signal light pulse, and the coded state signal light pulse is obtained after a long distance transmission. Weak signal light pulses.

[0054] Figure 4 This is the representation of the QPSK coding format in the phase space. The horizontal and vertical coordinates are X and P components respectively, and the four quadrants represent the coherent states |α0>, |α1>, |α2>, and |α3> under the QPSK coding format. Figure 4 (a) represents the standard QPSK coding format of the signal light pulse, (b) represents the influence of the intensity noise on the standard QPSK coding state after the channel transmission, and (c) represents the influence of the phase noise on the standard QPSK coding state after the channel transmission. As the signal light pulse is transmitted through the optical fiber 12 in the channel, in addition to the attenuation caused by the optical fiber, it will also be affected by the intensity noise and phase noise, corresponding to the evolution of the QPSK coding state as shown in Figure 4 Phase space representation in (b) and (c).

[0055] The local oscillator light generation module includes a narrow linewidth continuous light fiber laser 1, an acousto-optic modulator 2, an electro-optic phase modulator 3, an electro-optic intensity modulator 5, and a compensation adjustment module 6. The narrow linewidth continuous light fiber laser 1 is used to generate continuous linearly polarized light, and the acousto-optic modulator 2 is used to modulate the continuous linearly polarized light into a light pulse with a pulse width of T0, wherein the pulse width satisfies The electro-optical phase modulator 3 and the electro-optical intensity modulator 5 are used to complete the encoding and intensity adjustment of the optical pulse. The encoding format is consistent with the signal optical pulse (also the QPSK encoding format. The specific correspondence between the local oscillator optical pulse generated by the modulation and the signal optical pulse in the time domain is as follows: Figure 6 (c)(d)), the compensation adjustment module 6 includes a fiber polarization controller and a fiber delay line, which are used to adjust the polarization state and pulse front of the local oscillator light pulse to be consistent with the signal light pulse, ensuring a high degree of interference in the interference module.

[0056] The interference module includes a fiber coupler 7, which has two input ends, one input end is used to access the signal light pulse whose intensity becomes weak and is affected by the channel noise after being transmitted through the long section of optical fiber 12, and the other input end is used to access the local oscillator light pulse. There is one output end, and the output end is used to output the interference light pulse. The coupling ratio of the fiber coupler 7 can be 99:1. The fiber coupler 7 is used to use the displacement operation of the local oscillator light pulse through interference to convert the weak signal light pulse from a coherent state to a photon number state. The displacement operation of interference is represented by the phase space of QPSK encoding as follows Figure 5 As shown in (a)(b).

[0057] The photon number resolvable detection module includes a photon number resolvable detector (PNRD) 8, which is used to measure the interference light pulse and output a level signal corresponding to the photon number distribution through a built-in electrical amplifier module. Figure 5 The displacement operation (interference) process shown in (a) and (b) and the different coding states corresponding to the interference light pulses and the photon number distribution results obtained by the photon number resolvable detector 8 are shown in FIG. Figure 5 As shown in (c): When the photon number is shifted to the zero state, the photon number distribution is a Gaussian distribution with an average value of 0 (μ = 0); when the photon number is not shifted to the zero state, the photon number distribution is related to the intensity of the interference light pulse and is a Gaussian distribution with different average values. (μ, σ 2 ), μ reflects the average value of the photon number distribution when PNRD receives a certain light intensity light pulse (also the photon number corresponding to the maximum peak value of the detected photon distribution), σ 2 It reflects the fluctuation of the photon number distribution around the average value when PNRD receives a light pulse of a certain light intensity (the detection performance of PNRD will vary when facing light pulses of different light intensities).

[0058] The data acquisition and adaptive feedback module includes a field programmable gate array 9 (Field Programmable Gate Array, referred to as FPGA), a host computer 10 communicating with the FPGA 9, and a digital-to-analog conversion module 11. Among them, the FPGA 9 is used to collect the photon number distribution results detected by the photon number resolvable detector 8 And the photon number distribution results and the encoding state of the local oscillator light matching this time |β k > Forming a Dataset The data set is uploaded to the host computer 10, and the host computer 10 As the input data of the simplified U-Net neural network based on deep learning, a channel noise compensation model is constructed; through the channel noise compensation model, the compensation corresponding to the selection / channel noise influence of the coding state preparation of the next local oscillator light is calculated and determined, and a feedback instruction is formed, and the feedback instruction is output to FPGA9.

[0059] The digital-to-analog conversion module 11 is used to convert the feedback instruction into a level signal of modulation phase and intensity, which is loaded into the electro-optical phase modulator 3 and the electro-optical intensity modulator 5 respectively, so as to compensate for the modulation / channel noise influence of the coding state of the next local oscillator light.

[0060] In this embodiment, the pulse width T of the prepared signal light pulse is taken as a cycle, and the preparation of local oscillator light pulses with the same coding format but different coding states is repeated M times in each cycle, and the influence of channel noise is compensated to complete the interference of M local oscillator light pulses on the signal light pulse. The standard coding format of the weak signal light pulse (taking QPSK as an example) is characterized by phase space as follows Figure 6 As shown in (a), after being transmitted over a long distance through an optical fiber and affected by channel noise (intensity noise, phase noise), the coding state of the signal light pulse changes as follows: Figure 6 (b) as shown.

[0061] The relationship between the coding state of the signal light pulse (taking the QPSK coding format as an example) and the preparation of the coding state of the local oscillator light and noise compensation in the time domain is as follows: Figure 6 (c) (d) As shown. The FPGA 9 collects the photon number distribution result of each time the photon number resolvable detector 8 detects the interference light pulse, as shown in Figure 6 As shown in (c)(d), in the first stage (Phase I), the host computer 10 uses a deep learning neural network to complete the selection of the next coding state preparation of the local oscillator light within the cycle, completes all possible standard coding state tests under the QPSK standard coding format one by one through polling, modulates the local oscillator light pulse to complete multiple interference measurements, and determines the most likely coding state through an algorithm based on the photon number distribution result of the interference light pulse; then, in the second stage (Phase II), based on the most likely coding state of the local oscillator light, combined with the photon number distribution result of the interference light pulse, the host computer 10 uses a deep learning neural network to calculate the influence of intensity / phase noise on the signal light coding state in channel transmission, and feeds back to FPGA 9, and uses the digital-to-analog conversion module 11 to complete the intensity / phase modulation compensation based on the standard coding format.

[0062] The present embodiment provides a channel noise compensation device based on a photon number resolvable detector, which has the following beneficial effects:

[0063] In the photon number resolvable detection module, a photon number resolvable detector (PNRD) is used to replace the single photon detector (SPD) or superconducting nanowire single photon detector (SN-SPD) commonly used in quantum enhanced detection receivers. When the coded state local oscillator light with the same coding format is prepared to convert the signal light from the coherent state to the photon number state through a displacement operation (interference), the traditional SPD and SN-SPD can only distinguish the "0" state (no photon) and the non-"0" state (with photons) of the photon number in the detection and receiving system. Therefore, when facing a coding format such as QPSK with a coding state number higher than 2, the detection result of the non-"0" state will make it difficult to effectively distinguish the inconsistent results of the LO light and the signal light coding state. The photon number resolvable detector (PNRD) can make a more detailed distinction of the photon number distribution results for the non-"0" state interference light detection results, which can effectively reduce the misjudgment rate of the detection results. In addition, when the signal light is difficult to maintain a stable standard coding state under the influence of channel noise, it is difficult to produce a "0" state result when interfering with the prepared standard coding state local oscillator light pulse. At this time, PNRD can make a good distinction between all non-"0" state detection results, providing sufficient judgment basis for the preparation of subsequent coding state local oscillator light pulses.

[0064] The following is a further description of a method for compensating weak signal transmission channel noise based on a photon number resolvable detector. The method comprises the following steps:

[0065] Step 1, the continuous light generated by the narrow linewidth continuous light fiber laser 1 is output through the acousto-optic modulator 2 and the electro-optic phase modulator 3 to prepare a coded signal light pulse of a certain coding format (taking the QPSK coding format as an example), and then passes through the adjustable optical attenuator 4 and the long section of optical fiber 12 to generate a weak QPSK-coded signal light pulse after long-distance optical fiber transmission. The devices and equipment are connected through a flange.

[0066] Step 2, another narrow linewidth continuous light fiber laser 1 is connected to the acousto-optic modulator 2, electro-optic phase modulator 3, and electro-optic intensity modulator 5 through a flange to generate a coded state local oscillator light pulse with the same coding format as the signal light, and the pulse front edge and pulse polarization state are regulated by the compensation adjustment module 6 composed of the optical fiber delay line and the optical fiber polarization controller to keep consistent with the signal light pulse after the long-distance optical fiber channel transmission. The level signal of the corresponding coded state formed by modulating the electro-optic phase modulator 3 for the first time is output and recorded by the FPGA.

[0067] The narrow linewidth continuous light fiber laser 1 generating the local oscillator light in step 2 and the narrow linewidth continuous light fiber laser 1 generating the signal light in step 1 are set by the operating software to output linearly polarized light of the same frequency (same wavelength), the same polarization and the same spatial mode.

[0068] Step 3, the weak coded signal light pulse transmitted through the long-distance optical fiber channel and affected by the channel noise is connected to the two input ends of the optical fiber coupler 7 at the same time as the prepared local oscillator light pulse with the same coding format, and the coded signal light pulse is converted into a photon digital state output through the interference displacement operation.

[0069] Step 4: Input the interfered light pulse into the photon number resolvable detector (PNRD) 8 to complete the photon number distribution. (μ, σ 2 ) measurement.

[0070] Step 5, the detected photon number distribution result and the corresponding coding state of the local oscillator light are collected and recorded by FPGA9, and uploaded to the host computer 10. The feedback instruction is output through the digital-to-analog conversion module in a polling manner (taking QPSK as an example, four coding states are prepared and tested one by one), and the electro-optical phase modulator 3 is adjusted to complete the preparation of the coding state of the next local oscillator light. Through multiple interferences between the local oscillator light of each coding state under the coding format and the signal light of the same coding state, the host computer 10 combines the photon number distribution obtained by PNRD 8 to screen and obtain the photon number distribution. (μ, σ 2 ) is the coded state of the local oscillator light corresponding to the detection result closest to μ=0.

[0071] Step 6: Keep the coded state of the local oscillator light selected in step 5, and interfere with the signal light again, and form a data set with the photon number distribution detection result of the light pulse after interference and the coded state of the local oscillator light The FPGA 9 is uploaded to the host computer 10, and the influence of the channel noise on the coding state of the signal light is calculated by combining the channel noise compensation network. The coding state of the local oscillator light is obtained by the output result calculated by the channel noise compensation model. k > intensity and phase data, judge the selection of the coding state preparation of the next local oscillator light / the corresponding compensation of the channel noise influence, and form a feedback instruction to output to FPGA 9. Finally, the phase / intensity compensation of the coding state of the local oscillator light is clearly determined in combination with the channel noise compensation network, and is output to FPGA 9 as a feedback instruction. The phase is modulated by the digital-to-analog conversion module 11, and the electro-optical intensity modulator 5 compensates the local oscillator light prepared next time until the detected photon number distribution satisfies μ=0.

[0072] The electrical signal output after conversion by the digital-to-analog conversion module 11 matches the parameters of the RF input end of the electro-optical phase modulator 3. The duration of the single feedback process in steps 5 and 6 (the feedback process includes PNRD measurement, FPGA data acquisition and upload, host computer processing and calculation, and feedback instructions sent back to the FPGA, and the electro-optical phase modulator and electro-optical intensity modulator are controlled by the digital-to-analog conversion module to complete the preparation of the new coding state / noise compensation and then generate a new coding state local oscillator light output) is equal to T / M, and the pulse width of the local oscillator light is less than T / M, where M is the number of time domain partitions of the coded signal light pulse, and its value is generally greater than the number of codable states of the selected coding format. For example, for the QPSK coding format, M>4 is satisfied. When executing step 5, the first 4 partitions of the M partitions in the time domain are used to poll and traverse to clarify the most likely coding state under the QPSK coding format, corresponding to Figure 6 (d) in Phase I; the remaining (M-4) partitions are executed in step 6. Based on the setting of the most likely coding state of the local oscillator light, the influence of the channel noise on the signal light is calculated through the channel noise compensation algorithm to form a noise compensation feedback for the coding state of the local oscillator light, corresponding to Figure 6 (d) Phase II.

[0073] Step 7, taking the pulse width T of the prepared coded state signal light as a cycle, and repeating steps 1 to 6 within each cycle for each coded state signal light pulse. After completing multiple measurement feedbacks within each cycle, the system bit error rate of the entire detection and intelligent compensation device can be calculated by comparing the coded state of the signal light and the coded state of the finally selected local oscillator light.

[0074] In one embodiment, in step 5, the host computer 10 extracts the photon number distribution obtained by polling. (μ, σ 2 ), as a data set to train the embedded channel noise compensation model based on the simplified U-Net network structure based on deep learning. Among them, the data set can be represented by a two-dimensional matrix, the column vector of the two-dimensional matrix represents the distribution of the number of detected photons, and the row vector represents the local oscillator light of different coding states of QPSK.

[0075] refer to Figure 7 The process of training a channel noise compensation model (also called a channel noise compensation algorithm or a channel noise compensation network) based on a simplified U-Net network structure based on deep learning includes the following steps:

[0076] I. Determine the sample set for neural network training by the operations of steps 1-5, the sample set includes the photon number distribution matrix And the distribution of the number of detected photons The corresponding LO light encoding state |β k >intensity and phase data.

[0077] II. Based on the simplified U-Net network structure in deep learning, a channel noise compensation algorithm is established. The specific simplified U-Net channel noise compensation network is as follows: the simplified U-Net encoding network is the main structure, using 32×3×3 convolution layers to extract shallow features F1, 64×3×3 convolution layers to extract middle features F2, and 128×3×3 convolution layers to extract high-level features F3; the high-level features F3 extracted by the convolutional neural network are input into the simplified U-Net decoding network, and after 64×2×2 and 32×2×2 upsampling convolution layers, the decoding features F2 are obtained in turn. ′ (second decoding feature) and F1 ′ (First decoding feature), during the decoding process, F2 ′ Concatenate with F2 to get the first decoding network output features F4, F1 ′ The output feature F5 of the second decoding network is finally obtained by concatenating it with F1; finally, the output feature F5 of the simplified U-Net decoding network is flattened to obtain one-dimensional feature data and input into the fully connected layer, where the size of the fully connected layer is (32, 2). The output result of the final fully connected layer is the encoding state of the local oscillator light |β k >intensity and phase data.

[0078] III. Initialize the training process at the same time, including initializing the channel noise compensation algorithm model parameters, the optimizer, and the learning rate. Preferably, the LeakyRelu activation function is used in the above operations.

[0079] IV. Sample data (including photon number distribution matrix and the distribution of the number of detected photons The corresponding LO light encoding state |β k >intensity and phase data) are input into the channel noise compensation model for training, and the loss function is determined, specifically, the mean square error loss function is determined as the overall loss function of the channel noise compensation model.

[0080] V. Back propagate the error of the channel noise compensation model, adjust the parameters of the model, and optimize the channel noise compensation model. Return to step I, iteratively train the channel noise compensation model until the overall loss function converges, and obtain the final channel noise compensation model after the training is completed.

[0081] The present embodiment provides a method for compensating channel noise based on a photon number resolvable detector, which has the following beneficial effects:

[0082] In the actual context of long-distance optical fiber signal transmission, the intensity and phase noise introduced in the channel transmission will have a disturbing effect on the signal light of the standard coding state, resulting in deviations in both intensity and phase between the signal light in the phase space and the local oscillator light of the standard coding state using the same coding format, making it difficult to form a high-sensitivity, low-bit-error-rate detection of weak signal light through the combination of "displacement operation (interference) to photon number state + SPD / SN-SPD" in the traditional quantum enhanced detection receiver. On the basis of PNRD, the present invention introduces a deep learning neural network algorithm, and establishes a channel noise compensation algorithm based on a simplified U-Net network structure. It can identify and extract the influence of intensity / phase noise in channel transmission from the detection results, and rely on the feedback module to form intelligent and real-time channel noise compensation. On the basis of keeping the detection efficiency, interference degree and other parameters consistent with the reported experiment, the overall system can achieve a lower bit error rate for weak coded state signal light under the influence of channel intensity / phase noise. Therefore, the "PNRD+intelligent noise compensation algorithm" provided by the present invention can effectively assist the coherent optical communication system in detecting weak intensity and unknown coded state signal light under the influence of long-distance transmission and channel noise, thereby achieving a lower bit error rate and improved overall performance.

[0083] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0084] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0085] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

[0086] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0087] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for compensating channel noise of a photon number resolvable detector, characterized in that: include: The transmitting end generates a signal light pulse with a known coding format and an unknown coding state, and divides the signal light pulse into time partitions within a time period corresponding to each pulse width of the signal light pulse; The receiving end prepares local oscillator optical pulses with the same coding format and different coding states for the signal optical pulses in different time partitions after attenuation through a long-distance noisy channel transmission; Using the local oscillator light pulse as a displacement operator, within a period corresponding to a pulse width, the signal light pulse of the same coding state is repeatedly displaced in different time partitions by means of interference, and an interference light pulse is output; The interference light pulse is detected by using a photon number resolvable detector to obtain a photon number distribution result of the interference light pulse; By matching the photon number distribution results each time with the corresponding coded state of the prepared local oscillator light, combined with the deep learning network feedback, the offset of the coded state of the local oscillator light relative to the coded state of the signal light under the influence of channel noise is calculated, and the next local oscillator light is guided to prepare the coded state of the noise using intensity and phase modulation based on the standard coding format.

2. The method according to claim 1, characterized in that The method matches the photon number distribution result each time with the coding state of the corresponding prepared local oscillator light, calculates the offset of the coding state of the local oscillator light relative to the coding state of the signal light under the influence of channel noise in combination with the deep learning network feedback, and guides the next local oscillator light to prepare the coding state of the noise using intensity and phase modulation compensation in the standard coding format, including: The photon number distribution result and the corresponding coded state of the prepared local oscillator light (|β k >) Form a data set The dataset As input data of the deep learning network, a channel noise compensation model is constructed; The influence of the channel noise on the coding state of the signal light is calculated by a channel noise compensation model, and the intensity and phase modulation compensation of the coding state of the local oscillator light is calculated based on the influence of the channel noise on the coding state of the signal light to form a feedback instruction; The preparation of the encoding state of the next local oscillator light is compensated according to the feedback instruction until the average value of the photon number distribution result is 0.

3. The method according to claim 1, characterized in that The method further comprises: A pulse width of the signal light pulse is T, a time partition of the signal light pulse is M, and a pulse width of the prepared local oscillator light pulse is less than T / M.

4. The method according to claim 2, characterized in that The method further comprises: The photon number distribution result of the interference light pulse conforms to the Gaussian distribution μ represents the average value of the photon number distribution, σ 2 Represents the fluctuation of the photon number distribution around the average value.

5. The method according to claim 4, characterized in that The step of compensating the preparation of the next encoding state of the local oscillator light according to the feedback instruction until the average value of the photon number distribution result is 0 includes: When μ=0, it is determined that the coding state of the signal light pulse is consistent with the coding state of the local oscillator light pulse; When μ≠0, the photon number distribution of the interference light pulse Prepare new coded state local oscillator light pulses under the same coding format and combine μ and σ 2 The numerical value of and the channel noise compensation model are used to calculate the intensity and phase noise caused to the signal light pulse during channel transmission, and the modulation compensation of the intensity and phase is completed on the basis of preparing a new standard coded state local oscillator light under the coding format.

6. A channel noise compensation device based on a photon number resolvable detector, characterized in that: include: A signal light generating module, used for generating a signal light pulse with a known coding format and an unknown coding state; A local oscillator light generation module, used to generate a local oscillator light pulse having the same coding format as the signal light pulse and an adjustable coding state; An interference module, used for interfering the signal light pulse and the local oscillator light pulse, forming a displacement operation of the local oscillator light pulse on the signal light pulse, and outputting an interference light pulse; A photon number resolvable detection module, used to detect the interference light pulse and obtain the photon number distribution result of the interference light pulse; The data acquisition and adaptive feedback module is used to calculate the offset of the coding state of the local oscillator light relative to the coding state of the signal light under the influence of channel noise according to the photon number distribution result, and to provide feedback guidance for the preparation of a new coding state of the local oscillator light pulse having the same coding format as the signal light pulse and taking into account noise compensation.

7. The device according to claim 6, characterized in that The signal light generation module comprises a narrow linewidth continuous light fiber laser, an acousto-optic modulator, an electro-optic phase modulator, an adjustable optical attenuator and a long-distance transmission optical fiber connected in sequence; The local oscillator light generation module includes a narrow linewidth continuous light fiber laser, an acousto-optic modulator, an electro-optic phase modulator, an electro-optic intensity modulator, and a compensation adjustment module connected in sequence. The compensation adjustment module includes a fiber polarization controller and a fiber delay line, which are used to adjust the local oscillator light pulse and the signal light pulse to maintain the same polarization and consistent pulse front.

8. The device according to claim 6, characterized in that The interference module includes a fiber coupler, and the photon number resolvable detection module includes a photon number resolvable detector.

9. The device according to claim 6, characterized in that The data acquisition and adaptive feedback module includes an FPGA, a host computer communicating with the FPGA, and a digital-to-analog conversion module; The FPGA is used to synchronously complete the recording of the coded state of the prepared local oscillator light and the matching photon number distribution result, form a data set, and upload it to the host computer; The host computer is used to input the data set into a channel noise compensation model to calculate the influence of channel noise, and output the output result as a feedback instruction to the FPGA; The digital-to-analog conversion module is used to convert the feedback instruction into an electrical signal, complete the modulation of the electro-optical phase modulator and the electro-optical intensity modulator in the local oscillator light generation module, and realize the compensation of the modulation / channel noise influence of the coding state of the next local oscillator light.

10. The device according to claim 9, characterized in that The training method of the channel noise compensation model includes: Step I, taking the photon number distribution matrix and the intensity and phase data of the local oscillator light of the coded state corresponding to the detected photon number distribution as sample data; Step II, based on the simplified U-Net network structure of deep learning, a channel noise compensation model is established, wherein the simplified U-Net network structure is a main structure that uses 32×3×3 convolutional layers to sequentially extract shallow features, uses 64×3×3 convolutional layers to sequentially extract middle features, and uses 128×3×3 convolutional layers to sequentially extract high-level features; the high-level features extracted by the convolutional neural network are input into the simplified U-Net decoding network, and after 64×2×2 and 32×2×2 upsampling convolutional layers, the second decoding features and the first decoding features are obtained in sequence, and during the decoding process, the second decoding features and the middle-level features are concatenated to obtain the first decoding network output features, and the first decoding features and the shallow features are concatenated to obtain the second decoding network output features; the second decoding network output features are flattened to obtain one-dimensional feature data, which is input into the fully connected layer, wherein the size of the fully connected layer is (32, 2), and the output result of the fully connected layer is the intensity and phase data of the coded state local oscillation light; Step III, initializing the channel noise compensation algorithm model parameters, optimizer, and learning rate; Step IV, inputting the sample data into the channel noise compensation model for training, and determining a loss function; In step V, the error of the channel noise compensation model is back-propagated, the parameters of the model are adjusted, and the channel noise compensation model is optimized; returning to step I, the channel noise compensation model is iteratively trained until the loss function converges, and the final channel noise compensation model is obtained after the training is completed.

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