A method for estimating signal demodulation confidence based on noise vector

Abstract

The present invention provides a method for estimating signal demodulation confidence based on noise vector, including obtaining constellation data after demodulation of received signal and calculating constellation discreteness of received signal according to constellation data; matching constellation discreteness with constellation discreteness data based on noise vector under different channel conditions, and determining channel condition of received signal according to matching result; matching bit error condition data and signal quality data under current channel condition based on determined channel condition, and using bit error condition data and signal quality data as demodulation confidence estimation data of received signal; constellation discreteness data, bit error condition data and signal quality data based on noise vector are all pre-calculated under different channel conditions using different modulation mode signals, and have one-to-one correspondence under the same channel condition. The present invention can estimate current bit error condition when only signal constellation data is obtained, thereby providing a strong basis for judging whether captured information is reliable and accurate.

Description

Technical Field

[0001] The invention belongs to the field of communication technology, and in particular relates to a method for estimating signal demodulation confidence based on noise vector. Background Art

[0002] In the modern military field, information-based confrontation is becoming more and more intense, and radio reconnaissance technology has gradually become a key means. By intercepting and deciphering a large amount of interactive information between the sender and the receiver, it can provide important support for military decision-making. With the increasing complexity of the electromagnetic environment in modern warfare, the application scenarios of non-cooperative communication are becoming more and more common. In this context, the ideal receiving equipment should not only have conventional capabilities such as large dynamic frequency range, high sensitivity and low power consumption, but also meet the specific needs of users, try to accurately detect various signals with a 100% interception probability in the full frequency band, and then complete the interpretation and processing, so as to evaluate the value of the intercepted information in real time. Under the current international situation, "electromagnetic information warfare" is becoming more and more intense. Because it is wirelessly transmitted, information has become a "public resource" in a sense, increasing the possibility of information being intercepted and used. Based on the recognition of signals, information is intercepted and cracked, so as to reasonably use the acquired intelligence and provide guidance for various decisions.

[0003] In existing technologies, evaluating the demodulation quality of the processed signal is an important link, among which the bit error situation is the most direct reference physical quantity. However, obtaining the bit error situation under conventional processes has certain feasibility and real-time issues, and also faces high complexity. Traditional evaluation methods usually require a lot of computing resources and time, and are difficult to run efficiently in a real-time environment. In addition, these methods often rely on detailed channel models and prior knowledge, which is often not feasible in non-cooperative communication scenarios. Therefore, how to quickly and accurately estimate the current bit error situation when only the signal constellation data is obtained has become a problem that needs to be solved urgently. Summary of the invention

[0004] In view of this, the present invention proposes a method for estimating signal demodulation confidence based on noise vector, which can estimate the current bit error situation when only signal constellation data is obtained, thereby providing a strong basis for determining whether the captured information is reliable and accurate.

[0005] In order to achieve the above object, the technical solution provided by the present invention is as follows:

[0006] A method for estimating signal demodulation confidence based on noise vector comprises the following steps:

[0007] Obtaining constellation data of the demodulated received signal and calculating the constellation dispersion of the received signal according to the constellation data;

[0008] Matching the constellation discreteness with constellation discreteness data based on noise vectors under different channel conditions, and determining the channel condition of the received signal according to the matching result;

[0009] Based on the determined channel conditions, the error condition data and signal quality data under the current channel conditions are matched, and the error condition data and signal quality data are used as demodulation confidence estimation data of the received signal; the constellation discreteness data, the error condition data and the signal quality data based on the noise vector are all pre-calculated under different channel conditions using signals of different modulation modes, and have a one-to-one correspondence under the same channel conditions.

[0010] Furthermore, constellation dispersion data, bit error data and signal quality data based on noise vectors are calculated using signals of different modulation modes under different channel conditions, including:

[0011] Acquire first constellation data of a first modulation mode signal based on a noise vector under a first channel condition, and calculate distances between each constellation in the first constellation data and a plurality of standard mapping constellations corresponding to the current modulation mode;

[0012] Grouping the constellations in the first constellation data and the standard mapping constellation based on the minimum distance principle;

[0013] Based on the result of data grouping, a hard decision is made on the position of each constellation in the first constellation data, and bit data of each constellation corresponding to a standard mapping constellation is obtained;

[0014] Compare the original bit data of the first modulation mode signal with the bit data corresponding to the standard mapping constellation to obtain the bit error data of the first modulation mode signal under the first channel condition;

[0015] Calculating the variance of each constellation in the first constellation data of the group to which the standard mapping constellation belongs and calculating the expectation of the variance of all standard mapping constellation groups to serve as constellation discreteness data of the first modulation mode signal under the first channel condition;

[0016] Calculate the carrier-to-noise ratio according to the signal-to-noise ratio under the first channel condition to serve as signal quality data of the first modulation mode signal under the first channel condition;

[0017] Modulation modes and channel conditions are changed and calculations are performed to obtain constellation discreteness data, bit error data and signal quality data based on noise vectors that correspond uniformly to signals of different modulation modes under different channel conditions.

[0018] Furthermore, the constellation discreteness is calculated according to the following formula:

[0019]

[0020] Where D(snr) is the discrete degree under the signal-to-noise ratio snr, M is the number of mapped constellation types, i is the type index, and D(C′ i ) is the group C′ to which the i-th mapping constellation belongs i The variance, N i is the number of constellations in the group to which the i-th mapping constellation belongs, k i is the data index of the i-th mapping constellation, C′ i (k i ) is C′ i k i Constellation data, E(C′ i ) is the group C′ to which the i-th mapping constellation belongs i expectations.

[0021] Furthermore, the constellation discreteness is matched with constellation discreteness data based on noise vectors under different channel conditions, including:

[0022] Calculate the difference between the constellation discreteness and the constellation discreteness data based on the noise vector under different channel conditions;

[0023] The signal-to-noise ratio is obtained according to the channel condition corresponding to the minimum difference, thereby determining the channel condition of the received signal.

[0024] Furthermore, the bit error data is calculated according to the following formula:

[0025]

[0026] Where P b (snr) is the bit error rate under the signal-to-noise ratio snr, that is, the bit error situation data, N is the number of bit data, b(k) is the original bit data sequence, b′(k) is the mapped bit data sequence, and mod[·] is the modulo-2 addition operation.

[0027] Furthermore, the signal quality data is calculated according to the following formula:

[0028]

[0029] In the formula, is the carrier-to-noise ratio, that is, signal quality data, and snr is the signal-to-noise ratio.

[0030] Furthermore, the constellation data of the standard mapping constellation is as follows:

[0031] C i =x i +j·y i i=0,1,…,M-1

[0032]

[0033] In the formula, C i is the constellation data of the i-th standard mapping constellation, (x i ,y i ) is C i The constellation coordinates in the two-dimensional complex plane, n is the nth circle index from the inside to the outside, R n is the radius of the nth circle, M n is the number of PSK constellation points on the nth circle, θ n is the initial phase of the nth PSK constellation, i n is the mapping constellation point on the nth concentric circle.

[0034] Further, obtaining first constellation data based on the noise vector includes:

[0035] Building a signal model based on predefined signal parameters to represent the current modulation mode signal;

[0036] Add a noise vector to the signal model and perform sampling to obtain the receiving end signal;

[0037] According to the current signal modulation mode, the receiving end signal is coherently demodulated to obtain constellation data of the receiving end signal based on the noise vector.

[0038] Furthermore, the expression of the signal model is as follows:

[0039]

[0040] Where s(t) is the modulation signal, a k is the kth code element, i.e., the predefined signal parameter, g T is the pulse shaping impulse response, T s is the symbol period, f c is the carrier frequency, θ c is the initial phase of the carrier, t is the time, and j is the imaginary unit;

[0041] Add a noise vector to the signal model as follows:

[0042]

[0043] Where r(t) represents the signal with the added noise vector, and w(t) is the additive Gaussian white noise;

[0044] Sampling is performed to obtain the receiving end signal, as follows:

[0045]

[0046] Where r(n) is the receiving end signal, fs is the sampling frequency, τ is the transmission delay of the Gaussian channel, and θ is the phase offset of the Gaussian channel.

[0047] Furthermore, based on the Monte Carlo statistical method, multiple simulation calculations are performed on signals of different modulation modes under different channel conditions, so as to obtain the mapping relationship between the constellation discreteness data based on the noise vector, the bit error data and the signal quality data.

[0048] In summary, the present invention provides a method for estimating signal demodulation confidence based on noise vector, including obtaining constellation data after demodulation of the received signal and calculating the constellation discreteness of the received signal according to the constellation data; matching the constellation discreteness with the constellation discreteness data based on the noise vector under different channel conditions, and determining the channel condition of the received signal according to the matching result; matching the bit error situation data and signal quality data under the current channel conditions based on the determined channel conditions, and using the bit error situation data and signal quality data as demodulation confidence estimation data for the received signal; the constellation discreteness data, bit error situation data and signal quality data based on the noise vector are all pre-calculated under different channel conditions using different modulation mode signals, and have a one-to-one correspondence under the same channel condition. The present invention can estimate the current bit error situation when only the signal constellation data is obtained, thereby providing a strong basis for judging whether the captured information is reliable and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 A flowchart of a method for estimating signal demodulation confidence based on a noise vector according to the present invention;

[0051] Figure 2 A block diagram of a method for estimating signal demodulation confidence based on noise vector according to the present invention;

[0052] Figure 3 It is the constellation pattern diagram of 16-APSK of the present invention;

[0053] Figure 4 It is the constellation pattern diagram of 16-QAM of the present invention;

[0054] Figure 5 It is the constellation pattern diagram of 16-PSK of the present invention;

[0055] Figure 6 The Gray constellation mapping diagram of the present invention in which the modulation mode is 16-APSK;

[0056] Figure 7 The Gray constellation mapping diagram of the present invention in which the modulation mode is 16-APSK;

[0057] Figure 8 The Gray constellation mapping diagram of the present invention when the modulation mode is 16-PSK;

[0058] Fig. 9 It is a bit error rate curve diagram of 16-APSK under different signal-to-noise ratios of the present invention;

[0059] Fig.10 It is a bit error rate curve diagram of 16-QAM under different signal-to-noise ratios of the present invention;

[0060] Fig.11 It is a bit error rate curve diagram of 16-PSK under different signal-to-noise ratios of the present invention;

[0061] Fig.12 It is a deviation variance curve diagram of 16-APSK under different signal-to-noise ratios of the present invention;

[0062] Fig.13 It is a deviation variance curve diagram of 16-QAM under different signal-to-noise ratios of the present invention;

[0063] Fig.14 It is a deviation variance curve diagram of 16-PSK under different signal-to-noise ratios of the present invention;

[0064] Fig.15 It is the constellation diagram of 16-APSK when the signal-to-noise ratio of the present invention is 20dB;

[0065] Fig.16 This is the constellation diagram of 16-APSK when the signal-to-noise ratio of the present invention is 30 dB;

[0066] Fig.17 It is the constellation diagram of 16-QAM when the signal-to-noise ratio of the present invention is 20dB;

[0067] Fig.18 It is the constellation diagram of 16-QAM when the signal-to-noise ratio of the present invention is 30dB;

[0068] Fig.19 It is the constellation diagram of 16-PSK when the signal-to-noise ratio of the present invention is 20dB;

[0069] Fig. 20 It is the constellation diagram of 16-PSK when the signal-to-noise ratio of the present invention is 30dB. DETAILED DESCRIPTION

[0070] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] See also Figure 1 This embodiment provides a method for estimating signal demodulation confidence based on a noise vector, comprising the following steps:

[0072] S1: Obtain constellation data of the demodulated received signal and calculate the constellation discreteness of the received signal according to the constellation data.

[0073] It should be noted that the received signal is demodulated to restore the constellation data, and the demodulated constellation data can be expressed as P'(k). The discrete degree of the demodulated signal point P'(k) on the complex plane is calculated to express its constellation discrete degree. The method that can be used is to calculate the average distance or variance between the signal point and the standard constellation point.

[0074] S2: Matching the constellation discreteness with the constellation discreteness data based on the noise vector under different channel conditions, and determining the channel condition of the received signal according to the matching result.

[0075] It should be noted that noise vector data (i.e., constellation discreteness data based on noise vectors) under different channel conditions (such as different signal-to-noise ratios, multipath effects, etc.) are generated in advance by simulation. For each channel condition, the constellation discreteness of the demodulated signal point is calculated to form a database. The constellation discreteness calculated in step S1 is matched with the predefined noise vector data. The closest predefined discreteness value is found to determine the channel condition of the received signal.

[0076] S3: Match the bit error data and signal quality data under the current channel conditions based on the determined channel conditions, and use the bit error data and signal quality data as demodulation confidence estimation data for the received signal; the constellation discreteness data, bit error data and signal quality data based on the noise vector are all pre-calculated under different channel conditions using signals of different modulation modes, and have a one-to-one correspondence under the same channel conditions.

[0077] It should be noted that signal quality data such as bit error rate (BER) and signal-to-noise ratio (SNR) under different channel conditions are generated in advance through simulation. These data have a one-to-one correspondence with the noise vector data under the same channel condition. According to the channel condition determined in step S2, predefined bit error condition data and signal quality data are searched. The found data such as bit error rate (BER) and signal-to-noise ratio (SNR) are used as demodulation confidence estimation data of the received signal.

[0078] This embodiment provides a method for estimating signal demodulation confidence based on noise vectors, which can estimate the current bit error situation when only signal constellation data is obtained, thereby providing a strong basis for determining whether the captured information is reliable and accurate.

[0079] In one embodiment, using signals of different modulation modes under different channel conditions to calculate constellation dispersion data based on noise vectors, bit error data and signal quality data includes:

[0080] S21: Acquire first constellation data of a first modulation mode signal based on a noise vector under a first channel condition, and calculate distances between each constellation in the first constellation data and a plurality of standard mapping constellations corresponding to the current modulation mode.

[0081] First, define P′ as the constellation data obtained after demodulation, then:

[0082] P′=x′+j·y′ (1)

[0083] Wherein, x′ and y′ are the constellation coordinates of P′ in the two-dimensional complex plane.

[0084] The distances between the M mapping constellations corresponding to the current modulation mode and the corresponding standard mapping constellations are calculated as follows:

[0085] H i (k)=|P′(k)-C i (k)|i=0,1,…,M -1 (2)

[0086] Adhering to the principle of reducing computational complexity, after simplification, we have:

[0087] H i (k) = (x′(k)-x i (k)) 2 +(y′(k)-y i (k)) 2 (3)

[0088] S22: grouping each constellation in the first constellation data and the standard mapping constellation based on the minimum distance principle.

[0089] Then, the minimum distance grouping idea is adopted to take the minimum value of the distance between the current constellation coordinate point and the M standard constellation positions, then:

[0090] H min (k) = min(H i (k)) (4)

[0091] Therefore, the data can be divided into standard constellation positions based on the maximum probability (i.e. the closest distance) to obtain the index value i of the standard constellation, where i is an integer and its value range is [0, M-1]. Then, the data corresponding to each mapped constellation are:

[0092] C′ i (k i ) = x′ i (k i )+j·y′ i (k i )i=0,1,…,M-1 (5)

[0093] Among them, k i is the data index of the i-th mapping constellation point.

[0094] S23: Based on the result of data grouping, a hard decision is made on the position of each constellation in the first constellation data, and bit data of each constellation corresponding to the standard mapping constellation is obtained.

[0095] By making a hard decision on the original position of each constellation point, the bit data corresponding to the kth mapped constellation data P′(k) can be obtained:

[0096] {b′ mk b′ mk+1 …b′ m(k+1)-1} (6)

[0097] S24: Compare the original bit data of the first modulation mode signal with the bit data corresponding to the standard mapping constellation to obtain the bit error data of the first modulation mode signal under the first channel condition.

[0098] Statistical analysis of the signal error rate under the current channel conditions. That is, based on the above assumptions, the main source of error in the system comes from the noise in the channel. Let the bit data of the transmitting source be b(k), which is then modulated by the transmitting end, transmitted through the channel, and demodulated by the receiving end to obtain the received bit data b′(k). Compare b(k) and b′(k). When the two are not equal, it means that an error has occurred in the communication system.

[0099] By counting the total number of transmitted bits and the number of errors, the Monte Carlo estimate of the bit error rate BER can be obtained as:

[0100]

[0101] Where N is the total number of bits sent, N e The number of times the error occurred.

[0102] S25: Calculate the variance of each constellation in the first constellation data of the group to which the standard mapping constellation belongs and calculate the expectation of the variance of all standard mapping constellation groups to serve as constellation discreteness data of the first modulation mode signal under the first channel condition.

[0103] S26: Calculate the carrier-to-noise ratio according to the signal-to-noise ratio under the first channel condition to serve as signal quality data of the first modulation mode signal under the first channel condition.

[0104] S27: Modulation mode and channel conditions are changed and calculations are performed to obtain constellation discreteness data, bit error data and signal quality data based on noise vectors that correspond uniformly to signals of different modulation modes under different channel conditions.

[0105] Based on the calculation process of the constellation dispersion degree data, the bit error condition data and the signal quality data proposed in the above embodiments, in one embodiment, the constellation dispersion degree in step S25 is calculated according to the following process:

[0106] First, we need to calculate the expected E(C′) of the i-th mapping constellation i ),but:

[0107]

[0108] Then, according to the definition of variance, the variance D(C′) of the i-th mapping constellation can be obtained: i )but:

[0109]

[0110] Finally, we need to calculate the expectation of the variances of the M constellations to obtain the data sample variance under the current signal-to-noise ratio:

[0111]

[0112] Where D(snr) is the discrete degree under the signal-to-noise ratio snr, M is the number of mapped constellation types, i is the type index, and D(C′ i ) is the group C′ to which the i-th mapping constellation belongs i The variance, N i is the number of constellations in the group to which the i-th mapping constellation belongs, k i is the data index of the i-th mapping constellation, C′ i (k i ) is C′ i ki Constellation data, E(C′ i ) is the group C′ to which the i-th mapping constellation belongs i expectations.

[0113] Based on the calculation process of the constellation dispersion data, the bit error data and the signal quality data proposed in the above embodiment, in one embodiment, the bit error data of step S24 is calculated according to the following formula:

[0114]

[0115] Where P b (snr) is the bit error rate under the signal-to-noise ratio snr, that is, the bit error situation data, N is the number of bit data, b(k) is the original bit data sequence, b′(k) is the mapped bit data sequence, and mod[·] is the modulo-2 addition operation.

[0116] Based on the calculation process of the constellation dispersion data, bit error data and signal quality data proposed in the above embodiment, in one embodiment, the signal quality data of step S26 is calculated according to the following formula:

[0117]

[0118] In the formula, is the carrier-to-noise ratio, that is, signal quality data, and snr is the signal-to-noise ratio.

[0119] In order to make the calculated discreteness more credible and universal, in a further embodiment, it is necessary to conduct multiple simulation experiments based on the Monte Carlo statistical concept according to actual applications to obtain the mapping relationship between the signal-to-noise ratio, the bit error rate and the variance.

[0120] In one embodiment, obtaining first constellation data based on a noise vector includes:

[0121] S31: constructing a signal model based on predefined signal parameters to represent the current modulation mode signal.

[0122] The following is an introduction to defining code elements as signal parameters to construct a signal model.

[0123] First, define the random bit data as b k , the stationary complex random sequence (i.e., symbol sequence) with zero mean under the current modulation mode is a k If the signal modulation order is m, then each code element a k The m bits of data {b mk b mk+1 …b m(k+1)-1}, let M = 2 m , then code element a kThe value range is [0,M-1].

[0124] Then, a signal model is constructed to represent digital information in phase:

[0125]

[0126] Among them, g T represents the pulse shaping impulse response, T s represents the symbol period, f c represents the carrier frequency, θ c It represents the initial phase of the carrier (usually θ c =0).

[0127] According to the above definition, let A k is the current code element a k The amplitude of is the current code element a k The phase of , then:

[0128]

[0129] Wherein, k is the index of the codeword sequence.

[0130] Using Euler's formula, we have:

[0131]

[0132] According to space theory, the triangular code element is mapped to the two-dimensional complex plane. The position of the complex plane signal constellation point contains the amplitude phase modulation information. The Euclidean distance between each point on the constellation diagram and the origin coordinate axis represents the amplitude information of the signal, and the angle between the complex vector and the horizontal axis represents the phase information of the signal. Then, a k The corresponding space vector is:

[0133]

[0134] The signal sequence is divided into two vertical vectors, namely the in-phase component I of the horizontal axis projection and the orthogonal component Q of the vertical axis projection. Therefore, by modulating the signal on the constellation diagram, the amplitude and phase modulation of the signal can be realized simultaneously.

[0135] The constellation diagrams of M-APSK and M-QAM signals can be viewed as circles with multiple radii, and the layout of the constellation points on the circles presents a PSK mapping. Each circle can be viewed as an M-QAM with a constant radius. n -PSK, while M-PSK type signals can be regarded as special cases where all constellation patterns are distributed on a circle with equal phase intervals. Definition C iis the standard constellation point mapped without noise influence, then the unified expression of M-APSKM-QAM and M-PSK constellation point set is:

[0136]

[0137] Where n is the nth circle index from inside to outside; R n It represents the radius of the nth circle; M n It represents the number of PSK constellation points on the nth circle; θ n It represents the initial phase of the nth PSK constellation; i n It represents a specific mapping constellation point on the nth concentric circle, and the value range is i n =0,1,…,M n -1.

[0138] S32: Add a noise vector to the signal model and perform sampling to obtain a receiving end signal.

[0139] The model of the received signal r(t) is generally defined as:

[0140] r(t)=s(t)+w(t) (18)

[0141] Among them, s(t) represents the modulation signal and w(t) represents the additive white Gaussian noise.

[0142] The so-called additive Gaussian white noise refers to a sequence whose probability density function presents a normal distribution and whose power spectrum density function is a uniform distribution, that is:

[0143]

[0144] Correspondingly, f(x) represents the probability density function of n(k); R(τ) represents the autocorrelation function of n(k): S(f) is the Fourier transform of R(τ), which represents the power spectral density function of R(τ). 0 is the power spectral density in (W / Hz); δ(τ) is the unit impulse response.

[0145] Under constant parameter channel conditions, signal transmission will be interfered by noise, and errors may occur, resulting in bit errors. After the signal is superimposed with channel noise and processed by the receiver, the signal vector endpoint distribution diagram at the receiving end is no longer the ideal M points, but will be diffused to a certain extent, where the noise usually follows a normal distribution with a mean of zero.

[0146] At this point, it is necessary to introduce the common physical quantity signal-to-noise ratio (SNR) for evaluating signal quality. Its physical meaning is the ratio of signal power to noise power, usually in the form of:

[0147]

[0148] Among them, P S It represents the signal power value. Similarly, P N It represents the noise power value. According to the discrete time signal power calculation expression, we can get:

[0149]

[0150] Combined with the signal-to-noise ratio expression, we have:

[0151]

[0152] According to the above expression of the modulation signal s(t), we can get:

[0153]

[0154] Sampling at the receiving end, the sampling rate is f S , we can get its discretized form:

[0155]

[0156] Among them, τ represents the transmission delay of the Gaussian channel, and θ represents the phase offset of the Gaussian channel.

[0157] S33: coherently demodulate the receiving end signal according to the current signal modulation mode to obtain constellation data of the receiving end signal based on the noise vector.

[0158] According to the current signal modulation mode, the received signal r(n) is coherently demodulated to obtain the constellation data of the current signal: From the above, {C i} is the symbol sequence under the current modulation mode, then:

[0159] C i =x i +j·y i i=0,1,…,M-1 (28)

[0160] Among them, (x i ,y i ) corresponds to the constellation coordinates of the two-dimensional complex plane, that is:

[0161]

[0162] According to the amplitude R n First determine which circle of the constellation the symbol point falls on, and then calculate the value based on the real and imaginary parts y iThe phase determines which codeword neighborhood the symbol point falls in on the circle, thereby determining the codeword on the output constellation. Then, if P′ is defined as the constellation data obtained after demodulation (i.e., the constellation data based on the noise vector), then:

[0163] P′=x+j·y′ (31)

[0164] Based on the calculation method provided in the above embodiment, steps S1-S3 can be implemented as follows:

[0165] S1-1: Obtain the signal constellation data required for estimation for:

[0166]

[0167] S1-2: Calculate the square of the distance from each standard constellation position, that is:

[0168]

[0169] M types of modulation constellation pattern data are grouped according to the minimum distance:

[0170]

[0171] Then, the data corresponding to each mapping constellation are:

[0172]

[0173] Among them, k i is the data index of the i-th mapping constellation point.

[0174] S2-1: Calculate the expectation of M types of mapping constellations respectively and variance Right now:

[0175]

[0176] S2-2: Calculate the received constellation data samples The degree of dispersion

[0177]

[0178] S2-3: According to the discrete degree, traverse the previously defined discrete degree data based on the noise vector, that is, the variance array D(snr), and calculate its minimum difference to obtain its corresponding signal-to-noise ratio

[0179]

[0180] S3-1: Based on signal-to-noise ratio The bit error rate estimated by the currently received constellation data can be obtained and carrier-to-noise ratio Right now:

[0181]

[0182] Figure 2 The following shows an implementation method of a method for estimating signal demodulation confidence based on a noise vector designed based on the above embodiment. The method is verified by taking three modulation modes, 16-APSK, 16-QAM and 16-PSK, as examples.

[0183] 1. Use 16-APSK as the simulation modulation method

[0184] (1) Generate random bits. In this simulation modulation mode, the modulation order is m=4, so each code element a k The 4-bit data {b 4k b 4k+1 b 4k+2 b 4K+3}, let:

[0185] M=2 4 =16

[0186] Then, code element a k The value range is [0,15].

[0187] Then, based on equations (13)-(17), a unified expression of the 16-APSK constellation point set is obtained.

[0188] (2) Mapping standard constellation. The constellation diagram of the M-APSK signal can be seen as a circle with multiple radii, and the layout of the constellation points on the circle shows a PSK mapping situation. Each circle can be regarded as an M-APSK signal with a constant radius. n -PSK.

[0189] Combination Figure 3 As shown in the figure, taking the 16-APSK signal as the object, the "4+12" constellation point layout is adopted. The inner circle is equivalent to QPSK modulation, and the outer circle is equivalent to 12-PSK modulation. For 16-APSK, the constellation point amplitude, phase and distribution are as follows:

[0190]

[0191] in, is the general formula of the constellation points on the inner circle of 16-APSK, is the general formula of the constellation points on the outer circle of 16-APSK; R 1 is the inner radius, R 2 is the outer circle radius.

[0192] (3) Then, Gaussian white noise is added and the received signal is coherently demodulated according to the 16-APSK modulation pattern to obtain its constellation data.

[0193] Definition {C i} is the symbol sequence under 16-APSK modulation mode, then:

[0194] C i =x i +j·y i i=0,1,…,15

[0195] Among them, (x i ,y i ) corresponds to the constellation coordinates of the two-dimensional complex plane, that is:

[0196] Define P' as the constellation data obtained after demodulation, then:

[0197] P′=x′+j·y′.

[0198] (4) Then calculate the signal vector endpoint distribution variance. Based on equations (2)-(5) and (8)-(10), calculate the constellation dispersion as the signal vector endpoint distribution variance.

[0199] (5) Then calculate the bit error rate. Figure 6 Based on the mapping bit relationship of the 16-APSK Gray constellation, the original position of each constellation point can be hard-determined, so the bit data {b' corresponding to the kth mapped constellation data P'(k) can be obtained. 4k b' 4k+1 b' 4k+2 b' 4k+3}. The bit error rate is calculated based on formula (11).

[0200] (6) Establish a corresponding relationship table between distribution variance, signal-to-noise ratio and bit error rate. In order to make the calculated discrete degree more credible and universal, 1000 simulation experiments were carried out based on the Monte Carlo statistical idea to obtain the mapping relationship between signal-to-noise ratio, bit error rate and variance. Fig. 9 Bit error rate curves of 16-APSK under different signal-to-noise ratios and Fig.12 Deviation variance curve of 16-APSK under different signal-to-noise ratios.

[0201] (7) Constellation data grouping. Fig.15 and Fig.16 , respectively, are the constellation discreteness of 16-APSK under the conditions of signal-to-noise ratio SNR of 20dB and 30dB. The constellation data required to estimate the 16-APSK signal is recorded. for: Based on equations (33)-(35), data are grouped according to the 16 modulation constellation patterns based on the minimum distance.

[0202] (8) Calculate the variance of the current constellation data distribution. Based on equations (36)-(38), the expectations and variances of the 16 mapping constellations are calculated respectively, and then the received constellation data samples are calculated. The degree of dispersion

[0203] (9) Traverse the relationship table with distribution variance as the comparison object. According to the degree of dispersion The previously defined constellation variance array D(snr) based on the noise vector is traversed, and its minimum value is calculated based on equation (39) to obtain its corresponding signal-to-noise ratio, so that the estimated bit error rate and carrier-to-noise ratio of the currently received 16-APSK constellation data can be obtained based on equations (40) and (41).

[0204] 2. Use 16-QAM as the simulation modulation method

[0205] (1) Generate random bits. In this simulation modulation mode, the modulation order is m=4, so each code element a k The 4-bit data {b 4k b 4k+1 b 4k+2 b 4K+3}, let:

[0206] M=2 4 =16

[0207] Then, code element a k The value range is [0,15].

[0208] Then, based on equations (13)-(17), a unified expression of the 16-QAM constellation point set is obtained.

[0209] (2) Mapping standard constellation. The constellation diagram of the M-QAM signal can be seen as a circle with multiple radii, and the layout of the constellation points on the circle shows a PSK mapping. Each circle can be regarded as an M-QAM signal with a constant radius. n -PSK.

[0210] Combination Figure 4 As shown in the figure, taking 16-QAM signal as the object, adopting the "4+8+4" constellation point layout, the inner circle is equivalent to QPSK modulation, the middle circle is equivalent to 12-PSK modulation, and the outer circle is equivalent to QPSK modulation. Then for 16-QAM, the constellation point amplitude, phase and distribution are as follows:

[0211]

[0212] in, is the general formula of the constellation points on the inner circle of 16-QAM, is the general formula of the constellation points on the circle in 16-QAM, is the general formula of the constellation points on the outer circle of 16-QAM; R 1 is the inner radius, R 2 is the radius of the middle circle, R 3 is the outer circle radius.

[0213] (3) Then, Gaussian white noise is added and the received signal is coherently demodulated according to the 16-QAM modulation style to obtain its constellation data.

[0214] Definition {C i} is the symbol sequence under 16-QAM modulation mode, then:

[0215] C i =x i +j·y i i=0,1,…,15

[0216] Among them, (x i ,y i ) corresponds to the constellation coordinates of the two-dimensional complex plane, that is:

[0217] Define P' as the constellation data obtained after demodulation, then:

[0218] P′=x′+j·y′.

[0219] (4) Then calculate the signal vector endpoint distribution variance. Based on equations (2)-(5) and (8)-(10), calculate the constellation dispersion as the signal vector endpoint distribution variance.

[0220] (5) Then calculate the bit error rate. Figure 7 Based on the mapping bit relationship of the 16-QAM Gray constellation, the original position of each constellation point can be hard-determined, so the bit data {b' corresponding to the kth mapped constellation data P'(k) can be obtained. 4k b' 4k+1 b' 4k+2 b' 4k+3}. The bit error rate is calculated based on formula (11).

[0221] (6) Establish a corresponding relationship table between distribution variance, signal-to-noise ratio and bit error rate. In order to make the calculated discrete degree more credible and universal, 1000 simulation experiments were carried out based on the Monte Carlo statistical idea to obtain the mapping relationship between signal-to-noise ratio, bit error rate and variance. Fig.10 Bit error rate curves of 16-QAM under different signal-to-noise ratios and Fig.13 Deviation variance curves of 16-QAM at different signal-to-noise ratios.

[0222] (7) Constellation data grouping. Fig.17 and Fig.18 , respectively, are the constellation discreteness of 16-QAM under the conditions of signal-to-noise ratio SNR of 20dB and 30dB. The constellation data required to estimate the 16-QAM signal is recorded. for: Based on equations (33)-(35), data are grouped according to the 16 modulation constellation patterns based on the minimum distance.

[0223] (8) Calculate the variance of the current constellation data distribution. Based on equations (36)-(38), the expectations and variances of the 16 mapping constellations are calculated respectively, and then the received constellation data samples are calculated. The degree of dispersion

[0224] (9) Traverse the relationship table with distribution variance as the comparison object. According to the degree of dispersion The previously defined noise vector-based constellation variance array D(snr) is traversed, and its minimum value is calculated based on equation (39) to obtain its corresponding signal-to-noise ratio, so that the estimated bit error rate and carrier-to-noise ratio of the currently received 16-QAM constellation data can be obtained based on equations (40) and (41).

[0225] 3. Use 16-PSK as the simulation modulation method

[0226] (1) Generate random bits. In this simulation modulation mode, the modulation order is m=4, so each code element a k The 4-bit data {b 4k b 4k+1 b 4k+2 b 4K+3}, let:

[0227] M=2 4 =16

[0228] Then, code element a k The value range is [0,15].

[0229] Then, based on equations (13)-(17), a unified expression of the 16-PSK constellation point set is obtained.

[0230] (2) Mapping standard constellation. The constellation diagram of the M-PSK signal can be seen as a circle with multiple radii, and the layout of the constellation points on the circle shows the mapping of the PSK form. Each circle can be regarded as an M-PSK signal with a constant radius. n -PSK.

[0231] Combination Figure 5As shown, taking the 16-PSK signal as the object, the constellation point amplitude, phase and distribution are as follows:

[0232]

[0233] Among them, C i is the constellation point formula of 16-PSK; R is the radius of the circle.

[0234] (3) Then, Gaussian white noise is added and the received signal is coherently demodulated according to the 16-PSK modulation pattern to obtain its constellation data.

[0235] Definition {C i} is the symbol sequence under 16-PSK modulation mode, then:

[0236] C i =x i +j·y i i=0,1,…,15

[0237] Among them, (x i ,y i ) corresponds to the constellation coordinates of the two-dimensional complex plane, that is:

[0238] Define P' as the constellation data obtained after demodulation, then:

[0239] P′=x′+j·y′.

[0240] (4) Then calculate the signal vector endpoint distribution variance. Based on equations (2)-(5) and (8)-(10), calculate the constellation dispersion as the signal vector endpoint distribution variance.

[0241] (5) Then calculate the bit error rate. Figure 8 Based on the mapping bit relationship of the 16-PSK Gray constellation, the original position of each constellation point can be hard-determined, so the bit data {b' corresponding to the kth mapped constellation data P'(k) can be obtained. 4k b' 4k+1 b' 4k+2 b' 4k+3}. The bit error rate is calculated based on formula (11).

[0242] (6) Establish a corresponding relationship table between distribution variance, signal-to-noise ratio and bit error rate. In order to make the calculated discrete degree more credible and universal, 1000 simulation experiments were carried out based on the Monte Carlo statistical idea to obtain the mapping relationship between signal-to-noise ratio, bit error rate and variance. Fig.11 Bit error rate curves of 16-PSK under different signal-to-noise ratios and Fig.14 Deviation variance curves of 16-PSK under different signal-to-noise ratios.

[0243] (7) Constellation data grouping. Fig.19 and Fig. 20 , respectively, are the constellation discreteness of 16-PSK under the conditions of signal-to-noise ratio SNR of 20dB and 30dB. The constellation data required to estimate the 16-PSK signal is recorded. for: Based on equations (33)-(35), data are grouped according to the 16 modulation constellation patterns based on the minimum distance.

[0244] (8) Calculate the variance of the current constellation data distribution. Based on equations (36)-(38), the expectations and variances of the 16 mapping constellations are calculated respectively, and then the received constellation data samples are calculated. The degree of dispersion

[0245] (9) Traverse the relationship table with distribution variance as the comparison object. According to the degree of dispersion The previously defined constellation variance array D(snr) based on the noise vector is traversed, and its minimum value is calculated based on equation (39) to obtain its corresponding signal-to-noise ratio, so that the estimated bit error rate and carrier-to-noise ratio of the currently received 16-PSK constellation data can be obtained based on equations (40) and (41).

[0246] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating signal demodulation confidence based on noise vector, characterized in that: The steps include: Acquire constellation data of the demodulated received signal and calculate the constellation discreteness of the received signal according to the constellation data; Matching the constellation discreteness with constellation discreteness data based on noise vectors under different channel conditions, and determining the channel condition of the received signal according to the matching result; Matching the bit error data and signal quality data under the current channel condition based on the determined channel condition, and using the bit error data and the signal quality data as demodulation confidence estimation data of the received signal; the constellation discreteness data based on the noise vector, the bit error data and the signal quality data are all pre-calculated under different channel conditions using signals of different modulation modes, and have a one-to-one correspondence under the same channel condition; Calculating the constellation discreteness data based on the noise vector, the bit error data and the signal quality data under different channel conditions using signals of different modulation modes, including: Acquire first constellation data of a first modulation mode signal based on a noise vector under a first channel condition, and calculate distances between each constellation in the first constellation data and a plurality of standard mapping constellations corresponding to the current modulation mode; Grouping each constellation in the first constellation data and the standard mapping constellation based on a minimum distance principle; Based on the result of the data grouping, a hard decision is made on the position of each constellation in the first constellation data, and bit data of each constellation corresponding to the standard mapping constellation is obtained; Compare the original bit data of the first modulation mode signal with the bit data corresponding to the standard mapping constellation to obtain the bit error data of the first modulation mode signal under the first channel condition; Calculating the variance of each constellation in the first constellation data of the group to which the standard mapping constellation belongs and calculating the expectation of the variance of all the standard mapping constellation groups as the constellation discreteness data of the first modulation mode signal under the first channel condition; Calculating a carrier-to-noise ratio according to the signal-to-noise ratio under the first channel condition to serve as the signal quality data of the first modulation mode signal under the first channel condition; Modulation mode and channel conditions are changed and calculations are performed to obtain the noise vector-based constellation discreteness data, the bit error data and the signal quality data that correspond one-to-one to signals of different modulation modes under different channel conditions.

2. The method for estimating signal demodulation confidence based on noise vector according to claim 1, characterized in that: The constellation dispersion is calculated according to the following formula: ; ; ; In the formula, Signal-to-noise ratio The degree of dispersion under To map the number of constellation types, is the category index, For the i Mapping constellations to groups The variance of For the i The number of constellations in the group to which the constellation belongs is mapped. For the i A data index that maps the constellation, for Middle Constellation data, For the i Mapping constellations to groups expectations.

3. The method for estimating signal demodulation confidence based on noise vector according to claim 2, characterized in that: Matching the constellation discreteness with constellation discreteness data based on noise vectors under different channel conditions, including: Calculating the difference between the constellation discreteness and the constellation discreteness data based on the noise vector under different channel conditions; The signal-to-noise ratio is obtained according to the channel condition corresponding to the minimum difference, thereby determining the channel condition of the received signal.

4. The method for estimating signal demodulation confidence based on noise vector according to claim 1, characterized in that: The bit error data is calculated according to the following formula: ; In the formula, Signal-to-noise ratio The bit error rate under the condition, that is, the bit error situation data, is the number of bits of data, is the original bit data sequence, To map the bit data sequence, is a modulo-2 addition operation, The bit number index.

5. The method for estimating signal demodulation confidence based on noise vector according to claim 1, characterized in that: The signal quality data is calculated according to the following formula: ; In the formula, is the carrier-to-noise ratio, i.e., the signal quality data, is the signal-to-noise ratio.

6. The method for estimating signal demodulation confidence based on noise vector according to claim 1, characterized in that: The constellation data of the standard mapping constellation is as follows: ; ; ; In the formula, For the i constellation data of said standard mapped constellation, for The constellation coordinates in the two-dimensional complex plane, From the inside out Circumference index, For the The radius of the circle, For the On a circle The number of constellation points, For the indivual The initial phase of the constellation, For the The mapped constellation points on concentric circles, is the number of mapped constellation types.

7. The method for estimating signal demodulation confidence based on noise vector according to claim 1, characterized in that: Acquiring the first constellation data based on the noise vector includes: Building a signal model based on predefined signal parameters to represent the current modulation mode signal; Adding a noise vector to the signal model and performing sampling to obtain a receiving end signal; According to the current signal modulation mode, the receiving end signal is coherently demodulated to obtain constellation data of the receiving end signal based on the noise vector.

8. The method for estimating signal demodulation confidence based on noise vector according to claim 7, characterized in that: The expression of the signal model is as follows: ; In the formula, is the modulation signal, For the k Code elements, i.e. predefined signal parameters, is the pulse shaping impulse response, is the symbol period, is the carrier frequency, is the initial phase of the carrier, t For time, j is an imaginary unit, k is the index of the number of code elements; Add a noise vector to the signal model as follows: ; In the formula, represents the signal with the added noise vector, is additive Gaussian white noise; Sampling is performed to obtain the receiving end signal, as follows: ; In the formula, is the receiving end signal, is the sampling frequency, is the transmission delay of the Gaussian channel, is the phase shift of the Gaussian channel, The sampling point index.

9. The method for estimating signal demodulation confidence based on noise vector according to claim 1, characterized in that: Based on the Monte Carlo statistical method, multiple simulation calculations are performed on signals of different modulation modes under different channel conditions, so as to obtain the mapping relationship between the constellation discreteness data based on the noise vector, the bit error situation data and the signal quality data.

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