Power line channel signal-to-noise ratio estimation method and system based on delay autocorrelation algorithm

Abstract

The invention provides a power line channel signal-to-noise ratio estimation method and system based on a delay autocorrelation algorithm, and belongs to the technical field of power line communication, and the method comprises the steps: obtaining a power line signal to be analyzed, the obtained power line signal to be analyzed being a digital sequence generated by a leader sequence generation module; calculating a delay autocorrelation function, wherein the autocorrelation function describes the similarity of the power line signal between two adjacent time points; windowing processing of the signal reduces the edge effect of the power line signal and improves the accuracy of an autocorrelation function; and normalizing the self-correlation function to make the maximum value equal to 1, comparing the self-correlation characteristics of the same signal in different time periods, and performing power line channel signal-to-noise ratio estimation by using the normalized self-correlation function. The method has high precision under the condition of high signal-to-noise ratio, and compared with a traditional channel signal-to-noise ratio blind estimation method, the algorithm is high in precision, and occupied bandwidth resources are relatively small.

Description

Technical Field

[0001] The present invention relates to the technical field of power line communication, and in particular to a method and system for estimating a signal-to-noise ratio of a power line channel based on a delayed autocorrelation algorithm. Background Art

[0002] With the rapid development of smart grids and smart homes, higher requirements are placed on high-speed and reliable power line communications. Orthogonal frequency division multiplexing (OFDM) is a type of multi-carrier modulation. OFDM technology provides a high-speed data communication solution for bandwidth-limited power line channels. The OFDM system can select its modulation mode according to the communication quality of each sub-channel to ensure the most efficient transmission rate based on the reliability of information transmission. The signal-to-noise ratio (SNR) is the ratio of the average power of the signal to the average power of the noise within the bandwidth. It is an important parameter to measure the quality of the OFDM system. The accuracy of the signal-to-noise ratio estimation is directly related to the modulation type identification and power regulation of the signal, which in turn affects the efficiency and accuracy of the subsequent demodulation algorithm. Accurate signal-to-noise ratio estimation can ensure that the OFDM system can operate normally in various complex environments. Therefore, finding a signal-to-noise ratio estimation algorithm suitable for power line channels is of great significance to improving the performance of power line communication OFDM systems.

[0003] There are two main methods for existing signal-to-noise ratio estimation:

[0004] The data-assisted SNR estimation method uses a reference signal known at the receiving end to compare with the received signal to infer the characteristics of the noise. Since the reference signal is known, the difference between the signal and the noise can be calculated, and then the power of the noise can be estimated, and finally the estimated value of the SNR can be obtained. Common data-assisted SNR estimation methods include maximum likelihood estimation (ML), subspace decomposition algorithm (SD), least squares algorithm (LSE), etc. This method can estimate the SNR more accurately, especially when the channel conditions are poor or the noise level is high. However, this algorithm will increase the overhead of the entire communication system to a certain extent, thereby reducing the effective data transmission rate.

[0005] The blind SNR estimation technology allows the SNR of a signal to be estimated by analyzing the high-order statistical characteristics of the signal itself without prior knowledge. Commonly used blind SNR estimation methods include the second-order-fourth-order moment estimation method (M2M4), the second-order-fourth-sixth-order moment algorithm (M2M4M6), the high-order accumulation estimation method (CUM), and the SNR estimation algorithm based on the sample characteristic function (ECF). This method has poor performance but high efficiency, and can estimate the SNR more effectively without specific information about the environment.

[0006] Reference [1] proposed an algorithm for estimating the signal-to-noise ratio of a power line channel. By using virtual subcarriers in a repetitive structure synchronization training sequence or virtual carriers in a protection sideband, data-assisted estimation or blind estimation can be performed flexibly. However, the complexity of this algorithm is relatively high, and the accuracy of the algorithm is affected by the number of virtual subcarriers. The more virtual subcarriers there are, the more accurate the signal-to-noise ratio estimation value is. When the number of virtual subcarriers is too large, more bandwidth resources will be occupied.

[0007] Reference [2] proposed a method to identify OFDM signals with cyclic prefix (CP-OFDM) and OFDM signals with zero padding (ZP-OFDM) by analyzing the composition of the OFDM signal guard interval. Simulation results showed that this method can effectively estimate the signal-to-noise ratio of CP-OFDM signals and ZP-OFDM signals. However, the method has poor universality and cannot prove the superiority of its algorithm for other types of OFDM derivative technologies.

[0008] Reference [3] proposed a signal-to-noise ratio estimation method based on the autocorrelation function, which has a simple algorithm, low computational complexity and high accuracy. Simulations show that the accuracy is high when the signal-to-noise ratio is lower than 5 dB. However, this method is only suitable for use in wireless communication systems, and its applicability in power line channels is not mentioned.

[0009] The power line communication network connects numerous grid devices and power loads. The frequent switching of these devices and loads has a great interference on the power line channel. At the same time, the power line noise is diverse and changeable, which makes the communication of the power line channel much more complicated than other wired channels.

[0010] At present, the SNR estimation method under Gaussian white noise (AWGN) channel is relatively mature. However, due to the existence of frequency selective fading, severe power line background noise and impulse noise, the estimation performance of traditional SNR estimation algorithms such as minimum mean square error (MMSE) algorithm, second fourth moment (M2M4) algorithm and maximum likelihood (ML) algorithm has been seriously reduced. The SNR estimated by the traditional SNR estimation method is quite different from the actual channel SNR.

[0011] At the same time, in order to ensure the transmission bandwidth of effective data in the power line channel as much as possible, some schemes use the channel signal-to-noise ratio blind estimation method. The estimation accuracy of this scheme is low and it is difficult to meet the requirements of current technological development, or the accuracy is high but the algorithm is very complex and difficult to implement. At the same time, there are few literatures on power line channel signal-to-noise ratio estimation, and most of them are for wireless communication channel signal-to-noise ratio estimation.

[0012] In addition, the existing SNR estimation method, even if the autocorrelation algorithm is mentioned, only extracts features from the acquired signal, and then further processes it through a subsequent algorithm to obtain an estimated value of the channel SNR. The algorithm is relatively complex.

[0013] [1] Liang Dong, Zhang Baohui, Niu Dongwen, et al. OFDM signal-to-noise ratio estimation method for broadband power line communication [J]. Electric Power Automation Equipment, 2013, 33(12): 105-110.

[0014] [2] Zhang Haichuan, Lei Yingke. A method for estimating signal-to-noise ratio of orthogonal frequency division multiplexing signals[J]. Optical Communications Research, 2016(04):8-11+41.DOI:10.13756 / j.gtxyj.2016.04.003.

[0015] [3] Li Yinyong, Chen Jin, Zhang Yuming, et al. Signal-to-noise ratio estimation method based on autocorrelation function [J]. Military Communication Technology, 2011, 32(02): 18-21. Summary of the invention

[0016] The object of the present invention is to provide a method and system for estimating the signal-to-noise ratio of a power line channel based on a delayed autocorrelation algorithm, so as to solve at least one technical problem existing in the above-mentioned background technology.

[0017] In order to achieve the above object, the present invention adopts the following technical solutions:

[0018] In a first aspect, the present invention provides a method for estimating a signal-to-noise ratio of a power line channel based on a delayed autocorrelation algorithm, comprising:

[0019] Acquire a power line signal to be analyzed, wherein the acquired power line signal to be analyzed is a digital sequence generated by a preamble sequence generation module;

[0020] Calculate the delayed autocorrelation function, which describes the similarity of the power line signal between two adjacent time points;

[0021] Windowing of signals to reduce edge effects of power line signals and improve the accuracy of autocorrelation functions;

[0022] The autocorrelation function is normalized to make its maximum value equal to 1, the autocorrelation characteristics of the same signal in different time periods are compared, and the normalized autocorrelation function is used to estimate the signal-to-noise ratio of the power line channel.

[0023] Furthermore, the delayed autocorrelation function, autocorrelation function R xx [τ] describes the similarity between the signal x[t] at time points t and t+D; for a discrete signal x[n], the autocorrelation function is defined as:

[0024]

[0025] Where L is the signal length and D is the delay time.

[0026] Furthermore, the delay D is the length of an OFDM symbol, and the received digital sequence is r n The received sequence passes through two sliding windows C and P, with a window size of L=D. Window C is the delayed correlation detection window, and window P is the energy window of autocorrelation after a delay of D clocks. Comparing the two sliding windows can realize signal power normalization and ensure that the threshold value is independent of signal attenuation.

[0027] Furthermore, the functional relationship between the signal-to-noise ratio and the delayed autocorrelation estimate is that the delayed autocorrelation estimate is equal to a value with 2 as the base and "the sum of the delayed autocorrelation peak values ​​of all sampling points of the entire leading sequence under impulse noise interference" as the exponent.

[0028] Furthermore, the sum of the delayed autocorrelation peaks of all sampling points of the pilot sequence under impulse noise interference is solved by the following steps: n An autocorrelation operation is performed to obtain the autocorrelation function R(k); R(k) is searched traversally, and the peak value in R(k) is found by calculation, and all calculated peak values ​​are added to obtain the sum of the delayed autocorrelation peak values ​​of all sampling points of the entire leading sequence under impulse noise interference.

[0029] In a second aspect, the present invention provides a power line channel signal-to-noise ratio estimation system based on a delayed autocorrelation algorithm, comprising:

[0030] An acquisition module, used for acquiring a power line signal to be analyzed, wherein the acquired power line signal to be analyzed is a digital sequence generated by a leading sequence generation module;

[0031] A calculation module, used for calculating a delayed autocorrelation function, where the autocorrelation function describes the similarity between two adjacent time points of the power line signal;

[0032] Window processing module, used for window processing of signals, reducing edge effects of power line signals and improving the accuracy of autocorrelation functions;

[0033] The normalization module is used to normalize the autocorrelation function so that its maximum value is equal to 1, compare the autocorrelation characteristics of the same signal in different time periods, and use the normalized autocorrelation function to estimate the signal-to-noise ratio of the power line channel.

[0034] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the power line channel signal-to-noise ratio estimation method based on the delayed autocorrelation algorithm as described in the first aspect is implemented.

[0035] In a fourth aspect, the present invention provides a computer device comprising a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the power line channel signal-to-noise ratio estimation method based on the delayed autocorrelation algorithm as described in the first aspect.

[0036] In a fifth aspect, the present invention provides an electronic device, comprising: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the power line channel signal-to-noise ratio estimation method based on the delayed autocorrelation algorithm as described in the first aspect.

[0037] Terminology explanation: The physical meaning of the delayed autocorrelation peak is that it reveals the maximum value of the similarity between a signal or time series and itself at different time delays. OFDM (Orthogonal Frequency Division Multiplexing) is used for high-speed data transmission. OFDM works by dividing the available spectrum bandwidth into multiple smaller, overlapping but mutually orthogonal subcarriers, and then transmitting data in parallel on these subcarriers. This technology utilizes the concept of frequency division multiplexing (FDM) and avoids interference by ensuring orthogonality between subcarriers.

[0038] Beneficial effects of the present invention: Compared with the data-assisted channel signal-to-noise ratio estimation algorithm, the algorithm proposed by the present invention is simple and relatively easy to implement in hardware. It is theoretically proved that when there is no correlation between noise and signal, and between noise and noise, there is a close relationship between the normalized peak value Mn of the delayed autocorrelation and the signal-to-noise ratio SNR, and the channel signal-to-noise ratio is estimated by Mn. At the same time, the power line channel signal-to-noise ratio estimation method proposed by the present invention is implemented using an actual hardware circuit, proving that the power line channel signal-to-noise ratio estimation method proposed by the present invention has higher accuracy under high signal-to-noise ratio conditions. Compared with the traditional channel signal-to-noise ratio blind estimation method, the algorithm proposed by the present invention has higher accuracy and occupies relatively less bandwidth resources.

[0039] Additional advantages of the present invention will be more clearly given in the following description or learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0041] Figure 1 This is a flow chart of a delayed autocorrelation algorithm according to an embodiment of the present invention.

[0042] Figure 2 This is a schematic diagram of the delayed autocorrelation algorithm described in an embodiment of the present invention.

[0043] Figure 3 This is a schematic diagram of the simulation results of the signal-to-noise ratio estimation based on delayed autocorrelation according to an embodiment of the present invention. Figure 3 (a) is the theoretical derivation diagram; Figure 3 (b) is the algorithm modeling simulation diagram.

[0044] Figure 4 It is a schematic diagram of a communication model for signal-to-noise ratio estimation using a delayed autocorrelation algorithm according to an embodiment of the present invention.

[0045] Figure 5 This is a peak simulation diagram of delayed autocorrelation under different signal-to-noise ratio conditions according to an embodiment of the present invention.

[0046] Figure 6 This is a diagram of an input 2Vp-p noise signal according to an embodiment of the present invention.

[0047] Figure 7 This is a diagram of an input 2.5Vp-p noise signal according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below by the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0049] It should be understood by those skilled in the art that unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0050] It should also be understood that terms, such as those defined in commonly used dictionaries, should be understood to have a meaning consistent with that in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless as defined herein.

[0051] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or groups thereof.

[0052] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. Different embodiments or examples described in this specification and features of different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

[0053] To facilitate understanding of the present invention, the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings, and the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0054] Those skilled in the art should understand that the drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily necessary for implementing the present invention.

[0055] Combined with the requirements of the current power line OFDM communication system resource allocation for the accuracy of signal-to-noise ratio estimation, the present invention adopts a data-assisted signal-to-noise ratio estimation technology to perform signal-to-noise ratio estimation. On the basis of analyzing the principle of the delayed autocorrelation algorithm, a power line channel signal-to-noise ratio estimation method based on the delayed autocorrelation algorithm that takes noise into consideration is proposed. It is theoretically proved that when there is no correlation between noise and signal, and between noise and noise, there is a close relationship between the normalized peak value Mn of the delayed autocorrelation and the signal-to-noise ratio SNR, so Mn can be used to estimate the SNR. It is further analyzed through theoretical analysis and simulation modeling that when the signal-to-noise ratio is high, the algorithm proposed by the present invention has better performance.

[0056] Example 1

[0057] In this embodiment 1, a power line channel signal-to-noise ratio estimation system based on a delayed autocorrelation algorithm is first provided, including: an acquisition module, used to acquire a power line signal to be analyzed, wherein the acquired power line signal to be analyzed is a digital sequence generated by a leading sequence generation module; a calculation module, used to calculate a delayed autocorrelation function, wherein the autocorrelation function describes the similarity between the power line signal at two adjacent time points; a window processing module, used for window processing of the signal, reducing the edge effect of the power line signal and improving the accuracy of the autocorrelation function; a normalization module, used to normalize the autocorrelation function so that its maximum value is equal to 1, compare the autocorrelation characteristics of the same signal in different time periods, and use the normalized autocorrelation function to estimate the power line channel signal-to-noise ratio.

[0058] like Figure 1 As shown, in this embodiment, the above-mentioned system is used to implement a power line channel signal-to-noise ratio estimation method based on a delayed autocorrelation algorithm, including: obtaining a power line signal to be analyzed, wherein the acquired power line signal to be analyzed is a digital sequence generated by a leading sequence generation module; calculating a delayed autocorrelation function, wherein the autocorrelation function describes the similarity between the power line signal at two adjacent time points; windowing the signal to reduce the edge effect of the power line signal and improve the accuracy of the autocorrelation function; normalizing the autocorrelation function to make its maximum value equal to 1, comparing the autocorrelation characteristics of the same signal in different time periods, and using the normalized autocorrelation function to estimate the power line channel signal-to-noise ratio.

[0059] Power line communication channels usually contain a lot of noise and interference, including additive Gaussian white noise, impulse noise, and multipath effects caused by electrical loads on the power line. In such a complex communication environment, the autocorrelation function of the signal can effectively distinguish signal energy from noise energy. The delayed autocorrelation algorithm uses autocorrelation operations and frequency estimation of two sinusoidal signals to convert complex two-dimensional searches into two one-dimensional searches, reducing the complexity of the calculation.

[0060] In this embodiment, the signal-to-noise ratio is estimated by mainly utilizing the autocorrelation characteristic of the signal and analyzing the correlation between the signal and its delayed version.

[0061] Figure 2 The principle of the delay correlation algorithm is described. The delay D is generally the length of an OFDM symbol, and the received digital sequence is r n The received sequence passes through two sliding windows C and P, with a window size of L=D. Window C is the delayed correlation detection window, and window P is the energy window of autocorrelation after a delay of D clocks. Comparing the two sliding windows can realize signal power normalization and ensure that the threshold value is independent of signal attenuation.

[0062] Figure 2 In, C n is the delay correlation, Pn is the received signal energy, M n is the decision variable of the delay-related algorithm. n , P n 、M n It can be expressed by equations (2), (3) and (4) respectively.

[0063]

[0064] Among them, r n+k Represents the time series r n The observed value at time k; r n+k+D Represents the time series r n The observation at time k is delayed by D clocks; r * n+k+D Represents r n+k+D The conjugation of.

[0065] In this embodiment, based on the theoretical delayed autocorrelation algorithm, a delayed autocorrelation algorithm considering noise is studied and analyzed, and the demonstration process is shown in formula (5):

[0066]

[0067] Among them, N n+k Represents the noise signal N n The observed value at time k; N n+k+D Represents the noise signal N n The observation at time k is delayed by D clocks.

[0068] From the above formula, it can be analyzed that when the noise and the signal, and the noise and the noise are uncorrelated, the above formula (5) can be simplified as follows:

[0069]

[0070] From equation (6), we can find the normalized peak value M of the delayed autocorrelation: n There is a close relationship between M and the signal-to-noise ratio SNR, so it can be used n To estimate SNR. When SNR<<1, M n is approximately equal to SNR, but at this time M n is very small and is drowned by noise. Therefore, under low SNR conditions, accurate SNR estimation cannot be performed, and the estimated SNR is only used to determine the communication status. When SNR>>1, M n It is always greater than 0 and approximately equal to 1. At this time, the following relationship can be approximately obtained:

[0071]

[0072] The signal-to-noise ratio SNR at this time can be solved as follows:

[0073]

[0074] Therefore, under high signal-to-noise ratio conditions, the signal-to-noise ratio can be estimated more accurately.

[0075] This embodiment uses the MATLAB simulation platform to plot the above formula (6) to analyze the functional relationship between the signal-to-noise ratio and the delayed autocorrelation algorithm, and further performs simulation modeling to analyze the functional relationship between the signal-to-noise ratio and the delayed autocorrelation estimation value. Figure 3 (a) is a functional relationship diagram of the signal-to-noise ratio and the delayed autocorrelation algorithm reflected by formula (6). It can be seen from the theoretical derivation formula diagram that when the signal-to-noise ratio is high, the delayed autocorrelation normalized peak value is approximately 1. Combined with formula (8), it is theoretically explained that the algorithm proposed in this embodiment has a more accurate estimation value of the signal-to-noise ratio of the power line channel under high signal-to-noise ratio conditions.

[0076] like Figure 3 (b) shows the functional relationship between the signal-to-noise ratio and the delayed autocorrelation estimate obtained by algorithm simulation modeling, where the delayed autocorrelation estimate is equal to a value with base 2 and exponentially the sum of the delayed autocorrelation peaks of all sampling points of the entire leading sequence under impulse noise interference.

[0077] Among them, the sum of the delayed autocorrelation peak values ​​of all sampling points of the entire leading sequence under impulse noise interference can be solved by the following steps:

[0078] Step 1: For the received digital sequence R n Perform autocorrelation operation to obtain the autocorrelation function R(k)

[0079] Step 2: Detect the peak value, traverse and search R(k), find the peak value in R(k) by calculation, this part is to calculate the local maximum value of R(k), that is, satisfy R(k)>R(k+1) and R(k)>R(k-1), and record the maximum value as p m (m=0, 1, ..., k-1);

[0080] Step 3: Calculate the peak value and detect all p m , then the sum of the delayed autocorrelation peaks of all sampling points of the entire leading sequence under impulse noise interference can be expressed as:

[0081] S=p0+p1+…+p m +…+p k (9)

[0082] In addition, this embodiment simulates and models the proposed method for estimating the signal-to-noise ratio based on the delayed autocorrelation algorithm, and verifies the feasibility of the proposed method for estimating the signal-to-noise ratio based on the delayed autocorrelation algorithm through simulation and modeling. Figure 4 Diagram of the communication model for signal-to-noise ratio estimation based on the delayed autocorrelation algorithm.

[0083] The transmitting end is composed of a preamble sequence generation module, which is used to send a preamble sequence generated by a P symbol length parameter. The receiving end can estimate the channel parameters according to the preamble sequence. In this embodiment, the preamble sequence is used to estimate the signal-to-noise ratio of the power line channel.

[0084] The channel modeling part includes a power line channel model and a noise production module. The power line channel model is mainly designed to analyze the transmission behavior of signals in the power line network. The channel modeling part in this embodiment includes a channel bypass selection function and a noise bypass selection function. When the channel bypass selection or the noise bypass selection is 0, it means that the current channel is not affected by attenuation and noise, that is, the channel at this time is an ideal channel; when the channel bypass selection or the noise bypass selection is 1, it means that the current channel is affected by attenuation and noise. In addition, the channel bypass selection and noise bypass selection mentioned in this embodiment can be flexibly configured to analyze the influence of channel attenuation and noise interference on the signal separately. At the same time, the power line channel model mentioned in this embodiment can be flexibly selected. For example, the channel model through which the signal generated by the transmitting end passes can be an AWGN model, a multipath model, a model based on ABCD parameters, etc. The noise generation module is mainly used to generate pulse noise. At the same time, the power of the noise generated by the noise generation module mentioned in this embodiment can be flexibly adjusted; at the receiving end, the signal-to-noise ratio estimation method based on the delayed autocorrelation algorithm is used to estimate the signal-to-noise ratio.

[0085] This embodiment uses MATLAB simulation software to perform modeling simulation analysis based on the above-mentioned signal-to-noise ratio estimation modeling theoretical analysis based on the delayed autocorrelation algorithm. Figure 5 The peak value simulation diagram of delayed autocorrelation under different signal-to-noise ratio conditions. The horizontal axis is the number of sampling points. The number of sampling points proposed by the present invention is 8192, and the vertical axis is the delayed autocorrelation peak value. The present invention simulates the delayed autocorrelation peak value under -10dB, -5dB, 0dB, 5dB, and 10dB signal-to-noise ratio conditions. Analysis Figure 5 It can be concluded that under high signal-to-noise ratio conditions, the delayed autocorrelation peak is larger, and is greater than 0.8 under a signal-to-noise ratio of 10 dB; under low signal-to-noise ratio conditions, the delayed autocorrelation peak is smaller.

[0086] Through the above simulation analysis, it can be concluded that the conclusions obtained from the simulation are the same as those from the theoretical analysis, which verifies the feasibility of the power line channel signal-to-noise ratio estimation method based on the delayed autocorrelation algorithm proposed in the present invention and its reliability under high signal-to-noise ratio conditions.

[0087] Based on the above theoretical analysis and simulation modeling analysis, this embodiment further implements hardware verification of the proposed signal-to-noise ratio estimation method based on the delayed autocorrelation algorithm. Figure 6 The peak-to-peak value of the noise signal is 2V, which is generated by the hardware circuit. The estimated value of the signal-to-noise ratio under the condition of input peak-to-peak value of 2V noise (high channel signal-to-noise ratio) is 08. Figure 7 The peak-to-peak value of the noise signal is 2.5V, which is generated by the hardware circuit. The estimated signal-to-noise ratio under the condition of input peak-to-peak value of 2.5V noise (low channel signal-to-noise ratio). At this time, the digital tube display value is 03. The larger the digital tube display value, the higher the channel signal-to-noise ratio. The larger the peak-to-peak value of the noise signal, the lower the channel signal-to-noise ratio.

[0088] Depend on Figure 6 , Figure 7 It can be concluded that when the peak-to-peak value of the input noise signal is 2V, that is, under the condition of high channel signal-to-noise ratio, the digital tube display value in the signal-to-noise ratio estimation hardware circuit is 08, indicating that the channel signal-to-noise ratio is good; when the peak-to-peak value of the input signal noise is 2.5V, that is, under the condition of low channel signal-to-noise ratio, the digital tube display value in the signal-to-noise ratio estimation hardware circuit is 03, indicating that the channel signal-to-noise ratio is poor. This result corresponds to the theoretical analysis results and simulation modeling results. While proving the algorithm proposed in this embodiment, the hardware implements the algorithm proposed in this invention, indicating that the algorithm proposed in this embodiment can be actually applied to specific scenarios in reality.

[0089] Example 2

[0090] This embodiment 2 provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the power line channel signal-to-noise ratio estimation method based on the delayed autocorrelation algorithm as described above is implemented. The method includes:

[0091] Acquire a power line signal to be analyzed, wherein the acquired power line signal to be analyzed is a digital sequence generated by a preamble sequence generation module;

[0092] Calculate the delayed autocorrelation function, which describes the similarity of the power line signal between two adjacent time points;

[0093] Windowing of signals to reduce edge effects of power line signals and improve the accuracy of autocorrelation functions;

[0094] The autocorrelation function is normalized to make its maximum value equal to 1, the autocorrelation characteristics of the same signal in different time periods are compared, and the normalized autocorrelation function is used to estimate the signal-to-noise ratio of the power line channel.

[0095] Example 3

[0096] This embodiment 3 provides a computer device, including a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, the processor calls the program instructions to execute the power line channel signal-to-noise ratio estimation method based on the delayed autocorrelation algorithm as described above, the method comprising:

[0097] Acquire a power line signal to be analyzed, wherein the acquired power line signal to be analyzed is a digital sequence generated by a preamble sequence generation module;

[0098] Calculate the delayed autocorrelation function, which describes the similarity of the power line signal between two adjacent time points;

[0099] Windowing of signals to reduce edge effects of power line signals and improve the accuracy of autocorrelation functions;

[0100] The autocorrelation function is normalized to make its maximum value equal to 1, the autocorrelation characteristics of the same signal in different time periods are compared, and the normalized autocorrelation function is used to estimate the signal-to-noise ratio of the power line channel.

[0101] Example 4

[0102] This embodiment 4 provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes instructions for implementing the power line channel signal-to-noise ratio estimation method based on the delayed autocorrelation algorithm as described above, the method including:

[0103] Acquire a power line signal to be analyzed, wherein the acquired power line signal to be analyzed is a digital sequence generated by a preamble sequence generation module;

[0104] Calculate the delayed autocorrelation function, which describes the similarity of the power line signal between two adjacent time points;

[0105] Windowing of signals to reduce edge effects of power line signals and improve the accuracy of autocorrelation functions;

[0106] The autocorrelation function is normalized to make its maximum value equal to 1, the autocorrelation characteristics of the same signal in different time periods are compared, and the normalized autocorrelation function is used to estimate the signal-to-noise ratio of the power line channel.

[0107] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0109] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0111] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative work on the basis of the technical solution disclosed in the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for estimating signal-to-noise ratio of a power line channel based on a delayed autocorrelation algorithm, characterized in that: include: Acquire a power line signal to be analyzed, wherein the acquired power line signal to be analyzed is a digital sequence generated by a preamble sequence generation module; Calculate the delayed autocorrelation function, which describes the similarity of the power line signal between two adjacent time points; Windowing of signals to reduce edge effects of power line signals and improve the accuracy of autocorrelation functions; The autocorrelation function is normalized to make its maximum value equal to 1, the autocorrelation characteristics of the same signal in different time periods are compared, and the normalized autocorrelation function is used to estimate the signal-to-noise ratio of the power line channel.

2. The method for estimating the signal-to-noise ratio of a power line channel based on a delayed autocorrelation algorithm according to claim 1, characterized in that: Calculate the delayed autocorrelation function, autocorrelation function R xx [τ] describes the similarity between the signal x[t] at time points t and t+D; for a discrete signal x[n], the autocorrelation function is defined as: Where L is the signal length and D is the delay time.

3. The method for estimating the signal-to-noise ratio of a power line channel based on a delayed autocorrelation algorithm according to claim 2, characterized in that: The delay D is the length of an OFDM symbol, and the received digital sequence is r n The received sequence passes through two sliding windows C and P, with a window size of L=D. Window C is the delayed correlation detection window, and window P is the energy window of autocorrelation after a delay of D clocks. Comparing the two sliding windows can realize signal power normalization and ensure that the threshold value is independent of signal attenuation.

4. The method for estimating the signal-to-noise ratio of a power line channel based on a delayed autocorrelation algorithm according to claim 2, characterized in that: The functional relationship between the signal-to-noise ratio and the delayed autocorrelation estimate, wherein the delayed autocorrelation estimate is equal to a value with a base of 2 and an exponent of "the sum of the delayed autocorrelation peaks of all sampling points of the entire leading sequence under impulse noise interference".

5. The method for estimating the signal-to-noise ratio of a power line channel based on a delayed autocorrelation algorithm according to claim 4, characterized in that: The sum of the delayed autocorrelation peaks of all sampling points of the pilot sequence under impulse noise interference is solved by the following steps: n An autocorrelation operation is performed to obtain the autocorrelation function R(k); R(k) is searched traversally, and the peak value in R(k) is found by calculation, and all calculated peak values ​​are added to obtain the sum of the delayed autocorrelation peak values ​​of all sampling points of the entire leading sequence under impulse noise interference.

6. A power line channel signal-to-noise ratio estimation system based on delayed autocorrelation algorithm, characterized in that: include: An acquisition module, used for acquiring a power line signal to be analyzed, wherein the acquired power line signal to be analyzed is a digital sequence generated by a leading sequence generation module; A calculation module, used for calculating a delayed autocorrelation function, where the autocorrelation function describes the similarity between two adjacent time points of the power line signal; Window processing module, used for window processing of signals, reducing edge effects of power line signals and improving the accuracy of autocorrelation functions; The normalization module is used to normalize the autocorrelation function so that its maximum value is equal to 1, compare the autocorrelation characteristics of the same signal in different time periods, and use the normalized autocorrelation function to estimate the signal-to-noise ratio of the power line channel.

7. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by the processor, the power line channel signal-to-noise ratio estimation method based on the delayed autocorrelation algorithm as described in any one of claims 1-5 is implemented.

8. A computer device, characterized in that: It includes a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the power line channel signal-to-noise ratio estimation method based on the delayed autocorrelation algorithm as described in any one of claims 1-5.

9. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the power line channel signal-to-noise ratio estimation method based on the delayed autocorrelation algorithm as described in any one of claims 1 to 5.

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