A method for distributed energy equipment and related devices

Abstract

The application discloses a kind of distributed energy equipment-oriented license-free access method and related equipment, belong to wireless communication license-free random access technical field, central processing unit utilizes complex sequence convex optimization iteration decorrelation algorithm to optimize non-orthogonal structure pilot matrix, and the low coherence pilot obtained is distributed to equipment;Active equipment sends signal frame with pilot and data to wireless access point;Central processing unit estimates equipment active state using pilot in signal frame received by wireless access point, and on this basis, using linear minimum mean square error algorithm carries out channel estimation;Central processing unit uses data in signal frame received by wireless access point, and using successive interference cancellation technology decodes equipment data;Central processing unit adds combination filter in each iteration process of successive interference cancellation, compares signal-to-interference-and-noise ratio of each iteration process with threshold, and verifies whether decoding is successful.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless communication license-free random access, and particularly relates to a license-free access method for distributed energy devices and related equipment. BACKGROUND

[0002] With the rapid development of renewable energy and the continuous progress of smart grid technology, distributed energy is increasingly widely used in smart distribution networks to realize real-time data transmission and interconnection. The connection of distributed energy and smart distribution networks requires the use of advanced communication technologies such as the Internet of Things and 5G networks to establish reliable data channels to monitor the status and performance of the distributed energy system. A large number of Internet of Things devices in the network need to meet the requirements of low latency, low power consumption, and low cost communication, as well as the connection requirements of a large number of devices.

[0003] However, the traditional license-based random access technology has the disadvantages of high access failure probability and large signaling overhead. In order to solve these two problems, a license-free random access technology is proposed, which allows active devices to send pilot sequences without waiting for the authorization of the central processor, and directly sends data signals, thereby reducing the access delay and signaling overhead. The key task of license-free random access is to use the received signal to detect the activity of the device and decode the data.

[0004] However, the device activity detection of license-free random access also has some problems:

[0005] On the one hand, in the license-free access scenario without a cell, there may be a preamble collision problem;

[0006] On the other hand, the traditional hybrid automatic repeat request mechanism triggers retransmission when the previous transmission detects a fault, which is difficult to meet the low latency requirement. SUMMARY

[0007] The technical problem to be solved by the present application is to provide a license-free access method for distributed energy devices and related equipment to solve the technical problems of high access failure probability and large signaling overhead of the existing license-based random access technology, and to improve the performance of license-free random access in device activity detection, channel estimation, and payload data decoding.

[0008] The application adopts the following technical solutions:

[0009] A license-free access method for distributed energy devices, comprising the following steps:

[0010] Optimizing the non-orthogonal structure pilot matrix with the central processor, and distributing the obtained low-coherence pilot to the device;

[0011] The central processor sends the signal frame with pilot and data to the wireless access point through the device;

[0012] The central processor estimates the active state of the device using the pilot in the signal frame received by the wireless access point, and performs channel estimation using a linear minimum mean square error algorithm;

[0013] The central processor decodes the device data using successive interference cancellation technology using the data in the signal frame received by the wireless access point, and adds a combining filter in each iteration process of successive interference cancellation, compares the signal-to-interference-and-noise ratio of each iteration process with a threshold value, and when decoding is successful, the central processor allocates the time-frequency resource block required for grant-free random access to the device according to the current load and available resources of the network.

[0014] Preferably, the non-orthogonal structure pilot matrix is optimized, and the obtained low coherence pilot is allocated to the device, specifically:

[0015] The central processor optimizes the non-orthogonal pilot matrix using a complex sequence iterative convex optimization decorrelation algorithm, and optimizes the obtained x n is restored to the pilot sequence f n and allocated to the nth device.

[0016] Preferably, the non-orthogonal pilot matrix is optimized using a complex sequence iterative convex optimization decorrelation algorithm, specifically:

[0017] P1:

[0018] s.t. A n,R,1 x n ≤0,

[0019] A n,R,2 x n ≤0,

[0020] A n,I,1 x n ≤0,

[0021] A n,I,2 x n ≤0,

[0022]

[0023] wherein, is an optimization variable, τ p is the pilot length, Φ is a block matrix, and I2 represents a two-order unit diagonal matrix, is a full zero matrix with a dimension of 2τ p , A n,R,1 , A n,R,2 , A n,I,1 , A n,I,2is the corresponding coefficient matrix, represents the real part symbol, represents the imaginary part symbol, Ξ is a block matrix, b n is the intercept matrix F n is the n-th column vector in the intercept matrix F n is a given unit norm matrix, T n is a search radius, x n is obtained by performing successive and iterative de-correlation of column vectors of the initial random pilot matrix, x n is recombined to obtain a low coherence pilot sequence f p .

[0024] Preferably, the wireless access point m receives the pilot matrix is:

[0025]

[0026] where P n is the transmission power of each pilot symbol; a nr represents the active state indication of device n; α mnr is the access mode of device n, indicating whether device n uses resource block r; h mr is the r-th resource block channel matrix between device n and wireless access point m; N is the variance of the additive white Gaussian noise.

[0027] Preferably, the linear minimum mean square error algorithm is used for channel estimation, specifically:

[0028] The central processor uses the pilots in the signal frame received by the wireless access point to converge to the local minimum of the objective function f n (d) using a low complexity component iterative algorithm, and estimates the active state of the device using a relaxed maximum likelihood estimation algorithm The channel estimation matrix is obtained by performing channel estimation on the detected active state device, and is:

[0029]

[0030] where Ψ p is the inverse of the covariance matrix related to the received pilot signal at the access point m and resource block r, is the unit matrix of τ p × τ p , and τ * is the pilot length.

[0031] Preferably, the device active state is estimated using a relaxation maximum likelihood estimation algorithm In particular,

[0032] The device active state is initialized The initial pilot vector variance received by the wireless access points, the iteration index

[0033] The step size d is calculated using a low complexity component iteration algorithm *

[0034] The optimal iteration step size is updated as The relaxation active coefficient is updated Before calculating the next device active state coefficient, update

[0035] Calculate When The algorithm is determined to be converged, and the value Otherwise, update The step size is recalculated using a low complexity component iteration algorithm, and the output and a predetermined threshold θ are compared one by one to determine the final estimated active coefficient

[0036] Preferably, the step size d is calculated using a low complexity component iteration algorithm * As follows:

[0037]

[0038] Where M is the number of wireless access points, is the set of resource blocks used by device n, λ mnr is the quality of the pilot signal received by device n on resource block r at the mth wireless access point, χ mnr is the interference caused by other devices on the same resource block.

[0039] Preferably, The calculation is as follows:

[0040]

[0041] Where tr(·) is the sum of the diagonal elements of the matrix, is the initial pilot vector variance received by the wireless access points.

[0042] Preferably, the device data is decoded using a successive interference cancellation technique

[0043] In the uplink data decoding stage, two sets are defined Where is defined as the​ The device data after i iterations is correctly decoded by the redundancy code check, The set of devices that are not correctly decoded after successive interference cancellation iterations, i represents an index value, S i includes S Initialization i = 1, s = 1;

[0044] The central processor centrally processes the data sent by the wireless access point;

[0045] The central processor designs a combination filter Processes the signal to obtain a filtered combination signal

[0046] The central processor decodes the signal, and repeats until The set is empty.

[0047] Preferably, the data sent by the wireless access point is centrally processed, and the received signal vector is updated as follows:

[0048]

[0049] Where y rt is the initial signal matrix received by the central processor on the resource block r, p s is the transmit power of device n', P u is the maximum available power of the device, c st and c n′t are code words transmitted on available resource blocks, η rt is Gaussian white noise, h n'r is the channel estimation vector between the wireless access point and device n' on the rth available resource block.

[0050] Preferably, the decoding process of the central processor on the signal is specifically:

[0051] The estimated instantaneous effective signal-to-interference-and-noise ratio after the i-th iteration satisfies the following inequality constraint, indicating that the central processor successfully decodes the device data using the successive interference cancellation technique, and the obtained code word is denoted as The s-th device in the set is placed in the set , and the set is updated, i = i + 1; from any element is assigned to s, and the central processing of the data sent by the wireless access point is returned to continue;

[0052] The inequality constraint is specifically as follows:

[0053]

[0054] wherein γ th is a signal-to-interference-and-noise ratio threshold, is a received interference and noise power matrix.

[0055] In a second aspect, embodiments of the present application provide a distributed energy device-oriented grant-free access system, comprising:

[0056] a distribution module, which optimizes a non-orthogonal pilot matrix with a central processor and distributes the obtained low-coherence pilots to devices;

[0057] a sending module, which sends a signal frame with pilots and data to a wireless access point through a device by the central processor;

[0058] an estimation module, which estimates the active state of the device using the pilots in the signal frame received by the wireless access point and performs channel estimation using a linear minimum mean square error algorithm by the central processor;

[0059] an output module, which decodes device data using a successive interference cancellation technology by the central processor using the data in the signal frame received by the wireless access point, adds a combining filter in each iteration process of the successive interference cancellation, compares the signal-to-interference-and-noise ratio of each iteration process with a threshold, and allocates time-frequency resource blocks required for grant-free random access to the device successfully decoded according to the current load and available resources of the network by the central processor after successful decoding.

[0060] Preferably, the optimization of the non-orthogonal pilot matrix and the distribution of the obtained low-coherence pilots to the devices are specifically as follows:

[0061] the central processor optimizes the non-orthogonal pilot matrix using a complex sequence iterative convex optimization decorrelation algorithm, restores the obtained x n to a pilot sequence f n and distributes it to the nth device; and the optimization of the non-orthogonal pilot matrix using the complex sequence iterative convex optimization decorrelation algorithm is specifically as follows:

[0062] P1:

[0063] s.t. A n,R,1 x n ≤0,

[0064] A n,R,2 x n ≤0,

[0065] A n,I,1 x n ≤0,

[0066] A n,I,2 xn ≤0,

[0067]

[0068] wherein, are optimization variables, τ p is the pilot length, Φ is a block matrix, I2 represents a two-order unit diagonal matrix, is a zero matrix with dimension 2τ p , A n,R,1 , A n,R,2 , A n,I,1 , A n,I,2 are the corresponding coefficient matrices, represents taking the real part symbol, represents taking the imaginary part symbol, Ξ is a block matrix, b n is the n-th column vector in the intercept matrix F n and a vector composed of zero elements, F n is a given unit norm matrix, T n is a search radius, by performing continuous and iterative decorrelation of column vectors on the initial random pilot matrix, the optimized x n is recombined with the real and imaginary parts to obtain a pilot sequence f n with low coherence.

[0069] Preferably, the pilot matrix received by the wireless access point m at the rth resource block is:

[0070]

[0071] wherein, P p is the transmission power of each pilot symbol; a n represents the active state indication of the device n; α nr is the access mode of the device n, indicating whether the device n uses the resource block r; h mnr is the rth resource block channel matrix between the device n and the wireless access point m; N mr is an independent and identically distributed additive white Gaussian noise matrix, is the variance of the additive white Gaussian noise.

[0072] Preferably, the channel estimation is performed using a linear minimum mean square error algorithm, specifically:

[0073] The central processor uses the pilots in the signal frame received by the wireless access point, converges to the local minimum value of the objective function f n (d) using a low-complexity component iteration algorithm, estimates the device active state ​The channel estimation is performed on the detected active devices to obtain a channel estimation matrix H = (Hn,m), where Hn,m is the channel estimation matrix of the device n at the access point m.

[0074]

[0075] where Ψn,m is the inverse of the covariance matrix of the received pilot signal at the access point m and the resource block r, mr is the identity matrix of size τ p × τ p , and τ p is the pilot length.

[0076] Preferably, the device active state is estimated using a relaxed maximum likelihood estimation algorithm Specifically,

[0077] The device active state is initialized is the initial pilot vector variance received by the wireless access point, and the iteration index

[0078] The step size d is calculated using a low complexity component iteration algorithm * ;

[0079] The optimal iteration step size is updated as The relaxed active coefficient is updated as Before calculating the next device active state coefficient, the iteration index is updated as

[0080] The iteration step size is calculated as When the algorithm is determined to have reached convergence, and the value is output.Otherwise, the iteration index is updated as The step size d is recalculated using a low complexity component iteration algorithm, and the output and a predetermined threshold value θ are compared one by one to determine the final estimated active coefficient

[0081] Preferably, the step size d is calculated using a low complexity component iteration algorithm as follows *

[0082]

[0083] where M is the number of wireless access points, is the set of resource blocks used by the device n, and λ mnr is a quality indicator of the pilot signal received by the device n at the resource block r at the mth wireless access point, and χ mnr is the interference caused by other devices at the same resource block.

[0084] ​Preferably, the device data is decoded using successive interference cancellation technique, in particular:

[0085] In the uplink data decoding phase, two sets are defined wherein is defined as the set of devices whose data is correctly decoded after i iterations over the , and is defined as the set of devices whose data is not correctly decoded after successive interference cancellation iterations, and i represents the index value, includes S i elements whose index value is initialized i = 1, s = 1;

[0086] The central processor centrally processes the data sent by the wireless access point;

[0087] The central processor designs a combination filter to process the signal to obtain a filtered combination signal

[0088] The central processor decodes the signal, and repeats until is an empty set.

[0089] Preferably, the data sent by the wireless access point is centrally processed, and the received signal vector is updated as:

[0090]

[0091] wherein y rt is the initial signal matrix received by the central processor on the resource block r, p s is the transmit power of device n', P u is the maximum available power of the device, c st and c n′t are the code words transmitted on the available resource blocks, η rt is the Gaussian white noise, h n'r is the channel estimation vector between the wireless access point and device n' on the rth available resource block.

[0092] Preferably, the decoding of the signal by the central processor is in particular:

[0093] The estimated instantaneous effective signal-to-interference-and-noise ratio after the ith iteration satisfies the following inequality constraint, indicating that the central processor successfully decodes the device data using the successive interference cancellation technique, and the obtained code word is denoted as The s-th device in the set is placed in the set , and the set i = i + 1; from Any element is assigned to s, and the centralized processing of the data sent by the wireless access point is returned to continue;

[0094] The inequality constraint is specifically as follows:

[0095]

[0096] Wherein, γ th The signal-to-interference noise ratio threshold, The received interference and noise power matrix.

[0097] In a third aspect, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above-mentioned method for license-free access to distributed energy devices.

[0098] In a fourth aspect, an embodiment of the present application provides an electronic device including a computer program, and the computer program implements the steps of the above-mentioned method for license-free access to distributed energy devices when executed by the electronic device.

[0099] Compared with the prior art, the present application has at least the following beneficial effects:

[0100] A method for license-free access to distributed energy devices, first, by optimizing the non-orthogonal pilot matrix, a pilot with low coherence is designed to reduce the pilot pollution between devices and improve the capacity of the system and the number of access devices; channel estimation is performed by using the known pilot sequence to provide the necessary channel state information for data decoding; in addition, the device directly sends the pilot and data frame to the wireless access point without the need for a complex access request and authorization process, improving access efficiency; the central processor uses the linear minimum mean square error algorithm for channel estimation and estimates the active state of the device, and combines the successive interference cancellation technology to eliminate the interference between the decoded signals, and the successfully decoded devices can obtain the time-frequency resources allocated by the central processor, and the entire access method can realize efficient use of resources and fast response.

[0101] Further, the central processor uses the complex sequence iterative convex optimization decorrelation algorithm to optimize the non-orthogonal pilot matrix, and continuously adjusts the elements in the pilot sequence by iteration, and uses convex optimization to solve the minimum correlation between the pilot sequences of different devices, reduces the pilot pollution, and improves the accuracy of channel estimation.

[0102] Further, the central processor uses the linear minimum mean square error algorithm for channel estimation, considers the statistical characteristics of the signal and interference, minimizes the mean square error between the channel estimation value and the true value, thereby improving the accuracy of channel estimation.

[0103] Further, the relaxed maximum likelihood estimation algorithm gradually approaches the real device activity state by introducing a low complexity component iteration algorithm. The working environment of the distributed energy device is complex and changeable, and there are many factors such as signal attenuation, interference, noise and the like. Therefore, the algorithm introduces a relaxation factor in each iteration process, which can tolerate these adverse factors to a certain extent and enhance the system stability.

[0104] Further, the continuous interference cancellation technology is used to gradually eliminate the interference of the decoded device on the data signal in the decoding process, improve the accuracy and reliability of decoding, and thus support more devices to access the system at the same time and improve the overall performance of the system.

[0105] Further, in the continuous interference cancellation technology, by setting the inequality constraint of the instantaneous effective signal-to-interference-and-noise ratio, the central processor can judge whether the data of a certain device is successfully decoded in the current iteration, and ensure the accuracy and reliability of decoding.

[0106] It can be understood that the beneficial effects of the above-mentioned second aspect to the fourth aspect can be referred to the related description in the first aspect, which will not be repeated here.

[0107] In summary, the present application effectively improves the accuracy of grant-free random access in device activity detection, channel estimation and data decoding, and has lower computational complexity.

[0108] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0109] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the accompanying drawings used in the relative embodiment description will be briefly introduced. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and other accompanying drawings can be obtained by those skilled in the art without creative labor.

[0110] Figure 1 The present application is based on the grant-free random access of the no-cell multi-input and output network model diagram;

[0111] Figure 2 The present application is a method flowchart;

[0112] Figure 3 The present application is a schematic diagram of a computer device provided by an embodiment;

[0113] Figure 4 The present application is a block diagram of an electronic device according to an embodiment;

[0114] Figure 5Fig. 4 is a pilot correlation comparison chart for the uplink device number N = 100;

[0115] Figure 6 Fig. 5 is a failed probability influence chart for different decoding schemes under different devices and available resource blocks. DETAILED DESCRIPTION

[0116] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0117] In the description of the present application, it should be understood that the terms "include" and "contain" indicate the presence of described features, whole, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.

[0118] It should also be understood that the terms used in the present application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, unless otherwise clear from the context, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0119] It should be further understood that the term "and / or" used in the present application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations, for example, A and / or B can represent three cases of A alone, A and B together, and B alone. In addition, the character " / " in the present application generally represents an "or" relationship between the front and rear associated objects.

[0120] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present application to describe the preset ranges, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, the first preset range can also be referred to as the second preset range, and similarly, the second preset range can also be referred to as the first preset range without departing from the scope of the embodiments of the present application.

[0121] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "while" or "in response to determining" or "in response to detecting." Similarly, the phrase "if it is determined" or "if [a stated condition or event] is detected" can be interpreted to mean "when it is determined" or "in response to determining" or "when [a stated condition or event] is detected" or "in response to detecting [a stated condition or event]."

[0122] The various structural diagrams according to the disclosed embodiments of the present application are shown in the drawings. These diagrams are not drawn to scale, in which certain details are exaggerated for clarity and others omitted. The shapes and relative sizes of the various regions, layers, and the relative positions of these regions / layers shown in the drawings are merely examples and can deviate in practice due to manufacturing tolerances or technical limitations, and regions / layers with different shapes, sizes, and relative positions can be additionally designed according to actual needs by those skilled in the art.

[0123] The application provides a kind of distributed energy equipment-oriented free authorization access method, central processing unit utilizes complex sequence convex optimization iteration decorrelation algorithm to optimize non-orthogonal structure pilot matrix, and low coherence pilot obtained is distributed to equipment;Active equipment sends signal frame with pilot and data to wireless access point;Central processing unit estimates equipment active state using the pilot in the signal frame received by wireless access point, and on this basis, channel estimation is carried out using linear minimum mean square error algorithm;Central processing unit uses the data in the signal frame received by wireless access point, and uses successive interference cancellation technology to decode equipment data.Central processing unit adds combination filter in each iteration process of successive interference cancellation, compares the signal-to-interference-and-noise ratio of each iteration process with threshold, and verifies whether decoding is successful, the application effectively improves detection performance in free authorization random access system, and has lower computational complexity.

[0124] Please refer to Figure 1 The large-scale multiple-input multiple-output system without cell described in the embodiment includes M wireless access points, wherein each wireless access point is equipped with L antenna arrays, connected to central processor through wireless front link, and serves N single-antenna devices on the same time-frequency resource.

[0125] Please refer to Figure 2 The application provides a kind of distributed energy equipment-oriented free authorization access method, comprising the following steps:

[0126] S1, central processor utilizes complex sequence convex optimization iteration decorrelation algorithm to optimize non-orthogonal structure pilot matrix, and low coherence pilot obtained is distributed to equipment;

[0127] Consider a non-cellular MIMO scenario with N devices, 1 central processor, M wireless access points, each equipped with L antennas and R available resource blocks. Step S1 includes the following steps:

[0128] S101. In order to reduce the pilot length while maintaining the linear independence between the vectors in the pilot matrix as much as possible, the central processing unit optimizes the non-orthogonal pilot matrix using a complex sequence iterative convex optimization decorrelation algorithm, that is, solving problem P1:

[0129] P1:

[0130] st A n,R,1 x n ≤0,

[0131] A n,R,2 x n ≤0,

[0132] A n,I,1 x n ≤0,

[0133] A n,I,2 x n ≤0,

[0134]

[0135] in, is the optimization variable, τ p is the pilot length, Φ is a dimension (2τ p +2), and only the lower right corner of Φ has a two-dimensional unit matrix, and the rest are all zero elements. I2 represents a second-order unit diagonal matrix, The dimension is 2τ p The all-zero matrix, is the corresponding coefficient matrix, represents the real part sign, Represents the imaginary part sign, Ξ is a dimension (2τ p +2), and only the upper left corner of Ξ has a two-dimensional identity matrix, and the rest are all zero elements. F n For a given unit norm matrix, T n is the search radius.

[0136] S102, the central processing unit will optimize the obtained x n Restore to pilot sequence f n And assigned to the nth device.

[0137] Specifically, take x n The front τp a vector of elements take x n the τ p +1 to 2τ p a vector of elements form and as real and imaginary parts, form a complex vector f n is assigned to device n as a pilot.

[0138] S2, the active device sends a signal frame with pilot and data to the wireless access point;

[0139] S2, the active device sends a signal frame with pilot and data to the wireless access point; the wireless access point m receives a pilot matrix is:

[0140]

[0141] where P p is the transmission power of each pilot symbol; a n represents the active state indication of device n, a n =1 indicates that device n is in an active state, otherwise in an inactive state; a nr is the access mode of device n, indicating whether device n uses resource block r; when a n =0, for any r, a nr =0; when a n =1, a nr =1 indicates that device n uses resource block r for transmission, otherwise does not use; h mnr is the rth resource block channel matrix between device n and the wireless access point m; N mr is an independent and identically distributed additive white Gaussian noise matrix, is the variance of the additive white Gaussian noise.

[0142] S3, the central processor estimates the device active state using the pilot in the signal frame received by the wireless access point, and on this basis uses a linear minimum mean square error algorithm for channel estimation;

[0143] S301, the central processor estimates the device active state using a relaxed maximum likelihood estimation algorithm using the pilot in the signal frame received by the wireless access point;

[0144] The central processor uses a low-complexity component iteration algorithm to converge to the local minimum of the objective function f n (d), in order to further reduce the computational complexity, a relaxed maximum likelihood estimation algorithm is used to estimate the device active state The implementation is achieved by the following steps:

[0145] S3011, initialization of device active state The initial pilot vector variance received by the wireless access point, iteration index

[0146] S3012, calculation of step size by using low-complexity component iteration algorithm

[0147]

[0148] Wherein, M represents the number of wireless access points, is the resource block set used by device n, β mn is the large-scale path loss between the mth wireless access point and the nth device, L is the number of antennas of the wireless access point, represents taking the conjugate transpose matrix.

[0149] In order to further reduce the calculation complexity, the central processor only considers the M C wireless access points most favorable to the signal propagation conditions of the nth device, and the set of these access points is and the large-scale path loss of these access points satisfies Therefore, the objective function f n (d) is approximated as The derivative of is obtained, and the real root set of the quadratic polynomial is

[0150] S3013, since the relaxation active state coefficient is a non-negative number, the optimal iteration step size is updated as The relaxation active coefficient is updated Before calculating the next device active state coefficient, update

[0151] S3014, calculation of tr(·) represents the sum of the diagonal elements of the matrix. When it is determined that the algorithm has reached convergence, and the output value otherwise, update Repeat from step S3012;

[0152] S3015, compare the output and the predetermined threshold θ one by one to determine the final estimated active coefficient

[0153] Active coefficient Specifically:

[0154]

[0155] S302: Perform channel estimation on the device detected to be in active state.

[0156] Since large-scale machine-type communications have very low false alarm and missed detection probabilities, the central processor can use the channel state information at the wireless access point and the estimated device activity status to perform channel estimation.

[0157] The set of active devices detected is for The pilot matrix sent by the device in the channel is obtained as follows:

[0158]

[0159] in, is τ p ×τ p The identity matrix of .

[0160] S4. The central processing unit uses the data in the signal frames received by the wireless access point to decode the device data using a continuous interference cancellation technique. The central processing unit applies a combination filter to each iteration of the continuous interference cancellation process and compares the signal-to-interference-and-noise ratio (SIR) of each iteration with a threshold to verify decoding success.

[0161] The central processing unit utilizes data from signal frames received by the wireless access point and decodes the device data using a continuous interference cancellation technique to improve the accuracy of the estimated device data during a continuous iteration process. The steps include:

[0162] S401: In the uplink data decoding phase, two sets are defined. in Defined as After i iterations, the device data is correctly decoded after the redundancy code check. It is defined as the set of devices that are not correctly decoded after consecutive interference cancellation iterations, i represents the index value, Including S i elements, whose index values initialization i=1, s=1;

[0163] S402, the central processing unit centrally processes the data sent by the wireless access point;

[0164] The signal vector received at the CPU Updated to:

[0165]

[0166] wherein, is the initial signal matrix received by the central processor on resource block r, p s ≤ P u is the transmit power of device n', P u is the maximum available power of the device, c st and c n′t is the codeword transmitted on the available resource blocks, is the Gaussian white noise, h n'r = [h 1n'r , h 2n'r ,..., h Mn'r ] is the channel estimation vector between the wireless access point and device n' on the rth available resource block.

[0167] S403, in order to further improve the accuracy of data decoding, the central processor designs a combining filter to process the signal, and obtain a filtered combining signal;

[0168] The filtered combining signal is:

[0169]

[0170] wherein, is the designed combining filter, is the signal matrix received by the central processor on resource block r after the ith iteration.

[0171] S404, the central processor decodes the signal;

[0172] If the estimated instantaneous effective signal-to-interference-and-noise ratio after the ith iteration satisfies the following inequality constraint:

[0173]

[0174] wherein, γ th is the signal-to-interference-and-noise ratio threshold, R n′r = blockdiag(R 1n′r ,...,R Mn′r ), blockdiag(·) represents a block diagonal matrix operation,

[0175] then it indicates that the central processor successfully decodes the device data by using the successive interference cancellation technology, and the obtained codeword is represented as wherein, indicates a codebook, ||·| 2 denotes the square of the 2-norm of a vector; and the s-th device in the set is put into the set , the set i = i + 1; from any element is assigned to s, and the process returns to step S402 for further execution.

[0176] S405, the above process is repeated until is an empty set.

[0177] Those skilled in the art can understand that various aspects of the present application can be implemented as a system, a method or a program product. Therefore, various aspects of the present application can be embodied as a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "platform" herein.

[0178] In another embodiment of the present application, a distributed energy device-oriented grant-free access system is provided, which can be used to implement the above-mentioned distributed energy device-oriented grant-free access method. Specifically, the distributed energy device-oriented grant-free access system includes an allocation module, a sending module, an estimation module and an output module.

[0179] The allocation module optimizes the non-orthogonal structure pilot matrix using a central processor, and distributes the obtained low-coherence pilot to the device.

[0180] The sending module sends the signal frame with pilot and data to the wireless access point through the device by the central processor.

[0181] The estimation module estimates the active state of the device using the pilot in the signal frame received by the wireless access point, and performs channel estimation using a linear minimum mean square error algorithm.

[0182] The output module decodes the device data using the continuous interference cancellation technology using the data in the signal frame received by the wireless access point, and adds a combining filter in each iteration process of the continuous interference cancellation. The signal-to-interference-and-noise ratio of each iteration process is compared with a threshold value. When the decoding is successful, the central processor allocates the time-frequency resource block required for grant-free random access to the device that has successfully decoded according to the current load and available resources of the network.

[0183] In still another embodiment of the present application, a terminal device is provided, which comprises a processor and a memory, the memory is configured to store a computer program, the computer program comprises program instructions, and the processor is configured to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are particularly suitable for loading and executing one or more instructions to implement a corresponding method flow or a corresponding function; the processor in the embodiments of the present application can be used for the operation of the distributed energy device-oriented grant-free access method, which comprises:

[0184] The central processor optimizes the non-orthogonal structure pilot matrix, and distributes the obtained low-coherence pilot to the device; the central processor sends a signal frame with a pilot and data to the wireless access point through the device; the central processor estimates the active state of the device by using the pilot in the signal frame received by the wireless access point, and performs channel estimation using a linear minimum mean square error algorithm; the central processor decodes the device data using a successive interference cancellation technology by using the data in the signal frame received by the wireless access point, and adds a combining filter in each iteration process of the successive interference cancellation, compares the signal-to-interference-and-noise ratio of each iteration process with a threshold, and when the decoding is successful, the central processor allocates the time-frequency resource block required for the grant-free random access to the successfully decoded device according to the current load and available resources of the network.

[0185] In still another embodiment of the present application, a computer readable storage medium, specifically a computer readable storage medium (Memory) is also provided. The computer readable storage medium is a memory device in the terminal equipment, for storing programs and data. It can be understood that the computer readable storage medium herein can include the built-in storage medium in the terminal equipment, and of course can also include the expansion storage medium supported by the terminal equipment, and can be any tangible medium containing or storing programs, which can be used by or in combination with the instruction execution system, device or apparatus. The computer readable storage medium provides a storage space, which stores the operating system of the terminal. Moreover, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer readable storage medium include: an electrical connection with one or more conductive wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0186] The computer readable storage medium also includes a data signal carried in the baseband or as a part of a carrier wave, in which the readable program code is borne. Such a propagated data signal can take any of a variety of forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. The computer readable storage medium can also be any medium that can be read by the instruction execution system, device or apparatus, and can send, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, device or apparatus. The program code contained in the computer readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical, RF, etc., or any suitable combination thereof.

[0187] The program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++, etc., and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet Service Provider.

[0188] The one or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the method for distributed energy equipment oriented grant-free access in the above embodiments; the one or more instructions stored in the computer-readable storage medium are loaded and executed by the processor to implement the following steps:

[0189] The central processor optimizes the non-orthogonal structure pilot matrix, and allocates the obtained low coherence pilot to the device; the central processor sends the signal frame with the pilot and data to the wireless access point through the device; the central processor estimates the active state of the device by using the pilot in the signal frame received by the wireless access point, and performs channel estimation using the linear minimum mean square error algorithm; the central processor decodes the device data using the successive interference cancellation technology by using the data in the signal frame received by the wireless access point, and adds a combining filter in each iteration process of the successive interference cancellation, compares the signal-to-interference-and-noise ratio of each iteration process with a threshold, and when the decoding is successful, the central processor allocates the time-frequency resource block required for grant-free random access to the device successfully decoded according to the current load and available resources of the network.

[0190] Please refer to Figure 3 , the terminal device is a computer device, the computer device 60 of the embodiment includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61, and the computer program 63 implements the method for distributed energy equipment oriented grant-free access in the embodiment when executed by the processor 61, to avoid repetition, which will not be described here. Alternatively, the computer program 63 implements the functions of various models / units in the system for distributed energy equipment oriented grant-free access in the embodiment when executed by the processor 61, to avoid repetition, which will not be described here.

[0191] The computer device 60 can be a desktop computer, a notebook, a palm computer, and a cloud server, etc. The computer device 60 can include, but is not limited to, the processor 61, the memory 62. Those skilled in the art can understand that Figure 3 is only an example of the computer device 60, and does not constitute a limitation on the computer device 60, and can include more or fewer components than the illustrated components, or combine certain components, or different components, for example, the computer device can also include an input / output device, a network access device, a bus, etc.

[0192] The processor 61 can be a central processing unit (CPU), and can also be other general-purpose processors, central processing units, graphics processing units, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic components, quantum computing-based data processing logic components, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0193] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or a memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 60.

[0194] Further, the memory 62 can include both an internal storage unit and an external storage device of the computer device 60. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0195] Any reference to storage, databases or other media used to store data in the embodiments provided herein is intended to include at least one of volatile and non-volatile storage. Non-volatile storage can include, for example, optical, floppy disks, hard disks, or solid state drives. Volatile storage can include, for example, random access memory (RAM). A basic input / output system (BIOS), containing the basic routines that help to transfer information between elements within the electronic device, such as during startup, can typically be stored in non-volatile memory. By way of illustration, and not limitation, a basic input / output system based on the BIOS, can include a BIOS, a unified extensible firmware interface (UEFI), or the like, including without limitation basic input / output system software stored in nonvolatile memory that

[0196] The databases involved in the embodiments provided herein can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, and the like, without being limited thereto. The processor involved in the embodiments provided herein can be a general processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, and the like, without being limited thereto.

[0197] Referring to Figure 4 , the terminal device 600 is an electronic device, which is manifested in the form of a general computing device. The components of the electronic device can include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components including the storage unit 620 and the processing unit 610, a display unit 640, and the like.

[0198] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present application described in the method part of the present specification. For example, the processing unit 610 can perform the steps as shown in Figure 2 .

[0199] The storage unit 620 can include a readable medium in the form of volatile storage such as random access memory (RAM) 6201 and / or cache memory 6202, and also can include a non-volatile storage such as read only memory (ROM) 6203.

[0200] The storage unit 620 also can include a program / utility 6204 having a set of programs / modules 6205, including an operating system, one or more application programs, other program modules, and program data, each of which can implement aspects of a network environment, for example, as each or some combination of these examples.

[0201] The bus 630 can represent one or more of several types of bus structures, including a storage bus or bus for storage controller, peripheral bus, graphics bus, processor or local bus using any of a variety of bus architectures.

[0202] The electronic device 600 also can communicate with one or more external devices 700 such as a keyboard or pointing device, a Bluetooth device, etc.; other devices that enable a user to interact with the electronic device 600; and / or any devices (e.g., a router, a modem, a printer, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication can occur via an input / output (I / O) interface 650. Still yet, the electronic device 600 can communicate with one or more networks, such as a local area network (LAN), a general wide area network (WAN), and / or a public network such as the Internet, via a network adapter 660. The network adapter 660 can be communicatively coupled to the other components of the electronic device 600 via the bus 630. It should be appreciated that the electronic device 600 can be a part of one or more networks, such as virtual networks, which further can include more than one network.

[0203] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application but not all embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present application belong to the scope of protection of the present application.

[0204] As shown in Figure 5 The proposed sequence convex optimization iterative decorrelation algorithm is compared with the existing pilot construction methods (including random Gaussian and truncated discrete Fourier transform matrix), such as Figure 5 As shown in the comparison, the method of the present application reduces the computational complexity while approaching the Welch bound, and significantly reduces the correlation between pilot sequences compared with the other two methods. In addition, the simulation results reveal that in the highly non-orthogonal scenario, the sequence convex optimization iterative decorrelation algorithm can effectively alleviate the interference problem between users and greatly reduce the required pilot length.

[0205] As shown in Figure 6 The present application successfully reduces the probability of failure in the decoding process of distributed energy devices by optimizing the K-repetition scheme in the grant-free random access process. Specifically, by increasing the number of resource blocks used for active device detection in each frame, although this may increase the collision probability and inter-user interference, it also provides more received signal samples, thereby improving the decoding performance. In addition, the successive interference cancellation decoding strategy used in the present patent can effectively reduce inter-user interference and further reduce the probability of decoding failure compared with linear decoding through its unique interference cancellation mechanism.

[0206] In summary, the grant-free access method for distributed energy devices and related equipment of the present application optimizes the pilot matrix using the complex sequence convex optimization iterative decorrelation algorithm, reduces the interference between pilots, and improves the accuracy of the system's estimation of the active state of the device. The linear minimum mean square error algorithm is used to further improve the accuracy of channel estimation. The continuous interference cancellation technology is used to decode device data, and a combination filter is added in each iteration process, thereby reducing the number of unnecessary iterations and reducing the computational complexity while ensuring decoding quality. The present application significantly improves the detection performance in the grant-free random access system, including improving the accuracy of the estimation of the active state of the device, enhancing the reliability of channel estimation, and optimizing the efficiency of data decoding. At the same time, the computational complexity is reduced, making the method of the present application more efficient and feasible in practical applications.

[0207] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be realized in the form of hardware or software. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0208] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0209] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0210] In the embodiments provided by the present application, it should be understood that the disclosed devices / terminals and methods can be implemented by other ways. For example, the device / terminal embodiments described above are only schematic, and the division of the modules or units is only a logical function division, and there can be another division way in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection between each other can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0211] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0212] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0213] The integrated module / unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such understanding, the present application realizes all or part of the processes in the above-mentioned embodiment methods, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can realize the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer-readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0214] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one flow or multiple flows and / or blocks Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks.

[0215] These computer program instructions can also be stored in a computer-readable storage medium that can guide the computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer-readable storage medium produce a manufactured product that includes instruction devices that implement the functions specified in the flowcharts and / or block diagrams. Figure 1one or more processes and / or blocks Figure 1 the function specified in the one or more blocks.

[0216] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable data processing devices to generate a computer-implemented process, so that the instructions executed on the computer or other programmable data processing devices provide a process for implementing the flow Figure 1 one or more processes and / or blocks Figure 1 the function specified in the one or more blocks.

[0217] The above is only to illustrate the technical idea of the present application, and cannot limit the protection scope of the present application. Any modification made according to the technical idea of the present application on the basis of the technical scheme falls within the protection scope of the claims of the present application.

Claims

1. A method for unauthorized access to distributed energy equipment, characterized in that: The following steps are involved: Utilize a central processing unit to optimize a non-orthogonal structure pilot matrix and distribute the obtained low-coherence pilots to devices; The central processor sends a signal frame with pilot and data to the wireless access point through the device; The central processor uses the pilot signal in the signal frame received by the wireless access point to estimate the activity status of the device, and uses the linear minimum mean square error algorithm for channel estimation and the relaxed maximum likelihood estimation algorithm to estimate the activity status of the device. Specifically: Initialize device active state , is the initial pilot vector variance received by the wireless access point, and the iteration index , ; Calculate step size using low-complexity component-wise iterative algorithm ; Update the optimal iteration step size to , update the relaxation activity coefficient , before calculating the next device active state coefficient, update ; calculate ;when , then the algorithm is judged to have reached convergence, and the output value Otherwise, update , re-execute the low-complexity component iteration algorithm to calculate the step size, and output and a predetermined threshold Perform a comparison one by one to determine the final estimated activity coefficient , using a low-complexity component-wise iterative algorithm to calculate the step size as follows: in, M is the number of wireless access points, For devices n The set of resource blocks used, To represent the mth wireless access point, for device n in resource block The quality of the pilot signal received on is the interference caused by other devices on the same resource block; The calculation is as follows: in, is the sum of the diagonal elements of the matrix, is the variance of the initial pilot vector received by the wireless access point; The central processor uses the data in the signal frame received by the wireless access point to decode the device data using continuous interference cancellation technology, and adds a combined filter in each iteration of continuous interference cancellation. The signal-to-interference-noise ratio of each iteration is compared with the threshold. When the decoding is successful, the central processor allocates the time-frequency resource blocks required for unlicensed random access to the device that successfully decoded based on the current network load and available resources.

2. The method for unauthorized access to distributed energy devices according to claim 1, characterized in that: Optimize the non-orthogonal structure pilot matrix and assign the obtained low-coherence pilots to the devices as follows: The CPU uses the complex sequence iterative convex optimization decorrelation algorithm to optimize the non-orthogonal pilot matrix and converts the optimized Restore to pilot sequence and assigned to the n devices.

3. The method for unauthorized access to distributed energy devices according to claim 2, characterized in that: The optimization of the non-orthogonal pilot matrix using the complex sequence iterative convex optimization decorrelation algorithm is as follows: in, To optimize the variables, is the pilot length, is a block matrix, The dimension is The all-zero matrix, 、 , , is the corresponding coefficient matrix, is a block matrix, To intercept the matrix The vector consisting of the nth column vector and zero elements in , is a given unit norm matrix, The search radius is obtained by continuously and iteratively decorrelating the column vectors of the initial random pilot matrix. Recombine the real and imaginary parts to obtain a low-coherence pilot sequence .

4. The method for unauthorized access to distributed energy devices according to claim 1, characterized in that: Wireless Access Point m In the r The pilot matrix received by resource blocks for: in, is the transmission power of each pilot symbol; Representative equipment Active status indication; For devices Access mode, indicating the device Whether to use resource blocks ; For devices The first Resource block channel matrix; is an independent and identically distributed additive Gaussian white noise matrix, ; is the variance of the additive white Gaussian noise.

5. The method for unauthorized access to distributed energy equipment according to claim 1, characterized in that: The channel estimation using the linear minimum mean square error algorithm is as follows: The central processor uses the pilot in the signal frame received by the wireless access point and uses a low-complexity component iterative algorithm to converge to the target function The local minimum of , using the relaxed maximum likelihood estimation algorithm to estimate the device activity state ; Perform channel estimation on the device detected as active, and obtain the channel estimation matrix as follows: in, For access points m and resource blocks r Above, the inverse of the covariance matrix associated with the received pilot signal, is the pilot length.

6. The method for unauthorized access to distributed energy devices according to claim 1, characterized in that: The device data decoded using successive interference cancellation technology is as follows: In the uplink data decoding stage, two sets are defined ,in Defined as After i iterations, the device data is correctly decoded after the redundancy code check. Defined as the set of devices that are not correctly decoded after successive interference cancellation iterations, i Represents the index value, Included elements, whose index values ;initialization , , , ; The central processing unit centrally processes the data sent by the wireless access points; CPU designs combined filters Process the signal to obtain the filtered combined signal ; The central processing unit decodes the signal and repeats until Is an empty set.

7. The method for unauthorized access to distributed energy devices according to claim 6, characterized in that: Centrally process the data sent by the wireless access point and convert the received signal vector Updated to: in, For the CPU in the resource block r The initial signal matrix received on is the codeword transmitted on the available resource blocks, For the r Wireless access points and devices on available resource blocks The channel estimation vector between .

8. The method for unauthorized access to distributed energy devices according to claim 6, characterized in that: The CPU decodes the signal as follows: No. i After iterations, the estimated instantaneous effective signal-to-interference-and-noise ratio satisfies the following inequality constraint, indicating that the central processor successfully decodes the device data using the continuous interference cancellation technique. The obtained codeword is expressed as , will be collected The first s Devices into collection Update the collection 、 , , ;from Take any element and assign it to , returns to continue to centrally process the data sent by the wireless access point; The inequality constraints are as follows: in, is the signal-to-interference-noise ratio threshold, is the received interference and noise power matrix.

9. An unauthorized access system for distributed energy equipment, characterized in that: include: A distribution module, which uses a central processing unit to optimize the non-orthogonal structure pilot matrix and distributes the obtained low-coherence pilots to the devices; The sending module, the central processor sends the signal frame with pilot and data to the wireless access point through the device; The central processing unit uses the pilot signal in the signal frame received by the wireless access point to estimate the activity status of the device, and uses the linear minimum mean square error algorithm for channel estimation and the relaxed maximum likelihood estimation algorithm to estimate the activity status of the device. Specifically: Initialize device active state , is the initial pilot vector variance received by the wireless access point, and the iteration index , ; Calculate step size using low-complexity component-wise iterative algorithm ; Update the optimal iteration step size to , update the relaxation activity coefficient , before calculating the next device active state coefficient, update ; calculate ;when , then the algorithm is judged to have reached convergence, and the output value Otherwise, update , re-execute the low-complexity component iteration algorithm to calculate the step size, and output and a predetermined threshold Perform a comparison one by one to determine the final estimated activity coefficient , using a low-complexity component-wise iterative algorithm to calculate the step size as follows: in, M is the number of wireless access points, For devices n The set of resource blocks used, To represent the mth wireless access point, for device n in resource block The quality of the pilot signal received on is the interference caused by other devices on the same resource block; The calculation is as follows: in, is the sum of the diagonal elements of the matrix, is the variance of the initial pilot vector received by the wireless access point; In the output module, the central processor uses the data in the signal frame received by the wireless access point to decode the device data using the continuous interference cancellation technology, and adds a combined filter in each iteration of the continuous interference cancellation process. The signal-to-interference-noise ratio of each iteration process is compared with the threshold. When the decoding is successful, the central processor allocates the time-frequency resource blocks required for unlicensed random access to the device that successfully decoded according to the current network load and available resources.

10. The unauthorized access system for distributed energy devices according to claim 9, characterized in that: Optimize the non-orthogonal structure pilot matrix and assign the obtained low-coherence pilots to the devices as follows: The CPU uses the complex sequence iterative convex optimization decorrelation algorithm to optimize the non-orthogonal pilot matrix and converts the optimized Restore to pilot sequence and assigned to the n Device; use the complex sequence iterative convex optimization decorrelation algorithm to optimize the non-orthogonal pilot matrix specifically as follows: in, To optimize the variables, is the pilot length, is a block matrix, The dimension is The all-zero matrix, 、 , , is the corresponding coefficient matrix, is a block matrix, To intercept the matrix The vector consisting of the nth column vector and zero elements in , is a given unit norm matrix, The search radius is obtained by continuously and iteratively decorrelating the column vectors of the initial random pilot matrix. Recombine the real and imaginary parts to obtain a low-coherence pilot sequence .

11. The unauthorized access system for distributed energy devices according to claim 9, characterized in that: Wireless Access Point m In the r The pilot matrix received by resource blocks for: in, is the transmission power of each pilot symbol; Representative equipment Active status indication; For devices Access mode, indicating the device Whether to use resource blocks ; For devices The first Resource block channel matrix; is an independent and identically distributed additive Gaussian white noise matrix, ; is the variance of the additive white Gaussian noise.

12. The unauthorized access system for distributed energy devices according to claim 9, characterized in that: The channel estimation using the linear minimum mean square error algorithm is as follows: The central processor uses the pilot in the signal frame received by the wireless access point and uses a low-complexity component iterative algorithm to converge to the target function The local minimum of , using the relaxed maximum likelihood estimation algorithm to estimate the device activity state ; Perform channel estimation on the device detected as active, and obtain the channel estimation matrix as follows: in, For access points m and resource blocks r Above, the inverse of the covariance matrix associated with the received pilot signal, is the pilot length.

13. The unauthorized access system for distributed energy devices according to claim 9, characterized in that: The device data decoded using successive interference cancellation technology is as follows: In the uplink data decoding stage, two sets are defined ,in Defined as After i iterations, the device data is correctly decoded after the redundancy code check. Defined as the set of devices that are not correctly decoded after successive interference cancellation iterations, i Represents the index value, Included elements, whose index values ;initialization , , , ; The central processing unit centrally processes the data sent by the wireless access points; CPU designs combined filters Process the signal to obtain the filtered combined signal ; The central processing unit decodes the signal and repeats until Is an empty set.

14. The unauthorized access system for distributed energy devices according to claim 13, characterized in that: Centrally process the data sent by the wireless access point and convert the received signal vector Updated to: in, For the CPU in the resource block r The initial signal matrix received on is the codeword transmitted on the available resource blocks, For the r Wireless access points and devices on available resource blocks The channel estimation vector between .

15. The unauthorized access system for distributed energy devices according to claim 13, characterized in that: The CPU decodes the signal as follows: No. i After iterations, the estimated instantaneous effective signal-to-interference-and-noise ratio satisfies the following inequality constraint, indicating that the central processor successfully decodes the device data using the continuous interference cancellation technique. The obtained codeword is expressed as , will be collected The first s Devices into collection Update the collection 、 , , ;from Take any element and assign it to , returns to continue to centrally process the data sent by the wireless access point; The inequality constraints are as follows: in, is the signal-to-interference-noise ratio threshold, is the received interference and noise power matrix.

16. A chip, characterized in that: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 8.

17. An electronic device, characterized in that: Comprising the chip as claimed in claim 16.

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