Authorization-free access method for distributed energy equipment and related equipment

Abstract

The invention discloses an unlicensed access method for distributed energy equipment and related equipment, and belongs to the technical field of wireless communication unlicensed random access, a central processing unit optimizes a non-orthogonal structure pilot frequency matrix by using a complex sequence convex optimization iterative decorrelation algorithm, and allocates obtained low-coherence pilot frequency to equipment; the active equipment sends a signal frame with pilot frequency and data to the wireless access point; the central processing unit estimates the active state of the equipment by using the pilot frequency in the signal frame received by the wireless access point, and performs channel estimation by using a linear minimum mean square error algorithm on the basis; the central processing unit decodes the equipment data by using the continuous interference cancellation technology according to the data in the signal frame received by the wireless access point; and the central processing unit adds a combined filter in each iteration process of continuous interference elimination, compares the signal to interference plus noise ratio of each iteration process with a threshold value, and checks whether decoding succeeds or not.

Description

Background Art

[0002] With the rapid development of renewable energy and the continuous advancement of smart grid technology, distributed energy is increasingly being used in smart distribution networks to achieve real-time data transmission and interconnection. The connection between 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 distributed energy systems. Due to the large number of IoT devices in the network, low latency, low power consumption, and low-cost communications are required, as well as the connection requirements of massive devices.

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

[0004] However, there are some problems with the detection of device activity in random access without authorization:

[0005] On the one hand, in the unlicensed access scenario without a cellular cell, there may be a problem of preamble collision;

[0006] On the other hand, the traditional hybrid automatic repeat request mechanism will trigger retransmission when a failure is detected in the previous transmission, which is difficult to meet the low latency requirements. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide an unauthorized access method and related equipment for distributed energy equipment in view of the deficiencies in the above-mentioned prior art, which is used to solve the technical problems of high access failure probability and large signaling overhead in the existing authorization-based random access technology, and can improve the performance of unauthorized random access in device activity detection, channel estimation and load data decoding.

[0008] The present invention adopts the following technical solutions:

[0009] A method for unauthorized access to distributed energy equipment, comprising the following steps:

[0010] Utilizing a central processing unit to optimize a non-orthogonal structure pilot matrix and assigning the obtained low-coherence pilots to the devices;

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

[0012] The central processor estimates the activity status 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 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 to compare the signal-to-interference-noise ratio of each iteration with the threshold. When the decoding is successful, the central processor allocates the time-frequency resource blocks required for unlicensed random access to the successfully decoded device based on the current network load and available resources.

[0014] Preferably, optimizing the non-orthogonal structure pilot matrix and allocating the obtained low-coherence pilots to the devices is specifically as follows:

[0015] The CPU uses a complex sequence iterative convex optimization decorrelation algorithm to optimize the non-orthogonal pilot matrix and converts the optimized x n Restore to pilot sequence f n And assigned to the nth device.

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

[0017] P1:

[0018] st 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] in, is the optimization variable, τ p is the pilot length, Φ is the block matrix, I2 represents a second-order unit diagonal matrix, is of dimension 2τ p The all-zero matrix, A n,R,1 , A n,R,2 , A n,I,1 , A n,I,2 is the corresponding coefficient matrix, represents the real part sign, represents the imaginary part sign, Ξ is the block matrix, b n is the intercept matrix F n The vector consisting of the nth column vector and zero elements in F n is a given unit norm matrix, T n is the search radius, and the optimized x is n Recombine the real part and the imaginary part to obtain a low-coherence pilot sequence f n .

[0024] Preferably, the pilot matrix received by the wireless access point m in the rth resource block for:

[0025]

[0026] Among them, P p is the transmission power of each pilot symbol; a n Represents the active status indication of device n; α nr is the access mode of device n, indicating whether device n uses resource block r; h mnr is the rth resource block channel matrix between device n and wireless access point m; N mr is an independent and identically distributed additive Gaussian white noise matrix, is the variance of additive white Gaussian noise.

[0027] Preferably, the linear minimum mean square error algorithm is used to perform channel estimation as follows:

[0028] The central processor uses the pilot in the signal frame received by the wireless access point and uses a low-complexity component iteration algorithm to converge to the target function f n The local minimum of (d) uses the relaxed maximum likelihood estimation algorithm to estimate the device activity state Channel estimation is performed on the device detected as active, and the channel estimation matrix is ​​obtained as follows:

[0029]

[0030] Among them, mr is the inverse of the covariance matrix associated with the received pilot signal at access point m and resource block r, is τ p ×τ p The identity matrix, τ p is the pilot length.

[0031] Preferably, a relaxed maximum likelihood estimation algorithm is used to estimate the device activity state Specifically:

[0032] Initialize device active state is the initial pilot vector variance received by the wireless access point, and the iteration index

[0033] The step size d is calculated using a low-complexity component-wise iterative algorithm. * ;

[0034] Update the optimal iteration step size to Update the relaxation activity coefficient Before calculating the next device active state coefficient, update

[0035] calculate when 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 Compare each of them with the predetermined threshold θ to determine the final estimated activity 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 To represent 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 variance of the initial pilot vector received by the wireless access point.

[0042] Preferably, the device data is decoded using the successive interference cancellation technique as follows:

[0043] In the uplink data decoding stage, two sets are defined in Defined as After i iterations, the device data is correctly decoded after the redundant 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, Includes S i elements, whose index values initialization i=1, s=1;

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

[0045] CPU design combined filter Process the signal to obtain the filtered combined signal

[0046] The CPU decodes the signal and repeats until Is an empty set.

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

[0048]

[0049] Among them, y rt is the initial signal matrix received by the CPU on resource block r, p s is the transmission power of device n', P u is the maximum available power of the device, c st and c n′t are the codewords transmitted on the 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 central processing unit decodes the signal specifically as follows:

[0051] The estimated instantaneous effective signal to noise ratio after the i-th iteration satisfies the following inequality constraint, indicating that the central processor successfully decodes the device data using the continuous interference cancellation technique, and the obtained codeword is expressed as Will gather The sth device in the collection In the update collection i=i+1; from Randomly assign an element to s, and return to continue centrally processing the data sent by the wireless access point;

[0052] The inequality constraints are as follows:

[0053]

[0054] Among them, γ th is the signal-to-interference-noise ratio threshold, is the received interference and noise power matrix.

[0055] In a second aspect, an embodiment of the present invention provides an unauthorized access system for distributed energy devices, including:

[0056] An allocation module, which optimizes the non-orthogonal structure pilot matrix using a central processing unit and allocates the obtained low-coherence pilots to the devices;

[0057] The sending module, the central processor sends the signal frame with pilot and data to the wireless access point through the device;

[0058] The estimation module,the central processor estimates the active state of the device using the pilot in the signal frame received by the wireless access point,and uses the linear minimum mean square error algorithm to perform channel estimation;

[0059] 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 to compare the signal-to-interference-noise ratio of each iteration process with the threshold. When the decoding is successful, the central processor allocates the time-frequency resource blocks required for unlicensed random access to the successfully decoded device based on the current network load and available resources.

[0060] Preferably, optimizing the non-orthogonal structure pilot matrix and allocating the obtained low-coherence pilots to the devices is specifically as follows:

[0061] The CPU uses a complex sequence iterative convex optimization decorrelation algorithm to optimize the non-orthogonal pilot matrix and converts the optimized x n Restore to pilot sequence f n And assigned to the nth device; the non-orthogonal pilot matrix is ​​optimized using the complex sequence iterative convex optimization decorrelation algorithm as follows:

[0062] P1:

[0063] st 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 x n ≤0,

[0067]

[0068] in, is the optimization variable, τ p is the pilot length, Φ is the block matrix, I2 represents a second-order unit diagonal matrix, is of dimension 2τ p The all-zero matrix, A n,R,1 , A n,R,2 , A n,I,1 , A n,I,2 is the corresponding coefficient matrix, represents the real part sign, represents the imaginary part sign, Ξ is the block matrix, b n is the intercept matrix F n The vector consisting of the nth column vector and zero elements in F n is a given unit norm matrix, T n is the search radius, and the optimized x is n Recombine the real part and the imaginary part to obtain a low-coherence pilot sequence f n .

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

[0070]

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

[0072] Preferably, the linear minimum mean square error algorithm is used to perform channel estimation as follows:

[0073] The central processor uses the pilot in the signal frame received by the wireless access point and uses a low-complexity component iteration algorithm to converge to the target function f n The local minimum of (d) uses the relaxed maximum likelihood estimation algorithm to estimate the device activity state Channel estimation is performed on the device detected as active, and the channel estimation matrix is ​​obtained as follows:

[0074]

[0075] Among them, mr is the inverse of the covariance matrix associated with the received pilot signal at access point m and resource block r, is τ p ×τ p The identity matrix, τ p is the pilot length.

[0076] Preferably, a relaxed maximum likelihood estimation algorithm is used to estimate the device activity state Specifically:

[0077] Initialize device active state 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-wise iterative algorithm. * ;

[0079] Update the optimal iteration step size to Update the relaxation activity coefficient Before calculating the next device active state coefficient, update

[0080] calculate when 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 Compare each of them with the predetermined threshold θ to determine the final estimated activity 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 device n, λ mnr To represent 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.

[0084] Preferably, the device data is decoded using the successive interference cancellation technique as follows:

[0085] In the uplink data decoding stage, two sets are defined in Defined as After i iterations, the device data is correctly decoded after the redundant 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, Includes S i elements, whose index values initialization i=1, s=1;

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

[0087] CPU design combined filter Process the signal to obtain the filtered combined signal

[0088] The CPU 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 Updated to:

[0090]

[0091] Among them, y rt is the initial signal matrix received by the CPU on resource block r, p s is the transmission power of device n', P u is the maximum available power of the device, c st and c n′t are the codewords transmitted on the 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.

[0092] Preferably, the central processing unit decodes the signal specifically as follows:

[0093] The estimated instantaneous effective signal to noise ratio after the i-th iteration satisfies the following inequality constraint, indicating that the central processor successfully decodes the device data using the continuous interference cancellation technique, and the obtained codeword is expressed as Will gather The sth device in the collection In the update collection i=i+1; from Randomly assign an element to s, and return to continue centrally processing the data sent by the wireless access point;

[0094] The inequality constraints are as follows:

[0095]

[0096] Among them, γ th is the signal-to-interference-noise ratio threshold, is 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, wherein the processor implements the steps of the above-mentioned method for unauthorized access to distributed energy devices when executing the computer program.

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

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

[0100] An unauthorized access method for distributed energy devices. First, by optimizing a non-orthogonal pilot matrix, a pilot with low coherence is designed to reduce pilot pollution between devices, thereby increasing the system capacity and the number of access devices. Channel estimation is performed through a known pilot sequence to provide necessary channel state information for data decoding. In addition, the device directly sends the pilot and data frames to a wireless access point without the need for complex access request and authorization processes, thereby improving access efficiency. A central processing unit uses a linear minimum mean square error algorithm to perform channel estimation and estimate the active state of the device, and combines continuous interference elimination technology to eliminate interference between decoded signals. Devices that successfully decode can obtain time-frequency resources allocated by the central processing unit, and the entire access method can achieve efficient resource utilization and rapid response.

[0101] Furthermore, the central processing unit uses a complex sequence iterative convex optimization decorrelation algorithm to optimize the non-orthogonal pilot matrix, continuously adjusts the elements in the pilot sequence through iteration, and uses convex optimization to solve the minimum correlation between pilot sequences of different devices, thereby reducing pilot contamination and improving the accuracy of channel estimation.

[0102] Furthermore, the central processing unit uses a linear minimum mean square error algorithm to perform channel estimation, taking into account the statistical characteristics of signals and interference, and minimizes the mean square error between the channel estimation value and the true value, thereby improving the accuracy of channel estimation.

[0103] Furthermore, the relaxed maximum likelihood estimation algorithm gradually approaches the actual active state of the device by introducing a low-complexity component iteration algorithm. The working environment of distributed energy equipment is complex and changeable, with various factors such as signal attenuation, interference, and noise. Therefore, the algorithm introduces a relaxation factor in each iteration process, which can tolerate these unfavorable factors to a certain extent and enhance the stability of the system.

[0104] Furthermore, continuous interference cancellation technology is used to gradually eliminate the interference of decoded devices on data signals during the decoding process, thereby improving the accuracy and reliability of decoding, thereby supporting more devices to access the system at the same time and improving the overall performance of the system.

[0105] Furthermore, in the continuous interference cancellation technology, by setting an inequality constraint on the instantaneous effective signal to interference and noise ratio, the central processor can determine whether the data of a certain device has been successfully decoded in the current iteration, thereby ensuring the accuracy and reliability of the decoding.

[0106] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

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

[0108] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings to be used in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0110] Figure 1 This is a diagram of a non-cellular multiple input and output network model based on unlicensed random access according to the present invention;

[0111] Figure 2 It is a schematic diagram of the method flow of the present invention;

[0112] Figure 3 A schematic diagram of a computer device provided by an embodiment of the present invention;

[0113] Figure 4 A block diagram of an electronic device provided according to an embodiment of the present invention;

[0114] Figure 5This is a pilot signal correlation comparison diagram when the number of uplink devices N=100;

[0115] Figure 6 The figure shows the impact of different decoding schemes on the failure probability under different devices and available resource blocks. DETAILED DESCRIPTION

[0116] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0117] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

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

[0119] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to 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: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

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

[0121] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0122] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0123] The present invention provides an unlicensed access method for distributed energy equipment. The central processing unit optimizes the non-orthogonal structure pilot matrix using a complex sequence convex optimization iterative decorrelation algorithm, and allocates the obtained low-coherence pilot to the equipment; the active equipment sends a signal frame with pilot and data to a wireless access point; the central processing unit estimates the active state of the equipment using the pilot in the signal frame received by the wireless access point, and uses a linear minimum mean square error algorithm to perform channel estimation on this basis; the central processing unit uses the data in the signal frame received by the wireless access point to decode the equipment data using a continuous interference elimination technology. The central processing unit adds a combined filter in each iterative process of continuous interference elimination, compares the signal-to-noise ratio of each iterative process with a threshold, and verifies whether the decoding is successful. The present invention effectively improves the detection performance in the unlicensed random access system and has a low computational complexity.

[0124] See also Figure 1 The non-cellular massive multi-input and multi-output system described in this embodiment includes M wireless access points, wherein each wireless access point is equipped with L antenna arrays, connected to a central processor via a wireless fronthaul link, and serves N single-antenna devices on the same time-frequency resources.

[0125] See also Figure 2 The present invention provides an authorization-free access method for distributed energy equipment, comprising the following steps:

[0126] S1. The central processing unit optimizes the non-orthogonal structure pilot matrix using a complex sequence convex optimization iterative decorrelation algorithm, and distributes the obtained low-coherence pilots to the devices;

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

[0128] S101. In order to reduce the pilot length and keep 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, solves 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, is of dimension 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 unit matrix, and the rest are all zero elements. F n is a given unit norm matrix, T n is the search radius.

[0136] S102, the CPU 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 Build a vector with elements Take x n The τth p +1 to 2τ p Build a vector with elements Will and As real and imaginary parts, we form the complex vector f n 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] The active device sends a signal frame with pilot and data to the wireless access point; the pilot matrix received by the wireless access point m in the rth resource block is for:

[0140]

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

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

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

[0144] The central processor uses the pilot in the signal frame received by the wireless access point and uses a low-complexity component iteration algorithm to converge to the target function f n (d) is the local minimum. In order to further reduce the computational complexity, the relaxed maximum likelihood estimation algorithm is used to estimate the device activity state. This is accomplished by the following steps:

[0145] S3011, Initialize device active status is the initial pilot vector variance received by the wireless access point, and the iteration index

[0146] S3012, using low-complexity component iteration algorithm to calculate step size

[0147]

[0148] Where M represents the number of wireless access points. is the set of resource blocks 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 at the wireless access point, Representative The conjugate transposed matrix of .

[0149] To further reduce the computational complexity, the CPU only considers the M that is most favorable for the signal propagation conditions of the nth device. C wireless access points, which are grouped as And the large-scale path loss of these access points satisfies So the objective function f n (d) is approximately right Take the derivative and get the real root set of the quadratic polynomial

[0150] S3013, due to the relaxation active state coefficient is a non-negative number, and the optimal iteration step length is updated to Update the relaxation activity coefficient Before calculating the next device active state coefficient, update

[0151] S3014, Calculation tr(·) represents the sum of the diagonal elements of the matrix. The algorithm is judged to have reached convergence, and the output value Otherwise, update Repeat from step S3012;

[0152] S3015, output Compare each of them with the predetermined threshold θ to determine the final estimated activity coefficient

[0153] Activity 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 for channel estimation.

[0157] The detected active device set 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 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. The central processor adds a combined filter in each iteration of the continuous interference cancellation process, compares the signal-to-interference-noise ratio of each iteration process with the threshold, and verifies whether the decoding is successful.

[0161] 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 to improve the accuracy of the estimated device data in the continuous iteration process; including the following steps:

[0162] S401: In the uplink data decoding stage, two sets are defined in Defined as After i iterations, the device data is correctly decoded after the redundant 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, Includes 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] in, is the initial signal matrix received by the CPU on resource block r, p s ≤P u is the transmission 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 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 processing unit designs a combined filter Processing the signal to obtain a filtered combined signal;

[0168] The combined signal after filtering for:

[0169]

[0170] in, For the designed combined filter, is the signal matrix received by the central processor on resource block r after iteration i.

[0171] S404, the central processing unit decodes the signal;

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

[0173]

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

[0175] This means that the CPU successfully decodes the device data using the continuous interference cancellation technique, and the resulting codeword is expressed as in, represents the codebook, ||·|| 2 represents taking the square of the vector 2 norm; and the set The sth device in the collection In the update collection i=i+1; from Randomly assign an element to s, and return to step S402 to continue execution.

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

[0177] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0178] In another embodiment of the present invention, an authorization-free access system for distributed energy devices is provided, which can be used to implement the above-mentioned authorization-free access method for distributed energy devices. Specifically, the authorization-free access system for distributed energy devices includes a distribution module, a sending module, an estimation module and an output module.

[0179] The allocation module optimizes the non-orthogonal structure pilot matrix using the central processing unit and allocates the obtained low-coherence pilots to the devices;

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

[0181] The estimation module,the central processor estimates the active state of the device using the pilot in the signal frame received by the wireless access point,and uses the linear minimum mean square error algorithm to perform channel estimation;

[0182] 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 to compare the signal-to-interference-noise ratio of each iteration process with the threshold. When the decoding is successful, the central processor allocates the time-frequency resource blocks required for unlicensed random access to the successfully decoded device based on the current network load and available resources.

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

[0184] The central processing unit is used to optimize the non-orthogonal structure pilot matrix and allocate the obtained low-coherence pilot to the device; the central processing unit sends the signal frame with pilot and data to the wireless access point through the device; the central processing unit uses the pilot in the signal frame received by the wireless access point to estimate the active state of the device and uses the linear minimum mean square error algorithm for channel estimation; the central processing unit uses the data in the signal frame received by the wireless access point to decode the device data using the continuous interference elimination technology, and adds a combined filter in each iteration of the continuous interference elimination process, and compares the signal-to-interference-noise ratio of each iteration process with the threshold. When the decoding is successful, the central processing unit allocates the time-frequency resource blocks required for unauthorized random access to the successfully decoded device according to the current network load and available resources.

[0185] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these 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 here include: an electrical connection with one or more 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] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0187] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0188] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the method for unauthorized access to distributed energy devices in the above embodiment; one or more instructions in the computer-readable storage medium may be loaded and executed by a processor as follows:

[0189] The central processing unit is used to optimize the non-orthogonal structure pilot matrix and allocate the obtained low-coherence pilot to the device; the central processing unit sends the signal frame with pilot and data to the wireless access point through the device; the central processing unit uses the pilot in the signal frame received by the wireless access point to estimate the active state of the device and uses the linear minimum mean square error algorithm for channel estimation; the central processing unit uses the data in the signal frame received by the wireless access point to decode the device data using the continuous interference elimination technology, and adds a combined filter in each iteration of the continuous interference elimination process, and compares the signal-to-interference-noise ratio of each iteration process with the threshold. When the decoding is successful, the central processing unit allocates the time-frequency resource blocks required for unauthorized random access to the successfully decoded device according to the current network load and available resources.

[0190] See also Figure 3 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for unauthorized access to distributed energy devices in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the unauthorized access system for distributed energy devices in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0191] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 3 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0192] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics 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, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0193] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may 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] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0195] Any reference to memory, database or other media used in the embodiments provided in the present application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0196] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0197] See also Figure 4 The terminal device 600 is an electronic device, and the electronic device is in the form of a general computing device. The components of the electronic device may 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, etc.

[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 invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 2 Follow the steps shown in .

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

[0200] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0201] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0202] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0203] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0204] See also 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 matrices), such as Figure 5 As shown in the figure. It can be clearly seen from these comparisons that the method of the present invention reduces the computational complexity while approaching the Welch bound, and significantly reduces the correlation between pilot sequences compared to the other two methods. In addition, the simulation results reveal that in highly non-orthogonal scenarios, the sequence convex optimization iterative decorrelation algorithm can effectively alleviate the interference problem between users and can significantly reduce the required pilot length.

[0205] See also Figure 6 , the present invention successfully reduces the probability of failure of distributed energy devices during the decoding process by optimizing the K repetition scheme in the process of unlicensed random access. Specifically, by increasing the number of resource blocks used for active device detection in each frame, although this may lead to an increase in collision probability and interference between users, it also provides more received signal samples, thereby improving decoding performance. In addition, compared with linear decoding, the continuous interference cancellation decoding strategy used in this patent can effectively reduce interference between users through its unique interference cancellation mechanism, further reducing the probability of decoding failure.

[0206] In summary, the present invention provides an unlicensed access method and related equipment for distributed energy equipment. The pilot matrix is ​​optimized by using a complex sequence convex optimization iterative decorrelation algorithm, which reduces the interference between pilots and improves the accuracy of the system's estimation of the active state of the equipment; the linear minimum mean square error algorithm is used to further improve the accuracy of channel estimation; the continuous interference elimination technology is used to decode the equipment data, and a combined filter is added in each iteration process, thereby reducing the number of unnecessary iterations and reducing the computational complexity while ensuring the decoding quality; the present invention achieves a significant improvement in detection performance in the unlicensed random access system, including improving the accuracy of the estimation of the active state of the equipment, enhancing the reliability of the channel estimation, and optimizing the efficiency of data decoding. At the same time, the computational complexity is reduced, making the method of the present invention more efficient and feasible in practical applications.

[0207] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0208] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0209] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0210] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

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

[0212] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0213] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained 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, computer-readable media do not include electric carrier signals and telecommunication signals.

[0214] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate 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.

[0215] 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 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0216] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0217] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for unauthorized access to distributed energy equipment, characterized in that: The following steps are involved: Utilizing a central processing unit to optimize a non-orthogonal structure pilot matrix and assigning the obtained low-coherence pilots to the devices; The central processor sends a signal frame with pilot and data to the wireless access point through the device; The central processor estimates the activity status 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; 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 to compare the signal-to-interference-noise ratio of each iteration with the threshold. When the decoding is successful, the central processor allocates the time-frequency resource blocks required for unlicensed random access to the successfully decoded device 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: Optimizing the non-orthogonal structure pilot matrix and allocating the obtained low-coherence pilots to the devices is as follows: The CPU uses a complex sequence iterative convex optimization decorrelation algorithm to optimize the non-orthogonal pilot matrix and converts the optimized x n Restore to pilot sequence f n And assigned to the nth device.

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: P1： s.t.A n,R,1 x n ≤0, A n,R,2 x n ≤0, A n,I,1 x n ≤0, A n,I,2 x n ≤0, in, is the optimization variable, τ p is the pilot length, Φ is the block matrix, I2 represents a second-order unit diagonal matrix, is of dimension 2τ p The all-zero matrix, A n,R,1 , A n,R,2 , A n,I,1 , A n,I,2 is the corresponding coefficient matrix, represents the real part sign, represents the imaginary part sign, Ξ is the block matrix, b n is the intercept matrix F n The vector consisting of the nth column vector and zero elements in F n is a given unit norm matrix, T n is the search radius, and the optimized x is n Recombine the real part and the imaginary part to obtain a low-coherence pilot sequence f n .

4. The method for unauthorized access to distributed energy devices according to claim 1, characterized in that: The pilot matrix received by wireless access point m in the rth resource block is for: Among them, P p is the transmission power of each pilot symbol; a n Represents the active status indication of device n; α nr is the access mode of device n, indicating whether device n uses resource block r; h mnr is the rth resource block channel matrix between device n and wireless access point m; N mr is an independent and identically distributed additive Gaussian white noise matrix, is the variance of additive white Gaussian noise.

5. The method for unauthorized access to distributed energy devices 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 iteration algorithm to converge to the target function f n The local minimum of (d) uses the relaxed maximum likelihood estimation algorithm to estimate the device activity state Channel estimation is performed on the device detected as active, and the channel estimation matrix is ​​obtained as follows: Among them, mr is the inverse of the covariance matrix associated with the received pilot signal at access point m and resource block r, is τ p ×τ p The identity matrix, τ p is the pilot length.

6. The method for unauthorized access to distributed energy equipment according to claim 5, characterized in that: Estimating device activity using the relaxed maximum likelihood estimation algorithm Specifically: Initialize device active state is the initial pilot vector variance received by the wireless access point, and the iteration index The step size d is calculated using a 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 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 Compare each of them with the predetermined threshold θ to determine the final estimated activity coefficient 7. The method for unauthorized access to distributed energy equipment according to claim 6, characterized in that: The step size d is calculated using a low-complexity component-wise iterative algorithm. * as follows: Where M is the number of wireless access points. is the set of resource blocks used by device n, λ mnr To represent 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.

8. The method for unauthorized access to distributed energy devices according to claim 6, characterized in that: The calculation is as follows: where tr(·) is the sum of the diagonal elements of the matrix, is the variance of the initial pilot vector received by the wireless access point.

9. The method for unauthorized access to distributed energy devices according to claim 1, characterized in that: The specific decoding device data using the successive interference cancellation technique is: In the uplink data decoding stage, two sets are defined in Defined as After i iterations, the device data is correctly decoded after the redundant 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, Includes S i elements, whose index values initialization i=1, s=1; The central processor centrally processes the data sent by the wireless access point; CPU design combined filter Process the signal to obtain the filtered combined signal The CPU decodes the signal and repeats until Is an empty set.

10. The method for unauthorized access to distributed energy equipment according to claim 9, characterized in that: Centrally process the data sent by the wireless access point and convert the received signal vector Updated to: Among them, y rt is the initial signal matrix received by the CPU on resource block r, p s is the transmission power of device n', P u is the maximum available power of the device, c st and c n′t are the codewords transmitted on the 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.

11. The method for unauthorized access to distributed energy equipment according to claim 9, characterized in that: The central processing unit decodes the signal as follows: The estimated instantaneous effective signal to noise ratio after the i-th iteration satisfies the following inequality constraint, indicating that the central processor successfully decodes the device data using the continuous interference cancellation technique, and the obtained codeword is expressed as Will gather The sth device in the collection is put into Update the collection i=i+1; from Randomly assign an element to s, and return to continue centrally processing the data sent by the wireless access point; The inequality constraints are as follows: Among them, γ th is the signal-to-interference-noise ratio threshold, is the received interference and noise power matrix.

12. An unauthorized access system for distributed energy equipment, characterized in that: include: An allocation module, which optimizes the non-orthogonal structure pilot matrix using a central processing unit and allocates 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 estimation module,the central processor estimates the active state of the device using the pilot in the signal frame received by the wireless access point,and uses the linear minimum mean square error algorithm to perform channel estimation; 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 to compare the signal-to-interference-noise ratio of each iteration process with the threshold. When the decoding is successful, the central processor allocates the time-frequency resource blocks required for unlicensed random access to the successfully decoded device based on the current network load and available resources.

13. The unauthorized access system for distributed energy devices according to claim 12, characterized in that: Optimizing the non-orthogonal structure pilot matrix and allocating the obtained low-coherence pilots to the devices is as follows: The CPU uses a complex sequence iterative convex optimization decorrelation algorithm to optimize the non-orthogonal pilot matrix and converts the optimized x n Restore to pilot sequence f n And assigned to the nth device; The optimization of the non-orthogonal pilot matrix using the complex sequence iterative convex optimization decorrelation algorithm is as follows: P1： s.t.A n,R,1 x n ≤0, A n,R,2 x n ≤0, A n,I,1 x n ≤0, A n,I,2 x n ≤0, in, is the optimization variable, τ p is the pilot length, Φ is the block matrix, I2 represents a second-order unit diagonal matrix, is of dimension 2τ p The all-zero matrix, A n,R,1 , A n,R,2 , A n,I,1 , A n,I,2 is the corresponding coefficient matrix, represents the real part sign, represents the imaginary part sign, Ξ is the block matrix, b n is the intercept matrix F n The vector consisting of the nth column vector and zero elements in F n is a given unit norm matrix, T n is the search radius, and the optimized x is n Recombine the real part and the imaginary part to obtain a low-coherence pilot sequence f n .

14. The unauthorized access system for distributed energy devices according to claim 12, characterized in that: The pilot matrix received by wireless access point m in the rth resource block is for: Among them, P p is the transmission power of each pilot symbol; a n Represents the active status indication of device n; α nr is the access mode of device n, indicating whether device n uses resource block r; h mnr is the rth resource block channel matrix between device n and wireless access point m; N mr is an independent and identically distributed additive Gaussian white noise matrix, is the variance of additive white Gaussian noise.

15. The unauthorized access system for distributed energy devices according to claim 12, 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 iteration algorithm to converge to the target function f n The local minimum of (d) uses the relaxed maximum likelihood estimation algorithm to estimate the device activity state Channel estimation is performed on the device detected as active, and the channel estimation matrix is ​​obtained as follows: Among them, mr is the inverse of the covariance matrix associated with the received pilot signal at access point m and resource block r, is τ p ×τ p The identity matrix, τ p is the pilot length.

16. The unauthorized access system for distributed energy devices according to claim 15, characterized in that: Estimating device activity status using relaxed maximum likelihood estimation algorithm Specifically: Initialize device active state is the initial pilot vector variance received by the wireless access point, and the iteration index The step size d is calculated using a 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 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 Compare each of them with the predetermined threshold θ to determine the final estimated activity coefficient 17. The unauthorized access system for distributed energy devices according to claim 16, characterized in that: The step size d is calculated using a low-complexity component-wise iterative algorithm. * as follows: Where M is the number of wireless access points. is the set of resource blocks used by device n, λ mnr To represent 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.

18. The unauthorized access system for distributed energy devices according to claim 12, characterized in that: The specific decoding device data using the successive interference cancellation technique is: In the uplink data decoding stage, two sets are defined in Defined as After i iterations, the device data is correctly decoded after the redundant 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, Includes S i elements, whose index values initialization i=1, s=1; The central processor centrally processes the data sent by the wireless access point; CPU design combined filter Process the signal to obtain the filtered combined signal The CPU decodes the signal and repeats until Is an empty set.

19. The unauthorized access system for distributed energy devices according to claim 18, characterized in that: Centrally process the data sent by the wireless access point and convert the received signal vector Updated to: Among them, y rt is the initial signal matrix received by the CPU on resource block r, p s is the transmission power of device n', P u is the maximum available power of the device, c st and c n′t are the codewords transmitted on the 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.

20. The unauthorized access system for distributed energy devices according to claim 19, characterized in that: The central processing unit decodes the signal as follows: The estimated instantaneous effective signal to noise ratio after the i-th iteration satisfies the following inequality constraint, indicating that the central processor successfully decodes the device data using the continuous interference cancellation technique, and the obtained codeword is expressed as Will gather The sth device in the collection is put into Update the collection i=i+1; from Randomly assign an element to s, and return to continue centrally processing the data sent by the wireless access point; The inequality constraints are as follows: Among them, γ th is the signal-to-interference-noise ratio threshold, is the received interference and noise power matrix.

21. 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 11.

22. An electronic device, characterized in that: Comprising the chip as claimed in claim 13.

Images