Resource allocation method and related equipment

Abstract

The invention provides a resource allocation method and related equipment. The method comprises the following steps: calculating a horizontal positioning error based on acquired first power spectral density, second power spectral density and third power spectral density of a positioning user; taking a negative number of the maximized horizontal positioning error as a target function; the constraint conditions of the target function comprise a communication user service quality constraint, a total power constraint and a far and near interference constraint; converting the constraint condition of the target function into an unconstrained target function by using a Lagrange multiplier, and splitting the target function by using a dual decomposition method to obtain sub-target functions; and iteratively updating the sub-objective functions by using a subgradient method, and allocating resources based on an iteration result. According to the embodiment of the invention, the non-convex problem is converted into the convex problem, the sub-gradient method is used for iterative solution, and the optimal solution can be gradually approached, so that the positioning error of the system is minimized, the communication quality and the positioning precision can be effectively balanced, and the reasonable allocation of resources is finally realized.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a resource allocation method and related equipment. Background Art

[0002] Although there are solutions to the power allocation problem of the Reconfigurable Intelligence Surface (RIS)-assisted Non-orthogonal multiple access technology (NOMA) collaborative positioning system in the prior art, the impact of communication on positioning is not considered, and there is a lack of solutions to improve the positioning accuracy of positioning users while ensuring the communication quality of communication users in complex situations. Summary of the invention

[0003] In view of this, the purpose of the present application is to propose a resource allocation method and related equipment.

[0004] Based on the above purpose, the present application provides a resource allocation method, including:

[0005] Calculate the horizontal positioning error based on the acquired first power spectrum density of the positioning user, the second power spectrum density of the positioning user receiving other cluster communication signals, and the third power spectrum density of the positioning user receiving positioning signals from other base stations;

[0006] The negative number of the maximized horizontal positioning error is used as the objective function; the constraint conditions of the objective function include the communication user service quality constraint, the total power constraint and the near-far interference constraint;

[0007] The constraint conditions of the objective function are converted into an unconstrained objective function by using Lagrange multipliers, and the objective function is split by using a dual decomposition method to obtain sub-objective functions;

[0008] The sub-objective function is iteratively updated using a sub-gradient method, and resources are allocated based on the iterative result.

[0009] In a possible implementation, the horizontal positioning error is calculated based on the first power spectrum density of the positioning user, the second power spectrum density of the positioning user received from other cluster communication signals, and the third power spectrum density of the positioning user received from other base stations, including:

[0010] Calculate the positioning user ranging error based on the first power spectral density, the second power spectral density and the third power spectral density;

[0011] The horizontal positioning error is calculated based on the positioning user ranging error.

[0012] In a possible implementation, the positioning user ranging error is expressed by the following formula:

[0013]

[0014] in, Indicates the positioning user ranging error, a=B L (1-0.5B L T coh ), B L represents the loop noise bandwidth, T coh represents the coherent integration time, B0 represents the center frequency of the NOMA signal, and B fe represents the RF front-end bandwidth, N0 represents the power spectrum density of the ambient noise, represents the second power spectral density, represents the third power spectral density, represents the first power spectral density, β represents the amplitude coefficient, A represents the cascade channel coefficient, represents the power allocation coefficient of the positioning user, P s represents the transmission power of the base station, f represents the frequency, D represents the distance between the front and rear correlators, T p Represents the communication signal period, represents the channel gain from base station k′ to the positioning user m.

[0015] In a possible implementation, the horizontal positioning error is expressed by the following formula:

[0016]

[0017] Among them, m represents the horizontal positioning error, K represents the number of base stations, represents the (i, k)th element in the channel matrix of the positioned user m, Indicates the positioning user ranging error.

[0018] In a possible implementation manner, the communication user service quality constraint is determined by the following steps:

[0019] Based on the interference of the positioning user to the communication user, the transmission bit error rate of the communication user is calculated;

[0020] Determining a first constraint condition for the transmission bit error rate;

[0021] Converting the first constraint condition of the transmission bit error rate into a second constraint condition of interference of the positioning user to the communication user;

[0022] The second constraint condition is determined as the communication user quality of service constraint.

[0023] In a possible implementation, the total power constraint is determined based on an upper limit of the total transmit power; and the near-far interference constraint is determined based on a ratio of autocorrelation and cross-correlation of the positioning signal and an energy efficiency level of the receiving device.

[0024] Based on the same inventive concept, the embodiment of the present application further provides a resource allocation device, including:

[0025] A calculation module is configured to calculate a horizontal positioning error based on a first power spectrum density of the positioning user, a second power spectrum density of other cluster communication signals received by the positioning user, and a third power spectrum density of positioning signals received by the positioning user from other base stations;

[0026] A constraint module is configured to take the negative of the maximized horizontal positioning error as an objective function; the constraint conditions of the objective function include a communication user service quality constraint, a total power constraint, and a near-far interference constraint;

[0027] A conversion and splitting module is configured to convert the constraint conditions of the objective function into an unconstrained objective function using Lagrange multipliers, and split the objective function using a dual decomposition method to obtain sub-objective functions;

[0028] The iteration module is configured to iteratively update the sub-objective function using a sub-gradient method and allocate resources based on the iteration result.

[0029] Based on the same inventive concept, an embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a resource allocation method as described in any one of the above is implemented.

[0030] Based on the same inventive concept, an embodiment of the present application further provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute any of the resource allocation methods described above.

[0031] Based on the same inventive concept, an embodiment of the present application further provides a computer program product, which includes computer program instructions, and the computer instructions are used to enable the computer program product to execute any of the resource allocation methods described above.

[0032] From the above, it can be seen that the resource allocation method and related equipment provided by the present application calculate the horizontal positioning error based on the first power spectrum density of the positioned user, the second power spectrum density of the communication signals received by the positioned user from other clusters, and the third power spectrum density of the positioning signals received by the positioned user from other base stations; the negative of the maximized horizontal positioning error is used as the objective function; the constraints of the objective function include the communication user service quality constraint, the total power constraint, and the near and far interference constraint; the constraints of the objective function are converted into an unconstrained objective function using Lagrange multipliers, and the objective function is split using the dual decomposition method to obtain sub-objective functions; the sub-objective function is iteratively updated using the sub-gradient method, and resources are allocated based on the iterative results. The embodiment of the present application considers the influence of multiple base stations and multiple users on the positioning user, and combines the signal normalized power spectral density function (PSD) to derive the relationship between the communication user signal and the communication quality; combined with the positioning signal of the positioning user, under the premise of ensuring the communication quality, the horizontal error of the positioning user is used as the evaluation benchmark, and the base station power and the power allocation coefficient are combined to derive the closed expression of the positioning accuracy using the Lagrange dual method; in the face of the non-convex problem mentioned above, the problem is decomposed into multiple sub-problems, and the Lagrange dual method and the sub-gradient method are used to transform the non-convex problem into a convex problem and solve it using the CVX tool, which effectively reduces the complexity of the problem. At the same time, the sub-gradient method is used to iteratively solve, which can gradually approach the optimal solution, thereby minimizing the system positioning error, and can effectively balance the communication quality and positioning accuracy, and finally achieve reasonable allocation of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 A schematic diagram of a resource allocation method flow chart of an embodiment of the present application;

[0035] Figure 2 A schematic diagram of an application scenario of an embodiment of the present application;

[0036] Figure 3 A schematic diagram showing a comparison of positioning performance between the RIS-NOMA and NOMA systems of an embodiment of the present application;

[0037] Figure 4 A schematic diagram showing a comparison of the positioning performance of the RIS-NOMA system under different reflecting surfaces according to an embodiment of the present application;

[0038] Figure 5 This is a schematic diagram of the change of the average positioning error with the total power constraint in an embodiment of the present application;

[0039] Figure 6 This is a performance diagram of the average positioning error under different BER constraints of an embodiment of the present application;

[0040] Figure 7 This is a schematic diagram of the structure of a resource allocation device according to an embodiment of the present application;

[0041] Figure 8 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0043] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0044] It is understandable that before using the technical solutions of each embodiment of the present disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0045] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0046] As an optional but non-limiting implementation, in response to receiving the user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0047] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0048] As described in the background technology section, although there are solutions to the power allocation problem of the reconfigurable intelligent metasurface (RIS) assisted non-orthogonal multiple access technology (NOMA) collaborative positioning system in the prior art, the impact of communication on positioning is not considered, and there is a lack of solutions to improve the positioning accuracy of positioning users while ensuring the communication quality of communication users in complex situations.

[0049] Based on the above considerations, an embodiment of the present application proposes a resource allocation method, which calculates the horizontal positioning error based on the first power spectral density of the positioned user, the second power spectral density of the communication signals received by the positioned user from other clusters, and the third power spectral density of the positioning signals received by the positioned user from other base stations; the negative of the maximized horizontal positioning error is used as the objective function; the constraints of the objective function include the communication user service quality constraint, the total power constraint, and the near and far interference constraint; the constraints of the objective function are converted into an unconstrained objective function using Lagrange multipliers, and the objective function is split using the dual decomposition method to obtain sub-objective functions; the sub-objective function is iteratively updated using the sub-gradient method, and resources are allocated based on the iterative results. The embodiment of the present application considers the influence of multiple base stations and multiple users on the positioning user, and combines the signal normalized power spectral density function (PSD) to derive the relationship between the communication user signal and the communication quality; combined with the positioning signal of the positioning user, under the premise of ensuring the communication quality, the horizontal error of the positioning user is used as the evaluation benchmark, and the base station power and the power allocation coefficient are combined to derive the closed expression of the positioning accuracy by using the Lagrange dual method; in the face of the non-convex problem mentioned above, the problem is decomposed into multiple sub-problems, and the Lagrange dual method and the sub-gradient method are used to transform the non-convex problem into a convex problem and solve it using the CVX tool, which effectively reduces the complexity of the problem. At the same time, the sub-gradient method is used to iteratively solve, which can gradually approach the optimal solution, thereby minimizing the system positioning error, and can effectively balance the communication quality and positioning accuracy, and finally achieve reasonable allocation of resources.

[0050] The technical solutions of the embodiments of the present application are described in detail below through specific examples.

[0051] refer to Figure 1 The resource allocation method of the embodiment of the present application comprises the following steps:

[0052] Step S101, calculating a horizontal positioning error based on a first power spectrum density of a positioning user, a second power spectrum density of other cluster communication signals received by the positioning user, and a third power spectrum density of positioning signals received by the positioning user from other base stations;

[0053] Step S102, taking the negative number of the maximized horizontal positioning error as the objective function; the constraint conditions of the objective function include the communication user service quality constraint, the total power constraint and the near-far interference constraint;

[0054] Step S103, using Lagrange multipliers to transform the constraint conditions of the objective function into an unconstrained objective function, and using a dual decomposition method to split the objective function to obtain sub-objective functions;

[0055] Step S104, iteratively updating the sub-objective function using a sub-gradient method, and allocating resources based on the iteration result.

[0056] refer to Figure 2 , which is a schematic diagram of an application scenario of an embodiment of the present application.

[0057] Specifically, this application considers the RIS-NOMA communication positioning system, the system model is as follows Figure 2 As shown in the figure. Assume that K base stations (BS) can provide positioning functions. K base stations contain J reflection units, providing communication positioning links for NOMA users, and users are equipped with a single antenna. There are N legal users in the system, namely K communication users (u c ), M positioning users (u p ). Due to the existence of multiple BSs, the positioning users in the coverage of multiple BSs will be affected by the remaining base stations. In order to reduce this impact, the communication users with high signal-to-noise ratio are paired with the positioning users with low signal-to-noise ratio to form a cluster of NOMA users. Assume that the signal-to-noise ratio of the communication user is greater than that of the positioning user, satisfying γ1≥γ2≥γ3≥…≥γ m >>γ m+1 ≥…≥γ N , where 1 to k are communication users, m+1 to N are positioning users, and the users are divided into N / 2 clusters. The first cluster has: {γ1,γ N}, the second cluster has: The second cluster has: {γ2,γ N-1}, and so on, where Uc is a far user and Up is a near user. Assume that the direct link between the BS and the NOMA user is unreachable due to the presence of obstacles. It is particularly important to note that each communication user is connected to only one BS, while the positioning user is connected to multiple BSs, so the indices of these two types of users are kn and m, respectively. It represents the instantaneous channel gain from user ky to user kx through the delay-free and error-free feedback channel, which is specifically shown in the following Table 1.

[0058] Table 1 Definition of instantaneous channel gain

[0059]

[0060] Tr(X),|X|,X m,n Respectively represent the trace, determinant, and value of the mth row and nth column of the matrix X; represents the matrix space; diag(·) represents a diagonal matrix with the bracket elements (·) as diagonal elements; ||·|| represents the Euclidean norm of the matrix; (·) H represents the conjugate transpose of a matrix or vector; erfc(·) represents the complementary error function; Indicates that the variable x has a mean of μ and a variance of σ 2 The complex Gaussian distribution of , j is the imaginary unit.

[0061] For step S101, the horizontal positioning error is calculated based on the acquired first power spectrum density of the positioning user, the second power spectrum density of the positioning user receiving other cluster communication signals, and the third power spectrum density of the positioning user receiving positioning signals from other base stations.

[0062] In some embodiments, the horizontal positioning error is calculated based on the acquired first power spectrum density of the positioning user, the second power spectrum density of the positioning user received from other cluster communication signals, and the third power spectrum density of the positioning user received from other base stations, including: calculating the positioning user ranging error based on the first power spectrum density, the second power spectrum density and the third power spectrum density; and calculating the horizontal positioning error based on the positioning user ranging error.

[0063] In some embodiments, the positioning user ranging error is expressed by the following formula:

[0064]

[0065] in, Indicates the positioning user ranging error, B L represents the loop noise bandwidth, T coh represents the coherent integration time, B0 represents the center frequency of the NOMA signal, and B fe represents the RF front-end bandwidth, N0 represents the power spectrum density of the ambient noise, represents the second power spectral density, represents the third power spectral density, represents the first power spectral density, β represents the amplitude coefficient, A represents the cascade channel coefficient, a p represents the power allocation coefficient of the positioning user, P s represents the transmission power of the base station, f represents the frequency, D represents the distance between the front and rear correlators, T p Represents the communication signal period, represents the channel gain from base station k′ to the positioning user m.

[0066] In some embodiments, the horizontal positioning error is expressed by the following formula:

[0067]

[0068] Among them, m represents the horizontal positioning error, K represents the number of communication users, represents the (i, k)th element in the channel matrix of the positioned user m, Indicates the positioning user ranging error.

[0069] In this embodiment, the RIS-NOMA collaborative positioning system with multiple users and multiple base stations is considered. However, since the positioning user will be interfered by the signals of the intra-cluster communication users and the adjacent BS reflected by the RIS, the interference needs to be analyzed. Since the horizontal positioning error is related to the ranging error, the receiver can use a delay locked loop (DLL, Delay Locked Loop) to track the positioning component, and then represent the ranging error according to the PSD of the received signal to obtain the intrinsic connection between the ranging error and the interference error. It should be noted that due to the existence of RIS, the positioning user will also be affected by RIS when it receives interference from other signals. Specifically, the positioning user u p PSD (first power spectrum density), positioning user u p The PSD (second power spectrum density) of the received communication signal of other clusters and the PSD (third power spectrum density) of the received positioning signal of other BSs can be respectively expressed as:

[0070]

[0071]

[0072] Where c represents the matrix space, Δf p Indicates the subcarrier spacing of the positioning user, represent the instantaneous channel gain coefficients from other k'th communication signals and other base stations, T c is the signal period of the communication user, Δf c is the communication user subcarrier spacing. Further, the positioning user ranging error can be expressed as:

[0073]

[0074] in, Indicates the positioning user ranging error, B L represents the loop noise bandwidth, T coh represents the coherent integration time, B0 represents the center frequency of the NOMA signal, and B fe represents the RF front-end bandwidth, N0 represents the power spectrum density of the ambient noise, represents the second power spectral density, represents the third power spectral density, represents the first power spectral density, β represents the amplitude coefficient, A represents the cascade channel coefficient, a p represents the power allocation coefficient of the positioning user, P srepresents the transmission power of the base station, f represents the frequency, D represents the distance between the front and rear correlators, T p Represents the communication signal period, represents the channel gain from base station k′ to the positioning user m.

[0075] Furthermore, in order to simplify the expression, the above formula can be approximated to obtain the following formula.

[0076]

[0077] in, represents the channel capacity, (CPR) km←k′ Indicates the communication interference strength ratio relative to the positioning signal, (PPR) km←k′m Indicates the ratio of the positioning interference signal to its own strength,

[0078] The specific derivation process is as follows:

[0079]

[0080] in, Specifically expressed as:

[0081]

[0082]

[0083] Where A represents the channel gain coefficient after RIS reflection, represents the power allocation coefficient of the mth positioned user at the kth base station, It represents the third power spectrum density of the mth located user at the kth base station.

[0084] Furthermore, assume that the positioning user bandwidth is much smaller than the total bandwidth B, and the front-end bandwidth is also greater than B. Therefore, the previous formula can be further written as Furthermore, in the above formula It can be expressed as:

[0085]

[0086]

[0087] Among them, P c represents the communication user power, T c represents the signal period of the communication user, G represents the spectrum efficiency coefficient, and n represents the number of communication users.

[0088] Substitute the formula into:

[0089]

[0090] Further, Represents the communication interference intensity ratio relative to the positioning signal, represents the ratio of the positioning interference signal to its own strength, the original formula can be re-expressed as:

[0091]

[0092] For the convenience of description, The horizontal positioning error of the positioning user is derived according to the ranging error, and the horizontal positioning error of the mth positioning user can be expressed as:

[0093]

[0094] Furthermore, the geometric sparsity is recorded as The above formula can be restated as:

[0095]

[0096] The specific derivation process is as follows:

[0097] Assumptions represents the ranging error vector of the mth positioned user, where represents the ranging error of the user from the km-th user, then the positioning error of the m-th user can be expressed as:

[0098]

[0099] Among them, G mm The Jacobian determinant is shown below:

[0100]

[0101] in X = [x, y, z] T represents the coordinates, and the subscripts b and p are used to identify the base station and the positioning user, respectively. Since the distance deviations between base stations are independent of each other, their covariance matrix has the characteristics of a diagonal line, and its covariance matrix is ​​expressed as:

[0102]

[0103] Among them, the ranging error from the km positioning link is The covariance of the positioning error is shown as follows:

[0104]

[0105] Among them, H m The elements in If it is expressed as: then the horizontal positioning error can be obtained:

[0106]

[0107] After the horizontal positioning error is calculated, for step S102, the negative number of the maximized horizontal positioning error is used as the objective function; the constraints of the objective function include the communication user service quality constraint, the total power constraint and the near-far interference constraint.

[0108] In some embodiments, the communication user service quality constraint is determined by the following steps: based on the interference of the positioning user on the communication user, the transmission bit error rate of the communication user is calculated; a first constraint condition for the transmission bit error rate is determined; the first constraint condition for the transmission bit error rate is converted into a second constraint condition for the interference of the positioning user on the communication user; and the second constraint condition is determined as the communication user service quality constraint.

[0109] In some embodiments, the total power constraint is determined based on an upper limit of the total transmit power; and the near-far interference constraint is determined based on a ratio of autocorrelation and cross-correlation of the positioning signal and an energy efficiency level of the receiving device.

[0110] In this embodiment, the optimization problem is to consider the near-far interference problem of the positioned user, the need for at least three base stations to position the positioned user, and ensure the quality of communication service (QoS) in the RIS-NOMA communication positioning system, and jointly consider the base station power and power allocation coefficient, and use the positioning error as a measurement indicator to minimize the system positioning error. However, due to the near-far interference problem and the randomness of the channel, the horizontal positioning error of the positioned user becomes more complicated. At the same time, RIS is coupled with the base station cascade channel, beamforming matrix, user pairing, and base station power, making it difficult to solve the aforementioned non-convex optimization problem directly. Therefore, this application reduces the complexity of the problem by decoupling and decomposing the Lagrangian dual method, and uses the subgradient method to iteratively solve and gradually approach the optimal solution, thereby minimizing the system positioning error, effectively balancing the communication quality and positioning accuracy, and ultimately optimizing the system performance.

[0111] According to the above analysis, considering its constraints, the positioning error of the RIS-NOMA communication positioning system is minimized. However, in order to better use the Lagrangian dual method, this application adopts an equivalent method, that is, maximizing the negative value of the average positioning error of all positioning users, indirectly minimizing the positioning error of the system. The objective function is expressed by the following formula:

[0112]

[0113] in, represents the horizontal positioning error of the mth positioning user, represents the interference tolerance of the nth communication user at the kth base station, represents the total power upper limit of K communication users, C1 is the communication user service quality constraint, C2 is the total power constraint, and C3 is the near and far interference constraint. The specific derivation process is as follows:

[0114] C1: Communication user service quality constraints.

[0115] This application optimizes the positioning accuracy of the positioning user on the basis of ensuring the service quality of the communication user. In order to ensure the communication service within the network, the limit of the bit error rate of the communication user is preset, and it is stipulated that the bit error rate of all users must be kept below this limit. From the transmission bit error rate, it can be obtained:

[0116]

[0117] in, Indicates the upper limit of the bit error rate.

[0118] Substituting the above formula into the bit error rate formula, we can get:

[0119]

[0120] It should be noted that the interference tolerance limit of the knth communication user is defined as This value is closely related to the quality of service.

[0121] In this embodiment, the present application is in the communication positioning system scenario, assuming that the kth BS sends a composite signal to the communication positioning user Among them, x c For communication user u c The signal x p To locate user u p signal, assuming the base station power is the same, P s is the transmission power of BS, a c and a p u c and u p The power distribution coefficient satisfies a c +a p =1, and a c >a p . Locate user u p and communication user u c The received signal y p and c Respectively expressed as

[0122]

[0123] in, represents additive white Gaussian noise, represents the RIS reflection coefficient matrix, β∈[0,1] and φ i ∈[0, 2π) represent the amplitude coefficient and the i-th phase shift coefficient respectively. Considering that the amplitude coefficients of the RIS reflection units are the same, In order to simplify the calculation formula, let represents the channel gain coefficient after RIS reflection, then u c The signal to interference plus noise ratio (SINR) of the decoded signal itself is expressed as:

[0124]

[0125] in represents the interference of the positioning user to the communication user, T p represents the signal period of the positioning user, G represents the multiple of the positioning user subcarrier spacing relative to the communication user subcarrier spacing, considering that the system adopts binary phase shift keying (BPSK) modulation, then u c The transmission bit error rate is expressed as:

[0126]

[0127] C2: Total power constraint.

[0128] In order to ensure the quality of communication services and reduce interference to the receiver, an upper limit is set for the total transmission power. The total power can be expressed as:

[0129]

[0130] Furthermore, since it is assumed that the output power of all base stations to the communication users is consistent, the overall power limit condition of the positioning signal can be obtained, which is: It can be re-expressed as:

[0131]

[0132] C3: “Near and far interference” constraint.

[0133] Since the positioning of users will be interfered by the signals of adjacent base stations, there are signals that cannot distinguish between distant base station signals and adjacent base station signals. Therefore, by increasing the density of base stations and enhancing signal diversity, the accuracy of positioning is indirectly improved. Since the signals sent by each base station are independent of each other, the constraints are satisfied:

[0134]

[0135] Among them, Ω represents the ratio of the positioning signal autocorrelation to the cross-correlation, and ρ represents the efficiency level of the receiving device, which is usually greater than 1. It should be noted that if the maximum cross-correlation value meets certain conditions, the correlation values ​​of all other k' will also follow the same rule. The above formula can be further expressed as:

[0136]

[0137] Among them, k′ k m is defined as an indicator that does not include the specific positioning signal of the km-th user, but reflects the location identifier of other strongest signals received.

[0138] Furthermore, for step S103 and step S104, the constraints of the objective function are converted into unconstrained objective functions using Lagrange multipliers, and the objective function is split using the dual decomposition method to obtain sub-objective functions; the sub-objective functions are iteratively updated using the sub-gradient method, and resources are allocated based on the iterative results.

[0139] In this embodiment, due to the relationship between the shape of the beam and the phase adjustment of the RIS, the beamforming vector defines the direction of the beam, and the RIS phase shift matrix defines the phase adjustment of the signal on the STAR-RIS, making the constraints C1 and C3 non-convex. In addition, the interference problem of multiple base stations and communication users makes the original objective function more complicated, resulting in the original problem cannot be solved directly using CVX. The goal of this application is to maximize the performance of positioning users while considering the communication quality of communication users. It is necessary to solve the constraints separately to obtain the global optimal solution of the original problem. In order to solve the above problems, this application introduces Lagrange multipliers to transform the constraints of the original objective function into an unconstrained objective function and uses the dual decomposition method to deal with the non-convexity of the objective function. Finally, the complex objective function and its multiple constraints are transformed into a mathematical form that is easy to solve, thereby effectively handling the original coupled non-convex optimization problem.

[0140] CVX (Convex Optimization Toolbox) in MATLAB is a toolbox specifically designed for solving convex optimization problems. Convex optimization is a branch of mathematical optimization that studies optimization problems with convex feasible regions and convex objective functions. These problems are widely used in many engineering fields and scientific applications, such as signal processing, control theory, machine learning, financial engineering, etc. CVX provides MATLAB users with a concise and easy-to-use syntax, allowing non-professional optimization experts to easily express and solve convex optimization problems. CVX is a MATLAB toolbox designed specifically for solving convex optimization problems, which allows users to use MATLAB's language to describe and solve convex optimization problems. CVX simplifies the modeling and solution process of convex optimization problems, allowing researchers and engineers to more easily implement complex convex optimization algorithms without having to deeply understand the underlying optimization theory or algorithm details.

[0141] For the convenience of expression, Definition: When the target variable satisfies certain constraints, this problem is equivalent to the original problem. Then the Lagrangian form of the original problem can be expressed as:

[0142]

[0143] in, is the power allocation coefficient for km users, and the Lagrangian function of the original problem is It can be expressed as:

[0144]

[0145] Assumptions If it remains unchanged, then according to the original constraints, the dual optimization problem can be restated as:

[0146] ming(μ, v, w)

[0147] st.μ≥0,v≥0,β≥0

[0148] because Fixed, function The linear relationship between μ, v, and w means that the original problem is convex. At this point, the original problem is transformed into a convex function. For this problem, due to its excessive variables and complex calculations, it cannot be solved directly. Therefore, the dual decomposition strategy is used to solve the problem.

[11] , split the objective function into several small problems. This method can split a complex large-scale optimization problem into multiple small-scale sub-problems, each of which is relatively simple and easy to solve, thereby reducing the computational complexity. The above problem is decomposed into power allocation coefficient sub-problems to balance the power allocation between different users and ensure the performance of communication and positioning. It can be expressed as:

[0149]

[0150] So far, the dual function has been decomposed into K×M independent subtasks, and g k m(μ, v, w) contains M sub-problems. The objective function of the above sub-problems can be expressed as:

[0151]

[0152] Similarly, assume that the non-negative dual variables involved in the constraints are denoted by Then the pull-type expression of the above sub-problem can be expressed as:

[0153]

[0154] in The Lagrangian function can be expressed as:

[0155]

[0156] Then the subproblem dual optimization problem can be expressed as:

[0157]

[0158] According to the KKT (Karush-Kuhn-Tucker) condition, the ideal power allocation strategy of P2 can be determined. Under the condition of satisfying certain equality constraints, the optimal power allocation scheme can be obtained by accurate solution to maximize the system efficiency. The KKT condition is expressed as

[0159]

[0160] The optimal solution to this problem conforms to this equation, by Substituting and differentiating the formula, setting it equal to zero, we can get:

[0161]

[0162]

[0163] The constraint scale is The geometric dilution is λ km , the ranging factor is make J kn The value of is determined by the communication path loss coefficient from all base stations to kn users, v k is a paired variable associated with the total transmit power cap, and They are respectively related to the bit error rate limit of the communication user kn and the performance of the user receiver.

[0164] At this point, P1 can get its optimal solution, but for P2, since its closed expression cannot be obtained, P2 cannot be solved directly. In order to deal with this problem, this application uses the subgradient method to iteratively update the problem. The subgradient method does not require precise gradient information and can handle non-convex problems with discontinuity or non-differentiability, thereby improving the solution efficiency and robustness. k Related to the total power, when the total power is determined, v k is relatively fixed, so by changing v k The gradient estimate of The optimal solution for a specific set of dual variables Then on the variable s The gradient can be expressed as follows.

[0165]

[0166] The sub-problem in the above formula can be transformed into:

[0167]

[0168]

[0169] Based on the above formula, given the known parameter v k Under the premise of being able to effectively calculate The corresponding optimal power allocation strategy. By repeatedly adjusting v k The gradient estimate of , gradually approaches the ideal optimal solution.

[0170]

[0171] Through the above analysis, the optimization problem P1 has been transformed into a standard convex problem, which can be solved using the CVX tool, and this application will not go into details here.

[0172] When the solution is obtained, resources are allocated according to the solution, and specifically, an optimal power allocation strategy is formed.

[0173] In another feasible embodiment, the present application uses Matlab to perform performance analysis and verification on the proposed problem. Assume that there are 4 base station devices and consider that 20 positioning users are uniformly and randomly distributed in the coverage area. In order to improve the statistical reliability, 50 rounds of Monte Carlo simulation are performed for each simulation scenario. At the same time, in order to ensure fairness and generality, the total power threshold of the positioning signal is set. is the BER threshold of all communicating users.

[0174] refer to Figure 3, which is a schematic diagram comparing the positioning performance of the RIS-NOMA and NOMA systems of the embodiment of the present application.

[0175] exist Figure 3 In the experiment, the positioning performance of RIS-NOMA and traditional NOMA under the same resource allocation was compared. The experiment ensured that the resource allocation of the two was consistent to accurately evaluate the performance difference. If the user cannot receive positioning information from at least three base stations, it is considered impossible to locate and the error is counted as 0. The results show that under the same conditions, the average positioning error of RIS-NOMA is lower than that of NOMA, which is due to the fact that RIS technology optimizes the signal environment through intelligent reflective surfaces and improves positioning accuracy. Although NOMA has advantages in multiple access, its positioning performance is slightly inferior to RIS-NOMA.

[0176] refer to Figure 4 , which is a schematic diagram comparing the positioning performance of the RIS-NOMA system under different reflecting surfaces according to an embodiment of the present application.

[0177] Figure 4 The positioning performance of the RIS-NOMA system under different N values ​​is deeply analyzed. The results show that with the increase of N value, the positioning error of the RIS-NOMA system gradually decreases under the same resource allocation strategy. This shows that under the condition of consistent resource allocation, the increase of N value can significantly improve the positioning performance of RIS-NOMA. This conclusion is crucial to optimize the performance of the RIS-NOMA positioning system under given resources.

[0178] refer to Figure 5 , which is a schematic diagram of the change of the average positioning error with the total power constraint in an embodiment of the present application.

[0179] Figure 5 The relationship between the average positioning error and the total power constraint is clearly shown. As the total power constraint increases, it can be seen that the average positioning error gradually decreases. However, when the total power reaches a certain level, the error curve tends to be stable, which shows that further increasing the power has limited effect on improving positioning accuracy. It is worth noting that under the same total power constraint, the Qos constraint becomes the dominant factor affecting the positioning error. In addition, by comparing the curves of different colors, it can be observed that when the Qos standard decreases, the corresponding positioning error will increase accordingly. This observation highlights the key role of the Qos constraint in optimizing positioning accuracy.

[0180] refer to Figure 6 , which is a performance diagram of the average positioning error of an embodiment of the present application under different BER constraints.

[0181] exist Figure 6In the figure, the average positioning accuracy performance under different QoS conditions is shown. It can be clearly seen that with the gradual increase of the BER threshold, the average positioning error shows a gradually decreasing trend. However, when the BER threshold continues to increase to a certain extent, these curves tend to be stable and no longer show a significant decrease. The reason for this phenomenon is the limitation of total power, which restricts the further improvement of positioning performance. This result shows that a balance needs to be found between QoS constraints and power constraints to achieve the best positioning performance. Therefore, in order to ensure a high positioning accuracy, it is particularly important to reasonably set QoS constraints and power constraints.

[0182] It can be seen from the above embodiments that the resource allocation method described in the embodiments of the present application calculates the horizontal positioning error based on the first power spectral density of the positioned user, the second power spectral density of the communication signals received by the positioned user from other clusters, and the third power spectral density of the positioning signals received by the positioned user from other base stations; the negative of the maximized horizontal positioning error is used as the objective function; the constraints of the objective function include the communication user service quality constraints, the total power constraints, and the near and far interference constraints; the constraints of the objective function are converted into an unconstrained objective function using Lagrange multipliers, and the objective function is split using the dual decomposition method to obtain a sub-objective function; the sub-objective function is iteratively updated using the sub-gradient method, and resources are allocated based on the iterative results. The embodiment of the present application considers the influence of multiple base stations and multiple users on the positioning user, and combines the signal normalized power spectral density function (PSD) to derive the relationship between the communication user signal and the communication quality; combined with the positioning signal of the positioning user, under the premise of ensuring the communication quality, the horizontal error of the positioning user is used as the evaluation benchmark, and the base station power and the power allocation coefficient are combined to derive the closed expression of the positioning accuracy using the Lagrange dual method; in the face of the non-convex problem mentioned above, the problem is decomposed into multiple sub-problems, and the Lagrange dual method and the sub-gradient method are used to transform the non-convex problem into a convex problem and solve it using the CVX tool, which effectively reduces the complexity of the problem. At the same time, the sub-gradient method is used to iteratively solve, which can gradually approach the optimal solution, thereby minimizing the system positioning error, and can effectively balance the communication quality and positioning accuracy, and finally achieve reasonable allocation of resources.

[0183] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0184] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0185] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a resource allocation device.

[0186] refer to Figure 7 , the resource allocation device comprises:

[0187] The calculation module 71 is configured to calculate the horizontal positioning error based on the first power spectrum density of the positioning user, the second power spectrum density of the positioning user receiving other cluster communication signals, and the third power spectrum density of the positioning user receiving positioning signals from other base stations;

[0188] The constraint module 72 is configured to take the negative of the maximized horizontal positioning error as the objective function; the constraint conditions of the objective function include the communication user service quality constraint, the total power constraint and the near-far interference constraint;

[0189] The conversion and splitting module 73 is configured to use Lagrange multipliers to convert the constraint conditions of the objective function into an unconstrained objective function, and use a dual decomposition method to split the objective function to obtain sub-objective functions;

[0190] The iteration module 74 is configured to iteratively update the sub-objective function using a sub-gradient method and allocate resources based on the iteration result.

[0191] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0192] The apparatus of the above embodiment is used to implement the corresponding resource allocation method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0193] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the resource allocation method described in any of the above embodiments is implemented.

[0194] Figure 8 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0195] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0196] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0197] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0198] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0199] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0200] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0201] The electronic device of the above embodiment is used to implement the corresponding resource allocation method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0202] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the resource allocation method described in any of the above embodiments.

[0203] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0204] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the resource allocation method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0205] Based on the same inventive concept, corresponding to the resource allocation method described in any of the above embodiments, the present disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer so that the computer and / or the processor execute the resource allocation method. Corresponding to the execution subject corresponding to each step in each embodiment of the resource allocation method, the processor that executes the corresponding step can belong to the corresponding execution subject.

[0206] The computer program product of the above embodiment is used to enable the computer and / or the processor to execute the resource allocation method described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0207] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0208] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0209] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0210] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A resource allocation method, characterized in that: include: Calculate the horizontal positioning error based on the acquired first power spectrum density of the positioning user, the second power spectrum density of the positioning user receiving other cluster communication signals, and the third power spectrum density of the positioning user receiving positioning signals from other base stations; The negative number of the maximized horizontal positioning error is used as the objective function; the constraint conditions of the objective function include the communication user service quality constraint, the total power constraint and the near-far interference constraint; The constraint conditions of the objective function are converted into an unconstrained objective function by using Lagrange multipliers, and the objective function is split by using a dual decomposition method to obtain sub-objective functions; The sub-objective function is iteratively updated using a sub-gradient method, and resources are allocated based on the iterative result.

2. The method according to claim 1, characterized in that The calculating the horizontal positioning error based on the first power spectrum density of the positioning user, the second power spectrum density of the positioning user received from other cluster communication signals, and the third power spectrum density of the positioning signal received from other base stations by the positioning user includes: Calculate the positioning user ranging error based on the first power spectral density, the second power spectral density and the third power spectral density; The horizontal positioning error is calculated based on the positioning user ranging error.

3. The method according to claim 2, characterized in that The positioning user ranging error is expressed by the following formula: in, Indicates the positioning user ranging error, a=B l (1-0.5B l T co ), B L represents the loop noise bandwidth, T coh represents the coherent integration time, B0 represents the center frequency of the NOMA signal, and B fe represents the RF front-end bandwidth, N0 represents the power spectrum density of the ambient noise, represents the second power spectral density, represents the third power spectral density, represents the first power spectral density, β represents the amplitude coefficient, A represents the cascade channel coefficient, represents the power allocation coefficient of the positioning user, P s represents the transmission power of the base station, f represents the frequency, D represents the distance between the front and rear correlators, T p Indicates the communication signal period of the positioning user, Indicates that except for base station k ′ The channel gain from the external positioning user m.

4. The method according to claim 2, characterized in that: The horizontal positioning error is expressed by the following formula: Among them, m represents the horizontal positioning error, K represents the number of base stations, represents the (i,k)th element in the channel matrix of the positioned user m, Indicates the positioning user ranging error.

5. The method according to claim 1, characterized in that The communication user service quality constraint is determined by the following steps: Based on the interference of the positioning user to the communication user, the transmission bit error rate of the communication user is calculated; Determining a first constraint condition for the transmission bit error rate; Converting the first constraint condition of the transmission bit error rate into a second constraint condition of interference of the positioning user to the communication user; The second constraint condition is determined as the communication user quality of service constraint.

6. The method according to claim 1, characterized in that The total power constraint is determined based on the upper limit of the total transmission power; the near-far interference constraint is determined based on the ratio of the autocorrelation and cross-correlation of the positioning signal and the energy efficiency level of the receiving device.

7. A resource allocation device, characterized in that: include: A calculation module is configured to calculate a horizontal positioning error based on a first power spectrum density of the positioning user, a second power spectrum density of other cluster communication signals received by the positioning user, and a third power spectrum density of positioning signals received by the positioning user from other base stations; A constraint module is configured to take the negative of the maximized horizontal positioning error as an objective function; the constraint conditions of the objective function include a communication user service quality constraint, a total power constraint, and a near-far interference constraint; A conversion and splitting module is configured to convert the constraint conditions of the objective function into an unconstrained objective function using Lagrange multipliers, and split the objective function using a dual decomposition method to obtain sub-objective functions; The iteration module is configured to iteratively update the sub-objective function using a sub-gradient method and allocate resources based on the iteration result.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 6.

10. A computer program product, comprising computer program instructions, which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 6.

Images