A power allocation method for a de-cell large-scale MIMO system based on a particle swarm algorithm

Abstract

The application discloses a power distribution method for a de-cell large-scale MIMO system based on a particle swarm algorithm, which comprises the following steps: firstly, in a de-cell large-scale MIMO system adopting a non-ideal hardware and a superimposed pilot transmission scheme, a user transmission rate lower bound expression is obtained by using a UatF technology; then, an optimization problem is established by taking user pilot and data transmission power as independent variables and taking maximization of user total rate as an objective function; secondly, in a multivariable coupling condition, an optimal solution of the optimization problem is obtained by using a particle swarm algorithm; finally, pilot signals and data signals are controlled at a user end according to determined pilot and data transmission power, so that the optimization target is achieved. In the application, the user pilot and data transmission power are optimized in the condition that the pilot and data transmission power are coupled with each other, and compared with an equal power distribution scheme, the power distribution method designed in the application can greatly improve the user total rate.

Description

Technical Field

[0001] This invention relates to a power allocation method for decellularized massive MIMO systems based on particle swarm optimization, belonging to the field of dynamic power allocation technology in wireless communication. Background Technology

[0002] Cellular decellularized massive multiple-input multiple-output (MIMO) technology can provide strong macro diversity gain and uniform quality of service, and when combined with non-ideal hardware, it can significantly improve system energy efficiency. Therefore, it is considered a potential key technology in the future 6G (Sixth Generation).

[0003] As the number of users increases, pilot pollution becomes increasingly severe due to limitations in pilot resources, restricting further increases in transmission rates for decellularized massive MIMO systems. To suppress pilot pollution, a superimposed pilot transmission scheme can be adopted, which increases the number of orthogonal pilots by extending the pilot length. However, with the superimposed pilot transmission scheme, the pilot and data transmit power coefficients in the user transmission rate expression are coupled, making power optimization difficult for this expression.

[0004] Although decellularized massive MIMO technology can achieve high and uniform transmission rates, from the perspective of high-speed communication, corresponding power optimization algorithms are still needed to further improve the transmission rate. Considering the multivariate coupling problem in the user rate expression, convex optimization tools are difficult to use. Therefore, this invention designs a power allocation method based on particle swarm optimization. Summary of the Invention

[0005] Objective: To overcome the shortcomings of existing technologies, this invention provides a power allocation method for decellularized massive MIMO systems based on particle swarm optimization, which allows users to reasonably adjust the transmit power allocated to pilot and data signals, thereby significantly improving user transmission rates.

[0006] Technical solution: To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0007] The present invention discloses a power allocation method for decellularized massive MIMO systems based on particle swarm optimization, comprising the following steps:

[0008] Step 1: In a decellularized massive MIMO system employing non-ideal hardware and a superimposed pilot transmission scheme, the lower bound expression R of the user transmission rate is obtained using the Use-and-then-Forget (UatF) technique. k (ρ, q).

[0009] Step 2: Using user pilot and data transmit power as independent variables, and maximizing the total user rate as the objective function, combine this with the lower bound expression R for the user transmission rate. k Establish an optimization problem model (ρ, q).

[0010] Step 3: Under the condition of pilot and data transmission power coupling, the optimal solution of the optimization problem model is obtained by using the particle swarm optimization algorithm, thereby obtaining the optimal pilot and data transmission power.

[0011] Step 4: Based on the determined optimal pilot and data transmission power, control the user terminal to transmit pilot and data signals to achieve the optimization goal of maximizing the total user rate.

[0012] Furthermore, the lower bound expression for the user transmission rate R k (ρ, q), the calculation formula is as follows:

[0013]

[0014] in,

[0015]

[0016]

[0017]

[0018]

[0019] In the above formula, γ lk express The variance of any element Represents the channel coefficient g lk The LMMSE estimate (linear minimum mean square error estimate), where B represents the channel bandwidth, N represents the number of antennas equipped in the AP, and γ lk′ express The variance of any element Represents the channel coefficient g lk′ LMMSE estimate, ρ = [ρ1, ..., ρ K ] T and q = [q1, ..., q K ] T This represents a vector consisting of the pilot and data transmit power of all users, with the superscript T indicating the matrix transpose operator. lk g lk′ Let ρ represent the channel coefficients between the l-th AP and the k-th and k′-th users, respectively. l = 1, 2, ..., L, k = 1, 2, ..., K, where K is the total number of users, L is the total number of APs, and ρ k q kLet ρ represent the pilot transmit power and data transmit power of the k-th user, respectively. k′ q k′ Let κ represent the pilot transmit power and data transmit power of the k′-th user, respectively. t and κ r β represents the hardware quality of the transmitter and receiver, k′ represents other users different from k, k′∈{1,...,K}, and β lk / β lk′ σ represents the large-scale fading coefficient between the l-th AP and the k / k′-th user. 2 τ represents the power of Gaussian white noise. c Indicates the pilot length.

[0020] in,

[0021] Among them, P k This represents the set of all users who use the same pilot frequency as the k-th user.

[0022] Furthermore, The calculation formula is as follows:

[0023]

[0024] Among them, Y l This represents the superimposed signal received by the l-th AP. This represents the pilot signal for the k-th user.

[0025] Furthermore, Y l The calculation formula is as follows:

[0026]

[0027] Where the superscript H represents the conjugate transpose operator, s k This represents the data signal of the k-th user. and W l，r N represents the hardware impairments when the k-th user transmits the pilot signal, the hardware impairments when the k-th user transmits the data signal, and the hardware impairments when the l-th AP receives the superimposed signal, respectively. l This is a Gaussian white noise matrix.

[0028] Furthermore, the problem model is optimized, and the calculation formula is as follows:

[0029] Q:

[0030] s·t.C1:

[0031] C2:

[0032] C3:

[0033] in, P represents the total user rate. u This represents the user's maximum transmit power, and C1, C2, and C3 represent the first, second, and third constraints, respectively.

[0034] Furthermore, step 3 includes the following steps:

[0035] Step 3-1: Initialize the particle swarm, establishing a particle swarm containing S particles. The position information of the s-th particle is denoted as... The vector matrix at the i-th iteration is denoted as a K×2 matrix composed of the pilot signals and data transmission power of all users. The velocity matrix of the s-th particle at the i-th iteration is denoted as... Initialize the number of iterations i = 0, and set the maximum number of iterations to 0.

[0036] Step 3-2: Calculate the optimal solution of the s-th particle and the global optimal solution in the 0th iteration, and set... Among them, 1 K Represents a K-dimensional column vector consisting of the digit 1; set Among them, V min and V max These are the lower and upper limits of the particle velocity, respectively, and rand represents a randomly generated number between 0 and 1; Substituting into the optimization problem model, we get The optimal solution for the s-th particle is The globally optimal solution in the 0th iteration is f. opt =R(Φ opt ),

[0037] Step 3-3: Calculate the optimal solution of the s-th particle and the global optimal solution in the i-th iteration, including the following steps:

[0038] Step 3-3-1: Update particle velocity. And on Define it so that in V represents max All elements in the set are greater than or equal to In the corresponding elements, c1 and c2 are learning factors. This represents the inertia weight.

[0039] Step 3-3-2: Update particle positions. And on Define it so that Where, Φ min and Φmax These represent the lower and upper limits of the particle's position, respectively; for An element of the set is subjected to adaptive mutation, i.e., if rand > prob, where prob represents the adaptive mutation probability, then... Where m = ceil(rand × K), n = ceil(rand × 2), and ceil represents the round-up operator.

[0040] Step 3-3-3: Judgment Does the constraint condition of the optimization problem model meet? If not, If satisfied, Substituting into the optimization problem model, we get

[0041] Step 3-3-4: Update the current optimal solution and the global optimal solution: If make like Let f opt =R(Φ opt ),

[0042] Step 3-3-5: Judgment Check if the iteration has converged or reached the maximum number of iterations. If it has, end the iteration and proceed to step 3-4. If it has not, let i = i + 1 and repeat steps S3-3-1 to S3-3-5.

[0043] Steps 3-4: Output the optimal individual Φ opt Φ opt This refers to the optimal combination of user pilot and data transmission power based on the particle swarm optimization algorithm.

[0044] Furthermore, w max and w min d1 and d2 represent the maximum and minimum weight coefficients, respectively, and the control factors.

[0045] Furthermore, step 4 includes the following steps:

[0046] The CPU will calculate Φ opt Feedback is sent to the user via the backhaul link, and the user, based on Φ opt The optimal target is achieved by adjusting the transmit power allocated to the pilot and data signals.

[0047] Beneficial Effects: This invention provides a power allocation method for decellularized massive MIMO systems based on particle swarm optimization. First, in a decellularized massive MIMO system employing non-ideal hardware and a superimposed pilot transmission scheme, the lower bound expression for the user transmission rate is derived. Next, considering user pilot and data transmission power constraints, an optimization problem is established with maximizing the total user rate as the objective function. Then, addressing the multivariate coupling problem in this optimization problem, the optimal solution is obtained using particle swarm optimization. Finally, based on the determined power allocation method, the user-end pilot and data signal transmissions are controlled to achieve the optimization objective.

[0048] This invention optimizes the user pilot and data transmission power when the pilot and data transmission power are coupled together. Compared with the equal power allocation scheme, the power allocation method designed in this invention can significantly improve the total user rate and has broad application value and prospects. Attached Figure Description

[0049] Figure 1 This is a flowchart of the particle swarm optimization algorithm described in an embodiment of the present invention.

[0050] Figure 2 This is the user total rate convergence diagram described in the embodiment of the present invention.

[0051] Figure 3 This is a graph showing the relationship between the total user rate and the number of access points (APs) as described in an embodiment of the present invention. Detailed Implementation

[0052] The present invention will be further described below with reference to specific embodiments.

[0053] A power allocation method for decellularized massive MIMO systems based on particle swarm optimization (PSO) algorithm is disclosed. This proposed method dynamically adjusts the transmit power allocated to pilot and data signals by users, resulting in a significant increase in transmission rate. The invention will be further described in detail below with reference to the accompanying drawings.

[0054] Step (1): In a decellularized massive MIMO system employing non-ideal hardware and superimposed pilot transmission scheme, the lower bound expression for the user transmission rate is obtained using UatF technology.

[0055] This invention studies a non-ideal hardware AP access point and user system with decellularized MIMO, consisting of L APs, K users and 1 CPU. Each AP is equipped with N antennas and each user is equipped with a single antenna.

[0056] Assuming all users simultaneously transmit pilot and data signals to the AP, based on the error vector amplitude model, the superimposed signal Y received by the l-th AP is... l It can be modeled as:

[0057]

[0058] Where l = 1, 2, ..., L, k = 1, 2, ..., K, K is the total number of users, L is the total number of APs, and g lk Let ρ be the channel coefficient between the l-th AP and the k-th user. k q k These represent the pilot transmit power, data transmit power, pilot signal, and data signal of the k-th user, respectively. The superscript H indicates the conjugate transpose operator. Additionally, κ... t and k r This indicates the hardware quality of the transmitter and receiver. and w l，r N represents the hardware impairments when the k-th user transmits the pilot signal, the hardware impairments when the k-th user transmits the data signal, and the hardware impairments when the l-th AP receives the superimposed signal, respectively. l This is a Gaussian white noise matrix.

[0059] Based on Y l The channel coefficient g is estimated using the LMMSE criterion. lk Channel coefficient g lk LMMSE estimation for:

[0060]

[0061] Where, τ c Let β represent the pilot length (note: in the superimposed pilot transmission scheme, the pilot length and data length are equal), k′ represent other users different from k, k′∈{1,...,K}, and β represent the pilot length. lk / β lk′ P represents the large-scale fading coefficient between the l-th AP and the k / k′-th user. k Let σ represent the set of all users who use the same pilot signal as the k-th user. 2 ρ represents the power of Gaussian white noise. k′ q k′ These represent the pilot transmit power and data transmit power of the k′-th user, respectively. This represents the pilot signal for the k-th user.

[0062] To detect user data, it is necessary to utilize A maximum ratio combining receiver is established. Then, using the commonly used UatF technique to analyze performance limits, the lower bound expression R for the transmission rate of the k-th user can be derived. k (ρ, q) is...

[0063]

[0064] in,

[0065]

[0066]

[0067]

[0068]

[0069] In the above formula, γ lk express The variance of any element, B represents the channel bandwidth, γ lk′ express The variance of any element Channel coefficient g lk′ The LMMSE estimate. ρ = [ρ1, ..., ρ K ] T and q = [q1, ..., q K ] T This represents a vector consisting of the pilot and data transmit power of all users, with the superscript T indicating the matrix transpose operator.

[0070] Step (2): Establish an optimization problem with user pilot and data transmission power as independent variables and maximizing the total user rate as the objective function.

[0071] The optimization problem, with the overall rate of transmission as the objective and the user's pilot and data transmission power as independent variables, can be modeled as follows:

[0072] Q:

[0073] stC1:

[0074] C2:

[0075] C3:

[0076] in, P represents the total user rate. u This represents the user's maximum transmit power. Constraints C1 and C2 require that the transmit power allocated by the user to the pilot and data signals should be greater than 0. Constraint C3 requires that the sum of the pilot and data transmit powers cannot exceed P. u .

[0077] It can be seen that the variables ρ and q in the optimization problem Q are coupled together, making it a non-convex problem, which is difficult to solve using convex optimization mathematical tools. Considering the advantages of particle swarm optimization (PSO) algorithm, such as its strong adaptability and fast convergence, this invention will use the algorithm's approach to design a power allocation method in the above system.

[0078] like Figure 1 As shown, step (3): under the condition of pilot and data transmission power coupling, the optimal solution of the optimization problem is obtained by using the particle swarm algorithm, and then the optimal pilot and data transmission power is obtained.

[0079] S1 initializes the particle swarm.

[0080] Construct a particle swarm containing S particles, where the position information of the s-th particle is denoted as . The vector matrix at the i-th iteration is denoted as a K×2 matrix composed of the pilot signals and data transmission power of all users. The velocity matrix of the s-th particle at the i-th iteration is denoted as... Initialize the number of iterations i = 0, and set the maximum number of iterations to 0. K represents the number of users.

[0081] S2 calculates the optimal solution of the s-th particle and the global optimal solution in the 0th iteration.

[0082] set up Among them, 1 K Represents a K-dimensional column vector consisting of the digit 1; set Among them, V min and V max These are the lower and upper limits of the particle velocity, respectively, and rand represents a randomly generated number between 0 and 1; Substituting into the objective function in step (2), we get The optimal solution for the s-th particle is The globally optimal solution in the 0th iteration is f. opt =R(Φ opt ),

[0083] S3 calculates the optimal solution of the s-th particle and the global optimal solution in the i-th iteration.

[0084] S31 updates particle velocity. And on Define it so that in V represents max All elements in the set are greater than or equal to In the corresponding elements, c1 and c2 are learning factors. w represents the inertia weight. max and w mind1 and d2 represent the maximum and minimum weight coefficients, respectively, and the control factors.

[0085] S32 updates particle positions. And on Define it so that Where, Φ min and Φ max These represent the lower and upper limits of the particle's position, respectively; for An element of the set is subjected to adaptive mutation, i.e., if rand > prob, where prob represents the adaptive mutation probability, then... Where m = ceil(rand × K), n = ceil(rand × 2), and ceil represents the round-up operator.

[0086] S33 Judgment Does the constraint condition meet? If not, If satisfied, Substituting into the objective function in step (2), we get

[0087] S34 updates its own optimal solution and the global optimal solution: If make like Let f opt =R(Φ opt ),

[0088] S35 Judgment Check if the iteration has converged or reached the maximum number of iterations. If it has, end the iteration; if it has not, let i = i + 1 and repeat steps S31-S35.

[0089] S4 outputs the best individual Φ opt This matrix represents the optimal combination of user pilot and data transmit power based on the particle swarm optimization algorithm.

[0090] Step (4): Based on the determined pilot and data transmission power, control the user terminal to transmit pilot and data signals to achieve the optimization goal.

[0091] The CPU will calculate Φ opt Feedback is sent to the user via the backhaul link, and the user, based on Φ opt The optimal target is achieved by adjusting the transmit power allocated to the pilot and data signals.

[0092] The performance of the technical solution of the present invention will be further explained below with reference to simulation experiments.

[0093] Table 1

[0094]

[0095]

[0096] Unless otherwise specified, the simulation parameters selected in this invention are shown in Table 1.

[0097] Figure 2 The convergence plot of the total user rate under the particle swarm optimization scheme is given, where the horizontal axis represents the number of iterations and the vertical axis represents the total user rate. Figure 2 As shown, for decellularized massive MIMO systems with L=40, K=30 and L=40, K=20, the total user rate gradually converges as the number of iterations increases. Furthermore, the number of users K affects the dimensionality of the optimization problem; the more users there are, the higher the complexity of the optimization problem, and therefore the slower the convergence.

[0098] Figure 3 A graph showing the relationship between the total user rate and the number of access points (APs) under the optimized scheme proposed in this invention is presented, where the horizontal axis represents the number of APs and the vertical axis represents the total user rate. For comparison with the power allocation method designed in this invention, the total user rate under the equal power allocation scheme is also... Figure 3 The equal power allocation scheme refers to user k transmitting pilot signals and data signals at maximum power, with both having equal power, i.e., ρ. k =q k =0.5P u .like Figure 3 As shown, compared with the equal power allocation scheme, the power allocation scheme proposed in this invention can significantly improve the total user rate. Specifically, when L = 60 and N = 4, the power allocation method proposed in this invention increases the total user rate by approximately 30.3% under the equal power allocation scheme. Furthermore, it can be observed that as N increases, the total user rate under both the equal power allocation scheme and the power allocation method designed in this invention is significantly improved.

[0099] Although this invention only considers the optimization of pilot and data transmission power in decellularized massive MIMO systems using non-ideal hardware and superimposed pilot transmission schemes, it also provides a reference for other optimization problems in the same field. It can be used as a basis for expansion and extension, and applied to technical solutions of other algorithms in the same field, showing a very broad application prospect.

[0100] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A power allocation method for a decellularized massive MIMO system based on particle swarm optimization, characterized in that: Includes the following steps: Step 1: In a decellularized massive MIMO system employing non-ideal hardware and a superimposed pilot transmission scheme, the lower bound expression for the user transmission rate is obtained using the Use-and-then-Forget (UatF) technique. ; Step 2: Using user pilot and data transmit power as independent variables, and maximizing the total user rate as the objective function, combine this with the expression for the lower bound of the user transmission rate. Establish an optimization problem model; Step 3: Under the condition of pilot and data transmission power coupling, the optimal solution of the optimization problem model is obtained by using the particle swarm optimization algorithm, and then the optimal pilot and data transmission power are obtained; Step 4: Based on the determined optimal pilot and data transmission power, control the user terminal to transmit pilot and data signals to achieve the optimization goal of maximizing the total user rate; The lower bound expression for the user transmission rate The calculation formula is as follows: ； in, ； ； ； ； In the above formula, express The variance of any element Represents channel coefficients LMMSE estimate, Indicates channel bandwidth. N This indicates the number of antennas equipped on the AP. express The variance of any element Represents channel coefficients LMMSE estimate, and This represents a vector consisting of the pilot and data transmit power of all users, with superscript […]. This represents the matrix transpose operator; , Represented as the first The AP and the first , Channel coefficients between users; , , For the total number of users, The total number of APs. They represent the first Pilot transmit power and data transmit power for each user , They represent the first Pilot transmit power and data transmit power for each user and This indicates the hardware quality of the transmitter and receiver. For different Other users, , Indicates the first The AP and the first Large-scale fading coefficients between individual users Indicates the power of Gaussian white noise; Indicates the pilot length; in, ; in, Indicates the relationship with the first The set of all users who use the same pilot signal; The optimization problem model is calculated using the following formula: ； ； ； ； in, Indicates the total user rate. Indicates the user's maximum transmit power. , , These represent the first, second, and third constraints, respectively. Step 3 includes the following steps: Step 3-1: Initialize the particle swarm and establish a system containing... The particle swarm of the nth particles, the nth The position information of each particle is recorded as , indicating the first The next iteration consists of the pilot and data transmission power of all users. dimensional matrix, the first The particle in the first The velocity matrix at the next iteration is denoted as Initialize the number of iterations Set the maximum number of iterations to ; Step 3-2: Calculate the value of the first iteration in the 0th iteration. The individual optimal solution and the global optimal solution of each particle are set. ,in, It represents a composition consisting of the number 1. 3D column vector; setting ,in, and These are the lower and upper limits of particle velocity, respectively. Represents a randomly generated number between 0 and 1; Substituting into the optimization problem model, we get ;No. The optimal solution for each particle is , The global optimal solution at the 0th iteration is , ; Step 3-3: Calculate the first... During the nth iteration The process of finding the optimal solution for each particle and the global optimal solution includes the following steps: Step 3-3-1: Update particle velocity. And on Define it so that ,in express All elements in the set are greater than or equal to The corresponding element in the middle, and As a learning factor, Indicates inertia weight; Step 3-3-2: Update particle positions. And on Define it so that ,in, and These represent the lower and upper limits of the particle's position, respectively; for If a certain element undergoes adaptive mutation, that is, if ,in Describing the adaptive mutation probability, then ,in, , , This represents the floor function operator; Step 3-3-3: Judgment Does the constraint condition of the optimization problem model meet? If not, If satisfied, Substituting into the optimization problem model, we get ; Step 3-3-4: Update the current optimal solution and the global optimal solution: If ,make ;like ,make , ; Step 3-3-5: Judgment Check if the iteration has converged or reached the maximum number of iterations. If yes, end the iteration and proceed to step 3-4; if no, set... Repeat steps S3-3-1 to S3-3-5; Steps 3-4: Output the best individual , This refers to the optimal combination of user pilot and data transmission power based on the particle swarm optimization algorithm.

2. The power allocation method for a decellularized massive MIMO system based on particle swarm optimization algorithm according to claim 1, characterized in that: The calculation formula is as follows: ； in, Indicates the first Superimposed signals received by each AP Indicates the first Pilot signals for each user.

3. The power allocation method for a decellularized massive MIMO system based on particle swarm optimization algorithm according to claim 2, characterized in that: The calculation formula is as follows: ； Among them, superscript This represents the conjugate transpose operator. Indicates the first Data signals from individual users and They represent the first k Hardware damage when a user transmits pilot signals, the first k Hardware damage during user data signal transmission and the first l Hardware damage when an AP receives superimposed signals. This is a Gaussian white noise matrix.

4. The power allocation method for a decellularized massive MIMO system based on particle swarm optimization as described in claim 1, characterized in that: Step 4 includes the following steps: The CPU calculates the... Feedback is sent back to the user via the backhaul link, and the user then... The transmission power allocated to the pilot signal and data signal is adjusted to achieve the optimization goal.

5. The power allocation method for a decellularized massive MIMO system based on particle swarm optimization algorithm according to claim 1, characterized in that: , and For the maximum and minimum weight coefficients, and This represents the control factor.

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