An adjustable power distribution method suitable for 6G non-orthogonal mobile communication

Abstract

The application provides an adjustable power distribution method suitable for 6G non-orthogonal mobile communication, which comprises the following steps: setting a 6G non-orthogonal mobile communication environment and constraint conditions; according to the set environment and constraint conditions, establishing an optimization problem of maximum spectrum efficiency of a user group and an optimization problem of maximum energy efficiency of the user group and solving the optimization problems to obtain a maximum value of the spectrum efficiency and a maximum value of the energy efficiency; according to the maximum value of the spectrum efficiency and the maximum value of the energy efficiency, establishing an optimization problem of maximum comprehensive efficiency fairness of the user group and solving the optimization problem to obtain total transmission power and a power distribution coefficient of a terminal; and according to the total transmission power and the power distribution coefficient of the terminal, calculating power distributed to each terminal, a spectrum efficiency value, an energy efficiency value and a fairness measure value, and completing power distribution. The application has the advantages of clear structure, strong adaptability and autonomous execution, better meets the demand of 6G network construction and operation and maintenance and academic research, and has high practical value and popularization.

Description

Technical Field

[0001] This invention relates to a power allocation method, and more particularly to an adjustable power allocation method suitable for 6G non-orthogonal mobile communication. Background Technology

[0002] 6G networks, catering to the explosive growth in demand for wireless access and mobile data consumption, can support the expansion of new business opportunities and become a key tool for addressing socioeconomic digital inequality challenges by efficiently connecting unconnected areas. Wireless non-orthogonal multiple access (NOMA) is a novel multiple access technology that can be used in the physical layer of 6G mobile communications. It serves a greater number of terminals through non-orthogonal resource allocation and mitigates inter-terminal interference through interference cancellation.

[0003] Radio resource efficiency (RREE) in 6G networks is a key performance indicator of modern mobile communications, reflecting the overall system operation and the quality of service (QoS) level available to individual terminals. Specifically, RREE includes two aspects: spectrum efficiency and energy efficiency. In 6G non-orthogonal mobile communication, multiple terminals form a user group to share the same physical resource block. The spectrum efficiency is calculated by comparing the sum of data transmission rates obtained by each terminal within the user group to the system bandwidth; the energy efficiency is calculated by comparing the sum of data transmission rates obtained by each terminal within the user group to the total power consumption. Existing research and practice show that optimizing spectrum efficiency and optimizing energy efficiency are not always consistent and may even conflict.

[0004] Fairness in service access for terminals in 6G networks is a problem that needs to be addressed. Considering that non-orthogonal multiple access (NOMA) provides services to different terminals within the same physical resource block, maintaining fairness among terminals within the same user group is particularly important, and this is measured by calculating fairness metrics. Existing research and practice show that when resource efficiency is the sole optimization objective, terminals with better wireless channel conditions will receive a higher proportion of resources, significantly compromising fairness among terminals within the same user group.

[0005] Whether in the construction and operation of 6G networks or in its academic research, power dividers are needed to distribute the total wireless transmission power among various terminals. In particular, power dividers that can balance resource efficiency and terminal fairness and adjust the proportion of performance indicators such as resource efficiency and fairness in the optimization process are highly favored by the development of 6G non-orthogonal mobile communication. However, there is currently a lack of clear and easy-to-use power dividers of this type and corresponding process design. Summary of the Invention

[0006] Purpose of the invention: The technical problem to be solved by the present invention is to provide an adjustable power allocation method suitable for 6G non-orthogonal mobile communication, which addresses the shortcomings of the prior art.

[0007] To address the aforementioned technical problems, this invention discloses an adjustable power allocation method suitable for 6G non-orthogonal mobile communication, comprising the following steps:

[0008] Step 1, in a 6G non-orthogonal mobile communication environment, by Each terminal forms a user group. When allocating power, the calculation... achievable data rate per terminal The total transmission power and the achievable data rate for each terminal are used as constraints.

[0009] Step 2: Based on the environment and constraints set in Step 1, establish and solve the optimization problem of maximizing the spectral efficiency of the user group to obtain the maximum spectral efficiency. ;

[0010] Step 3: Based on the environment and constraints set in Step 1, establish and solve the optimization problem of maximizing energy efficiency for the user group to obtain the maximum energy efficiency. ;

[0011] Step 4, based on the maximum spectral efficiency and the maximum energy efficiency Establish and solve the optimization problem of maximizing the overall efficiency and fairness of user groups to obtain the total transmission power. and Power allocation coefficient of each terminal ;

[0012] Step 5, based on the total transmission power Power distribution coefficient Calculate the power allocated to each terminal. The power allocation is completed by measuring the spectral efficiency, energy efficiency, and fairness measures.

[0013] Beneficial effects:

[0014] 1. The power allocation method proposed in this invention has relatively independent parts and simple logic.

[0015] 2. The power allocation method of the present invention requires only 6 sets of inputs, including: a set of channel gain, a set of minimum data rate, the variance of Gaussian white noise, the channel bandwidth of the entire system, the maximum transmission power, and the circuit power consumption. There are only 3 adjustable weighting coefficients, all of which are general variables. In particular, there are no restrictions on the specific type of wireless channel and the order of channel gain corresponding to the input, which makes the present invention highly adaptable.

[0016] 3. This invention can meet the requirements of balancing resource efficiency and terminal fairness in 6G non-orthogonal mobile communication networks. After the initial adjustment of the weighting coefficients, the power allocator automatically completes the entire calculation process without human interruption / intervention, thus achieving high autonomous execution efficiency. Attached Figure Description

[0017] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0018] Figure 1 This is a schematic diagram of the overall architecture of the present invention.

[0019] Figure 2 This is a schematic diagram of the execution flow of the present invention. Detailed Implementation

[0020] This invention provides an adjustable power allocation method suitable for 6G non-orthogonal mobile communication that balances resource efficiency and terminal fairness. It is applied to allocating transmission power among multiple terminals belonging to the same user group in a 6G non-orthogonal access mobile communication network. This ensures that power allocation, after manually setting and adjusting weighting coefficients, can optimize both resource efficiency and terminal fairness, supporting network construction and maintenance as well as academic theoretical research. Figure 2 As shown, the execution process of this power allocation method includes the following steps:

[0021] Step 1: Based on the input of the power divider, establish a... The optimization problem of maximizing the spectral efficiency of a user group consisting of 10 terminals can be solved under the following two constraints to obtain the maximum spectral efficiency. :

[0022] Constraint (1) The total transmission power does not exceed the maximum transmission power. .

[0023] Constraint (2): The achievable data rate of each terminal must be greater than the minimum data rate required by that terminal. ( ).

[0024] The maximum value of the spectral efficiency is obtained by solving the problem. It includes the following steps:

[0025] 1-1. (Regarding) Channel gain corresponding to each terminal Arranged in ascending order, that is .

[0026] 1-2. Substitute the values ​​and simplify. achievable data rate per terminal The calculation formula is as follows:

[0027] (1) For the first ( There are ) terminals:

[0028]

[0029] (2) For the first ( There are ) terminals:

[0030]

[0031] Among them, the variance of Gaussian white noise The channel bandwidth of the entire system The value is known, and the total transmission power is... Power distribution coefficient As an optimization parameter, its value is unknown.

[0032] 1-3. A generalized optimization problem is presented to maximize spectral efficiency, namely...

[0033]

[0034] in, , The minimum data rate required by each terminal Maximum transmission power The value is known.

[0035] 1-4. For the optimization problem in steps 1-3, the classic sequential convex approximation SCA method is used (reference: Chen Baolin. Optimization Theory and Algorithms [M]. Tsinghua University Press, 2005). Based on a step-by-step iterative process, the optimal solution is obtained. , Numerical value.

[0036] 1-5. Based on the results of steps 1-4, calculate the corresponding... The numerical value, representing the maximum value of spectral efficiency. .

[0037] Step 2: Based on the input of the power divider, establish a... The optimization problem of maximizing energy efficiency for a user group consisting of 10 terminals can be solved under the following two constraints to obtain the maximum energy efficiency. The constraints are as follows:

[0038] (1) The total transmission power does not exceed the maximum transmission power. ;

[0039] (2) The achievable data rate of each terminal must be greater than the minimum data rate required by that terminal. ( )

[0040] The maximum energy efficiency can be obtained by solving the problem. ,as follows:

[0041] 2-1. (Regarding) Channel gain corresponding to each terminal Arranged in ascending order, that is .

[0042] 2-2. Substitute the values ​​and simplify. achievable data rate per terminal The calculation formula is as follows:

[0043] (1) For the first ( There are ) terminals:

[0044]

[0045] (2) For the first ( There are ) terminals:

[0046]

[0047] The variance of Gaussian white noise The channel bandwidth of the entire system The value is known, and the total transmission power is... Power distribution coefficient As an optimization parameter, its value is unknown.

[0048] 2-3. The problem presented is an optimization problem to maximize energy efficiency, namely...

[0049]

[0050] in, , The minimum data rate required by each terminal Maximum transmission power Circuit power consumption The value is known.

[0051] 2-4. Rewrite the optimization problem in step 2-3 into an equivalent optimization problem, that is...

[0052]

[0053] in It is a parameter that is introduced to adjust the weights.

[0054] 2-5. For the optimization problem in step 2-4, an iterative process is used to solve it. Define a factor based on the total transmission power. Power distribution coefficient Functions as independent variables At the beginning of the iteration process, set the maximum number of iterations, the maximum tolerance, and... The value is improved in each iteration. The value continues until it converges. This iterative process can be described as shown in Table 1:

[0055] Table 1 Iteration Process Table

[0056] 1: while based on the first The result of the next iteration Maximum tolerance or Maximum number of iterations (do) 2: 1. Given We use Lagrange multiplication to solve for the power distribution in the form of the expression in step 2-4; 3: 2. Settings and take ; / / Indicates the first During the next iteration Value / / Indicates based on the first Calculation of the result of the next iteration The obtained value 4: end while

[0057] When the above iterative process ends, the final result is obtained. , Numerical value.

[0058] 2-6. The final result obtained from step 2-5 , Numerical value, calculate the corresponding The numerical value, representing the maximum energy efficiency. .

[0059] Step 3: Based on the input of the power divider and the manually set adjustment weight coefficient... Establish a foundation about The optimization problem of maximizing overall efficiency and fairness for a user group composed of multiple terminals is given by the objective function expression as follows:

[0060]

[0061] in:

[0062] It is the formula for calculating energy efficiency, that is

[0063]

[0064] It is the formula for calculating spectral efficiency, i.e.

[0065]

[0066] It is a fair measure calculation formula, that is

[0067]

[0068] This refers to the achievable data rate of each terminal. It is the circuit power consumption. yes Total transmission power of each terminal It is the channel bandwidth of the entire system;

[0069] This is the value obtained in step 1. This is the value obtained in step 2. , , , The constraints of this optimization problem include:

[0070] (1) The total transmission power does not exceed the maximum transmission power. ;

[0071] (2) The achievable data rate of each terminal must be greater than the minimum data rate required by that terminal. ( The total transmission power suitable for this user group is obtained after solving the problem. ,this The power allocation coefficient obtained by each terminal ,as follows:

[0072] 3-1. (Regarding) Channel gain corresponding to each terminal Arranged in ascending order, that is .

[0073] 3-2. Substitute the values ​​and simplify. achievable data rate per terminal The calculation formula is as follows:

[0074] (1) For the first ( There are ) terminals

[0075]

[0076] (2) For the first ( There are ) terminals

[0077]

[0078] The variance of Gaussian white noise The channel bandwidth of the entire system The value is known, and the total transmission power is... Power distribution coefficient As an optimization parameter, its value is unknown.

[0079] 3-3. Substitute the values ​​and organize the data for measurement. Fairness measures of fairness among individual terminals The calculation formula is as follows:

[0080]

[0081] 3-4. Establish a framework for... The optimization problem of maximizing overall efficiency and fairness by forming a user group with multiple terminals is as follows:

[0082]

[0083] in, , The minimum data rate required by each terminal Maximum transmission power Circuit power consumption The value is known. It is the maximum energy efficiency obtained in step 2. It is the maximum spectral efficiency obtained in step 1, with the weighting coefficient adjusted. These are values ​​that are input externally to the power divider and set manually. It is worth noting that the optimization problem established in steps 3-4 is a complex non-convex optimization problem and cannot be solved directly.

[0084] 3-5. Rewrite the optimization problem in step 3-4 into an equivalent optimization problem, that is...

[0085] ,

[0086] in, It is a newly introduced slack variable used to characterize the process in steps 3-4. If this has some physical meaning, then a new constraint is added. It can be further rewritten as an equivalent group of expressions. ;

[0087] It is a newly introduced slack variable used to characterize the process in steps 3-4. If this has some physical meaning, then a new constraint is added. It can be further rewritten as an equivalent group of expressions. ;

[0088] It is a newly introduced slack variable used to characterize the process in steps 3-4. If this has some physical meaning, then a new constraint is added. It can be further rewritten as an equivalent group of expressions. .

[0089] expression group :

[0090] expression group This refers to the constraints in steps 3-4. Equivalently rewritten in second-order cone form;

[0091] expression group : ,

[0092] expression group :

[0093] ,

[0094] expression group This refers to the new constraints added in steps 3-5. The constraints are obtained by rearranging the expression, using a linear function approximated by a first-order Taylor series as the lower bound, and equivalently rewriting it into a second-order cone form; in the formula... These are all slack variables that, in a purely mathematical sense, assist in solving the optimization problem. Because an iterative process is used to solve this optimization problem, some slack variables are marked with a superscript label. Indicates the first In the next iteration, the slack variable takes an approximate value (i.e., the lower bound of the corresponding inequality).

[0095] expression group :

[0096] ,

[0097] expression group This refers to the new constraints added in steps 3-5. The constraints are obtained by rearranging the expression, using a linear function approximated by a first-order Taylor series as the lower bound, and equivalently rewriting it into a second-order cone form; in the formula... These are all slack variables that, in a purely mathematical sense, assist in solving the optimization problem. Because an iterative process is used to solve this optimization problem, some slack variables are marked with a superscript label. Indicates the first In the next iteration, the slack variable takes an approximate value (i.e., the lower bound of the corresponding inequality).

[0098] expression group :

[0099] ,

[0100] expression group This refers to the new constraints added in steps 3-5. The constraints are obtained by rearranging the expression, using a linear function approximated by a first-order Taylor series as the lower bound, and equivalently rewriting it into a second-order cone form; in the formula... These are all slack variables that, in a purely mathematical sense, assist in solving the optimization problem. Because an iterative process is used to solve this optimization problem, some slack variables are marked with a superscript label. Indicates the first In the next iteration, the slack variable takes an approximate value (i.e., the lower bound of the corresponding inequality).

[0101] In the above formula Let Euclidean norm be the norm of a vector. Including all optimization variables, i.e. .

[0102] For the equivalent optimization problem rewritten above, initialize... The solution is then obtained using an iterative process, and in each iteration, the solution is obtained based on the classic sequential convex approximation SCA method. The value is iterated until a preset accuracy is reached, at which point the iteration stops, thus obtaining the total transmission power suitable for this user group. ,this The power allocation coefficient obtained by each terminal .

[0103] Step 4: Based on the total transmission power obtained in Step 3 Power distribution coefficient Calculate the power allocated to each terminal. The spectral efficiency value, energy efficiency value, and fairness measure value, along with the power divider output corresponding to the input terminal order, are as follows:

[0104] 4-1. The total transmission power obtained in step 3 Power distribution coefficient Based on this, the power allocated to each terminal is calculated. Spectral efficiency value, energy efficiency value, and fairness measure value.

[0105] 4-2. Compare the power divider input with the input after sorting in step 3-1. The order of the terminals is rearranged according to the order in which the power divider inputs them. When the corresponding power divider is input... The serial number of each terminal, rearranged And output power dividers with spectral efficiency, energy efficiency, and fairness metrics.

[0106] Example 1:

[0107] This invention provides an adjustable power allocation method suitable for 6G non-orthogonal mobile communication, such as... Figure 1 As shown, this invention is applied to allocate transmission power among multiple terminals belonging to the same user group in a 6G non-orthogonal access mobile communication network. It ensures that power allocation, after manually setting and adjusting weighting coefficients, can balance resource efficiency and terminal fairness, supporting network construction and maintenance as well as academic theoretical research. In this embodiment, taking two terminals forming a user group and accessing a 6G non-orthogonal mobile communication network as an example, the method steps of this invention are specifically explained. Specifically, the number of terminals... Minimum data rate required by the terminal (Unit: Mbps), Channel gain of the terminal (Unit: dB) Maximum transmission power dBm, circuit power consumption W, the channel bandwidth of the entire system MHz, variance of Gaussian white noise mW, adjusting the weighting coefficient , , This embodiment includes the following steps:

[0108] Step 1: Based on the input of the power divider, establish a... The optimization problem of maximizing the spectral efficiency of a user group consisting of 10 terminals can be solved under the following two constraints to obtain the maximum spectral efficiency. :

[0109] Constraint (1) The total transmission power does not exceed the maximum transmission power. dBm.

[0110] Constraint (2): The achievable data rate of each terminal must be greater than the minimum data rate required by that terminal. ,Right now (Unit: Mbps)

[0111] The maximum value of the spectral efficiency is obtained by solving the problem. It includes the following steps:

[0112] 1-1. (Regarding) Channel gain corresponding to each terminal Arranged in ascending order, that is , , .

[0113] 1-2. Substitute the values ​​and organize this... achievable data rate per terminal The calculation formula is as follows:

[0114] (1) For the first There are: 1 terminal

[0115] (Unit: Mbps)

[0116] (2) For the first There are: [Number] users

[0117] (Unit: Mbps)

[0118] Total transmission power Power distribution coefficient As an optimization parameter, its value is unknown.

[0119] 1-3. A generalized optimization problem is presented to maximize spectral efficiency, namely...

[0120]

[0121] in, , The minimum data rate required by each terminal Maximum transmission power The numerical value is known. For Example 1, the optimization problem, after substituting the numerical value, can be written as follows:

[0122]

[0123]

[0124]

[0125] 1-4. For the optimization problem in steps 1-3, the classic sequential convex approximation SCA method is used. Based on a step-by-step iterative process, the optimal solution is obtained. , Numerical values, i.e., in Example 1 mW , .

[0126] 1-5. Based on the results of steps 1-4, calculate the corresponding... The numerical value, representing the maximum value of spectral efficiency. That is, in Example 1 (Unit: bps / Hz).

[0127] Step 2: Based on the input of the power divider, establish a... The optimization problem of maximizing energy efficiency for a user group consisting of 10 terminals can be solved under the following two constraints to obtain the maximum energy efficiency. :

[0128] Constraint (1) The total transmission power does not exceed the maximum transmission power. dBm.

[0129] Constraint (2): The achievable data rate of each terminal must be greater than the minimum data rate required by that terminal. ,Right now (Unit: Mbps)

[0130] The maximum energy efficiency can be obtained by solving the problem. It includes the following steps:

[0131] 2-1. (Regarding) Channel gain corresponding to each terminal Arranged in ascending order, that is , , .

[0132] 2-2. Substitute the values ​​and organize this... achievable data rate per terminal The calculation formula is as follows:

[0133] (1) For the first There are: 1 terminal

[0134] (Unit: Mbps)

[0135] (2) For the first There are: [Number] users

[0136] (Unit: Mbps)

[0137] Total transmission power Power distribution coefficient As an optimization parameter, its value is unknown.

[0138] 2-3. The problem presented is an optimization problem to maximize energy efficiency, namely...

[0139]

[0140] in, , The minimum data rate required by each terminal Maximum transmission power Circuit power consumption The numerical value is known. For Example 1, the optimization problem, after substituting the numerical value, can be written as follows:

[0141]

[0142]

[0143]

[0144] 2-4. Rewrite the optimization problem in step 2-3 into an equivalent optimization problem, that is...

[0145]

[0146] in This is a parameter introduced to adjust the weights. For Example 1, the equivalent optimization problem, after substituting the values, can be written as follows:

[0147]

[0148]

[0149]

[0150] 2-5. For the optimization problem in step 2-4, an iterative process is used to solve it. Define a factor based on the total transmission power. Power distribution coefficient Functions as independent variables At the beginning of the iteration process, set the maximum number of iterations, the maximum tolerance, and... The value is improved in each iteration. The value continues until it converges. This iterative process can be described as shown in Table 2:

[0151] Table 2 Iteration process table for this embodiment

[0152] 1: while based on the first The result of the next iteration Maximum tolerance or Maximum number of iterations (do) 2: 1. Given We use Lagrange multiplication to solve for the power distribution in the form of the expression in step 2-4; 3: 2. Settings and take ; / / Indicates the first During the next iteration Value / / Indicates based on the first Calculation of the result of the next iteration The obtained value 4: end while

[0153] When the above iterative process ends, the final result is obtained. , Numerical values, i.e., in Example 1 mW , .

[0154] 2-6. The final result obtained from step 2-5 , Numerical value, calculate the corresponding The numerical value, representing the maximum energy efficiency. That is, in Example 1 (Unit: Mbps / mW).

[0155] Step 3: Based on the input of the power divider and the manually set adjustment weight coefficient... , , Establish a foundation about The optimization problem of maximizing overall efficiency and fairness for a user group composed of multiple terminals is given by the objective function expression as follows:

[0156]

[0157] in:

[0158] It is the formula for calculating energy efficiency, that is

[0159] It is the formula for calculating spectral efficiency, i.e.

[0160] It is a fair measure calculation formula, that is

[0161] This refers to the achievable data rate of each terminal. It is the circuit power consumption. yes Total transmission power of each terminal It is the channel bandwidth of the entire system;

[0162] This is the value obtained in step 1. This is the value obtained in step 2. , , , The constraints of this optimization problem include:

[0163] (1) The total transmission power does not exceed the maximum transmission power. dBm.

[0164] (2) The achievable data rate of each terminal must be greater than the minimum data rate required by that terminal. ,Right now (Unit: Mbps)

[0165] The total transmission power suitable for this user group is obtained after solving. ,this The power allocation coefficient obtained by each terminal It includes the following steps:

[0166] 3-1. (Regarding) Channel gain corresponding to each terminal Arranged in ascending order, that is , , .

[0167] 3-2. Substitute the values ​​and organize this... achievable data rate per terminal The calculation formula is as follows:

[0168] (1) For the first There are: 1 terminal

[0169] (Unit: Mbps)

[0170] (2) For the first There are: [Number] users

[0171] (Unit: Mbps)

[0172] Total transmission power Power distribution coefficient As an optimization parameter, its value is unknown.

[0173] 3-3. Substitute the values ​​and organize the data for measurement. Fairness measures of fairness among individual terminals The calculation formula is as follows:

[0174]

[0175] 3-4. Establish a framework for... The optimization problem of maximizing overall efficiency and fairness by forming a user group with multiple terminals is as follows:

[0176]

[0177] in, , The minimum data rate required by each terminal Maximum transmission power Circuit power consumption The value is known. It is the maximum energy efficiency obtained in step 2. It is the maximum spectral efficiency obtained in step 1, with the weighting coefficient adjusted. These are values ​​input externally from the power divider and manually set. It is worth noting that the optimization problem established in steps 3-4 is a complex non-convex optimization problem and cannot be solved directly. For Example 1, the optimization problem, after substituting the values, can be written as follows:

[0178]

[0179]

[0180]

[0181] 3-5. Rewrite the optimization problem in step 3-4 into an equivalent optimization problem, that is...

[0182] ,

[0183] in, It is a newly introduced slack variable used to characterize the process in steps 3-4. If this has some physical meaning, then a new constraint is added. It can be further rewritten as an equivalent group of expressions. ;

[0184] It is a newly introduced slack variable used to characterize the process in steps 3-4. If this has some physical meaning, then a new constraint is added. It can be further rewritten as an equivalent group of expressions. ;

[0185] It is a newly introduced slack variable used to characterize the process in steps 3-4. If this has some physical meaning, then a new constraint is added. It can be further rewritten as an equivalent group of expressions. .

[0186] expression group :

[0187]

[0188] expression group This refers to the constraints in steps 3-4. Equivalently rewritten in second-order cone form; for Example 1, the expression group After substituting the values, it can be written as follows:

[0189] .

[0190] expression group : For Example 1, the expression group After substituting the values, it can be written as follows:

[0191] .

[0192] expression group :

[0193]

[0194] Das group This refers to the new constraints added in steps 3-5. The constraints are obtained by rearranging the expression, using a linear function approximated by a first-order Taylor series as the lower bound, and equivalently rewriting it into a second-order cone form; in the formula... These are all slack variables that, in a purely mathematical sense, assist in solving the optimization problem. Because an iterative process is used to solve this optimization problem, some slack variables are marked with a superscript label. Indicates the first In the next iteration, the slack variable takes an approximate value (i.e., the lower bound of the corresponding inequality). For Example 1, the expression group After substituting the values, it can be written as follows:

[0195]

[0196] expression group :

[0197] ,

[0198] expression group This refers to the new constraints added in steps 3-5. The constraints are obtained by rearranging the expression, using a linear function approximated by a first-order Taylor series as the lower bound, and equivalently rewriting it into a second-order cone form; in the formula... These are all slack variables that, in a purely mathematical sense, assist in solving the optimization problem. Because an iterative process is used to solve this optimization problem, some slack variables are marked with a superscript label. Indicates the first In the next iteration, the slack variable takes an approximate value (i.e., the lower bound of the corresponding inequality). For Example 1, the expression group After substituting the values, it can be written as follows:

[0199]

[0200] expression group :

[0201] ,

[0202] expression group This refers to the new constraints added in steps 3-5. The constraints are obtained by rearranging the expression, using a linear function approximated by a first-order Taylor series as the lower bound, and equivalently rewriting it into a second-order cone form; in the formula... These are all slack variables that, in a purely mathematical sense, assist in solving the optimization problem. Because an iterative process is used to solve this optimization problem, some slack variables are marked with a superscript label. Indicates the first In the next iteration, the slack variable takes an approximate value (i.e., the lower bound of the corresponding inequality). For Example 1, the expression group After substituting the values, it can be written as follows:

[0203]

[0204] In the above formula Let Euclidean norm be the norm of a vector. Including all optimization variables, i.e. .

[0205] For the equivalent optimization problem rewritten above, initialize... The solution is then obtained using an iterative process, and in each iteration, the solution is obtained based on the classic sequential convex approximation SCA method. The value is iterated until a preset accuracy is reached, at which point the iteration stops, thus obtaining the total transmission power suitable for this user group. ,this The power allocation coefficient obtained by each terminal That is, in Example 1 mW , .

[0206] Step 4: Based on the total transmission power obtained in Step 3 Power distribution coefficient Calculate the power allocated to each terminal. The power divider output, corresponding to the order of input terminals, includes the following steps: spectral efficiency value, energy efficiency value, fairness measure value.

[0207] 4-1. The total transmission power obtained in step 3 Power distribution coefficient Based on this, the power allocated to each terminal is calculated. Spectral efficiency value, energy efficiency value, and fairness measure value, i.e., in Example 1 mW , At that time, allocated to this The power of each terminal is (Unit: mW), spectral efficiency is 15.428 bps / Hz, energy efficiency is 0.065 Mbps / mW, and fairness measure is 1.000.

[0208] 4-2. Compare the power divider input with the input after sorting in step 3-1. The order of the terminals is rearranged according to the order in which the power divider inputs them. When the corresponding power divider is input... The serial number of each terminal, rearranged The power divider outputs spectral efficiency, energy efficiency, and fairness metrics. Specifically, in Example 1, the final output of the power divider includes: 185.842mW of power allocated to terminal 1 and 0.934mW of power allocated to terminal 2; spectral efficiency of 15.428bps / Hz; energy efficiency of 0.065Mbps / mW; and fairness metrics of 1.000.

[0209] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the invention's content regarding an adjustable power allocation method suitable for 6G non-orthogonal mobile communication, as well as some or all of the steps in various embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0210] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MCU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0211] This invention provides an idea and method for adjustable power allocation applicable to 6G non-orthogonal mobile communication. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. An adjustable power allocation method suitable for 6G non-orthogonal mobile communication, characterized in that, Includes the following steps: Step 1, in a 6G non-orthogonal mobile communication environment, by Each terminal forms a user group. When allocating power, the calculation... achievable data rate per terminal The total transmission power and the achievable data rate for each terminal are used as constraints. Step 2: Based on the environment and constraints set in Step 1, establish and solve the optimization problem of maximizing the spectral efficiency of the user group to obtain the maximum spectral efficiency. ; Step 3: Based on the environment and constraints set in Step 1, establish and solve the optimization problem of maximizing energy efficiency for the user group to obtain the maximum energy efficiency. ; Step 4, based on the maximum spectral efficiency and the maximum energy efficiency Establish and solve the optimization problem of maximizing the overall efficiency and fairness of user groups to obtain the total transmission power. and Power allocation coefficient of each terminal ; Step 5, based on the total transmission power Power distribution coefficient Calculate the power allocated to each terminal. The power allocation is completed by measuring the spectral efficiency, energy efficiency, and fairness measures.

2. The adjustable power allocation method for 6G non-orthogonal mobile communication according to claim 1, characterized in that, The 6G non-orthogonal mobile communication environment mentioned in step 1 includes: The channel gain of each terminal is represented as a set. , Indicates the first Channel gain of each terminal; The minimum data rate required by each terminal is expressed as: ,in, Indicates the first The minimum data rate required by each terminal; the variance of Gaussian white noise is The channel bandwidth of the entire communication environment is Maximum transmission power is The circuit power consumption is .

3. The adjustable power allocation method for 6G non-orthogonal mobile communication according to claim 2, characterized in that, The constraint conditions mentioned in step 1, which include the total transmission power and the achievable data rate of each terminal, specifically include: Constraint 1, Total Transmission Power Not exceeding the maximum transmission power ; Constraint 2: The achievable data rate of each terminal is greater than the minimum data rate required by that terminal. ; It is expressed as follows: ； 。 4. The adjustable power allocation method for 6G non-orthogonal mobile communication according to claim 3, characterized in that, The calculation described in step 1 achievable data rate per terminal ,include: right Channel gain corresponding to each terminal Arranged in ascending order, as follows: ； Calculate in sequence achievable data rate per terminal The calculation formula is as follows: when At that time, the first achievable data rate per terminal The calculation method is as follows: ； when At that time, the first achievable data rate per terminal The calculation method is as follows: ； in, For the set of power allocation coefficients, and The first The terminal and the first Power allocation coefficient for each terminal.

5. The adjustable power allocation method for 6G non-orthogonal mobile communication according to claim 4, characterized in that, Step 2 describes the optimization problem of maximizing spectral efficiency in establishing user groups, which includes: Step 2-1, establish the optimization problem of maximizing the spectral efficiency of the user group, as follows: ； Step 2-2: For the optimization problem in Step 2-1, the Sequential Convex Approximation (SCA) method is used. Based on a step-by-step iterative process, the total transmission power that satisfies the optimal solution is obtained. and power allocation coefficient set ; Step 2-3: Perform power allocation based on the solution results from Step 2-2, and determine the achievable data rate of the terminal. and channel bandwidth Calculate the maximum value of spectral efficiency , means as follows: 。 6. The adjustable power allocation method for 6G non-orthogonal mobile communication according to claim 5, characterized in that, Step 3, which describes the optimization problem of maximizing energy efficiency for establishing user groups, includes: Step 3-1, establish the optimization problem of maximizing energy efficiency for user groups, as follows: ； Step 3-2: Rewrite the optimization problem in Step 3-1 into an equivalent optimization problem, as follows: ； in, These are parameters used to adjust the weights; Step 3-3: Solve the optimization problem in step 3-2 using an iterative process, as follows: Step 3-3-1, define the total transmission power and power allocation coefficient set Function with independent variable , means as follows: ； Step 3-3-2 involves performing an iterative process to solve the optimization problem. The specific steps are as follows: Step 3-3-2-1: Initialize and set the maximum number of iterations, maximum tolerance, and initial parameters. The value; Step 3-3-2-2, based on the current parameters Solve the optimization problem in step 3-2 using the Lagrange multiplication method; Step 3-3-2-3: Set the number of iterations and take ; in, Indicates the first During the next iteration Values, Indicates based on the first Calculation of the result of the next iteration The obtained value; Step 3-3-2-4, repeat steps 3-3-2-2 and 3-3-2-3 in sequence, until based on the first... The result of the next iteration The value is less than or equal to the maximum tolerance or the number of iterations. If the number of iterations exceeds the maximum number of iterations, the iteration process ends, and the final total transmission power is obtained. and power allocation coefficient set ; Step 3-4: Based on the final total transmission power obtained in step 3-3 and power allocation coefficient set Calculate the maximum energy efficiency. , means as follows: 。 7. The adjustable power allocation method for 6G non-orthogonal mobile communication according to claim 6, characterized in that, Step 4 involves establishing and solving an optimization problem that maximizes overall efficiency and fairness for user groups, including: Step 4-1 establishes an optimization problem for maximizing overall efficiency and fairness for user groups, expressed as follows: ； in, It is the formula for calculating energy efficiency. This is the formula for calculating spectral efficiency. It is a fair measure calculation formula. , and The weighting coefficients are set manually to adjust the overall weighting. Step 4-2: Rewrite the optimization problem in Step 4-1 as an equivalent optimization problem, as follows: ； ； in, , and These are slack variables, used to characterize the three terms of the optimization problem in step 4-1, and expressions are added accordingly. , and As a new constraint; expression group To constrain conditions The equivalent rewrite in second-order cone form is as follows: ； expression group Original constraints ; expression group Specifically, it is expressed as follows: ； expression group Specifically, it is expressed as follows: ； expression group Specifically, it is expressed as follows: ； in, All are slack variables. Indicates the first In the next iteration, the approximate value of the slack variable corresponds to the lower bound of the inequality. Describes the Euclidean norm of a vector. This represents the set of all variables to be optimized, i.e.: ； Step 4-3: For the equivalent optimization problem in Step 4-2, initialize the set of variables to be optimized as follows: The solution is obtained using an iterative process, with the sequential convex approximation SCA method used in each iteration. The value is iterated until a preset accuracy is reached, at which point the iteration stops, and the optimal total transmission power for the user group is obtained. and power allocation coefficient set .

8. The adjustable power allocation method for 6G non-orthogonal mobile communication according to claim 7, characterized in that, The spectral efficiency value mentioned in step 5 is calculated using the spectral efficiency formula, and is expressed as follows: 。 9. The adjustable power allocation method for 6G non-orthogonal mobile communication according to claim 8, characterized in that, The energy efficiency value mentioned in step 5 is calculated using the energy efficiency formula, and is expressed as follows: 。 10. The adjustable power allocation method for 6G non-orthogonal mobile communication according to claim 9, characterized in that, The fairness measure value mentioned in step 5 is calculated by the fairness measure calculation formula, and is expressed as follows: 。

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