CN-116390232-B - Method for distributing power of de-honeycomb large-scale MIMO system based on particle swarm optimization

Abstract

The invention discloses a power distribution method of a de-cellular large-scale MIMO system based on a particle swarm algorithm, which comprises the steps of firstly, obtaining a user transmission rate lower bound expression by utilizing UatF technology in the de-cellular large-scale MIMO system adopting non-ideal hardware and a superimposed pilot transmission scheme, then, establishing an optimization problem by taking user pilot frequency and data transmission power as independent variables and maximizing a user total rate as an objective function, secondly, obtaining an optimal solution of the optimization problem by utilizing a particle swarm algorithm under the condition of multivariable coupling, and finally, controlling a user terminal to transmit pilot signals and data signals according to the determined pilot frequency and data transmission power so as to realize an optimization target. The invention optimizes the pilot frequency and data transmitting power of the user under the condition that the pilot frequency and the data transmitting power are mutually coupled, and compared with the equal power distribution scheme, the power distribution method designed by the invention can greatly improve the total rate of the user.

Inventors

• ZHANG YAO
• CHEN SAILI
• LIU HUIJUN
• YANG KAIQIAN
• YANG LONGXIANG

Assignees

• 浙江师范大学

Dates

Publication Date

20260512

Application Date

20230303

Claims (5)

1. 1. A power distribution method of a de-honeycomb large-scale MIMO system based on a particle swarm algorithm is characterized by comprising the following steps: Step 1, obtaining a user transmission rate lower bound expression by using Use-and-then-force (UatF) technology in a de-cellular massive MIMO system adopting non-ideal hardware and an overlapped pilot transmission scheme ; Step 2, combining the user pilot frequency and data transmitting power as independent variables and the maximized user total rate as objective function with the user transmission rate lower bound expression Establishing an optimization problem model; step 3, under the coupling condition of pilot frequency and data transmitting power, utilizing a particle swarm algorithm to obtain an optimal solution of the optimization problem model, and further obtaining optimal pilot frequency and data transmitting power; step 4, according to the determined optimal pilot frequency and data transmitting power, controlling the user terminal to transmit pilot frequency signals and data signals, and realizing the optimization target of maximizing the total user rate; the user transmission rate lower bound expression The calculation formula is as follows: ; Wherein, the ; ; ; ; In the above-mentioned method, the step of, Representation of The variance of any one element is used to determine, Representing channel coefficients Is used for the LMMSE estimation of (c), Indicating the channel bandwidth, N indicates the number of antennas equipped by the AP, Representation of The variance of any one element is used to determine, Representing channel coefficients Is used for the LMMSE estimation of (c), And Vectors representing pilot and data transmit power composition for all users, superscript Representing a matrix transpose operator; 、 Respectively denoted as the first AP and th 、 Channel coefficients between individual users; , , As a total number of users, As a total number of APs, Respectively represent the first Pilot transmit power and data transmit power for individual users, 、 Respectively represent the first Pilot transmit power and data transmit power for individual users, And Representing the hardware quality of the transmitter and receiver, Is different from Is used by the other user of the (c) device, , Represent the first AP and the first Large scale fading coefficients between individual users, Representing gaussian white noise power; Representing pilot length; Wherein, the ; Wherein, the Representation and the first A set of all users whose individual users use the same pilot; The problem model is optimized, and the calculation formula is as follows: ; ; ; ; Wherein, the Indicating the overall rate of the user, Indicating the maximum transmit power of the user, 、 、 Respectively representing a first constraint condition, a second constraint condition and a third constraint condition; the step 3 comprises the following steps: step 3-1, initializing particle swarm, and establishing a particle swarm comprising A particle group of individual particles, the first The positional information of the individual particles is noted as Indicated in the first Consisting of pilot and data transmit powers for all users at a single iteration Dimension matrix, the first The particles are at the first The velocity matrix at the time of iteration is recorded as Initializing the iteration number Setting the maximum iteration number as ; Step 3-2, calculate the 0 th iteration time Setting self optimal solution and global optimal solution of each particle , wherein, Representing a composition consisting of the number 1 Vector of dimension and column , wherein, And A lower limit and an upper limit of the particle velocity respectively, Representing a randomly generated number between 0 and 1 Substituting into an optimization problem model to obtain First, a third step Self-optimal solution of individual particles as , The globally optimal solution at iteration 0 is , ; Step 3-3 calculating the first The first iteration The self optimal solution and the global optimal solution of the individual particles comprise the following steps: step 3-3-1, updating the particle speed, And is opposite to Defining so that Wherein Representation of All elements in the formula are more than or equal to The corresponding element of the (c) is (are), And In order for the learning factor to be a function of, Representing inertial weights; Step 3-3-2, updating the particle position, And is opposite to Defining so that , wherein, And Respectively represent the lower limit and the upper limit of the particle position Is adaptively mutated, i.e. if Wherein Representing the adaptive mutation probability , wherein, , , Representing a rounding-up operator; step 3-3-3 judgment Whether the constraint condition of the optimization problem model is satisfied, if not, If it meets the requirement, then Substituting into an optimization problem model to obtain ; Step 3-3-4, updating the self optimal solution and the global optimal solution if Order-making If (1) Order-making , ; Step 3-3-5 judgment Whether the maximum iteration number is converged or reached, if so, ending the iteration, and entering step 3-4, and if not, making Repeating steps S3-3-1 to S3-3-5; Step 3-4. Outputting the best individual , The optimal user pilot frequency and data transmitting power combination based on the particle swarm algorithm is obtained.
2. 2. The method for power allocation of a de-cellular massive MIMO system based on a particle swarm algorithm according to claim 1, wherein: The calculation formula of (2) is as follows: ; Wherein, the Represent the first The superimposed signal received by the individual APs, Represent the first Pilot signals of individual users.
3. 3. The method for power allocation of a de-cellular massive MIMO system based on a particle swarm algorithm according to claim 2, wherein: The calculation formula of (2) is as follows: ; Wherein, superscript Represents the conjugate transpose operator and, Represent the first The data signal of the individual user is transmitted, And Respectively represent hardware damage when the kth user transmits a pilot signal, hardware damage when the kth user transmits a data signal, and hardware damage when the ith AP receives a superimposed signal, Is a gaussian white noise matrix.
4. 4. The method for power distribution of a de-cellular massive MIMO system according to claim 1, wherein step 4 comprises the step of the CPU performing the calculation Feedback to the user via the backhaul link, the user based on And adjusting the transmitting power allocated to the pilot signal and the data signal to realize the optimization target.
5. 5. The method for power allocation of a de-cellular massive MIMO system based on a particle swarm algorithm according to claim 1, wherein: , And For the maximum and minimum weight coefficients, And Representing the control factor.

Description

Method for distributing power of de-honeycomb large-scale MIMO system based on particle swarm optimization Technical Field The invention relates to a power distribution method of a large-scale MIMO system for removing cells based on a particle swarm algorithm, belonging to the technical field of dynamic power distribution in the field of wireless communication. Background The de-cellular large-scale Multiple-Input Multiple-Output (MIMO) technology can provide a strong macro-diversity gain and uniform quality of service, and the combination of non-ideal hardware can significantly improve the system energy efficiency, and is thus considered as a potential key technology in the future 6G (Sixth Generation). As the number of users increases, pilot pollution becomes more and more severe due to the restriction of pilot resources, which limits further improvement of the transmission rate of the de-cellular massive MIMO system. To suppress pilot pollution, a superimposed pilot transmission scheme may be employed, i.e., the number of orthogonal pilots may be increased by extending the pilot length. However, after the superimposed pilot transmission scheme is adopted, pilot and data transmission power coefficients in the user transmission rate expression are mutually coupled, and power optimization is difficult to perform for the expression. Although the de-cellular massive MIMO technology can achieve a higher and uniform transmission rate, from the perspective of high-rate communication, a corresponding power optimization algorithm needs to be designed in order to further increase the transmission rate. Considering that the user rate expression has the problem of multivariable coupling, the convex optimization tool is difficult to function, and the invention designs a power distribution method based on a particle swarm algorithm. Disclosure of Invention The invention aims to overcome the defects in the prior art, and provides a power distribution method for a de-cellular large-scale MIMO system based on a particle swarm algorithm, which can enable a user to reasonably adjust the transmitting power distributed to pilot frequency and data signals and greatly improve the transmission rate of the user. The technical scheme adopted by the invention is as follows: The invention discloses a power distribution method of a de-honeycomb large-scale MIMO system based on a particle swarm algorithm, which comprises the following steps: Step 1, in a cellular large-scale MIMO system adopting non-ideal hardware and a superimposed pilot transmission scheme, a user transmission rate lower bound expression R k (ρ, q) is obtained by using Use-and-then-force (UatF) technology. And 2, establishing an optimization problem model by taking the pilot frequency of the user and the data transmission power as independent variables and taking the maximum user total rate as an objective function and combining with a user transmission rate lower bound expression R k (rho, q). And 3, under the condition of coupling pilot frequency and data transmitting power, obtaining an optimal solution of the optimization problem model by using a particle swarm algorithm, and further obtaining the optimal pilot frequency and the data transmitting power. And 4, controlling the user terminal to transmit pilot signals and data signals according to the determined optimal pilot frequency and data transmission power, and realizing the optimization target of maximizing the total user rate. Further, the user transmission rate lower bound expression R k (ρ, q) has the following calculation formula: Wherein, the In the above formula, gamma lk representsThe variance of any one element is used to determine,LMMSE estimation (linear minimum mean square error estimation) representing channel coefficient g lk, B representing channel bandwidth, N representing the number of antennas equipped by the AP, y lk′ representingThe variance of any one element is used to determine,LMMSE estimates representing channel coefficients g lk′, ρ= [ ρ 1,...,ρK]T and q= [ q 1,...,qK]T ] represent vectors of pilot and data transmit power of all users, and the superscript T represents the matrix transpose operator. g lk、glk′ is denoted as the channel coefficient between the first AP and the k, k' th user, respectively. l=1, 2,..l, k=1, 2,..k, K being the total number of users, L being the total number of APs, ρ k,qk being the pilot transmit power, the data transmit power, respectively, of the kth user, ρ k′、qk′ being the pilot transmit power, the data transmit power, respectively, of the kth user, κ t and κ r being the hardware quality of the transmitter and receiver, K ' being other users than K, K ' being { e {1,..k, K }, β lk/βlk′ being the large scale fading coefficient between the ith AP and the kth/K ' user, σ 2 being the gaussian white noise power. τ c represents the pilot length. Wherein, the Where P k represents the set of all users that use the same pilot as the kth user. Further, the meth