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CN-120935594-B - Energy efficiency optimization method for active RIS (radio resource locator) assisted cell-free mMIMO system

CN120935594BCN 120935594 BCN120935594 BCN 120935594BCN-120935594-B

Abstract

The invention discloses an energy efficiency optimization method of an active RIS auxiliary cell-free mMIMO system, which comprises the steps of firstly establishing a downlink transmission data and system consumption total power model of the active RIS auxiliary cell-free mMIMO system, giving a downlink rate and energy efficiency expression, then designing a downlink transmission power distribution scheme of a communication node by formulating a multi-objective function optimization problem including maximizing downlink rate and minimizing total system consumption power, solving the non-convex optimization problem, converting the non-convex optimization problem into a generalized geometric planning problem by using a weighted sum method and an arithmetic mean inequality, solving in an iterative mode, and finally taking the obtained optimal solution as a final power distribution scheme and calculating system energy efficiency.

Inventors

  • YANG LONGXIANG
  • FANG HAO

Assignees

  • 南京邮电大学

Dates

Publication Date
20260512
Application Date
20251009

Claims (9)

  1. 1. An energy efficiency optimization method for an active RIS assisted cell-free mMIMO system, comprising the steps of: Step 1, an active RIS cell-free mMIMO system model is built, which specifically comprises a downlink data transmission model and a system consumption total power model; step 2, based on a system model, giving a downlink rate and energy efficiency expression; Step 3, the transmission power control coefficient of the communication node is used as an optimization variable, the communication node and the active RIS power budget are used as constraints, and a multi-objective function optimization problem is formulated by taking the downlink speed and maximization and the total consumption power of the system as an objective function; And 4, processing objective functions and polynomial constraints by using a weighted sum method and an arithmetic mean inequality, converting the original optimization problem into a generalized geometric programming problem and carrying out iterative solution to complete downlink power distribution, wherein the method specifically comprises the following steps of: representation of multiple objective function forms as single objective function forms using a weighted sum method by introducing weighting factors The problem of optimizing the single objective function is obtained, Introducing auxiliary variables And The single objective function optimization problem is converted into a generalized geometric programming problem by utilizing an arithmetic average inequality and a first-order taylor expansion inequality, Iterative solution of the geometric planning problem by utilizing MATLAB and CVX optimization tools to obtain an optimal downlink power distribution scheme The system energy efficiency is calculated.
  2. 2. The method for optimizing energy efficiency of active RIS assisted cell-free mMIMO system of claim 1, wherein in step 1, in the downlink data transmission model, the first Data received by individual users Represented as Wherein, the Indicating the number of communication nodes to be used, Indicating the number of users to be used, Representing the normalized signal-to-noise ratio for the downlink symbol transmission, Represent the first The communication node The transmission power control coefficients between the individual users, Represent the first The communication node A sequence of direct channels between individual users, Represent the first The communication node The sequence of beams between the individual users, Represent the first Data symbols of individual users, which satisfy , Representing active RIS and the first A sequence of channels between the individual users, A matrix of reflection coefficients representing the active RIS, Representing active RIS and the first A channel matrix between the individual communication nodes, Representing thermal noise generated by active RIS, each element of which obeys distribution , Represent the first Additive Gaussian noise received by individual users and subject to distribution , The expression is used for taking the absolute value operation, Represents the operation of conjugate transposition, Representing the desired operation of the solution, Representing a circularly symmetric complex gaussian distribution.
  3. 3. The method for optimizing energy efficiency of active RIS assisted cell mMIMO system of claim 1, wherein in step 1, the system consumes total power Is of the mathematical model of (a) Wherein, the Represent the first The power amplifier efficiency of the individual communication nodes, Representing the power amplifier efficiency of the active RIS, Represent the first The power consumed by the data transmitted by the individual communication nodes, Representing the power consumed by the active RIS amplified signal, The number of elements representing the active RIS, Representing the power consumed by the radio frequency circuitry of each communication node, Representing the power consumed by the control circuitry of each active RIS element, Representing the dc circuit power consumption of each active RIS element, Representing the circuit power consumption of each user.
  4. 4. An active RIS assisted cell-free mMIMO system energy efficiency optimization method as defined in claim 3, And The expression of (2) is Wherein, the Representing euclidean norms.
  5. 5. The method for optimizing energy efficiency of active RIS assisted cell-free mMIMO system of claim 4, wherein in step 2, the first step is Downlink rate of individual users The expression of (2) is Wherein, the Represent the first The communication node An aggregate channel sequence between individual users, Represent the first The communication node The transmission power control coefficients between the individual users, Represent the first The communication node The sequence of beams between the individual users, Represent the first The signal-to-interference-and-noise ratio of the individual users, The representation is equivalent to the system energy efficiency Is defined as the ratio of the downlink rate sum to the total power consumption of the system, expressed as , Wherein, the Represent the first The downlink rate of the individual user(s), Represent the first Signal-to-interference-and-noise ratio of individual users.
  6. 6. The method for optimizing energy efficiency of active RIS assisted cell-free mMIMO system of claim 5 wherein in step 3, the multi-objective function optimization problem P1 with respect to downstream rate and maximization and total power consumption minimization of the system is established as Wherein, the , , And Represents an intermediate variable which is referred to as, Representing a matrix of downlink transmission power control coefficients between all communication nodes and all users, Representing the maximum power budget the communication node uses for downlink data transmission, Representing the maximum power budget of the active RIS for amplifying the signal.
  7. 7. The method for optimizing energy efficiency of the active RIS assisted cell mMIMO system according to claim 5, wherein in step 4, the specific steps of solving the original optimization problem by using a weighted sum method and an arithmetic mean inequality are as follows: 4.1 introduction of weighting factors The original multiple objective function problem P1 is rewritten to a single objective function problem P2 by a weighted sum method, which is expressed as 4.2 Introduction of auxiliary variables And The problem P2 in step 4.1 is relaxed to Wherein, the Represent the first The data received by the individual user is transmitted, Represent the first The communication node The aggregate channel between the individual users, 4.3 For a given variable And Processing functions using arithmetic mean inequality And using a first order taylor expansion approximation The inequality is obtained as follows Wherein, the , , And Represents an intermediate variable; 4.4 control coefficient for given downlink power Based on the arithmetic mean inequality, the right part of the first constraint in the problem P3 in step 4.2 is Wherein, the , , Represents an intermediate variable; 4.5 based on the inequality in step 4.3 and step 4.4, the problem P3 in step 4.2 is rewritten as a generalized geometric programming problem as follows: 4.6 solve problem P4 in step 4.5 with MATLAB and CVX optimization tool and repeatedly execute steps 4.1-4.6 until termination condition is reached, obtaining the optimal solution ; 4.7 Optimal solution to be obtained And substituting the energy efficiency expression to calculate the energy efficiency of the system as a final downlink transmission power distribution scheme.
  8. 8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the program, implements a method of energy efficiency optimization for an active RIS assisted cell-free mMIMO system as claimed in any one of claims 1 to 7.
  9. 9. A computer readable storage medium having stored thereon computer instructions which when executed by a processor implement a method of energy efficiency optimization of an active RIS assisted cell-free mMIMO system as claimed in any one of claims 1 to 7.

Description

Energy efficiency optimization method for active RIS (radio resource locator) assisted cell-free mMIMO system Technical Field The invention belongs to the technical field of wireless communication, and particularly relates to an energy efficiency optimization method of an active RIS (radio resource locator) assisted cell-free mMIMO system. Background As a new network architecture, a cell-free large-scale multiple-input multiple-output (mMIMO) system does not divide a cell range with a large base station as a center, but randomly deploys a large number of small communication nodes around users, eliminating cell boundary effects, and providing uniform and uniform service quality. However, blocked by some obstacles such as large buildings and trees, the signal received by the user still experiences severe fading, causing degradation of service quality and even interruption of communication, and in order to solve this problem, a reconfigurable smart surface (reconfigurable intelligent surface, RIS) which is low-cost and flexible to deploy has been proposed and rapidly led to extensive research. Typically, RIS comprises a large number of reflective elements with only phase shifting circuitry, each of which can independently apply a phase to and reflect an incoming signal, effecting reshaping of the wireless propagation channel at the electromagnetic level. Because of no integrated complex radio frequency components, RIS has extremely low cost, so that we can flexibly deploy the RIS at a designated position to provide additional communication links, and a great deal of related researches also show that by deploying the RIS, the downlink rate of a cell-free mMIMO system is remarkably improved, the communication coverage is enlarged, and the fairness of users is further ensured. In contrast, the active RIS is to integrate an active load for each element additionally based on the RIS, so that the signal can be amplified while the signal is reflected, and the intensity of the reflected signal is enhanced. The transmission rate is increased, however, the system power consumption increases. In addition, the active RIS is limited by the amplified power budget, and conventional optimization methods based on the design without RIS power constraint problems cannot be applied, and the need for new optimization methods is urgent. Disclosure of Invention The invention aims to provide an energy efficiency optimization method for an active RIS cell-free mMIMO system, so as to solve the problem that the energy efficiency of the system is reduced due to the additional power consumption caused by using the active RIS. Under the constraint condition of considering the active RIS amplifying power, by designing a downlink transmission power distribution scheme, the signal transmission power consumption is saved, and the energy efficiency of the system is improved. In order to achieve the above purpose, the scheme of the invention is as follows, an energy efficiency optimization method of an active RIS assisted cell-free mMIMO system, which comprises the following steps, Step 1, an active RIS cell-free mMIMO system model is built, which specifically comprises a downlink data transmission model and a system consumption total power model; step 2, based on a system model, giving a downlink rate and energy efficiency expression; Step 3, the transmission power control coefficient of the communication node is used as an optimization variable, the communication node and the active RIS power budget are used as constraints, and a multi-objective function optimization problem is formulated by taking the downlink speed and maximization and the total consumption power of the system as an objective function; and 4, processing objective functions and polynomial constraints by using a weighted sum method and an arithmetic mean inequality, converting the original optimization problem into a generalized geometric programming problem, and carrying out iterative solution to complete downlink power distribution. Further, in the active RIS assisted cell-free mMIMO system, the aggregate channel between the communication node and the user includes two parts, i.e., a direct channel and a reflection channel, whose mathematical expression is that Wherein, the Represent the firstThe communication nodeThe aggregate channel between the individual users,Represent the firstThe communication nodeA sequence of direct channels between individual users,Representing active RIS and the firstA sequence of channels between the individual users,A matrix of reflection coefficients representing the active RIS,Representing active RIS and the firstA channel matrix between the communication nodes.Representing a conjugate transpose operation. Further, in the downlink data transmission model, a part of the signal transmitted from the communication node is transmitted through the direct connection channel, and a part of the signal is amplified by the RIS and reflected to the user,