CN114501503B - Energy efficiency maximization emission beam optimization method of general sense integrated system - Google Patents
Energy efficiency maximization emission beam optimization method of general sense integrated system Download PDFInfo
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- H04B7/00—Radio transmission systems, i.e. using radiation field
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- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
- H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
- H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
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Abstract
The invention discloses an energy efficiency maximization transmitting wave beam optimizing method of a general sense integrated system, which comprises the following steps: constructing an initial optimization problem, wherein the initial optimization problem takes the energy efficiency of maximized multi-user communication as an optimization target, and takes the base station transmitting power limitation, the communication minimum signal-to-interference-and-noise ratio requirement and the perception minimum beam intensity requirement as constraints; introducing auxiliary variables to equivalently convert the initial optimization problem into a new expression form; according to the new problem obtained by conversion, the rank constraint is removed by utilizing semi-definite relaxation, and a sequential convex approximation method is adopted for iterative solution; and constructing a group of optimal solutions meeting rank constraint based on the solutions obtained after algorithm iteration convergence. The invention researches the problem of optimizing the design of the energy efficiency maximized transmitting beam of the general sense integrated system, and can realize high-energy efficiency transmission while meeting the perception constraint.
Description
Technical Field
The invention belongs to the technical field of emission beam optimization in a general sense integrated system, and particularly relates to an energy efficiency maximization emission beam optimization method of the general sense integrated system.
Background
The 6G is used for constructing a neural center of the physical world and the digital world, everything perception and everything interconnection are taken as a development prospect of the 6G, a future communication system is required to be added with perception, and a communication perception integrated system is generated. The sense of general integration means that two functions of communication and sensing are fused, and the sensing function is synchronously executed in the information transmission process. The sense-of-general integrated system has the following two significant advantages. Firstly, the communication and sensing integrated system is different from the existing single communication or sensing system, allows the communication and sensing functions to share hardware equipment, a signal processing algorithm and time-frequency resources of the system, and can greatly save the system overhead. Secondly, the communication sense integration realizes the assistance and enhancement of the performance of the communication system by actively recognizing and analyzing the characteristics of the channel while transmitting information so as to perceive the surrounding environment and utilizing the perception result. For example, the base station can realize real-time measurement and even prediction of the direction angle prescribed by the mobile user through active sensing and assistance of a certain signal processing algorithm, so that the directivity precision of the transmitting beam is improved, a high-quality communication link is established, and the reliability of communication is improved. In summary, the integration of the sense of openness has become a key technology of a new generation communication system.
The optimization of the transmission beam design of the sense-of-general integrated system is an important research topic. The introduction of the sensing function brings new types of demand constraints which are not considered in various communication systems, and meanwhile, in order to improve the freedom degree of the sensing signal, a transmitting end usually introduces a special sensing signal in addition to a communication symbol which needs to be transmitted. The fact increases the difficulty in solving the problem of optimizing the design of the transmitting beam of the general sense integrated system.
With the continuous development of communication systems, the energy consumption of the communication systems is greatly increased due to the multiple increase of the number of base stations, the number of mobile terminals and the number of service flows, and how to reduce the energy consumption of the systems, so that the realization of wireless transmission with high energy efficiency has become an important concern in the design of mobile communication systems.
Disclosure of Invention
The invention aims to provide an energy efficiency maximization emission beam optimization method of a general sense integrated system, which meets the sensing requirement and realizes high-energy efficiency information transmission.
In order to solve the technical problems, the specific technical scheme of the invention is as follows:
an energy efficiency maximization transmitting wave beam optimizing method of a general sense integrated system comprises the following steps:
step S1, constructing an initial optimization problem, wherein in the initial optimization problem, the energy efficiency of the maximized general sense integrated system is taken as an optimization target, and the emission beam of the base station is optimized by taking the constraint of the limited emission power of the base station, the minimum signal-to-interference-and-noise ratio requirement of communication and the minimum beam intensity requirement of perception as constraintsWherein v is k Is the beamforming vector for the kth user of the base station, V 0 Is a special sense signal s transmitted by the base station 0 K is the number of users accessed by the system;
s2, introducing auxiliary variablesEquivalent transformation of the initial optimization problem into a new expression form, wherein (.) H Representing a conjugate transpose of the matrix;
step S3, discarding the matrix by using semi-definite relaxation according to the problem obtained by the conversion in step S2Constraint of rank, and adopting a sequential convex approximation method to carry out iterative solution;
s4, marking the optimal solution obtained after iteration convergence asBased on->Constructing a set of optimal solutionsThe rank constraint of semi-definite relaxation elimination in the step S3 is met;
s5, obtaining an optimal perception signal covariance matrixFor->Performing Cholesky decomposition to obtain optimal transmit beam +.>
Further, in the step S1, the initial optimization problem is constructed as follows:
the optimization targets are as follows: maximization of
The constraint conditions are as follows:
wherein log is 2 (-) represents a logarithmic function with a base of 2, |represents a modulo value, ||represents the two norms of the vector, tr (-) represents the trace of the matrix,x is a semi-positive definite matrix, h k Is the base station to kth user channel vector,is the noise power of the kth user, τ k Is the lowest SINR required by the kth user, θ m Is the angle of the mth perception direction Γ m Is the minimum beam intensity required for the mth perceived direction, η e (0, 1)]Is the working efficiency of the base station amplifier, P c Is the circuit power of the whole system, P max Is the maximum transmit power of the base station, M is the number of radar perceived directions,is the base station antenna array at theta m Direction vector of direction, (. Cndot.) T The matrix transpose is represented, N is the number of transmit antennas included in the base station antenna array, e is the natural base number, and j is the imaginary unit.
Further, in the step S2, an auxiliary variable is introduced to equivalently transform the initial optimization problem into the following problem:
the optimization targets are as follows: maximizing t
The constraint conditions are as follows:
t≥0,u≥0,
rank(V k )＝1,k＝1,…,K
wherein,for the introduced auxiliary variables rank (·) represents the matrix rank.
Further, according to the problem obtained by the conversion in the step S2, in the step S3, constraint rank (V k ) =1, k=1, …, K, and iteratively solved using a sequential convex approximation method; in the first iteration, the solved optimization problem is expressed as follows:
the optimization targets are as follows: maximizing t
The constraint conditions are as follows:
t≥0,u≥0,
wherein, u (l-1) ，t (l-1) and->The optimal solution obtained in the first-1 iteration is shown.
Further, in each iteration of step S3, the problem is a convex optimization problem, and the problem is solved by applying an interior point method.
Further, in the step S4, after the iteration converges, the obtained optimal solution is recorded asBased onConstructing a set of optimal solutions->
Wherein the method comprises the steps ofConstructed solution->Satisfying the constraint of semi-definite relaxation elimination, i.e. +.>Is the optimal solution of the problem described in step S2.
The energy efficiency maximization transmitting beam optimization method of the general sense integrated system has the following advantages:
1. the sequential convex approximation algorithm adopted by the invention is a general algorithm for indirectly solving the non-convex optimization problem, the original problem is monotonically approximated by iteratively solving a series of approximate convex optimization problems, and the local optimal solution of the original non-convex problem is obtained within acceptable time complexity.
2. The invention provides an energy efficiency maximization transmitting wave beam design method of a general sense integrated system. Step S1 is used for constructing an energy efficiency maximization emission beam design problem, step S2-step S5 are used for solving the problem, and the obtained optimal solution can realize high-energy efficiency transmission on the premise of guaranteeing a perception function.
Drawings
Fig. 1 is a flow chart of an energy efficiency maximizing transmit beam optimization method of a sense-of-general integrated system according to a first embodiment of the present invention;
fig. 2 is a diagram of simulation experiment results of an energy efficiency maximization transmit beam optimization method of a sense-of-general integrated system according to a first embodiment of the present invention.
Detailed Description
In order to better understand the purpose, structure and function of the present invention, a method for optimizing an energy efficiency maximizing transmit beam of a sense-of-general integrated system according to the present invention is described in further detail below with reference to the accompanying drawings.
First embodiment:
the embodiment provides an energy efficiency maximization emission beam optimization method of a general sense integrated system, which is applicable to general sense integrated scenes, and a base station sends signals expressed asBy optimally designing the transmitting wave beam of the base station end>Under the constraint of meeting the requirements of limited transmitting power, minimum SINR requirement of communication and minimum beam intensity requirement of perception, the energy efficiency of the general sense integrated system is maximized.
Specifically, in this embodiment, the flow of the energy efficiency maximizing beam optimizing method is shown in fig. 1, and specifically includes the following steps:
the initial optimization problem constructed in step S1 can be expressed as:
the optimization targets are as follows: maximization of
The constraint conditions are as follows:
wherein log is 2 (. Cndot.) represents a logarithmic function based on 2, |cndot.) represents a modulo value, |cndot.) represents the two norms of the vector, (. Cndot.) H Representing the conjugate transpose of the matrix, tr (·) representing the trace of the matrix,x is a semi-positive definite matrix, v k Is the beamforming vector for the kth user of the base station, V 0 Is a special sense signal s transmitted by the base station 0 Covariance matrix of h k Is the channel vector from the base station to the kth user, for example>Is the noise power of the kth user, τ k Is the lowest SINR required by the kth user, θ m Is the angle of the mth perception direction Γ m Is the minimum beam intensity required for the mth perceived direction, η e (0, 1)]Is the working efficiency of the base station amplifier, P c Is the circuit power of the whole system, P max Is the maximum transmitting power of the base station, K is the number of users accessed by the system, M is the number of radar sensing directions,/and/or>Is the base station antenna array at theta m Direction vector of direction, (. Cndot.) T The matrix transpose is represented, N is the number of transmit antennas included in the base station antenna array, e is the natural base number, and j is the imaginary unit.
The specific optimization solving steps of the problem are as follows:
step S2, by introducing the auxiliary variables t, u andthe initial optimization problem is equivalently converted into the following problems:
the optimization targets are as follows: maximizing t
The constraint conditions are as follows:
t≥0,u≥0,
rank(V k )＝1,k＝1,…,K
wherein rank (·) represents the matrix rank.
In step S3, the matrix is truncated using the semi-definite relaxationAnd adopting a sequential convex approximation method to carry out iterative solution. The optimization problem solved in the first iteration is described as:
the optimization targets are as follows: maximizing t
The constraint conditions are as follows:
t≥0,u≥0,
wherein, u (l-1) ，t (l-1) and->The optimal solution obtained in the first-1 iteration is shown.
Step S4, obtaining the related variable after iteration convergesIs marked as->Based onConstructing a set of optimal solutions->Satisfy rank constraint->
In this step, based onThe following calculation +.>
Wherein the method comprises the steps of
Constructed solutionsSatisfying the constraint of semi-definite relaxation elimination, i.e. +.> Is the optimal solution of the problem described in step S2.
Wherein the method comprises the steps ofIs a solution calculated by the interior point method, and generally does not satisfy the constraint that the rank of the semi-definite relaxation discard be one, that is,
thus, this step is based on an additional operation byConstructing a set of new solutions meeting rank constraintsAt this time, a-> The optimal objective function values obtained by the optimization problem described in step S2 are the same for both sets of solutions, the difference being +.>Satisfying rank 1 constraint->Not satisfied.
S5, obtaining an optimal perception signal covariance matrixFor->Performing Cholesky decomposition to obtain optimal transmit beam +.>
In order to verify the effect of the energy efficiency maximization transmitting beam optimization method provided by the embodiment, a simulation experiment is performed, and parameters related to the simulation experiment are shown in the following table:
TABLE 1 simulation experiment parameter Table
| Parameters (parameters) | Value taking |
| Number of base station transmitting antennas | 8 |
| Number of access users | 4 |
| Sensing direction number | 20 |
| Maximum transmit power of base station | 30dBm |
| System circuit power | 25dBm |
| Base station amplifier operating efficiency | 0.35 |
| Receiving end additive Gaussian white noise variance sigma 2 | -80dBm |
| Communication minimum SINR requirements | 5dB |
| Perceived minimum beam intensity requirements | 25dBm |
Fig. 2 is a comparison result of a simulation experiment, and the simulation result shows that: compared with the frequency spectrum efficiency maximization optimization scheme, the emission beam optimization method provided by the invention can obviously improve the energy efficiency of the system.
The present invention is not described in detail in the present application, and is well known to those skilled in the art.
It will be understood that the invention has been described in terms of several embodiments, and that various changes and equivalents may be made to these features and embodiments by those skilled in the art without departing from the spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (5)
1. The energy efficiency maximization transmitting beam optimizing method of the general sense integrated system is characterized by comprising the following steps of:
step S1, constructing an initial optimization problem, wherein in the initial optimization problem, the energy efficiency of the maximized general sense integrated system is taken as an optimization target, and the emission beam of the base station is optimized by taking the constraint of the limited emission power of the base station, the minimum signal-to-interference-and-noise ratio requirement of communication and the minimum beam intensity requirement of perception as constraintsWherein v is k Is the beamforming vector for the kth user of the base station, V 0 Is a special sense signal s transmitted by the base station 0 K is the number of users accessed by the system;
s2, introducing auxiliary variablesEquivalent transformation of the initial optimization problem into a new expression form, wherein (.) H Representing a conjugate transpose of the matrix;
step S3, discarding the matrix by using semi-definite relaxation according to the problem obtained by the conversion in step S2Constraint of rank, and adopting a sequential convex approximation method to carry out iterative solution;
s4, marking the optimal solution obtained after iteration convergence asBased on->Constructing a set of optimal solutionsThe rank constraint of semi-definite relaxation elimination in the step S3 is met;
s5, obtaining an optimal perception signal covariance matrixFor->Performing Cholesky decomposition to obtain optimal transmit beam +.>
In the step S1, the initial optimization problem is constructed as follows:
the optimization targets are as follows: maximization of
The constraint conditions are as follows:
wherein log is 2 (-) represents a logarithmic function with a base of 2, |·| represents a modulo value, |·|| represents the two norms of the vector, tr (-) represents the trace of the matrix,x is a semi-positive definite matrix, h k Is the channel vector from the base station to the kth user, for example>Is the noise power of the kth user, τ k Is the lowest SINR required by the kth user, θ m Is the angle of the mth perception direction Γ m Is the minimum beam intensity required for the mth perceived direction, η e (0, 1)]Is the working efficiency of the base station amplifier, P c Is the circuit power of the whole system, P max Is the maximum transmit power of the base station, M is the number of radar perceived directions, +.> Is the base station antenna array at theta m Direction vector of direction, (. Cndot.) T The matrix transpose is represented, N is the number of transmit antennas included in the base station antenna array, e is the natural base number, and j is the imaginary unit.
2. The method for optimizing an energy efficiency maximized transmission beam of a ventilation integrated system according to claim 1, wherein in said step S2, an auxiliary variable is introduced to equivalently transform said initial optimization problem into the following problem:
the optimization targets are as follows: maximizing t
The constraint conditions are as follows:
t≥0,u≥0,
rank(V k )＝1,k＝1,…,K
wherein,for the introduced auxiliary variables rank (·) represents the matrix rank.
3. The method for optimizing the transmission beam for maximizing the energy efficiency of the integrated system for the through-sense according to claim 2, characterized in that, according to the problem obtained by the conversion in step S2, in said step S3, constraint rank (V k ) =1, k=1, …, K, and iteratively solved using a sequential convex approximation method; in the first iteration, the solved optimization problem is expressed as follows:
the optimization targets are as follows: maximizing t
The constraint conditions are as follows:
t≥0,u≥0,
wherein, u (l-1) ，t (l-1) and->The optimal solution obtained in the first-1 iteration is shown.
4. The method for optimizing an energy efficiency maximized transmit beam in a motion sensing integrated system according to claim 3, wherein in each iteration of step S3, the problem is a convex optimization problem, and an interior point method is applied to calculate and solve the problem.
5. The method for optimizing an energy efficiency maximizing transmit beam of a joint-sense integrated system according to claim 4, wherein in said step S4, when iteration converges, the obtained optimal solution is noted asBased on->Constructing a set of optimal solutions->
Wherein the method comprises the steps ofConstructed solution->Satisfying the constraint of semi-definite relaxation elimination, i.e. +.>Is the optimal solution of the problem described in step S2.
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| CN111132297A (en) * | 2019-12-19 | 2020-05-08 | 东南大学 | Beam forming optimization method and device for minimizing transmission power of ultra-dense network |
| CN113596860A (en) * | 2021-07-29 | 2021-11-02 | 东南大学 | Low-overhead reflected beam optimization method of intelligent reflector OFDM system |
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| CN113596860A (en) * | 2021-07-29 | 2021-11-02 | 东南大学 | Low-overhead reflected beam optimization method of intelligent reflector OFDM system |
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