CN114978255B - Statistical channel characteristic-assisted radar communication coexistence energy efficiency optimization method - Google Patents

Statistical channel characteristic-assisted radar communication coexistence energy efficiency optimization method Download PDF

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CN114978255B
CN114978255B CN202210534505.0A CN202210534505A CN114978255B CN 114978255 B CN114978255 B CN 114978255B CN 202210534505 A CN202210534505 A CN 202210534505A CN 114978255 B CN114978255 B CN 114978255B
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radar
energy efficiency
user
matrix
optimization problem
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CN114978255A (en
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尤力
叶育琦
黄珂琳
伍诗语
王闻今
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Southeast University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Computer Networks & Wireless Communication (AREA)
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Abstract

The invention discloses a radar communication coexistence energy efficiency optimization method assisted by statistical channel characteristics, which comprises the following steps: constructing a scene where a monitoring radar and a large-scale MIMO communication system coexist; acquiring statistical channel information of users in a large-scale MIMO communication system; constructing a corresponding energy efficiency expression of the large-scale MIMO communication system according to the statistical channel information and establishing a radar communication coexistence energy efficiency optimization problem model under the constraint of the radar receiving signal-to-interference ratio and the respective transmitting power of the radar communication system; and solving the optimization problem by using alternating optimization, deterministic equivalence, an MM algorithm and a Dinkelbach algorithm. The invention fully utilizes the beam domain statistics channel state information of the user based on the coexistence scene of the monitoring radar and the large-scale MIMO communication system, remarkably improves the energy efficiency performance of the system by reducing the mutual interference between the large-scale MIMO communication system and the monitoring radar, and reduces the complexity of solving the optimization problem and realizing the physical layer.

Description

Statistical channel characteristic-assisted radar communication coexistence energy efficiency optimization method
Technical Field
The invention relates to the technical field of wireless communication and radars, in particular to a statistical channel characteristic-assisted radar communication coexistence energy efficiency optimization method.
Background
Radars have evolved for decades since the birth of the last half of the 20 th century. Modern radar systems are deployed worldwide with a variety of applications including traffic control, geophysical monitoring, weather observation, and national defense and security monitoring. Below 10GHz, most of the spectrum resources are mainly allocated to the radar, but in the advanced state of the art, the radar and the wireless communication system have new coexistence options. At higher frequencies, such as millimeter wave bands, communication and radar platforms are also expected to achieve harmonic coexistence and even beneficial cooperation in upcoming 5G networks and beyond. However, as available frequency bands are allocated to the above wireless technologies, interference in radar frequency bands is increasing and concerns of critical radar action guarantees by government and military organizations are raised. For this reason, research work on communication and radar spectrum sharing problems is underway. Radar communication coexistence is one of two main research directions for communication and radar spectrum sharing problems.
The existing radar communication coexistence energy efficiency optimization research mostly adopts instantaneous channel state information. It is only feasible to use instantaneous channel information if the terminal is not moving or the moving speed is very slow, resulting in no or slow channel change. When the terminal moves rapidly, the channel from the base station to the terminal will change rapidly, resulting in difficulty in instantaneous channel estimation. Furthermore, in practical massive MIMO downlink, it is difficult to obtain instantaneous channel state information at the transmitter. Note that it is relatively easy to obtain statistical channel information that varies relatively slowly from the base station, such as by extrapolation of the covariance or long-term feedback. Therefore, the invention provides a statistical channel characteristic-assisted radar communication coexistence energy efficiency optimization method.
Disclosure of Invention
In view of the above, the present invention aims to provide a method for optimizing the co-existence energy efficiency of radar communication with the assistance of statistical channel characteristics, which can optimize the energy efficiency of the system and reduce the complexity of implementing the optimization process aiming at the co-existence scene of a monitoring radar and a large-scale MIMO communication system, so that the implementation is convenient.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the invention relates to a statistical channel characteristic assisted radar communication coexistence energy efficiency optimization method, which comprises the following steps:
step 1, constructing a scene where radar and a communication system coexist;
the scene of coexistence of radar and communication system includes a massive MIMO downlink communication transmission system and a monitoring radar operating in the same bandwidth; wherein, the large-scale MIMO downlink communication transmission system transmits signals to communicate with users, and the radar transmits signals to detect targetsThe method comprises the steps of carrying out a first treatment on the surface of the According to the large-scale MIMO downlink communication scene, the base station side configures M t Large-scale antenna array of root transmitting antenna, K users in scene, each user is configured with M k A root receive antenna;
step 2, obtaining user statistics channel state information in a communication system;
step 3, constructing a communication system energy efficiency expression according to the user statistical channel state information and establishing a radar communication coexistence energy efficiency optimization problem model;
and step 4, solving the energy efficiency optimization problem.
Further, in step 2, the statistical channel state information of the user is obtained through user feedback, direct estimation by the base station, or by means of uplink probe signals.
Further, the method for acquiring the user statistical channel state information in the communication system in step 2 is as follows:
each user sends an uplink detection signal to a base station, and the base station estimates beam domain statistical channel state information according to the received detection signal, wherein the statistical channel state information of the kth user is as follows:
in the formula (1), G k For the beam domain channel matrix of the kth user,indicating the desired operation, ++indicates the matrix's Hadamard product, ++>As a matrix G k Is a conjugate matrix of (a).
Further, the method for constructing the communication system energy efficiency expression and establishing the radar communication coexistence energy efficiency optimization problem model in the step 3 is as follows:
the signal transmitted by the base station to the kth user is denoted as x k The total signal transmitted by the base station isLet x be equal to k' for k k And x k′ Not mutually related, then the covariance matrix of the kth user signal is +.>The achievable traversal rate for user k is expressed as:
in the case of the formula (1),wherein->For user k interference plus noise covariance matrix, P r For radar transmit power, q is the sequence used to modulate the radar pulse, +.>For the variance of the radar echo amplitude received by the user,/->For the noise variance received by the user, M k G is the number of antennas at the user side k For the beam domain channel matrix, V is the matrix composed of the eigenvectors of the base station side transmission correlation matrix, log is the log operation, det represents the determinant of the matrix, ++>Is M k ×M k Vitamin identity matrix (.) H Is the conjugate transpose of the matrix.
The power consumption model is as follows:
in equation (2), tr (·) is the operation of matrix apodization, the scaling factor η is the inverse of the transmission amplifier efficiency,p is the total transmit power d For dynamic power loss of each antenna, P t For static circuit power consumption, M t The number of antennas at the base station side.
Energy efficiency is defined as the ratio of the sum of all user achievable traversal rates to the power consumption:
in formula (3), EE represents energy efficiency.
Therefore, the radar communication coexistence energy efficiency optimization problem model is:
in formula (4), P c,max For maximum transmit power of a communication system ρ v For minimum received signal-to-interference ratio, P, required in radar resolution unit v r,max For maximum radar transmit power, SDR v The received signal-to-interference ratio of the radar in the radar resolution unit v is expressed asWherein->For the variance of the amplitude of the radar target echo, +.>For the variance of the amplitude of radar clutter, +.>For the variance of the noise received by the radar,the radar is received with a variance of the amplitude of the communication signal.
Further, the solving the energy efficiency optimization problem in the step 4 includes the following steps:
step 4.1, decomposing the radar communication coexistence energy efficiency optimization problem into communication precoding Q by using alternating optimization k Optimization problem and radar transmission power P r Optimizing a problem, and setting an iteration number indication d=0;
step 4.2, solving the communication precoding Q k The optimization problem specifically comprises:
step 4.2.1, decomposing with eigenvalues, Q k Represented asCommunication precoding Q k The optimization problem is decomposed into Guan k Sum lambda k Is a sub-problem of (2); wherein ψ is k Representing the subspace, Λ, in which the transmitted signal is located k Representing power corresponding to each dimension/direction of the transmission signal subspace;
step 4.2.2, solving for Guan k Which comprises in particular:
guan is provided with k The optimal solution of the sub-problem of (2) is
Step 4.2.3, solving for Guan k Which comprises in particular:
guan is provided with k Is expressed as
In the formula (5) of the present invention,
Λ＝{Λ 1 ,…,Λ k }；
step 4.2.3.1, initializing covariance matrix Λ of the transmit signal (0) Setting an iteration number indication
Step 4.2.3.2, find the desired operationA deterministic equivalent value, comprising in particular:
step 4.2.3.2.1, four deterministic equivalent auxiliary variables are introduced and iteratively calculated, expressed as:
wherein Γ is k (X) and y k (X) is a diagonal matrix function of user k, X meansInstead of general function variables, the diagonal elements are expressed as:
[Γ k (X)] t,t ＝tr{diag{([Ω k ] t,: )}X}；
[Υ k (X)] m,m ＝tr{diag{([Ω k ] m,: ) T }X}；
in the iteration process, all four auxiliary variables tend to converge, and iteration is stopped when the variation value of the auxiliary variables is smaller than a given threshold value;
in step 4.2.3.2.2 the process steps are performed,deterministic equivalent value ∈ ->Expressed as:
step 4.2.3.3 according to MM algorithmFirst order Taylor expansion->Replace->The method is specifically expressed as follows:
in the formula (7), it representsIs->Can be expressed as
In the formula (8) of the present invention, refers to omega k′ T line and->
Step 4.2.3.4, and applying the Dinkelbach algorithm, the energy efficiency optimization problem is changed into the following form:
step 4.2.3.5, since the optimization problem has become a convex problem, the power distribution matrix can be solved using a conventional convex optimization tool;
step 4.2.3.6, the firstThe energy efficiency value obtained by the iteration and the +.>Comparing the results of the iterations if the difference between the results is less than a set threshold ε 1 Terminating the iteration and taking the power allocation matrix obtained in step 4.2.3.5 as the final solution; otherwise, the iteration times are->Adding 1, jumping back to the step 4.2.3.2, substituting the solution of the iteration, recalculating the deterministic equivalent value of the user rate and the first-order Taylor expansion term of the MM algorithm, and repeating the above stepsThe steps are as follows;
step 4.3, solving the radar transmission power P r The optimization problem specifically comprises:
according to energy efficiency with radar transmitting power P r Is strictly decreased by the increase in (c), and can be obtained:
step 4.4, comparing the energy efficiency value obtained by the (d+1) th iteration with the result of the (d) th iteration, if the difference between the two results is smaller than the set threshold epsilon 2 Terminating the iteration, and taking the power distribution matrix obtained in the step 4.2.3.6 and the radar transmitting power obtained in the step 4.3 as final solutions; otherwise, the iteration times d are added with 1, and the step 4.2 is skipped.
The beneficial effects of the invention are as follows:
1. in the invention, the base station and the user communicate in the beam domain, and can be matched with the spatial characteristics of a large-scale MIMO wireless channel, so that the energy efficiency improvement caused by using a large-scale antenna array is obtained;
2. in the invention, the energy efficiency under the condition of the radar communication joint design is obviously improved compared with that of the non-joint design;
3. according to the method, the energy efficiency of the radar communication coexistence scene is optimized by using the alternating optimization method, the deterministic equivalent method, the MM algorithm and the Dinkelbach algorithm, the energy efficiency of the system can be obviously improved, the complexity of solving the optimization problem and realizing the physical layer is reduced, and the operation speed is increased.
Drawings
Fig. 1 is a flow chart of a statistical channel characteristic-assisted radar communication coexistence energy efficiency optimization method provided in embodiment 1;
FIG. 2 is a schematic flow chart of an iterative algorithm based on the alternating optimization, deterministic equivalence, MM algorithm and Dinkelbach algorithm provided in example 1;
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present embodiment provides a statistical channel characteristic-assisted radar communication coexistence energy efficiency optimization method, which includes the following steps:
step 1, constructing a scene where radar and a communication system coexist;
specifically, in this embodiment, the step 1 specifically includes:
the scene of coexistence of the radar and the communication system comprises a large-scale MIMO downlink communication transmission system and a monitoring radar, wherein the large-scale MIMO downlink communication transmission system and the monitoring radar work at the same bandwidth, and the two systems respectively transmit signals;
the large-scale MIMO downlink communication transmission system transmits signals to communicate with users, and the radar transmission signals are monitored to detect targets.
According to the large-scale MIMO downlink communication scene, the base station side configures M t Large-scale antenna array of root transmit antennas, M t Is 10 2 Or 10 3 Magnitude order. Assuming that there are K users in the scene, each user configures M k And a root receiving antenna. For surveillance radar, it is assumed that it transmits a pulse of repetition time T and average transmit power P r And the number of unambiguous distance cells is n=1. The radar pulse is modulated with a sequence q, and normalize q 2 ＝1。
Step 2, obtaining statistical channel information in a communication system;
in this embodiment, the statistical channel state information of the user is obtained by adopting modes of user feedback, direct estimation of the base station, or uplink detection signals.
Step 3, constructing a communication system energy efficiency expression according to the statistical channel information and establishing a radar communication coexistence energy efficiency optimization problem model:
specifically, in this embodiment, the step 3 includes:
obtaining an expression of the reachable traversing rate of each user by using the statistical channel information, wherein the energy efficiency is defined as the ratio of the sum of the reachable traversing rates of all users to the power consumption; the optimization objective of the optimization problem is an energy efficiency value, and the constraint condition is that the respective transmitting power of the base station and the radar is required to meet the power constraint and the radar receiving signal interference ratio is greater than or equal to an acceptable minimum value.
And 4, solving an optimization problem by using alternating optimization, deterministic equivalence, an MM algorithm and a Dinkelbach algorithm:
specifically, in this embodiment, the step 4 includes:
step 4.1, decomposing the original problem into communication precoding Q by using alternate optimization k Optimization problem and radar transmission power P r Optimizing the problem;
step 4.2, decomposing the communication precoding problem into an optimal transmitting direction problem and a power distribution problem by utilizing eigenvalue decomposition, wherein the optimal transmitting direction can be directly solved into a closed solution; the power distribution problem can be firstly solved to obtain a deterministic equivalent value of the velocity, then the sum velocity is changed into a concave function by utilizing an MM algorithm, then the concave-convex division problem can be converted into a convex problem by utilizing a Dinkelbach algorithm, and finally the problem can be solved by utilizing a traditional convex optimization tool;
step 4.3, calculating the radar transmission power P by utilizing the monotonicity between the radar transmission power and the energy efficiency r Is the optimal solution of (a);
and 4.4, continuously iterating until the energy efficiency value converges.
The above is a specific step of the method of the present embodiment, and the following describes the method of the present embodiment in more detail with reference to a specific scenario, and the method for optimizing the co-existence energy efficiency of the radar communication assisted by the statistical channel characteristics of the present invention includes the following steps:
step 1, constructing a radar communication coexistence model
In particular, consider a massive MIMO downlink communication transmission system and a monitor operating at the same bandwidthThe radars co-exist in the same scene. Large-scale MIMO communication system base station side configuration M t Large-scale antenna array of root transmit antennas, M t Is 10 2 Or 10 3 Magnitude order. Assuming that there are K users in the scene, each user configures M k And a root receiving antenna. For surveillance radar, it is assumed that it transmits a pulse of repetition time T and average transmit power P r And the number of unambiguous distance cells is n=1. The radar pulse is modulated with a sequence q, and normalize q 2 ＝1。
Step 2, obtaining statistical channel information in a communication system;
in the channel detection stage, each user transmits an uplink detection signal to a base station, and the base station estimates beam domain statistical channel state information according to the received detection signal, wherein the statistical channel state information of the kth user is as follows:
in the formula (1), G k For the beam domain channel matrix of the kth user,indicating the desired operation, ++indicates the matrix's Hadamard product, ++>As a matrix G k Is a conjugate matrix of (a).
Step 3, constructing a communication system energy efficiency expression according to the statistical channel information and establishing a radar communication coexistence energy efficiency optimization problem model;
the base station transforms the spatial domain signals sent to each user into the beam domain through unified unitary transformation, and sends signals to each user, and the signal sent to user k by the base station is assumed to be x k The signal covariance matrix isTraversal rate of user kCan be expressed as:
in the formula (2) of the present invention,wherein->Interference plus noise covariance matrix for user k,/>For the variance of the radar echo amplitude received by the user,/->For the noise variance received by the user, V is the matrix formed by the eigenvectors of the base station side transmitting correlation matrix, log is the logarithm operation, det represents the determinant of taking the matrix, < >>Is M k ×M k Vitamin identity matrix (.) H Is the conjugate transpose of the matrix.
The power consumption model is as follows:
in equation (3), tr (·) is the operation of matrix apodization, the scaling factor η is the inverse of the transmission amplifier efficiency,p is the total transmit power d For dynamic power loss of each antenna, P t Is static circuit power consumption.
Energy efficiency is defined as the ratio of the sum of all user achievable traversal rates to the power consumption:
in formula (4), EE represents energy efficiency.
Establishing a radar communication coexistence energy efficiency optimization problem model:
in formula (5), P c,max For maximum transmit power of a communication system ρ v For minimum received signal-to-interference ratio, P, required in radar resolution unit v r,max For maximum radar transmit power, SDR v The received signal-to-interference ratio of the radar in the radar resolution unit v is expressed asWherein->For the variance of the amplitude of the radar target echo, +.>For the variance of the amplitude of radar clutter, +.>For the variance of the noise received by the radar,the radar is received with a variance of the amplitude of the communication signal.
And 4, solving an optimization problem by using alternating optimization, deterministic equivalence, an MM algorithm and a Dinkelbach algorithm:
the implementation process of the iterative algorithm based on the alternating optimization, deterministic equivalence, MM method and Dinkelbach algorithm, as shown in fig. 2, specifically comprises the following steps:
step 4.1, decomposing the original problem into communication precoding Q by using alternate optimization k Optimization problem and radar transmission power P r Optimizing a problem, and setting an iteration number indication d=0;
step 4.2, solving the communication precoding Q k The optimization problem specifically comprises:
step 4.2.1, decomposing with eigenvalues, Q k Represented asCommunication precoding Q k The optimization problem is decomposed into Guan k Sum lambda k Is a sub-problem of (2); wherein ψ is k Representing the subspace, Λ, in which the transmitted signal is located k Representing power corresponding to each dimension/direction of the transmission signal subspace;
step 4.2.2, solution Guan k Which comprises in particular:
guan is provided with k The optimal solution of the sub-problem of (2) is
Step 4.2.3, solving for Guan k Which comprises in particular:
guan is provided with k Is expressed as
In the formula (5) of the present invention,
Λ＝{Λ 1 ,…,Λ k }；
step 4.2.3.1, initializing covariance matrix Λ of transmission signal (0) Setting an iteration number indicator l=0;
step 4.2.3.2, find the desired operationA deterministic equivalent value, comprising in particular:
step 4.2.3.2.1, the following four deterministic equivalent auxiliary variables are introduced and iteratively calculated, expressed as:
wherein Γ is k (X) and y k (X) is a diagonal matrix function of user k, X referring to a general function variable, the diagonal elements of which are expressed as:
[Γ k (X)] t,t ＝tr{diag{([Ω k ] t,: )}X}；
[Υ k (X)] m,m ＝tr{diag{([Ω k ] m,: ) T }X}；
in the iteration process, all four auxiliary variables tend to converge, and iteration is stopped when the variation value of the auxiliary variables is smaller than a given threshold value;
step 4.2.3.2.2,Deterministic equivalent value ∈ ->Expressed as:
step 4.2.3.3 according to MM algorithmFirst order Taylor expansion->Replace->The method is specifically expressed as follows:
in the formula (7), it representsIs->Can be expressed as
In the formula (8) of the present invention, refers to omega k′ T line and->
Step 4.2.3.4, and applying the Dinkelbach algorithm, the energy efficiency optimization problem is changed into the following form:
step 4.2.3.5, as the optimization problem has become a convex problem, the power distribution matrix can be solved by using a traditional convex optimization tool;
step 4.2.3.6, the firstThe energy efficiency value obtained by the iteration and the +.>Comparing the results of the iterations if the difference between the results is less than a set threshold ε 1 Terminating the iteration and taking the power allocation matrix obtained in step 4.2.3.5 as the final solution; otherwise, the iteration times are->Adding 1, jumping back to the step 4.2.3.2, substituting the solution of the iteration, recalculating the deterministic equivalent value of the user rate and the first-order Taylor expansion term of the MM algorithm, and repeating the steps;
step 4.3, solving the radar transmission power P r The optimization problem specifically comprises:
according to energy efficiency with radar transmitting power P r Is strictly decreased by the increase in (c), and can be obtained:
step 4.4, the (d+1) th iteration is performed to obtainComparing the energy efficiency value of (2) with the result of the d-th iteration, if the difference between the two results is smaller than the set threshold epsilon 2 Terminating the iteration and taking the power distribution matrix obtained in the step 4.2.3.6 and the radar transmitting power obtained in the step 4.3 as final solutions; otherwise, adding 1 to the iteration number d, and jumping back to the step 4.2;
specifically, in the present embodiment, the threshold ε 1 And epsilon 2 The setting can be performed by the skilled person according to the actual situation, and specifically, the amount is not limited in the present embodiment.
The present invention is not described in detail in the present application, and is well known to those skilled in the art.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.

Claims (2)

1. A statistical channel characteristic assisted radar communication coexistence energy efficiency optimization method is characterized by comprising the following steps:
step 1, constructing a scene where radar and a communication system coexist;
the scene of coexistence of radar and communication system includes a massive MIMO downlink communication transmission system and a monitoring radar operating in the same bandwidth; according to the large-scale MIMO downlink communication scene, the base station side configures M t Large-scale antenna array of root transmitting antenna, K users in scene, each user is configured with M k A root receive antenna;
step 2, obtaining user statistics channel state information in a communication system;
the method for acquiring the user statistical channel state information in the communication system in the step 2 is as follows:
each user sends an uplink detection signal to a base station, and the base station estimates beam domain statistical channel state information according to the received detection signal, wherein the statistical channel state information of the kth user is as follows:
in the formula (1), G k For the beam domain channel matrix of the kth user,indicating the desired operation, ++indicates the matrix's Hadamard product, ++>As a matrix G k Is a conjugate matrix of (a);
step 3, constructing a communication system energy efficiency expression according to the user statistical channel state information and establishing a radar communication coexistence energy efficiency optimization problem model;
the method for constructing the communication system energy efficiency expression and establishing the radar communication coexistence energy efficiency optimization problem model in the step 3 is as follows:
the signal transmitted by the base station to the kth user is denoted as x k The total signal transmitted by the base station isLet x be equal to k' for k k And x k′ Not mutually related, then the covariance matrix of the kth user signal is +.>The achievable traversal rate for user k is expressed as:
in the formula (2) of the present invention,wherein->For user k interference plus noise covariance matrix, P r For radar transmit power, q is the sequence used to modulate the radar pulse, +.>Variance of the amplitude of the radar echo received for the user, < >>For the noise variance received by the user, M k G is the number of antennas at the user side k For the beam domain channel matrix, V is the matrix composed of the eigenvectors of the base station side transmission correlation matrix, log is the log operation, det represents the determinant of the matrix, ++>Is M k ×M k Vitamin identity matrix (.) H Is the conjugate transpose of the matrix;
the power consumption model is as follows:
in equation (3), tr (·) is the operation of matrix apodization, the scaling factor η is the inverse of the transmission amplifier efficiency,p is the total transmit power d For dynamic power loss of each antenna, P t For static circuit power consumption, M t The number of antennas at the base station side;
energy efficiency is defined as the ratio of the sum of all user achievable traversal rates to the power consumption:
in formula (4), EE represents energy efficiency;
therefore, the radar communication coexistence energy efficiency optimization problem model is:
in formula (5), P c,max For maximum transmit power of a communication system ρ v For minimum received signal-to-interference ratio, P, required in radar resolution unit v r,max For maximum radar transmit power, SDR v For the radar received signal-to-interference ratio in the radar resolution unit v, expressed asWherein-> For the variance of the amplitude of the radar target echo, +.>For the variance of the amplitude of radar clutter, +.>Noise variance received for radar, +.>Receiving a variance of the amplitude of the communication signal for the radar;
step 4, solving the energy efficiency optimization problem;
and 4, solving the energy efficiency optimization problem, which comprises the following steps:
step 4.1, apply the intersectionDecomposing radar communication coexistence energy efficiency optimization problem into covariance matrix Q by alternative optimization k Optimization problem and radar transmission power P r Optimizing a problem, and setting an iteration number indication d=0;
step 4.2, solving the covariance matrix Q k The optimization problem specifically comprises:
step 4.2.1, decomposing with eigenvalues, Q k Represented asCovariance matrix Q k The optimization problem is decomposed into Guan k Sum lambda k Is a sub-problem of (2); wherein ψ is k Representing the subspace, Λ, in which the transmitted signal is located k Representing power corresponding to each dimension/direction of the transmission signal subspace;
step 4.2.2, solving for Guan k Which comprises in particular:
guan is provided with k The optimal solution of the sub-problem of (2) is
Step 4.2.3, solving for Guan k Which comprises in particular:
guan is provided with k Is expressed as
In the formula (6) of the present invention,
Λ＝{Λ 1 ,...,Λ k }；
step 4.2.3.1, initializing covariance matrix Λ of the transmit signal (0) Setting an iteration number indication
Step 4.2.3.2, find the desired operationA deterministic equivalent value, comprising in particular:
step 4.2.3.2.1, introducing and iteratively calculating four deterministic equivalent auxiliary variables, described below, expressed as
Wherein Γ is k (X) and y k (X) is a diagonal matrix function of user k, X referring to a general function variable, the diagonal elements of which are expressed as:
[Γ k (X)] t,t ＝tr{diag{([Ω k ] t,: )}X}；
[Υ k (X)] m,m ＝tr{diag{([Ω k ] m,: ) T }X}；
in the iteration process, all four auxiliary variables tend to converge, and iteration is stopped when the variation value of the auxiliary variables is smaller than a given threshold value;
in step 4.2.3.2.2 the process steps are performed,deterministic equivalent value ∈ ->Expressed as:
step 4.2.3.3 according to MM algorithmFirst order Taylor expansion->Replace->The method is specifically expressed as follows:
in the formula (8), it representsIs->Can be expressed as
In the formula (9) of the present invention, refers to omega k′ T line and->
Step 4.2.3.4, and applying the Dinkelbach algorithm, the energy efficiency optimization problem is changed into the following form:
step 4.2.3.5, since the optimization problem has become a convex problem, the power distribution matrix can be solved using a conventional convex optimization tool;
step 4.2.3.6, the firstThe energy efficiency value obtained by the iteration and the +.>Comparing the results of the iterations if the difference between the results is less than a set threshold ε 1 Terminating the iteration and taking the power allocation matrix obtained in step 4.2.3.5 as the final solution; otherwise, the iteration times are->Adding 1, jumping back to the step 4.2.3.2, substituting the solution of the iteration, recalculating the deterministic equivalent value of the user rate and the first-order Taylor expansion term of the MM algorithm, and repeating the steps；
Step 4.3, solving the radar transmission power P r The optimization problem specifically comprises:
according to energy efficiency with radar transmitting power P r Is strictly decreased by the increase in (c), and can be obtained:
step 4.4, comparing the energy efficiency value obtained by the (d+1) th iteration with the result of the (d) th iteration, if the difference between the two results is smaller than the set threshold epsilon 2 Terminating the iteration, and taking the power distribution matrix obtained in the step 4.2.3.6 and the radar transmitting power obtained in the step 4.3 as final solutions; otherwise, the iteration times d are added with 1, and the step 4.2 is skipped.
2. The method for optimizing the coexistence energy efficiency of radar communication assisted by statistical channel characteristics according to claim 1, wherein in step 2, the statistical channel state information of the user is obtained by means of user feedback, direct estimation by the base station or by means of uplink probe signals.
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