CN116388819A - Beam forming method of IRS auxiliary DFRC system under relevant channel - Google Patents
Beam forming method of IRS auxiliary DFRC system under relevant channel Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- 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
- H04B7/06—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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- 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
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- H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
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- H04B7/0842—Weighted combining
- H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
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Abstract
The invention discloses a beam forming method of an IRS auxiliary DFRC system under a relevant channel, which comprises the following steps: constructing a communication reachable rate model under the assistance of IRS, a radar detection power model of a target direction, a DFRC system related channel model and a self-adaptive user grouping strategy; based on the model and the strategy, constructing a beam forming optimization problem, wherein under the condition of meeting the power constraint and the IRS phase shift constraint, an active beam forming device at the DFRC base station and a passive beam forming device at the IRS are taken as solution parameters, and the optimization problem of maximizing the weighted sum rate of communication users and the detection power at the radar target is taken as a target; acquiring relevant information required for solving a beam forming optimization problem; and solving a beam forming optimization problem based on the acquired related information to obtain a beam forming scheme. The invention can reduce radar performance loss as much as possible while improving system communication performance, and improve system performance.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a beam forming method of an IRS (Intelligent Reflecting Surface ) auxiliary DFRC (Dual-functional Radar Communication, radar communication integration) system under a relevant channel.
Background
With the development of Multiple-Input-Multiple-Output (MIMO) technology in various fields, the academy begins to explore the combination of MIMO radar and MIMO communication, that is, the integration of radar communication is realized by using spatial degrees of freedom brought by the Multiple antenna technology. In civil aspects, with the development of transportation, functions of intelligent driving, vehicle-mounted anti-collision, vehicle positioning and the like in an intelligent transportation system are required to be realized through radar equipment and communication equipment. In military applications, combat platforms are required to be equipped with a variety of electronic devices such as communications, radar, etc. to enhance the ability to combat information. Therefore, the radar device and the communication device are combined to form a DFRC system, so that the DFRC system can communicate with communication users in a downlink, and meanwhile, radar targets can be detected, so that the space utilization rate can be improved, the cost of the device and the energy source can be reduced, and the operation flexibility of the device is enhanced.
The DFRC system is characterized in that the radar and the communication share the same hardware platform to transmit the wave beam, so that the aim of simultaneously realizing the radar detection and the communication functions is fulfilled. Thus, beamforming design is an important research direction for DFRC systems. In beamforming designs, by utilizing spatial degrees of freedom for beam forming radar targets and communication users, different beams of the same waveform are used to achieve dual functions of communication and radar, potentially supporting higher data rates and guaranteeing radar performance. In addition, the communication rate of the joint beam forming is not affected by the pulse repetition frequency, and multi-user communication can be efficiently realized.
However, in beamforming design, there is a tradeoff between radar performance and communication performance. Particularly, the performance of the communication system is greatly affected by the influence of the relevant channel, so that the performance of the radar needs to be reduced to improve the performance of the communication, which leads to the loss of the performance of the radar.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a beamforming method of an IRS-assisted DFRC system under a related channel.
The technical problems to be solved by the invention are realized by the following technical scheme:
a method of beamforming for an IRS-assisted DFRC system under a correlated channel, the system comprising: the system comprises a DFRC base station, an intelligent reflection surface IRS, a communication user and a radar target; the IRS is used for improving a wireless propagation environment so as to assist the DFRC base station to communicate with a communication user;
the method comprises the following steps:
constructing a communication reachable rate model of a communication user under the assistance of IRS;
constructing a radar detection power model at the target direction;
constructing a DFRC system related channel model;
constructing a self-adaptive user grouping strategy; dividing related communication users into different user groups in the self-adaptive user grouping strategy;
Constructing a beamforming optimization problem based on the communication radar rate model, the radar detection power model, the DFRC system-related channel model, and the adaptive user grouping strategy; the beam forming optimization problem is an optimization problem which aims at maximizing the weighting sum rate of communication users and the detection power at a radar target by taking an active beam forming device at a DFRC base station and a passive beam forming device at an IRS as solution parameters under the condition of meeting power constraint and IRS phase shift constraint;
acquiring relevant information required for solving a beam forming optimization problem; the related information comprises known channel parameters and radar target angles;
and solving the beam forming optimization problem based on the acquired related information to obtain a beam forming scheme.
Preferably, the communication achievable rate model is expressed as:
R k ＝log 2 (1+γ k )，
wherein R is k Representing the achievable rate at the kth communication user, gamma k Representing the signal-to-interference-and-noise ratio of the kth communication user;
indicating channel parameters of DFRC base station to kth communication user, superscript H indicating conjugate transpose,/>
Channel parameters representing IRS to kth communication subscriber,/->
Channel parameters representing DFRC base station to IRS; / >
For passive beamforming matrices on IRS,
τ n ∈[0,1]is the amplitude reflection coefficient of the array element with index n in the IRS,
is the phase shift reflection coefficient of an array element with an index of n in the IRS; m is the number of antennas of a uniform linear array equipped with the DFRC base station, N is the number of reflection array elements of the IRS equipment, and K is the number of communication users; />
Linear precoder representing the jth communication user,/->
Linear precoding representing the kth communication subscriber, < >>
Additive Gaussian noise satisfying complex Gaussian distribution at kth communication user +.>
Is a variance of (c).
Preferably, the radar detection power model is:
P radar ＝a H (θ 0 )Ra(θ 0 )；
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the direction vector of the transmitting antenna array, and the superscript H represents the conjugate transpose; />
Is covariance matrix of radar waveform, lambda is radar working wavelength, d is array element spacing of DFRC base station, theta 0 Representing radar target angle, N is the number of reflection array elements of IRS equipment, P radar Representing radar detection power, < >>
The linear precoding of the kth communication user is represented, and K is the number of communication users.
Preferably, the DFRC system-related channel model includes: half-correlated non-line-of-sight Rayleigh channel model between the DFRC base station and the communication user, half-correlated non-line-of-sight Rayleigh channel model between the IRS and the communication user, and baseband equivalent channel between the DFRC base station and the IRS are modeled as rice channel model; wherein, the liquid crystal display device comprises a liquid crystal display device,
The half-correlation non-line-of-sight rayleigh channel model between the DFRC base station and the kth communication user is expressed as:
the semi-correlated non-line-of-sight rayleigh channel model between the IRS and the kth communication user is expressed as:
the baseband equivalent channel between the DFRC base station and the IRS is modeled as a rice channel model, expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
indicating channel parameters of DFRC base station to kth communication user, superscript H indicating conjugate transpose,/>
Channel parameters, H, representing IRS through kth communication subscriber BI Representing channel parameters between the DFRC base station and the IRS; l (L) 1,k Is the path loss between the DFRC base station and the kth communication user, L 2,k Is the path loss between IRS and kth communication user, L BI Is the path loss between the DFRC base station and the IRS; />
Rayleigh fading channel vector, subject to zero mean, unit variance, representing the difference between DFRC base station and kth communication user,>
a rayleigh fading channel vector between the IRS and the kth communication user that is subject to zero mean, unit variance; />
Representing the inclusion of D between a DFRC base station and a kth communication user k Steering matrix of individual steering vectors,>
representing inclusion of D between IRS and kth communication subscriber k A steering matrix of the steering vectors; epsilon is the Lese factor, H BI,LoS Representing the line-of-sight component between the DFRC base station and the IRS,
Preferably, the adaptive user grouping strategy includes:
calculating a channel correlation coefficient between every two communication users;
for each pair of communication users with the channel correlation coefficient higher than a preset threshold, creating two different user groups for the pair of communication users, and dividing the pair of communication users into the created two user groups respectively;
for each communication user which remains ungrouped, calculating the sum of channel correlation coefficients of the communication user and all communication users in each user group, and dividing the communication users into user groups with the smallest sum of channel correlation coefficients;
the channel correlation coefficient is defined as:
wherein ρ is i,j Represents the channel correlation coefficient, h i And h j The channel parameters of the ith and jth communication users are respectively represented, and the I/I represents the second norm.
Preferably, under the adaptive user grouping strategy, the achievable weighted sum rate of all communication users is expressed as:
wherein R is sum Representing the achievable weighted sum rate for all K communication users, all K communication users are divided into G groups,
the achievable weighted sum rate, mu, representing all communication users of the g-th user group k Is the weight occupied by the kth communication user in the kth user group, t IRS Representing the proportion of time that the IRS spends configuring the IRS phase shift per scheduling period,
representing the achievable rate at the kth communication user in the kth user group, N g Indicating the number of communication subscribers of the g-th subscriber group.
Preferably, the beamforming optimization problem is expressed as:
wherein W represents a beamforming matrix of an active beamformer at the DFRC base station, Φ represents a beamforming matrix of a passive beamformer at the IRS; all K communication users are divided into G groups; t is t IRS Representing the proportion of time, N, consumed by the IRS for configuring the IRS phase shift per scheduling period g Representing the number of communication users of the g-th user group, wherein ρ is a regularization parameter and μ k Is the weight occupied by the kth communication user in the kth user group,
represents the achievable rate, a (θ 0 ) Direction vector, w, representing a transmit antenna array k Linear precoding representing the kth communication user, diag () representing a function constructing a diagonal matrix, P t For maximum transmitting power of DFRC base station, N is the number of reflection array elements of IRS equipment, 1 M×1 Representing a vector of length M, M being the number of antennas of a uniform linear array equipped with a DFRC base station, for>
τ n ∈[0,1]Is the amplitude reflection coefficient of the array element with index n in IRS, < >>
Is the phase shift reflection coefficient of the array element with index n in the IRS.
Preferably, solving the beamforming optimization problem based on the acquired related information, to obtain a beamforming scheme, including:
converting the beam forming optimization problem into a first sub-problem and a second sub-problem, and substituting the first sub-problem and the second sub-problem into the acquired related information; the first sub-problem is an optimization problem which aims at minimizing the weighted mean square error of a communication user and maximizing the detection power at a radar target by taking an active beam shaper at a DFRC base station as a solution parameter; the second sub-problem is an optimization problem targeting a passive beamformer at the IRS for solution parameters, maximizing the weighted sum rate of the communication users and the detected power at the radar target;
for each user group, iteratively optimizing the beam forming optimization problem by alternately solving the first sub-problem and the second sub-problem so as to obtain a beam forming scheme of the user group when the optimization target of the beam forming optimization problem is reached; wherein the solving parameter W of the first sub-problem is first solved at each iteration, and then the phi of the second sub-problem is solved based on the solved W.
Preferably, the first sub-problem is expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
indicating that the kth communication user employs a mean square error at the output of the minimum mean square error receiver,
Z(θ 0 )＝(MI-a(θ 0 )a H (θ 0 ) I) represents an identity matrix,
indicating channel parameters of DFRC base station to kth communication user, superscript H indicating conjugate transpose,/>
Channel parameters representing IRS to kth communication subscriber,/->
Channel parameters representing DFRC base station to IRS; />
Linear precoder representing kth communication user,/->
Linear precoder representing the jth communication user,/->
Additive gaussian noise satisfying complex gaussian distribution for kth communication subscriber
Is a function of the real part of the complex number, ω k Representing a linear decoder.
Preferably, the second sub-problem is expressed as:
where phi = vec (phi), vec () is a function of vectorizing the matrix,
ζ k and Z k Are auxiliary variables, and superscript indicates conjugation.
The beam forming method of the IRS auxiliary DFRC system under the related channel provided by the invention divides users into different groups based on the self-adaptive user grouping strategy, and maximizes the weighted sum rate of communication users and the detection power of radar detection targets by optimizing active beam forming and passive beam forming, thereby improving the communication performance of the system, reducing the loss of radar performance as much as possible, ensuring the performance of both radar and communication, and simultaneously meeting the power constraint and IRS phase shift constraint, and effectively improving the system performance.
The present invention will be described in further detail with reference to the accompanying drawings.
Drawings
Fig. 1 is a schematic structural diagram of an IRS-assisted DFRC system under a related channel according to an embodiment of the present invention;
fig. 2 is a flowchart of a beam forming method of an IRS-assisted DFRC system under a correlated channel according to an embodiment of the present invention;
FIG. 3 is a flow chart of iterative optimization of a beamforming optimization problem by alternately solving a first sub-problem and a second sub-problem in an embodiment of the present invention;
the effect of spatial correlation on radar communication integration system performance in two different scenarios is shown in fig. 4;
the effect of user grouping and IRS assistance on system communication performance is shown in fig. 5;
the impact of IRS assistance on system radar performance at different grouping thresholds is shown in fig. 6;
the effect of IRS element number on system communication performance is shown in fig. 7;
the effect of IRS array element number on system radar performance is shown in fig. 8.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but embodiments of the present invention are not limited thereto.
The embodiment of the invention provides a beam forming method of an IRS auxiliary DFRC system under a relevant channel, which considers that under the influence of the relevant channel, the beam forming design in a radar communication integrated system is assisted by an intelligent reflecting surface. Referring to fig. 1, the system includes: a DFRC Base Station (BS), an intelligent reflective surface IRS, a communication user (user), and a radar Target (Target); wherein the IRS is configured to improve a wireless propagation environment to facilitate communication between the DFRC base station and a communication user. The DFRC base station is equipped with a Uniform Linear Array (ULA) of M antennas and the IRS is equipped with N reflecting array elements serving the communication users of K single antennas. Because the communication user is located in a complex urban environment, a serious path loss is caused by multipath effect in the transmission process, and therefore the IRS is used for improving the wireless propagation environment so as to assist the communication between the DFRC base station and the communication user. In addition, the integrated system simultaneously adopts a radar tracking mode, and has azimuth angle theta 0 Is a radar target. In particular, MIMO radar transmits a separate waveform on each antenna, which provides the advantage of waveform diversity, allowing more degrees of freedom for system design. The MIMO radar transmits a spatial orthogonal waveform in a detection stage to form an omnibearing wave beam pattern so as to search a potential target in the whole angle domain, thereby acquiring prior position information of the radar target. Then, firstly, radar is used for targetThe position information is used as the interested direction to form the directional wave beam, so as to obtain a more accurate observed value.
As shown in fig. 2, the beamforming method of the IRS-assisted DFRC system under a relevant channel according to the embodiment of the present invention includes the following steps:
s10: a communication achievable rate model of the communication user with the aid of the IRS is constructed.
For communication users, the received signal y of the kth user is assisted by the IRS k Can be expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the communication channel from the DFRC base station to the kth user, for>
Channel parameters representing IRS to kth communication subscriber,/->
For passive beamforming matrices on IRS, 
Wherein make->
τ n ∈[0,1]Is the amplitude reflection coefficient of the array element with index n in IRS, < >>
Is the phase shift reflection coefficient of an array element with an index of n in the IRS; the superscript H denotes a conjugate transpose; />
Channel parameters representing DFRC base station to IRS, < +.>
Linear precoding, s, representing the kth communication user k Communication symbol representing the kth communication subscriber, < ->
Linear precoder s representing the jth communication user j Communication symbol representing the jth communication subscriber, < >>
For additive gaussian noise at communication user k,
additive Gaussian noise satisfying complex Gaussian distribution at kth communication user +.>
K is the number of communication users, M is the number of antennas of a uniform linear array equipped with the DFRC base station, and N is the number of reflection array elements of the IRS equipment.
SINR (signal to interference plus noise ratio) at the kth communication user is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
γ k the signal-to-interference-and-noise ratio of the kth communication user is indicated, and the rest of the explanation of the parameters is described above.
The achievable rate R at the kth communication user is according to shannon's formula k Can be expressed as:
R k ＝log 2 (1+γ k )；
thus, the constructed communication achievable rate model can be expressed as:
R k ＝log 2 (1+γ k )，
it will be appreciated that the communication achievable rate model presented herein is the communication achievable rate model of the kth communication user.
S20: a radar detection power model at the target direction is constructed.
Specifically, in order to improve the communication performance of the system and minimize the radar performance loss, the radar waveform may be designed to maximize the detection power in the radar target direction, so the constructed radar detection power model is expressed as:
P radar ＝a H (θ 0 )Ra(θ 0 )；
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the direction vector of the transmitting antenna array, where j in the exponent of the natural base e is the complex imaginary sign,/-, and>
is covariance matrix of radar waveform, lambda is radar working wavelength, d is array element spacing of DFRC base station, theta 0 Represents radar target angle, P radar The superscript T indicates the matrix transpose, and the rest of the parameters are explained above.
S30: and constructing a DFRC system related channel model.
In general, the fading correlation channel matrix H is modeled as:
wherein the method comprises the steps of
Is a receive correlation matrix, < >>
Is a transmission correlation matrix, H R Is a random matrix with independent, zero-mean, unit-variance complex terms, assuming a complex gaussian distribution that results in correlated rayleigh fading, < >>
Indicating the desire, tr () indicates the trace function.
In particular, in the implementation of the present invention, the channel between the DFRC base station and the user and the channel between the IRS and the user are modeled as semi-correlated non-line-of-sight (NLoS) rayleigh fading channels, where the fading is correlated on the DFRC base station side and the IRS side and uncorrelated on the communication user side, due to severe path loss caused by multipath effects during transmission by the communication user.
Thus, direct channel h of kth user 1,k Is modeled as:
wherein L is 1,k Is the path loss between the DFRC base station and the kth communication user,
rayleigh fading channel vector, subject to zero mean, unit variance, representing the difference between DFRC base station and kth communication user,>
representing the inclusion of D between a DFRC base station and a kth communication user k A steering matrix of steering vectors, each vector corresponding to a particular departure direction (DoD). In the form of ULA, steering matrix a 1,k Expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
ith DoD, i e 1, …, D representing kth communication subscriber k . Wherein it is assumed that dods are randomly and independently distributed in the expression +.>
The width of the angle spread represents the degree of channel correlation, and is fixed in the practice of the present invention.
Accordingly, the semi-correlated non-line-of-sight rayleigh channel model between the IRS and the kth communication user is expressed as:
wherein L is 2,k Is the path loss between the IRS and the kth communication user,
rayleigh fading channel vector, subject to zero mean, unit variance, representing the interference between IRS and kth communication user,>
representing inclusion of D between IRS and kth communication subscriber k Steering matrix of individual steering vectors.
The baseband equivalent channel between the DFRC base station and the IRS is modeled as a rice channel model, expressed as:
wherein L is BI Is the path loss between the DFRC base station and the IRS; epsilon is the Lese factor, H BI,LoS Representing the line-of-sight component between the DFRC base station and the IRS,
representing the angle of arrival of IRS, < >>
Indicating the departure angle of the DFRC base station, H BI,NLoS Representing non-line-of-sight components, H, between DFRC base station and IRS BI,NLoS Obeying a complex normal distribution with zero mean and unit variance.
S40: constructing a self-adaptive user grouping strategy; the adaptive user grouping strategy classifies the relevant communication users into different user groups.
Considering that the spatial correlation between two communicating users may result in increased interference between each other, thereby reducing the signal-to-interference-and-noise ratio (SINR) of the users and deteriorating the system communication performance. Therefore, in order to reduce strong interference among a plurality of communication users, the embodiment of the invention provides an adaptive user grouping strategy aiming at massive MIMO, and related communication users are divided into different user groups.
Wherein the adaptive user grouping strategy comprises:
(1) And calculating the channel correlation coefficient between every two communication users.
Here, the channel correlation coefficient is defined as:
wherein ρ is i,j Represents the channel correlation coefficient, h i And h j Channel parameters of the ith and jth communication users are respectively represented, and I.I.I. representsAnd (5) solving a binary norm.
It will be appreciated that K (K-1)/2 channel correlation coefficients can be calculated for K communication users in total.
(2) For each pair of communication users whose channel correlation coefficient is higher than a preset threshold, two different user groups are created for the pair of communication users, and the pair of communication users are respectively divided into the created two user groups.
Specifically, each pair of communication users with the channel correlation coefficient higher than the preset threshold value is found out by a searching method, and then the operation of the step (2) is executed for each pair of communication users.
It will be appreciated that after this step is performed, the number of user groups obtained is equal to twice the number of user pairs for which the channel correlation coefficient is greater than the preset threshold.
(3) For each communication user remaining ungrouped, a sum of channel correlation coefficients of the communication user and all communication users in each user group is calculated, and the communication user is divided into user groups having a sum of minimum channel correlation coefficients.
Specifically, for the remaining ungrouped communication users, the user groups are allocated one by one in the manner in this step (3).
In addition, if no channel correlation coefficient larger than a preset threshold is found through searching, all K communication users are uniformly classified into one group, and at the moment, the base station provides communication services for all users at the same time.
S50: constructing a beam forming optimization problem based on a communication reachable rate model, a radar detection power model, a DFRC system related channel model and a self-adaptive user grouping strategy; the beam forming optimization problem is an optimization problem which aims at solving parameters and maximizing the weighting sum rate of communication users and the detection power at a radar target by taking an active beam forming device at a DFRC base station and a passive beam forming device at an IRS as targets under the condition of meeting power constraint and IRS phase shift constraint.
Specifically, it is assumed that K communication users are divided into G user groups by using an adaptive grouping rule, wherein N is in the G group g And the communication users.The different user groups are allocated with orthogonal time slots, so they will not interfere with each other, and based on the communication reachable rate model of the single communication user constructed in step S10, the reachable weighted sum rate of all communication users in the g-th user group is obtained as follows:
wherein t is IRS Represents the proportion of time, μ, that is consumed by the IRS for configuring IRS phase shift per scheduling period k Is the weight occupied by the kth communication user in the kth user group,
representing the achievable rate at the kth communication user within the kth user group.
Thereby, the achievable weighted sum rate R of all K communication users sum Can be expressed as:
on this basis, it is considered to maximize the Weighted Sum Rate (WSR) of the communication users and the probe power at the radar probe target by jointly optimizing the active beamformer W at the DFRC base station and the passive beamformer Φ at the IRS, while satisfying both the power constraint and the IRS phase shift constraint. Thus, the build beamforming optimization problem is:
wherein ρ is a regularization parameter for adjusting the specific gravity of the system communication performance and the radar performance, a (θ 0 ) Representing the direction vector of the transmit antenna array, diag () representing the function of constructing the diagonal matrix, P t Maximum transmit power for DFRC base station, 1 M ×1 The full 1 vector of length M is shown and the rest of the parameters are explained above.
In this problem of beam forming optimization,
calculated is the weighted sum rate of the communication users,
calculated is the radar target angle theta 0 And the probe power. The first constraint condition is a constant mode constraint applied to the radar, which can ensure an optimal low peak-to-average power ratio, reduce radar channel distortion, and meet a total power constraint. The second constraint is the phase shift constraint for the IRS.
S60: and acquiring relevant information required for solving the beam forming optimization problem.
Here, the relevant information required to solve the beamforming optimization problem mainly includes known channel parameters and radar target angles. In addition, the method also comprises communication symbols of communication users, radar target angles, direction vectors of a transmitting antenna array, maximum transmitting power of an antenna, weights occupied by different communication users, regularization parameters rho, noise power, scheduling periods of IRS and the like.
S70: and solving a beam forming optimization problem based on the acquired related information to obtain a beam forming scheme.
Specifically, solving the beamforming optimization problem based on the acquired related information to obtain a beamforming scheme, including:
(1) And converting the beam forming optimization problem into a first sub-problem and a second sub-problem, and substituting the first sub-problem and the second sub-problem into the acquired related information.
Here, the first sub-problem is an optimization problem targeting an active beamformer at the DFRC base station for solution parameters, minimizing the weighted mean square error of the communication user and maximizing the probe power at the radar target; the second sub-problem is an optimization problem targeting the passive beamformer at the IRS for solution parameters, maximizing the weighted sum rate of the communication users and the detected power at the radar target.
(2) For each user group, carrying out iterative optimization on the beam forming optimization problem by alternately solving the first sub-problem and the second sub-problem so as to obtain a beam forming scheme of the user group when the optimization target of the beam forming optimization problem is reached; wherein, the solving parameter W of the first sub-problem is solved first at each iteration, and then the phi of the second sub-problem is solved based on the solved W.
The above-described beamforming optimization problem is a non-convex optimization problem and therefore needs to be solved by a solution. In the embodiment of the invention, the beam forming optimization problem is solved by converting the beam forming optimization problem into two sub-problems, converting the first sub-problem into a minimum mean square error (WMMSE) framework, converting the second sub-problem into a split planning (FP) problem, and then performing iterative optimization on the beam forming optimization problem by alternately solving the first sub-problem and the second sub-problem so as to obtain a beam forming scheme when the optimization target of the beam forming optimization problem is reached. The specific conversion and solving process is as follows:
for the beamforming optimization problem, it is first assumed that the passive beamformer Φ of the IRS is a constant, so that the constraint condition corresponding to the variable irrelevant to Φ in the beamforming optimization problem is eliminated, and the active beamformer W of the DFRC base station is optimized. At this time, the above beamforming optimization problem can be re-expressed as:
Wherein at the kth communication userBy using a linear decoder omega k The estimated sign is
Thus, communication user k demodulates the signal +.>
Transmitting a signal s with a base station k Is expressed as:
in this case, when w k When fixed, the omega of the formula can be obtained by k Derivation, i.e.
Obtain the optimal omega k . Through the optimal omega k The mean square error at the output can be obtained. Thus, the objective of maximizing the weighted sum rate of the communication users in the beamforming optimization problem can be translated into minimizing the weighted mean square error.
In addition, the radar target angle θ is calculated in the beamforming optimization problem 0 The above detection power term may be equivalent to:
thus, by using the WMMSE method, the weighted sum rate maximization problem of the communicating user is converted into an equivalent minimization weighted mean square error problem, the first sub-problem, expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
indicating that the kth communication user employs a mean square error at the output of the minimum mean square error receiver,
Z(θ 0 )＝(MI-a(θ 0 )a H (θ 0 ) I) represents the identity matrix, and the rest of the parameters are explained above.
The first sub-problem is an optimization problem which can be solved, and can be converted into a new homogeneous QQP (quadratic constraint programming) optimization problem by carrying out homogeneous treatment on the problem, and by omitting rank 1 constraint, the semi-positive relaxation (SDR) of the QQP problem is obtained, so that the problem can be solved by using a CVX tool to obtain a suboptimal solution, and the grouping weighted sum rate of communication users is maximized, thereby leading the system performance to be more optimal.
And substituting the first sub-problem into a beam forming optimization problem on the basis of W obtained by solving the first sub-problem, and continuously optimizing the passive beam forming matrix phi of the IRS. The beamforming optimization problem at this time can be expressed as:
according to the Lagrangian dual transform, the last optimization problem can be equivalent to:
wherein ζ k Is an auxiliary variable. If Φ is fixed, the above formula is an unconstrained convex optimization problem, corresponding to ζ= [ ζ ] 1 ,…,ζ K ]Optimal zeta * Is that
By making->
Obtaining the product. If ζ is fixed, let φ=vec (Φ) (vec () is a function that vectorizes the matrix), the above equation can be reduced to: />
Wherein, the liquid crystal display device comprises a liquid crystal display device,
for the rest of the parameters, please refer to above.
The above equation can be further converted into:
wherein z is k Is an auxiliary variable, z= [ z ] 1 ,z 2 ,…,z K ] T Superscript indicates conjugation. For a fixed phi-value, the reference to the fixed phi,
is an unconstrained convex problem about z by applying +.>
Can obtain the optimal solution
For a fixed z-axis, the reference to the z-axis,
phi of (F) H x k,j +y k,j 2 The term can be restated as:
thus, the problem of optimizing the passive beamforming matrix Φ of the IRS can be translated into a second sub-problem shown below:
this second sub-problem is a quadratic programming problem that can be solved with the CVX toolbox.
Therefore, the beam forming optimization problem can be iteratively optimized by alternately solving the first sub-problem and the second sub-problem, so that a beam forming scheme is obtained when the optimization target of the beam forming optimization problem is reached. The specific solving process is shown in FIG. 3, wherein i is an iteration number mark, f all The calculation formula of (2) is as follows:
in summary, according to the beam forming method of the IRS auxiliary DFRC system under the relevant channel provided by the embodiment of the invention, under the condition that the relevant channel causes loss to the system performance, the wireless propagation environment is improved through the IRS auxiliary DFRC system; on the basis, users are divided into different groups based on a self-adaptive user grouping strategy, and the beam forming optimization problem is built by optimizing active beam forming and passive beam forming and taking the maximization of the weighted sum rate of communication users and the maximization of the detection power at the radar detection target as targets, and an alternative optimization scheme is provided to solve the problem of indissolvable of the beam forming optimization problem, so that the solved beam forming scheme reduces the loss of radar performance as much as possible while improving the communication performance of the system, ensures the performance of both radar and communication, meets the power constraint and IRS phase shift constraint at the same time, and effectively improves the performance of the system.
The effectiveness of the embodiments of the present invention is verified by simulation experiments as follows.
Specifically, MATLAB simulation software is used for carrying out numerical simulation experiments on the scheme of the embodiment of the invention. In simulation experiments, consider an IRS-assisted DFRC system, where the DFRC base station is located at (0 m,0 m), the IRS is located at (200 m,0 m), and the user is located in a circular area with a radius of 10m around (200 m,30 m). The DFRC base station has an antenna number m=32, an irs element number n=64, and a communication user number k=30. Maximum transmit power P at BS t Set to 20dBm and the power of the noise set to-117 dBm. The rice factor of the rice channel is set to 5, and the spread angle and the discrete value of the half-correlated rayleigh channel are set to (5 °, 50). A path loss model is defined based on a 3GPP propagation environment, wherein the path loss of the direct path is 32.6+36.7log 10 (d) dB, the path loss of the reflection path is 35.6+22.0log 10 (d) dB (dB). The radar target angle is set to 0 DEG, t IRS Set to 1%.
The effect of spatial correlation on radar communication integration system performance in two different scenarios is shown in fig. 4. In a first uncorrelated channel scenario, direct channel H between a base station and K communicating users 1 ＝(h 1,1 ，h 1,2 ，…h 1,k …，h 1,K ) And reflection channel H between IRS and K communication users 2 ＝(h 2,1 ，h 2,2 ，…h 2,k …，h 2,K ) All assume independent co-distributed rayleigh channels with mean 0 and variance 1; in a second related channel scenario, H 1 And H 2 Then it is generated according to the semi-correlated non-line-of-sight rayleigh channel model shown above. As can be seen from fig. 4, when the communication user channels are correlated, the performance of the system is better than the uncorrelated time difference. This is due to the existence of spatial correlation between the communication user channel vectors, resulting in increased interference with each other and thus reduced system performance. Fig. 4 illustrates the necessity of modeling the channel between the DFRC base station and the communicating user and between the DFRC base station and the IRS as a semi-correlated non-line-of-sight rayleigh channel in the present invention.
The impact of user grouping and IRS assistance on system communication performance is shown in fig. 5. It can be seen from fig. 5 that the WSR with IRS assistance is significantly larger than the WSR without IRS assistance. And, regardless of the presence or absence of IRS assistance, the WSR when the adaptive grouping strategy is employed is always higher than the WSR when there is no grouping. Wherein, with the aid of IRS, WSR is maximum when the threshold α=0.85 of the channel correlation coefficient. It can also be seen from fig. 5 that when α is small, the users are divided into different groups, each group having a small number of users. The more groups that are created, the less time resources are per group. As a increases, there is a threshold to maximize the sum rate. When α=1, it means that all users are within a group, interference between users increases under the influence of the relevant channel, and WSR is the lowest at this time. It can be seen that the improvement would attenuate each group because of the reduced intra-group interference and the rate first increasing with the packet threshold, but because less time resources are available.
The impact of IRS assistance on system radar performance at different grouping thresholds is shown in fig. 6. Where radar systems represent systems that do not include a communication system, the radar performance is clearly optimal. As can be seen from fig. 6, in the case of using the adaptive strategy, IRS assistance improves radar target detection performance, and increases detection power at the target, which illustrates that the invention can effectively reduce radar performance loss while improving system communication performance.
The effect of IRS element number on system communication performance is shown in fig. 7. As can be seen from fig. 7, with the aid of IRS, WSR is improved as the number of IRS elements increases. However, the improvement is not infinite but gradually approaches an upper boundary. For example, when n=128, with the aid of IRS, WSR increases by 1bps/Hz; when n=224, WSR increases by 1.2bps/Hz.
The effect of IRS array element number on system radar performance is shown in fig. 8. As can be seen from fig. 8, as the IRS array element number increases, the radar beam pattern distribution changes, and the detection power at the target is higher, but the sidelobes also increase. For example, when N changes from 0 to 64, the main lobe increases by 3dBm; when N is changed from 128 to 256, the main lobe is increased by 0.5dBm and the side lobe is increased by 1dBm.
Fig. 7 and 8 illustrate that the IRS assistance is used in the present invention to improve the performance of the system in both communication and radar, and the improvement effect increases with the number of IRS elements, but not infinitely, but gradually approaches an upper boundary.
The beam forming optimization problem and the sub-problem thereof which are proposed in the embodiment of the invention can be loaded in the electronic equipment, so that the beam forming is realized in steps S60 to S70 in the beam forming method of the IRS auxiliary DFRC system under any relevant channel operated by the electronic equipment. In practical applications, the electronic device may be: computers, radars, etc.
The invention also provides a computer readable storage medium. The computer-readable storage medium stores a computer program which, when executed by a processor, implements steps S60 to S70 in the beam forming method of the IRS-assisted DFRC system under any of the above-described correlated channels.
Alternatively, the computer readable storage medium may be a Non-Volatile Memory (NVM), such as at least one disk Memory.
Optionally, the computer readable storage medium may also be at least one storage device located remotely from the aforementioned processor.
In yet another embodiment of the present invention, there is also provided a computer program product containing instructions that, when run on a computer, cause the computer to perform steps S60-S70 in a beam forming method of an IRS-assisted DFRC system under any of the above-described correlated channels.
It should be noted that, for the apparatus/electronic device/storage medium/computer program product embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference should be made to the description of the method embodiments in part.
It should be noted that the terms "first," "second," and the like are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the disclosed embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with aspects of the present disclosure.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Further, one skilled in the art can engage and combine the different embodiments or examples described in this specification.
Although the present application has been described herein with respect to various embodiments, other variations of the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed application, from a review of the figures and the disclosure. In the description of the present invention, the word "comprising" does not exclude other elements or steps, the "a" or "an" does not exclude a plurality, and the "a" or "an" means two or more, unless specifically defined otherwise. Moreover, some measures are described in mutually different embodiments, but this does not mean that these measures cannot be combined to produce a good effect.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
Claims (10)
1. A method for beamforming an IRS-assisted DFRC system under a correlated channel, the system comprising: the system comprises a DFRC base station, an intelligent reflection surface IRS, a communication user and a radar target; the IRS is used for improving a wireless propagation environment so as to assist the DFRC base station to communicate with a communication user;
the method comprises the following steps:
constructing a communication reachable rate model of a communication user under the assistance of IRS;
constructing a radar detection power model at the target direction;
constructing a DFRC system related channel model;
constructing a self-adaptive user grouping strategy; dividing related communication users into different user groups in the self-adaptive user grouping strategy;
constructing a beamforming optimization problem based on the communication radar rate model, the radar detection power model, the DFRC system-related channel model, and the adaptive user grouping strategy; the beam forming optimization problem is an optimization problem which aims at maximizing the weighting sum rate of communication users and the detection power at a radar target by taking an active beam forming device at a DFRC base station and a passive beam forming device at an IRS as solution parameters under the condition of meeting power constraint and IRS phase shift constraint;
Acquiring relevant information required for solving a beam forming optimization problem; the related information comprises known channel parameters and radar target angles;
and solving the beam forming optimization problem based on the acquired related information to obtain a beam forming scheme.
2. The method of beamforming for an IRS-assisted DFRC system under a correlation channel of claim 1, wherein the communication achievable rate model is expressed as:
R k ＝log 2 (1+γ k )，
wherein R is k Representing the achievable rate at the kth communication user, gamma k Representing the signal-to-interference-and-noise ratio of the kth communication user;
indicating channel parameters of DFRC base station to kth communication user, superscript H indicating conjugate transpose,/>
Channel parameters representing IRS to kth communication subscriber,/->
Channel parameters representing DFRC base station to IRS; />
For passive beamforming matrices on IRS,
τ n ∈[0,1]is the amplitude reflection coefficient of the array element with index n in the IRS,
is the phase shift reflection coefficient of an array element with an index of n in the IRS; m is the number of antennas of a uniform linear array equipped with the DFRC base station, N is the number of reflection array elements of the IRS equipment, and K is the number of communication users; />
Linear precoder representing the jth communication user,/->
Linear precoding representing the kth communication subscriber, < > >
Additive Gaussian noise satisfying complex Gaussian distribution at kth communication user +.>
Is a variance of (c).
3. The method for beamforming of an IRS-assisted DFRC system under a correlation channel of claim 1, wherein the radar detection power model is:
P radar ＝a H (θ 0 )Ra(θ 0 )；
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the direction vector of the transmitting antenna array, and the superscript H represents the conjugate transpose; />
Is covariance matrix of radar waveform, lambda is radar working wavelength, d is array element spacing of DFRC base station, theta 0 Representing radar target angle, N is the number of reflection array elements of IRS equipment, P radar Representing radar detection power, < >>
The linear precoding of the kth communication user is represented, and K is the number of communication users.
4. The method for beamforming of an IRS-assisted DFRC system under a correlation channel of claim 1, wherein the DFRC system correlation channel model comprises: half-correlated non-line-of-sight Rayleigh channel model between the DFRC base station and the communication user, half-correlated non-line-of-sight Rayleigh channel model between the IRS and the communication user, and baseband equivalent channel between the DFRC base station and the IRS are modeled as rice channel model; wherein, the liquid crystal display device comprises a liquid crystal display device,
the half-correlation non-line-of-sight rayleigh channel model between the DFRC base station and the kth communication user is expressed as:
The semi-correlated non-line-of-sight rayleigh channel model between the IRS and the kth communication user is expressed as:
the baseband equivalent channel between the DFRC base station and the IRS is modeled as a rice channel model, expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the channel parameters of the DFRC base station to the kth communication user, the superscript H represents the conjugate transpose,
representation IChannel parameters, H, of RS to kth communication user BI Representing channel parameters between the DFRC base station and the IRS; l (L) 1,k Is the path loss between the DFRC base station and the kth communication user, L 2,k Is the path loss between IRS and kth communication user, L BI Is the path loss between the DFRC base station and the IRS; />
Rayleigh fading channel vector, subject to zero mean, unit variance, representing the difference between DFRC base station and kth communication user,>
a rayleigh fading channel vector between the IRS and the kth communication user that is subject to zero mean, unit variance; />
Representing the inclusion of D between a DFRC base station and a kth communication user k Steering matrix of individual steering vectors,>
representing inclusion of D between IRS and kth communication subscriber k A steering matrix of the steering vectors; epsilon is the Lese factor, H BI,LoS Representing the line-of-sight component between the DFRC base station and the IRS,
representing the angle of arrival of IRS, < >>
Representing the departure angle of the DFRC base station, M is the DFRC base station device The number of the antennas of the prepared uniform linear array is N, the number of the reflection array elements of the IRS equipment is N, and the superscript T represents matrix transposition; h BI,NLoS Representing non-line-of-sight components, H, between DFRC base station and IRS BI,NLoS Obeying a complex normal distribution with zero mean and unit variance.
5. The beamforming method of an IRS-assisted DFRC system under a correlation channel of claim 1, wherein the adaptive user grouping strategy comprises:
calculating a channel correlation coefficient between every two communication users;
for each pair of communication users with the channel correlation coefficient higher than a preset threshold, creating two different user groups for the pair of communication users, and dividing the pair of communication users into the created two user groups respectively;
for each communication user which remains ungrouped, calculating the sum of channel correlation coefficients of the communication user and all communication users in each user group, and dividing the communication users into user groups with the smallest sum of channel correlation coefficients;
the channel correlation coefficient is defined as:
wherein ρ is i,j Represents the channel correlation coefficient, h i And h j The channel parameters of the ith and jth communication users are respectively represented, and the I/I represents the second norm.
6. The method for beamforming for an IRS-assisted DFRC system under a correlated channel of claim 5, wherein the achievable weighted sum rate for all communication users under the adaptive user grouping strategy is expressed as:
Wherein R is sum Representing the achievable weighted sum rate for all K communication users, all K communication users are divided into G groups,
the achievable weighted sum rate, mu, representing all communication users of the g-th user group k Is the weight occupied by the kth communication user in the kth user group, t IRS Indicating the proportion of time consumed by the IRS for configuring the IRS phase shift per scheduling period, +.>
Representing the achievable rate at the kth communication user in the kth user group, N g Indicating the number of communication subscribers of the g-th subscriber group.
7. The beamforming method of an IRS-assisted DFRC system under a correlation channel of claim 1, wherein the beamforming optimization problem is expressed as:
wherein W represents a beamforming matrix of an active beamformer at the DFRC base station, Φ represents a beamforming matrix of a passive beamformer at the IRS; all K communication users are coveredDividing into G groups; t is t IRS Representing the proportion of time, N, consumed by the IRS for configuring the IRS phase shift per scheduling period g Representing the number of communication users of the g-th user group, wherein ρ is a regularization parameter and μ k Is the weight occupied by the kth communication user in the kth user group,
represents the achievable rate, a (θ 0 ) Direction vector, w, representing a transmit antenna array k Linear precoding representing the kth communication user, diag () representing a function constructing a diagonal matrix, P t For maximum transmitting power of DFRC base station, N is the number of reflection array elements of IRS equipment, 1 M ×1 Representing a vector of length M, M being the number of antennas of a uniform linear array equipped with a DFRC base station, for>
τ n ∈[0,1]Is the amplitude reflection coefficient of the array element with index n in IRS, < >>
Is the phase shift reflection coefficient of the array element with index n in the IRS.
8. The beamforming method of the IRS-assisted DFRC system according to claim 7, wherein solving the beamforming optimization problem based on the obtained correlation information results in a beamforming scheme, comprising:
converting the beam forming optimization problem into a first sub-problem and a second sub-problem, and substituting the first sub-problem and the second sub-problem into the acquired related information; the first sub-problem is an optimization problem which aims at minimizing the weighted mean square error of a communication user and maximizing the detection power at a radar target by taking an active beam shaper at a DFRC base station as a solution parameter; the second sub-problem is an optimization problem targeting a passive beamformer at the IRS for solution parameters, maximizing the weighted sum rate of the communication users and the detected power at the radar target;
For each user group, iteratively optimizing the beam forming optimization problem by alternately solving the first sub-problem and the second sub-problem so as to obtain a beam forming scheme of the user group when the optimization target of the beam forming optimization problem is reached; wherein the solving parameter W of the first sub-problem is first solved at each iteration, and then the phi of the second sub-problem is solved based on the solved W.
9. The method for beamforming for an IRS-assisted DFRC system under a correlation channel of claim 8, wherein the first sub-problem is expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
indicating that the kth communication user employs a mean square error at the output of the minimum mean square error receiver,
Z(θ 0 )＝(MI-a(θ 0 )a H (θ 0 ) I) represents an identity matrix,
indicating channel parameters of DFRC base station to kth communication user, superscript H indicating conjugate transpose,/>
Channel parameters representing IRS to kth communication subscriber,/->
Channel parameters representing DFRC base station to IRS; />
Linear precoder representing kth communication user,/->
Linear precoder representing the jth communication user,/->
Additive gaussian noise satisfying complex gaussian distribution for kth communication subscriber
Is a function of the real part of the complex number, ω k Representing a linear decoder.
10. The method for beamforming for an IRS-assisted DFRC system under a correlation channel of claim 7, wherein the second sub-problem is expressed as:
where phi = vec (phi), vec () is a function of vectorizing the matrix,
ζ k and Z k Are auxiliary variables, and superscript indicates conjugation.
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