CN115175132B - Precoding and power distribution method supporting unmanned aerial vehicle communication perception integration - Google Patents
Precoding and power distribution method supporting unmanned aerial vehicle communication perception integration Download PDFInfo
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Abstract
The invention relates to a precoding and power distribution method supporting unmanned aerial vehicle communication perception integration, and belongs to the technical field of wireless communication. The method comprises the following steps: s1: establishing an unmanned aerial vehicle communication perception system model; s2: establishing an unmanned aerial vehicle energy transmission model; s3: modeling a joint waveform of the unmanned aerial vehicle communication perception system; s4: modeling the mutual information of the sensing system conditions of the unmanned aerial vehicle and the transmission data volume of the unmanned aerial vehicle communication system; s5: modeling a power constraint condition of a communication perception system of the unmanned aerial vehicle; s6: a joint precoding and power allocation strategy is determined based on system performance optimization. The invention takes the weighted sum of mutual information of communication users, speed and radar sensing links as an optimization target, and realizes the joint precoding, the energy transmission and sensing task time distribution and the joint optimization of sensing power distribution.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and relates to a precoding and power distribution method supporting integration of unmanned aerial vehicle communication perception.
Background
Unmanned Aerial Vehicle (UAV) is widely applied in military and civil fields because of the advantages of high maneuverability, low cost, strong concealment, easy deployment and the like. The unmanned aerial vehicle can be applied to the military field and can bear multi-type tasks such as reconnaissance detection, tracking and positioning, accurate guidance, electromagnetic interference, material delivery and the like, and the unmanned aerial vehicle communication perception integration is a primary foundation for realizing multi-machine cooperation high-precision, flexible target perception and information interaction. However, the unmanned aerial vehicle communication perception integrated system faces competition of communication and perception functions on multidimensional resources such as frequency spectrum, power and time slot, and interference inside the communication and perception system and among systems, and how to design a resource allocation strategy to realize performance optimization of the communication perception system is a problem to be solved.
At present, the problem of integration of unmanned aerial vehicle communication perception is studied in literature, if literature is based on the limiting condition of signal-to-interference-and-noise ratio of a receiving end of a perception system, a resource allocation scheme is designed to realize optimization of the transmission rate of a communication system; under the condition that the tolerable interference constraint condition of the communication system is met, the transmitting signals of the sensing system are designed to realize the optimization of the signal-to-interference-and-noise ratio of the sensing system, however, the joint optimization of the performance of the communication sensing system, the joint precoding and power distribution problems of the multi-antenna communication sensing system are less considered in the prior art, and the performance optimization of the communication sensing integrated system is difficult to realize.
Disclosure of Invention
In view of the above, the present invention aims to provide a precoding and power distribution method supporting integration of unmanned aerial vehicle communication perception. For a system comprising a wireless energy transmission scene, a UAV, a plurality of communication users and perceived targets, modeling a weighted sum of mutual information of the communication users, the speed and radar perceived links as an optimization target, and realizing joint precoding, energy transmission, sensory task time distribution and sensory power distribution joint optimization.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a precoding and power distribution method supporting integration of unmanned aerial vehicle communication perception aims at a system comprising a wireless energy transmission scene, a UAV, a plurality of communication users and perceived targets, and specifically comprises the following steps:
S1: establishing an unmanned aerial vehicle communication perception system model;
s2: establishing an unmanned aerial vehicle energy transmission model;
S3: modeling a joint waveform of the unmanned aerial vehicle communication perception system;
s4: modeling the mutual information of the sensing system conditions of the unmanned aerial vehicle and the transmission data volume of the unmanned aerial vehicle communication system;
s5: modeling a power constraint condition of a communication perception system of the unmanned aerial vehicle;
s6: a joint precoding and power allocation strategy is determined based on system performance optimization.
Further, in step S1, an unmanned aerial vehicle communication perception system model is established, which specifically includes: the working phase of the unmanned aerial vehicle communication perception system comprises an energy transmission phase and a joint communication perception phase; in the energy transmission stage, the radio station occupies time T 1 to transmit energy to the unmanned aerial vehicle; in the joint communication sensing stage, the unmanned aerial vehicle performs communication and sensing functions simultaneously by using the acquired energy, the duration is T 2, the total time T=T 1+T2, the unmanned aerial vehicle is configured with a multi-antenna system, the multi-antenna system is composed of an M-dimensional transmitting antenna array and an M-dimensional receiving antenna array, wherein the transmitting antenna array transmits communication sensing joint beams to communicate with K users, simultaneously transmits sensing signals to a target, and the receiving antenna array receives radar reflected signals to acquire detection target information.
Further, in step S2, an unmanned aerial vehicle energy transmission model is established, which specifically includes: in the energy transmission phase, the radio station transmits an energy radio frequency signal to the unmanned aerial vehicle, let N t denote the number of transmitting antennas configured by the radio station, the unmanned aerial vehicle configures a single receiving antenna, and the energy radio frequency signal y t received by the unmanned aerial vehicle from the radio station can be expressed as: Wherein the method comprises the steps of For the radio station to drone channel gain vector,Is N t multiplied by 1 dimensional matrix set, a is radio frequency energy signal of unit power,N t is additive white gaussian noise with zero mean value, which is an energy beam forming vector; the energy extracted by the drone can be expressed as: wherein eta epsilon [0,1] is the energy conversion efficiency.
Further, in step S3, modeling a joint waveform of the communication perception system of the unmanned aerial vehicle specifically includes: in the communication perception system constructed in step S1, the communication perception joint waveform x [ n ] in the nth slot may be expressed as: x [ n ] = W rs[n]+Wc c [ n ], n=0,..n-1, where s [ n ] = [ s 1[n],...,sm[n],...,sM[n]]T,sm [ n ] represents the radar waveform sent by the M-th antenna, and M is 1-M; c < n > = [ c 1[n],...,ck[n],...,cK[n]]T,ck < n > represents a communication symbol sent by a kth antenna, and K is more than or equal to 1 and less than or equal to K; w r is a sensing system precoding matrix, and W c is a communication precoding matrix; the received perceived signal y r n at the receiver can be expressed as: y r[n]＝Frs[n]+Fc c [ n ] +v [ n ], where F r＝HWr,Fc＝HWc is a radar equivalent channel, H is a perceptual channel matrix, and the signal y c [ n ] received by the communication user can be expressed as: y c[n]＝Fr′s[n]+Fc 'c [ n ] +v [ n ], where F r′＝H′Wr,Fc′＝H′Wc is a communication equivalent channel, H' is a communication channel matrix, and v [ n ] is Gaussian white noise.
Further, in step S4, the perceived system condition mutual information R MI is modeled as: Where γ m is the perceived signal-to-interference-and-noise ratio at the mth receive antenna, which can be expressed as: Where F r]m,m|2 represents the signal power of the mth perceived beam link, sigma i≠m|[Fr]m,i|2 is the interference from the perceived signal of the other link, Σ 2 is the power of white gaussian noise, which is the interference from the communication link.
Further, in step S4, the unmanned aerial vehicle communication system transmission data amount C (γ) is modeled as: where γ' k is the signal-to-interference-and-noise ratio of the kth communication user, which can be expressed as: Where F c′]k,k|2 represents the signal power of the kth user communication link, sigma i≠k|[Fc′]k,i|2 represents interference from other communication link signals, Representing interference from the radar link.
Further, in step S5, modeling the power constraint condition of the communication perception system of the unmanned aerial vehicle specifically includes: let r=e (xnx T n) represent the covariance matrix of the joint waveform x n, yielding r=w rWr H+WcWc H, the total power of the joint waveform modeled as Tr (R), where Tr () represents the trace of the matrix, and the power of target perception and user communication can be represented as Tr (W rWr H) and Tr (W cWc H), respectively;
the system energy constraints can be expressed as:
the radar energy constraint can be expressed as:
The communication energy constraint can be expressed as: where l is the duty cycle allocated for the target perceived power, i.e. the power allocation strategy; (1-l) is a duty cycle allocated for communication power;
the constraints on the radio station transmit energy can be expressed as: Where E max is the upper transmission energy limit.
Further, in step S7, determining a joint precoding and power allocation policy based on system performance optimization specifically includes: the modeling system performance metrics were: c (γ) +αr MI, wherein α represents a weight; the precoding matrix W r、Wc and the power allocation strategy l are determined based on system performance optimization, namely: { W r *,Wc *,l*,T1 *}＝argmax(C(γ)+αRMI), wherein W r *、Wc *、l*、T1 * is the optimal radar precoding matrix, communication precoding matrix, power allocation strategy and energy transmission time, respectively.
The invention has the beneficial effects that: the invention aims at a system comprising a wireless energy transmission scene, a UAV, a plurality of communication users and perceived targets, models the mutual information weighted sum of the communication users, the speed and radar perceived links as an optimization target, and realizes the joint precoding, the energy transmission and sensory communication task time distribution and the joint optimization of sensory communication power distribution.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
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For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in the following preferred detail with reference to the accompanying drawings, in which:
FIG. 1 is a schematic diagram of a communication system for energy transmission and perception according to the present invention;
Fig. 2 is a schematic flow chart of a precoding and power distribution method supporting unmanned aerial vehicle communication perception integration according to the present invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the illustrations provided in the following embodiments merely illustrate the basic idea of the present invention by way of illustration, and the following embodiments and features in the embodiments may be combined with each other without conflict.
Referring to fig. 1 to fig. 2, the precoding and power distribution method supporting the integration of unmanned aerial vehicle communication perception is provided. For a system comprising a wireless energy transmission scene, a UAV, a plurality of communication users and perceived targets, modeling a weighted sum of mutual information of the communication users, the speed and radar perceived links as an optimization target, and realizing joint precoding, energy transmission and unmanned aerial vehicle perceived communication time distribution and perceived communication power distribution joint optimization.
Fig. 1 is a schematic view of a scenario of an energy transmission and perception communication system, as shown in fig. 1, in which an unmanned aerial vehicle joint communication perception system acquires energy from a radio station, and in an energy transmission stage, the wireless energy station transmits the energy to the unmanned aerial vehicle, and the duration is T 1; in the unmanned aerial vehicle perception communication stage, the unmanned aerial vehicle utilizes the energy of acquireing to carry out radar and communication function simultaneously, and duration is T 2, and total time T=t 1+T2, unmanned aerial vehicle has built-in perception communication dual function system, comprises M dimension transmitting antenna array and M dimension receiving antenna array. The transmitting antenna array transmits communication perception joint beams to communicate with K users, and simultaneously transmits detection signals to a target. The receiving antenna array receives radar reflected signals to acquire information of a detected target.
Fig. 2 is a flow chart of the precoding and power distribution method of the present invention, as shown in fig. 2, the method specifically includes the following steps:
1) Establishing an unmanned aerial vehicle communication perception system model;
The unmanned aerial vehicle communication perception system model comprises: the working phase of the unmanned aerial vehicle communication perception system comprises an energy transmission phase and a joint communication perception phase; in the energy transmission stage, the radio station occupies time T 1 to transmit energy to the unmanned aerial vehicle; in the joint communication sensing stage, the unmanned aerial vehicle performs communication and sensing functions simultaneously by using the acquired energy, the duration is T 2, the total time T=T 1+T2, the unmanned aerial vehicle is configured with a multi-antenna system, the multi-antenna system is composed of an M-dimensional transmitting antenna array and an M-dimensional receiving antenna array, wherein the transmitting antenna array transmits communication sensing joint beams to communicate with K users, simultaneously transmits sensing signals to a target, and the receiving antenna array receives radar reflected signals to acquire detection target information.
2) Establishing an unmanned aerial vehicle energy transmission model;
the unmanned aerial vehicle energy transmission model includes: in the energy transmission stage, the radio station transmits an energy radio frequency signal to the unmanned aerial vehicle, let N t represent the number of transmitting antennas configured by the radio station, the unmanned aerial vehicle is configured with a single receiving antenna, and the energy radio frequency signal received by the unmanned aerial vehicle from the radio station can be expressed as: In the middle of For the radio station to drone channel gain vector, a is the radio frequency energy signal of unit power,N t is additive white gaussian noise with zero mean value, which is an energy beam forming vector; the energy extracted by the drone can be expressed as: wherein eta epsilon 0,1 is the energy conversion efficiency.
3) Combining waveform modeling by an unmanned aerial vehicle communication perception system;
The unmanned aerial vehicle communication perception system joint waveform modeling comprises: in the proposed communication aware joint system, the communication aware joint beam at the nth slot can be expressed as: x [ n ] = W rs[n]+Wc c [ n ], n=0, & gt, n-1, wherein s [ n ] = [ s 1[n],...,sm[n],...,sM[n]]T,sm [ n ] represents radar waveform sent by the M-th antenna, M is more than or equal to 1 and less than or equal to M, c [ n ] = [ c 1[n],...,ck[n],...,cK[n]]T,ck [ n ] represents communication symbol sent by the K-th antenna, K is more than or equal to 1 and less than or equal to K, W r is a sensing system precoding matrix, and W c is a communication precoding matrix; the perceived signal received by the receiver can be expressed as: y r[n]＝Frs[n]+Fc c [ n ] +v [ n ], where F r＝HWr,Fc＝HWc is a radar equivalent channel, H is a sensing channel matrix, and a signal received by a communication user can be expressed as: y c[n]＝Fr′s[n]+Fc 'c [ n ] +v [ n ], where F r′＝H′Wr,Fc′＝H′Wc is a communication equivalent channel, H' is a communication channel matrix, and v [ n ] is Gaussian white noise.
4) Modeling the condition mutual information of the unmanned aerial vehicle sensing system;
the unmanned aerial vehicle perception system condition mutual information modeling comprises the following steps: the mutual information modeling of the sensing system conditions is as follows: Where γ m is the received signal-to-interference-and-noise ratio of the mth sense antenna, which can be expressed as: Where F r]m,m|2 represents the signal power of the mth perceived beam link, sigma i≠m|[Fr]m,i|2 is the interference from the perceived signal of the other link, Σ 2 is the power of white gaussian noise, which is the interference from the communication link.
5) Modeling the maximum communication rate of the unmanned aerial vehicle communication system;
the modeling of the maximum communication data volume of the unmanned aerial vehicle communication perception system comprises the following steps: the amount of drone communication data may be expressed as: where γ' k is the received signal-to-interference-and-noise ratio of the kth communication user, it can be expressed as: Where F c′]k,k|2 represents the signal power of the kth user communication link, sigma i≠k|[Fc′]k,i|2 represents interference from other communication link signals, Representing interference from the radar link.
6) Modeling a power constraint condition of the unmanned aerial vehicle communication perception system;
The unmanned aerial vehicle communication perception system power constraint condition modeling comprises the following steps: let r=e (xnx T n) denote the covariance matrix of the joint waveform x n, where r=w rWr H+WcWc H is obtained, the total power of the joint waveform is modeled as Tr (R), where Tr () represents the trace of the matrix, and the power of target perception and user communication can be expressed as Tr (W rWr H) and Tr (W cWc H), respectively; the system energy constraints can be expressed as: the radar energy constraint can be expressed as: The communication energy constraint can be expressed as: Where l is the duty cycle allocated for the target perceived power and (1-l) is the duty cycle allocated for the communication power; the constraints on the radio station transmit energy can be expressed as: Where E max is the upper transmission energy limit.
7) Determining a joint precoding and power allocation strategy based on system performance optimization;
Determining a joint precoding and power allocation strategy based on system performance optimization includes: the modeling system performance metrics were: c (γ) +αr MI, wherein α represents a weight; the precoding matrix W r,Wc and the power allocation strategy l are determined based on system performance optimization, namely: { W r *,Wc *,l*,T1 *}＝argmax(C(γ)+αRMI), wherein W r *,Wc *,l*,T1 * is the optimal radar precoding, communication precoding, power allocation strategy and energy transmission time, respectively.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.
Claims (1)
1. The precoding and power distribution method supporting the integration of unmanned aerial vehicle communication perception is characterized by comprising the following steps:
S1: establishing an unmanned aerial vehicle communication perception system model, which specifically comprises the following steps: the working phase of the unmanned aerial vehicle communication perception system comprises an energy transmission phase and a joint communication perception phase; in the energy transmission stage, the radio station occupies time T 1 to transmit energy to the unmanned aerial vehicle; in the joint communication sensing stage, the unmanned aerial vehicle simultaneously executes communication and sensing functions by using the acquired energy, the duration is T 2, the total time T=T 1+T2, the unmanned aerial vehicle is configured with a multi-antenna system, the multi-antenna system is composed of an M-dimensional transmitting antenna array and an M-dimensional receiving antenna array, wherein the transmitting antenna array transmits communication sensing joint beams to communicate with K users, simultaneously transmits sensing signals to a target, and the receiving antenna array receives radar reflected signals to acquire detection target information;
S2: the unmanned aerial vehicle energy transmission model is established, and concretely comprises the following steps: in the energy transmission phase, the radio station transmits an energy radio frequency signal to the unmanned aerial vehicle, let N t denote the number of transmitting antennas configured by the radio station, the unmanned aerial vehicle configures a single receiving antenna, and the energy radio frequency signal y t received by the unmanned aerial vehicle from the radio station is expressed as: Wherein the method comprises the steps of For the radio station to drone channel gain vector,Is N t multiplied by 1 dimensional matrix set, a is radio frequency energy signal of unit power,N t is additive white gaussian noise with zero mean value, which is an energy beam forming vector; the energy acquired by the unmanned aerial vehicle is expressed as: wherein eta epsilon [0,1] is the energy conversion efficiency;
S3: modeling a joint waveform of a communication perception system of the unmanned aerial vehicle, and specifically comprises the following steps: in the communication perception system constructed in step S1, the communication perception joint waveform x [ n ] in the nth time slot is expressed as: x [ n ] = W rs[n]+Wc c [ n ], n=0,..n-1, where s [ n ] = [ s 1[n],...,sm[n],...,sM[n]]T,sm [ n ] represents the radar waveform sent by the M-th antenna, and M is 1-M; c < n > = [ c 1[n],...,ck[n],...,cK[n]]T,ck < n > represents a communication symbol sent by a kth antenna, and K is more than or equal to 1 and less than or equal to K; w r is a sensing system precoding matrix, and W c is a communication precoding matrix; the received perceptual signal y r n at the receiver is denoted as: y r[n]＝Frs[n]+Fc c [ n ] +v [ n ], wherein F r＝HWr,Fc＝HWc is a radar equivalent channel, H is a sensing channel matrix, and a signal y c [ n ] received by a communication user is expressed as: y c[n]＝F′rs[n]+F′c c n+v n, where F r′＝H′Wr,Fc′＝H′Wc is a communication equivalent channel, H' is a communication channel matrix, and v n is Gaussian white noise;
s4: modeling the mutual information of the sensing system conditions of the unmanned aerial vehicle and the transmission data volume of the unmanned aerial vehicle communication system;
In step S4, the perceived system condition mutual information R MI is modeled as: where γ m is the perceived signal-to-interference-and-noise ratio at the mth receive antenna, expressed as: Where F r]m,m|2 represents the signal power of the mth perceived beam link, sigma i≠m|[Fr]m,i|2 is the interference from the perceived signal of the other link, Σ 2 is the power of white gaussian noise, which is the interference from the communication link;
In step S4, the unmanned aerial vehicle communication system transmission data amount C (γ) is modeled as: where γ' k is the signal-to-interference-and-noise ratio of the kth communication user, expressed as: Where F c′]k,k|2 represents the signal power of the kth user communication link, sigma i≠k|[Fc′]k,i|2 represents interference from other communication link signals, Representing interference from the radar link;
S5: modeling a power constraint condition of a communication perception system of the unmanned aerial vehicle, specifically comprising: let r=e (x n x T n) denote the covariance matrix of the joint waveform x n, yielding r=w rWr H+WcWc H, the total power of the joint waveform modeled as Tr (R), where Tr () represents the trace of the matrix, and the power of target perception and user communication are denoted Tr (W rWr H) and Tr (W cWc H), respectively;
The system energy constraints are expressed as:
the radar energy constraint is expressed as:
the communication energy constraint is expressed as: where l is the duty cycle allocated for the target perceived power, i.e. the power allocation strategy; (1-l) is a duty cycle allocated for communication power;
the constraints on the radio station transmit energy are expressed as: Wherein E max is the upper transmission energy limit;
S6: determining a joint precoding and power allocation strategy based on system performance optimization, specifically comprising: the modeling system performance metrics were: c (γ) +αr MI, wherein α represents a weight; the precoding matrix W r、Wc and the power allocation strategy l are determined based on system performance optimization, namely: wherein W r *, And l *、T1 * is an optimal radar precoding matrix, a communication precoding matrix, a power allocation strategy and an energy transmission time respectively.
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