CN114584235A - Perception-based uplink communication security method for mobile aerial eavesdropper - Google Patents

Perception-based uplink communication security method for mobile aerial eavesdropper Download PDF

Info

Publication number
CN114584235A
CN114584235A CN202210164916.5A CN202210164916A CN114584235A CN 114584235 A CN114584235 A CN 114584235A CN 202210164916 A CN202210164916 A CN 202210164916A CN 114584235 A CN114584235 A CN 114584235A
Authority
CN
China
Prior art keywords
eavesdropper
communication
user
perception
base station
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202210164916.5A
Other languages
Chinese (zh)
Other versions
CN114584235B (en
Inventor
费泽松
张嘉慧
刘鹏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Institute of Technology BIT
Original Assignee
Beijing Institute of Technology BIT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Institute of Technology BIT filed Critical Beijing Institute of Technology BIT
Priority to CN202210164916.5A priority Critical patent/CN114584235B/en
Publication of CN114584235A publication Critical patent/CN114584235A/en
Application granted granted Critical
Publication of CN114584235B publication Critical patent/CN114584235B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H04B17/3913—Predictive models, e.g. based on neural network models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • 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
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses an uplink communication security method for a mobile aerial eavesdropper based on perception, and belongs to the field of communication physical layer security and radar interference suppression. Aiming at the characteristics of flexible deployment and high maneuverability of a mobile aerial eavesdropper, the radar signal and the receiving beam former are iteratively optimized by joint design through establishing a communication and perception integrated model, and balance optimization is carried out between interference AE and user communication; under the condition of ensuring the communication between the base station BS and a user, the path of the mobile AE and the channel state information CSI between the base station BS and the AE are predicted by using an extended Kalman filtering method, the accurate tracking of the AE is realized, the accurate CSI between the base station BS and the AE is obtained in real time, the interference capability of radar signals to the AE is further enhanced, and the confidentiality of reliable communication is improved; based on an alternate optimization algorithm, optimization of the radar signals is converted into a series of semi-positive definite planning SDP problems through a continuous convex approximation SCA technology, the radar signals and the receiving beams are designed in a combined mode, and the communication secrecy capability is further improved.

Description

Perception-based uplink communication security method for mobile aerial eavesdropper
Technical Field
The invention relates to an uplink communication security method for a mobile aerial eavesdropper based on perception, and belongs to the field of communication physical layer security and radar interference suppression.
Background
Unmanned Aerial Vehicles (UAVs) have the advantages of good communication links, flexible deployment, high maneuverability and the like, and are widely applied to public applications such as patrol monitoring, search and rescue and the like. However, due to the inherently open nature of wireless communications, the threat of information security and privacy by drones is also increasing, e.g., drones may be deployed as Airborne Eavesdroppers (AEs). To solve this problem, a Physical Layer Security (PLS) technique may be employed, which utilizes the difference in Channel State Information (CSI) between a legitimate link and an ongoing link to enhance confidentiality, thereby achieving information transmission security for AE.
One difficulty in implementing PLS in a practical communication system is to obtain CSI for AEs, since AEs are generally uncooperative. Communication awareness Integration (ISAC) is of great interest because it is capable of performing both awareness and communication functions, which can help estimate angle of arrival (AoA) information for AEs, which can then be used for security beamforming. Su considers the PLS problem in downlink MIMO ISAC systems, where a robust optimization method is proposed to improve the privacy rate under incomplete CSI. Fang investigated joint transmit beam pattern and phase shift optimization for reconfigurable smart surface (RIS) assisted safety DFRC systems. For scenarios with air AE, y.cai proposes a robust and secure resource allocation scheme to enhance the privacy performance of UAVs.
For the uplink, x.wang proposes a perceptually-assisted uplink communication security framework for AEs where the base station synthesizes a wide beam to detect unauthorized AEs while ensuring communication security. However, none of the existing research methods have considered tracking of moving AEs. Since the AE moves at high speed in the air, its position and CSI also change rapidly, tracking of the AE is crucial for the design of PLS. The present invention is directed to addressing uplink communication security with awareness assistance for mobile AE.
Disclosure of Invention
Aiming at the problem of uplink communication safety of mobile AE, the invention mainly aims to provide a perception-based uplink communication safety method for a mobile aerial eavesdropper on the basis of an ISAC system, and aims at the characteristics of flexible deployment and high maneuverability of the mobile aerial eavesdropper, an EKF (extended Kalman Filter) technology is utilized to predict the track and Channel State Information (CSI) of the mobile aerial eavesdropper, and the AE is accurately tracked to obtain accurate CSI; the method has the advantages that the noise ratio SINR of the base station BS is maximized under the constraints of AE signal and interference and noise ratio SINR of an aerial eavesdropper, tracking mean square error MSE and power budget, the radar signal and the receiving beam former are jointly designed, and the design is balanced between the AE signal interference and user communication, so that the confidentiality is maximized, and the communication confidentiality is enhanced.
The purpose of the invention is realized by the following technical scheme.
The invention discloses a perception-based uplink communication security method for a mobile aerial eavesdropper, which comprises the following steps:
aiming at the characteristics of flexible deployment and high mobility of a mobile aerial eavesdropper, a communication and perception Integration (ISAC) model is established, so that the base station BS can track and interfere AE while communicating with a user by applying the perception function of a radar signal, and the communication confidentiality in an ISAC system is expressed.
Step 1. A: setting the ISAC system to include a system having Nx×NyA full duplex ISAC base station of uniform planar array UPA, a single antenna legal user and a single antenna aerial eavesdropper; setting the positions of a base station and a user, and the initial position and the speed of AE, wherein the position of the base station is recorded as q0The location of the user is noted as qbAE initial position is qe[n]Corresponding speed of
Figure BDA0003515937920000021
q0＝[x0，y0，0]T (1)
qb＝[xb，yb，0]T (2)
qe[n]＝[xe[n]，ye[n]，ze[n]]T (3)
Figure BDA0003515937920000022
Where N denotes the discrete number of periods T into NSlots, each time slot
Figure BDA0003515937920000023
The nth time slot.
Step 1.B: according to the base station position q given in step 1.A0User location qbAnd the initial position q of AEe[n]And corresponding speed
Figure BDA0003515937920000024
Channels from user to BS, user to AE and BS to AE are respectively denoted as
Figure BDA0003515937920000025
Figure BDA0003515937920000026
Figure BDA0003515937920000027
Where ρ is0Indicates the reference distance d0Channel power at 1 m;
Figure BDA0003515937920000028
multipath fading components subject to a standard complex gaussian distribution; du，b＝||qb-q0||，du，e[n]＝||qe[n]-qbI and db，e[n]＝||qe[n]-q0The | | respectively represents the distance from the user to the BS, the distance from the user to the AE at the nth time slot and the distance from the BS to the AE; theta [ n ]]，φ[n]Represents azimuth angle (AoD) and altitude AoD from BS to Eve in the nth slot; a (theta n)]，φ[n]) Represents the steering vector of the UPA, expressed as
Figure BDA0003515937920000031
Step 1.C: from the channels from the user to the BS, from the user to the AE and from the BS to the AE given in step 1.B, the expressions of the uplink signal received at the BS and the signal received by the eavesdropper are found to be
rb＝hubs+HSIx+ξa*(θ，φ)aH(θ，φ)x+nb (9)
Figure BDA0003515937920000032
Wherein s represents an uplink signal of a user;
Figure BDA0003515937920000033
denotes self-interference (SI) caused by full-duplex operation, the elements of which
Figure BDA0003515937920000034
(ii) a complex gaussian distribution subject to independent and co-distribution; x is a radar signal transmitted by the BS; a (θ, Φ) ═ vec (a (θ, Φ)) means that the UPA steering vector is represented as a column vector;
Figure BDA0003515937920000035
representing Additive White Gaussian Noise (AWGN) at the BS,
Figure BDA0003515937920000039
denotes AWGN at AE;
Figure BDA0003515937920000036
ζ represents the radar cross-sectional area (RCS).
Step 1.D: from the channel and received signal expressions in step 1.B and step 1.C, SINR expressions at BS and AE are obtained, respectively expressed as
Figure BDA0003515937920000037
Wherein the content of the first and second substances,
Figure BDA0003515937920000038
representing the receive beamforming vector, P, of the BS at the time of receiving the superimposed signaluIs the signal transmission power of the user.
Step 1.E: substituting SINR expressions at BS and AE in step 1.D into the secret ability calculation formula to obtain secret ability of uplink communication
Cs[n]＝[RB-RE[n]]+ (12)
Wherein R isB＝log2(1+γb)，RE[n]＝log2(1+γe[n])[·]+Denotes max {. 0}
Step 1.F: by establishing a communication and perception integrated model shown in formulas (1), (2), (3), (4), (9) and (10), the base station BS can track and interfere AE by using the perception function of radar signals while communicating with users, and the uplink communication security capability of the ISAC system is shown as formula (12).
And step two, predicting the track of the mobile aerial eavesdropper AE and the channel state information CSI between the base station and the AE by using an extended Kalman filtering EKF method based on the communication and perception integrated model established in the step one, realizing accurate tracking of the mobile aerial eavesdropper AE, and obtaining accurate CSI between the base station and the AE in real time.
Step 2. A: in a communication and perception integrated model, observable parameters are obtained through a Matched Filter (MF) principle based on radar echo signals
Figure BDA0003515937920000041
Wherein the content of the first and second substances,
Figure BDA0003515937920000042
and
Figure BDA0003515937920000043
indicating the round trip time delay and doppler shift in time slot n.
Step 2. B: according to the observable parameters obtained in the step 2.A
Figure BDA0003515937920000044
Establishing the relationship between the observable parameters and the AE state to obtain the state measurement model of the AE
Figure BDA0003515937920000045
Equation (13) is written in the following form
Figure BDA0003515937920000046
Wherein
Figure BDA0003515937920000047
Indicates the state of AE at time slot n; gnIn (1)
Figure BDA0003515937920000048
fcAnd c represents the carrier frequency and speed of light; w [ n ]]＝[wτ[n]，wμ[n]，wsinθ[n]，wcosθ[n]，wsinφ[n]]TRepresenting a zero mean gaussian noise vector whose covariance matrix is expressed as
Figure BDA0003515937920000051
Figure BDA0003515937920000052
aiI is a constant related to the system configuration and the particular signal processing algorithm, and G denotes the matched filter gain.
Step 2. C: predicting the state of AE in n +1 time slot and the MSE matrix according to the state of the nth time slot AE obtained in the step 2.B and the MSE matrix obtained by calculation
Figure BDA0003515937920000053
Figure BDA0003515937920000054
Wherein S iscvRepresents a state transition matrix, represented as
Figure BDA0003515937920000055
In the above formula I3And 033 × 3 identity matrix and zero matrix respectively;
Figure BDA0003515937920000056
in the above formula
Figure BDA0003515937920000057
And
Figure BDA0003515937920000058
the variance of gaussian position noise position and velocity noise in the x, y, and z-axis directions are respectively represented.
Step 2. D: calculating a Kalman gain matrix by using the result obtained in the step 2.C
Figure BDA0003515937920000059
Wherein the content of the first and second substances,
Figure BDA00035159379200000510
the specific expression is
Figure BDA0003515937920000061
The slot index is omitted here, in the above formula
Figure BDA0003515937920000062
Figure BDA0003515937920000063
Step 2. E: and (3) correcting and updating the state and MSE matrix of the AE in the n +1 time slot, which are obtained by predicting in the step (2. C) according to formulas (20) and (21), predicting the track of the AE of the mobile aerial eavesdropper and the CSI between the base station and the AE, realizing accurate tracking of the AE of the mobile aerial eavesdropper, and obtaining the accurate CSI between the base station and the AE in real time.
Figure BDA0003515937920000064
M[n+1]＝(I-K[n+1]Gn)M[n+1|n] (21)
And step three, optimizing a receiving beam former u by using a closed optimal solution for the given radar signal x.
Step 3.A: given x, the SINR of the maximized BS is taken as an objective function, the power of a receiving beam is taken as a constraint condition, and the optimization problem is as follows
Figure BDA0003515937920000065
s.t.||u||＝1 (23)
Step 3.B: and (4) obtaining a closed optimal solution of u in the step (3. A) according to Rayleigh-Ritz theorem to obtain an optimized receiving beam former u.
And step four, for u given in the step three, converting the optimization problem of the radar signal into a series of semi-positive definite programming SDP problems to be solved based on a continuous convex approximation SCA method, and updating the optimal solution of the radar signal x.
Step 4.A: given u, taking the SINR of the maximized BS as an objective function, and taking the SINR of AE, the tracking Mean Square Error (MSE) and the radar transmission signal power budget as constraints, the optimization problem is as follows:
Figure BDA0003515937920000071
s.t.γe[n+1]≤γbq， (25)
Tr(M[n+1])≤Γm， (26)
||x||2≤PR (27)
wherein, γbpIs a pre-designed SINR threshold, Γ, of AEmIs a maximum tolerated tracking MSE threshold, PRRepresenting the maximum transmit power of the radar signal.
Step 4.B: a first constraint (25) on step 4.A, at a given point
Figure BDA0003515937920000072
Using a first order Taylor series expansion to obtain
Figure BDA0003515937920000073
Step 4.D: for the second constraint (26) in step 4.A, at the given point
Figure BDA0003515937920000074
Applying a first order Taylor series expansion to M-1[n+1]Conversion to the following linear form
Figure BDA0003515937920000075
Wherein the content of the first and second substances,
Figure BDA0003515937920000076
thus, the constraint is expressed as
Figure BDA0003515937920000077
Wherein v isiNot less than 0, i ═ {1, …,6} is an introduced auxiliary variable, eiIs a 16The ith column vector of (2).
Step 4.D: converting the problem in step 4.A into the following semi-definite planning SDP problem
Figure BDA0003515937920000078
Figure BDA0003515937920000079
Figure BDA0003515937920000081
||x||2≤PR
Step 4. E: and D, solving the convex optimization problem in the step 4.D, and updating x.
Step five: based on the repeated iteration of the third step and the fourth step, the radar signal and the receiving beam former are designed and optimized in a combined mode, balance optimization between interference AE and user communication is achieved, namely the noise ratio SINR of the base station BS is maximized under the constraint of the AE signal and interference noise ratio SINR of the aerial eavesdropper, the tracking mean square error MSE and the power budget, the mobile aerial eavesdropper AE is accurately tracked and predicted by utilizing a Kalman filtering method under the condition that the base station BS and the user communication are guaranteed, the interference capability of the radar signal to the AE is further enhanced, and the confidentiality of reliable communication is improved.
Judging whether the fraction increase of the value in the fourth step compared with the last iteration is smaller than a convergence precision threshold or not, or whether the fraction increase reaches the maximum iteration number, if not, jumping to the third step for iteration, if so, ending the iteration, outputting the radar signal and the receiving beam former after the joint design optimization, realizing the balance optimization between the interference AE and the user communication, namely, ensuring the interference capability of the AE signal and the interference and noise ratio SINR of the aerial eavesdropper to maximize the noise ratio SINR of the BS under the constraints of the tracking mean square error MSE and the power budget, ensuring that the BS and the user communication condition, accurately tracking and predicting the AE of the mobile aerial eavesdropper by using a Kalman filtering method, further enhancing the interference capability of the radar signal to the AE, and improving the security of reliable communication.
Has the advantages that:
1. the invention discloses a perception-based uplink communication safety method for a mobile aerial eavesdropper, which aims at the characteristics of flexible deployment and high maneuverability of the mobile aerial eavesdropper, realizes balance optimization between interference AE and user communication by establishing a communication and perception integrated model and combining design of an iterative optimization radar signal and a receiving beam former, namely, the interference capability of the AE signal and the interference and noise ratio SINR of the aerial eavesdropper is ensured, the noise ratio SINR of a base station BS is maximized under the constraints of a tracking mean square error MSE and a power budget, and the AE of the mobile aerial eavesdropper is accurately tracked and predicted by using a Kalman filtering method under the condition of ensuring the communication between the base station BS and a user, so that the interference capability of the radar signal to the AE is further enhanced, and the security of reliable communication is improved.
2. The invention discloses a perception-based uplink communication safety method for a mobile aerial eavesdropper, which aims at the characteristics of flexible deployment and high maneuverability of the mobile aerial eavesdropper, and realizes tracking and interference of AE by applying the perception function of radar signals while a base station BS communicates with a user by establishing a communication and perception integrated model.
3. The invention discloses a perception-based uplink communication safety method for a mobile aerial eavesdropper, which is based on a communication and perception integrated model and utilizes an Extended Kalman Filter (EKF) method to predict the track of the mobile aerial eavesdropper AE and the CSI between a base station and the AE, so that the mobile aerial eavesdropper AE is accurately tracked, the accurate CSI between the base station and the AE is obtained in real time, the CSI is effectively obtained, the tracking MSE is reduced, the interference capability of radar signals to the AE is further enhanced, and the confidentiality of reliable communication is improved.
4. The invention discloses a perception-based uplink communication security method for a mobile aerial eavesdropper, which is based on an alternating optimization algorithm, optimizes radar signals by a continuous convex approximation SCA technology and converts the optimization of the radar signals into a series of semi-definite programming SDP problems, and jointly designs the radar signals and receiving beams to further improve the communication confidentiality.
Drawings
FIG. 1 is a general flowchart of the upstream communication security method for mobile eavesdroppers based on perception and in embodiment 1 of the invention;
FIG. 2 is a flow chart of the method for establishing a communication integrated ISAC system and representing the uplink communication security capability in embodiment 1 of the present invention based on perception for mobile eavesdroppers in the air;
FIG. 3 is a flow chart of the EKF method in embodiment 1 and the uplink communication security method for mobile aerial eavesdroppers based on perception of the invention;
fig. 4 is a schematic diagram of the perception-based uplink communication security method for a mobile aerial eavesdropper and alternate optimization of the approximate form-based optimal solution and the continuous convex approximation SCA method in embodiment 1.
FIG. 5 is a graph of simulation results of tracking AE after the method for security of uplink communication for a mobile eavesdropper in the air based on perception and the method implemented in embodiment 1;
fig. 6 is a graph showing simulation results of average privacy capabilities of the upstream communication security method for a mobile eavesdropper based on sensing and the method implemented in embodiment 1.
Detailed Description
The method for security of upstream communication for mobile eavesdroppers based on perception according to the invention is described in detail below with reference to the accompanying drawings and specific embodiments.
Example 1
This example details the steps in the implementation of the upstream communication security method for mobile eavesdroppers based on perception.
This case considers that there is an uplink transmission of a mobile aerial eavesdropper AE in the ISAC system, and the BS also transmits a radar signal to obtain the location of the AE when receiving the uplink signal of the user, while the radar signal is also used as an interference signal to the AE. However, due to the high speed of the AE, its position and CSI also change rapidly, which has a great influence on the design of PLS. Therefore, an uplink communication security method aiming at the mobile aerial eavesdropper based on perception is adopted, and the uplink communication security capability is improved.
Fig. 2 is a flow chart of the method for establishing a universal-sense integrated ISAC system and representing the security capability of uplink communication in embodiment 1 according to the present invention.
As can be seen from fig. 2, according to the characteristics of flexible deployment and high mobility of a mobile aerial eavesdropper, by establishing an integrated communication and perception (ISAC) model, the base station BS is enabled to track and interfere with AE by applying a perception function of a radar signal while communicating with a user, and a detailed flow of an upstream communication security capability in an ISAC system is represented, specifically, in the embodiment, the method has the following operation flow:
step 1. A: setting the ISAC system to include a system having Nx×NyA full duplex ISAC base station of uniform plane array UPA, a single antenna legal user and a single antenna aerial eavesdropper; setting the positions of a base station and a user, and the initial position and the speed of AE, wherein the position of the base station is recorded as q0The location of the user is noted as qbAE initial position is qe[n]Corresponding speed of
Figure BDA0003515937920000101
q0＝[x0，y0，0]T (1)
qb＝[xb，yb，0]T (2)
qe[n]＝[xe[n]，ye[n]，ze[n]]T (3)
Figure BDA0003515937920000102
Wherein the period T is discretized into N time slots, each time slot
Figure BDA0003515937920000103
n denotes an nth slot.
In particular, in this embodiment, δ is 0.2s, N is 80, and q is0＝[0，0，0]T，
Figure BDA0003515937920000104
Figure BDA0003515937920000105
The AE flies above the user at a speed of 35m/s in any direction.
Step 1.B: according to the base station position q given in step 1.A0User location qbAnd AE initial position qe[n]And corresponding speed
Figure BDA0003515937920000111
Denote channels from user to BS, user to AE and BS to AE, respectively
Figure BDA0003515937920000112
Figure BDA0003515937920000113
Figure BDA0003515937920000114
Where ρ is0Indicates the reference distance d0Channel power at 1 m;
Figure BDA0003515937920000115
multipath fading components subject to a standard complex gaussian distribution; du，b＝||qb-q0||，du，e[n]＝||qe[n]-qb| | and db，e[n]＝||qe[n]-q0The | | respectively represents the distance from the user to the BS, the distance from the user to the AE at the nth time slot and the distance from the BS to the AE; theta [ n ]]，φ[n]Represents azimuth angle (AoD) and altitude AoD from BS to Eve in the nth slot; a (theta n)]，φ[n]) Represents the steering vector of the UPA, expressed as
Figure BDA0003515937920000116
In particular to the present embodiment, ρ0＝-50dB
Step 1.C: from the channels from the user to the BS, from the user to the AE and from the BS to the AE given in step 1.B, the expressions of the uplink signal received at the BS and the signal received by the eavesdropper are found to be
rb＝hubs+HSIx+ξa*(θ，φ)aH(θ，φ)x+nb (9)
Figure BDA0003515937920000117
Wherein s represents an uplink signal of a user;
Figure BDA0003515937920000118
denotes self-interference (SI) caused by full-duplex operation, the elements of which
Figure BDA0003515937920000119
(ii) a complex gaussian distribution subject to independent and co-distribution; x is a radar signal transmitted by the BS; a (θ, Φ) ═ vec (a (θ, Φ)) means that the UPA steering vector is represented as a column vector;
Figure BDA00035159379200001110
representing Additive White Gaussian Noise (AWGN) at the BS,
Figure BDA00035159379200001111
represents AWGN at AE;
Figure BDA00035159379200001112
ζ represents the radar cross-sectional area (RCS).
With particular reference to the present embodiment, it is,
Figure BDA00035159379200001113
step 1.D: from the channel and received signal expressions in step 1.B and step 1.C, SINR expressions at BS and AE are obtained, respectively expressed as
Figure BDA0003515937920000121
Wherein the content of the first and second substances,
Figure BDA0003515937920000122
representing the receive beamforming vector, P, of the BS at the time of receiving the superimposed signaluIs the signal transmission power of the user.
Specific to the present embodiment, Pu＝0dBm。
Step 1.E: substituting SINR expressions at BS and AE in step 1.D into the secret ability calculation formula to obtain secret ability of uplink communication
Cs[n]＝[RB-RE[n]]+ (12)
Wherein R isB＝log2(1+γb)，RE[n]＝log2(1+γe[n])[·]+Denotes max {. 0}
Step 1.F: by establishing a communication and perception integrated model shown in formulas (1), (2), (3), (4), (9) and (10), the base station BS can track and interfere AE by using the perception function of radar signals while communicating with users, and the uplink communication security capability of the ISAC system is shown as formula (12).
So far, from step 1.a to step 1.F, the establishment of the general-purpose integrated ISAC system model in the present embodiment is completed, and the uplink communication security capability is represented.
Fig. 3 is a flowchart of the upstream communication security method for mobile aerial eavesdroppers based on perception and the EKF method in embodiment 1.
As can be seen from fig. 3, based on the communication and perception integrated model established in the first step, the extended kalman filter EKF method is used to predict the track of the mobile aerial eavesdropper AE and the CSI of the channel state information between the base station and the AE, so as to accurately track the mobile aerial eavesdropper AE and obtain the accurate CSI between the base station and the AE in real time, specifically, in this embodiment, the method has the following operation flow:
step 2. A: based on radar echo signals, observable parameters are obtained through the MF principle of a matched filter
Figure BDA0003515937920000123
Wherein the content of the first and second substances,
Figure BDA0003515937920000124
and
Figure BDA0003515937920000125
indicating the round trip time delay and doppler shift in time slot n.
Step 2. B: according to the observable parameters obtained in the step 2.A
Figure BDA0003515937920000126
Establishing the relationship between the observable parameters and the AE state to obtain the state measurement model of the AE
Figure BDA0003515937920000131
Equation (13) is written in the form:
Figure BDA0003515937920000132
wherein
Figure BDA0003515937920000133
Indicates the state of AE at time slot n; gnIn (1)
Figure BDA0003515937920000134
fcAnd c represents the carrier frequency and speed of light; w [ n ]]＝[wτ[n]，wμ[n]，wsinθ[n]，wcosθ[n]，wsinφ[n]]TRepresenting a zero mean gaussian noise vector whose covariance matrix is expressed as
Figure BDA0003515937920000135
Figure BDA0003515937920000136
aiI is a constant related to the system configuration and the particular signal processing algorithm, and G denotes the matched filter gain.
Specific to the present embodiment, a1＝6.7×10-7，a2＝2×104，a3＝a4＝1，G＝104。
Step 2. C: predicting the state of AE in the n +1 time slot and the MSE matrix according to the state of the nth time slot AE obtained in the step 2.B and the MSE matrix obtained by calculation
Figure BDA0003515937920000137
Figure BDA0003515937920000138
Wherein S iscvRepresents a state transition matrix, represented as
Figure BDA0003515937920000141
In the above formula I3And 033 × 3 identity matrix and zero matrix respectively;
Figure BDA0003515937920000142
in the above formula
Figure BDA0003515937920000143
And
Figure BDA0003515937920000144
the variance of gaussian position noise position and velocity noise in the x, y, and z-axis directions are respectively represented.
Step 2. D: calculating a Kalman gain matrix by using the result obtained in the step 2.C
Figure BDA0003515937920000145
Wherein the content of the first and second substances,
Figure BDA0003515937920000146
the specific expression is
Figure BDA0003515937920000147
The slot index is omitted here, in the above formula
Figure BDA0003515937920000148
Figure BDA0003515937920000149
Step 2. E: and (3) correcting and updating the state and MSE matrix of the AE in the n +1 time slot, which are obtained by predicting in the step (2) and the step (C), according to the formulas (20) and (21), predicting the track of the AE of the mobile aerial eavesdropper and the CSI of the channel state between the base stations and the AE, accurately tracking the AE of the mobile aerial eavesdropper, and obtaining the accurate CSI between the base stations and the AE in real time.
Figure BDA00035159379200001410
M[n+1]＝(I-K[n+1]Gn)M[n+1|n] (21)
From step 2.A to step 2.E, the method for predicting the track of the mobile aerial eavesdropper AE and the channel state information CSI between the base station and the AE by using the extended Kalman filter EKF method is completed, the mobile aerial eavesdropper AE is accurately tracked, and the accurate CSI between the base station and the AE is obtained in real time.
Fig. 4 is a flowchart of the method for security of uplink communication for a mobile eavesdropper in the air based on perception and the alternate optimization method in embodiment 1.
As can be seen from fig. 4, the present invention optimizes the receive beamformer u using a closed-form optimal solution for a given radar signal x, specifically comprising the following sub-steps:
step 3.A: given x, the SINR of the maximized BS is taken as an objective function, the power of a receiving beam is taken as a constraint condition, and the optimization problem is as follows
Figure BDA0003515937920000151
s.t.||u||＝1 (23)
Step 3.B: and obtaining a closed optimal solution of u in the step 3.A according to Rayleigh-Ritz theorem.
By now, from step 3.a to step 3.B, it is done to optimize the receive beamformer u for a given radar signal x using an optimal solution in approximate form.
Based on the given u in fig. 4, the optimization problem of the radar signal is converted into a series of semi-definite planning SDP problems to be solved based on the continuous convex approximation SCA method, specifically to this embodiment, the method has the following operation flow:
step 4.A: given u, taking the SINR of the maximized BS as an objective function, taking the SINR of AE, tracking Mean Square Error (MSE) and radar transmission signal power budget as constraints, and taking the optimization problem as the following formula
Figure BDA0003515937920000152
s.t.γe[n+1]≤γbq， (25)
Tr(M[n+1])≤Γm， (26)
||x||2≤PR (27)
Wherein, γbpIs a pre-designed SINR threshold, Γ, of AEmIs a maximum tolerated tracking MSE threshold, PRRepresenting the maximum transmit power of the radar signal.
Specific to this example, γbp＝0.05，PR＝30dBm，ΓmTake 1.5 or 3.
Step 4.B: a first constraint (25) on step 4.A, at a given point
Figure BDA0003515937920000153
Using a first order Taylor series expansion to obtain
Figure BDA0003515937920000161
Step 4.D: for the second constraint in step 4.A, at the given point
Figure BDA0003515937920000162
Applying a first order Taylor series expansion to M-1[n+1]Conversion to the following linear form
Figure BDA0003515937920000163
Wherein the content of the first and second substances,
Figure BDA0003515937920000164
thus, the constraint is expressed as
Figure BDA0003515937920000165
Wherein v isiNot less than 0, i ═ {1, …,6} is an introduced auxiliary variable, eiIs I6The ith column vector of (2).
Step 4.D: converting the problem in the step 4.A into the following semi-definite programming SDP problem
Figure BDA0003515937920000166
Figure BDA0003515937920000167
Figure BDA0003515937920000168
||x||2≤PR
Step 4. E: solving the convex optimization problem in the step 4.D, and updating
Figure BDA0003515937920000169
From step 4.a to step 4.E, the method for optimizing radar signals for a given u according to the present embodiment is completed.
Based on the repeated iteration of the third step and the fourth step, the radar signal and the receiving beam former are designed and optimized in a combined mode, balance optimization between interference AE and user communication is achieved, namely the noise ratio SINR of the base station BS is maximized under the constraint of the AE signal and interference noise ratio SINR of the aerial eavesdropper, the tracking mean square error MSE and the power budget, the mobile aerial eavesdropper AE is accurately tracked and predicted by utilizing a Kalman filtering method under the condition that the base station BS and the user communication are guaranteed, the interference capability of the radar signal to the AE is further enhanced, and the confidentiality of reliable communication is improved.
Judging whether the fraction increase of the value in the fourth step compared with the last iteration is smaller than a convergence precision threshold or not, or whether the fraction increase reaches the maximum iteration number, if not, jumping to the third step for iteration, if so, ending the iteration, outputting the radar signal and the receiving beam former after the joint design optimization, realizing the balance optimization between the interference AE and the user communication, namely, ensuring the interference capability of the AE signal and the interference and noise ratio SINR of the aerial eavesdropper to maximize the noise ratio SINR of the BS under the constraints of the tracking mean square error MSE and the power budget, ensuring that the BS and the user communication condition, accurately tracking and predicting the AE of the mobile aerial eavesdropper by using a Kalman filtering method, further enhancing the interference capability of the radar signal to the AE, and improving the security of reliable communication.
So far, the flow of the upstream communication security method for the mobile aerial eavesdropper based on perception of the embodiment is completed from the step one to the step five.
Fig. 5 is a graph showing simulation results of tracking AE after the method for securing uplink communication to a mobile eavesdropper in the air according to the present invention based on sensing and the method implemented in embodiment 1.
In fig. 5, the abscissa is the number of time slots, values are sequentially 0,20,40,60, and 80, the ordinate is tracking MSE, the unit is dB, simulation experiments perform comparative analysis on prediction tracking based on EKF and tracking based on echo, and it can be seen from fig. 4 that the method provided by the present invention can obtain smaller tracking MSE, that is, tracking AE is more accurate, and better CSI can be obtained.
Fig. 6 is a graph of the simulation results of the average privacy capabilities of the upstream communication security method for mobile aerial eavesdroppers based on perception of the invention and the method implemented in embodiment 1.
In fig. 6, the abscissa is the distance between the user and the base station, which in turn takes 120,140,160,180,200,220 units of m, and the ordinate is the communication security capability, which is in bit/s/Hz. The simulation experiment carries out comparative analysis on three conditions of a Maximum Ratio Transmission (MRT) beam forming method, a Transient Zero Forcing (TZF) beam forming method and an EKF-based perception interference (EKF-SAJ) method, and fig. 5 shows that the method provided by the invention can obtain higher communication confidentiality under the condition of the same number of antennas, namely, the communication safety of an uplink is effectively improved.
While the foregoing is directed to the preferred embodiment of the present invention, it is not intended that the invention be limited to the embodiment and the drawings disclosed herein. Equivalents and modifications may be made without departing from the spirit of the disclosure, which is to be considered as within the scope of the invention.

Claims (6)

1. An uplink communication security method aiming at a mobile aerial eavesdropper based on perception is characterized in that: comprises the following steps of (a) preparing a solution,
aiming at the characteristics of flexible deployment and high mobility of a mobile aerial eavesdropper, a communication and perception integrated ISAC model is established, so that a base station BS can track and interfere AE while communicating with a user by applying the perception function of a radar signal, and the communication confidentiality in an ISAC system is expressed;
step two, predicting the track of the AE of the mobile aerial eavesdropper and the CSI between the base station and the AE by using an EKF (extended Kalman Filter) method based on the communication and perception integrated model established in the step one, realizing accurate tracking of the AE of the mobile aerial eavesdropper, and obtaining accurate CSI between the base station and the AE in real time;
step three, optimizing a receiving beam former u by using a closed optimal solution for a given radar signal x;
step four, for u given in the step three, converting the optimization problem of the radar signal into a series of semi-positive definite programming SDP problems to be solved based on a continuous convex approximation SCA method, and updating the optimal solution of the radar signal x;
step five: based on the repeated iteration of the third step and the fourth step, the radar signal and the receiving beam former are designed and optimized in a combined mode, balance optimization between interference AE and user communication is achieved, namely the noise ratio SINR of the base station BS is maximized under the constraint of the AE signal and interference noise ratio SINR of the aerial eavesdropper, the tracking mean square error MSE and the power budget, the mobile aerial eavesdropper AE is accurately tracked and predicted by utilizing a Kalman filtering method under the condition that the base station BS and the user communication are guaranteed, the interference capability of the radar signal to the AE is further enhanced, and the confidentiality of reliable communication is improved.
2. The perception-based upstream communication security method for a mobile eavesdropper on the air of claim 1, wherein: the first implementation method of the method is that,
step 1. A: setting the ISAC system to include a system having Nx×NyA full duplex ISAC base station of uniform planar array UPA, a single antenna legal user and a single antenna aerial eavesdropper; setting the positions of a base station and a user, and the initial position and the speed of AE, wherein the position of the base station is recorded as q0The location of the user is noted as qbAE initial position is qe[n]Corresponding speed of
Figure RE-FDA0003622378170000011
q0＝[x0,y0,0]T (1)
qb＝[xb,yb,0]T (2)
qe[n]＝[xe[n],ye[n],ze[n]]T (3)
Figure RE-FDA0003622378170000012
Where N denotes the discretization of the period T into N time slots, each time slot
Figure RE-FDA0003622378170000013
The nth time slot;
step 1.B, according to the base station position q given in step 1.A0User location qbAnd the initial position q of AEe[n]And corresponding speed
Figure RE-FDA00036223781700000212
Channels from user to BS, user to AE and BS to AE are respectively denoted as
Figure RE-FDA0003622378170000021
Figure RE-FDA0003622378170000022
Figure RE-FDA0003622378170000023
Where ρ is0Presentation GinsengDistance d to be examined0Channel power at 1 m;
Figure RE-FDA0003622378170000024
multipath fading components subject to a standard complex gaussian distribution; du,b＝||qb-q0||，du,e[n]＝||qe[n]-qb| | and db,e[n]＝||qe[n]-q0The | | respectively represents the distance from the user to the BS, the distance from the user to the AE at the nth time slot and the distance from the BS to the AE; theta [ n ]],φ[n]Indicates azimuth (AoD) and altitude AoD from BS to Eve in the nth slot; a (theta n)],φ[n]) Represents the steering vector of the UPA, expressed as
Figure RE-FDA0003622378170000025
Step 1.C, according to the channels from the user to the BS, from the user to the AE and from the BS to the AE given in the step 1.B, obtaining the expressions of the uplink signal received at the BS and the signal received by the eavesdropper as
rb＝hubs+HSIx+ξa*(θ,φ)aH(θ,φ)x+nb (9)
Figure RE-FDA0003622378170000026
Wherein s represents an uplink signal of a user;
Figure RE-FDA0003622378170000027
represents the self-interference (SI) caused by full-duplex operation, whose elements obey an independent and identically distributed complex gaussian distribution; x is a radar signal transmitted by the BS; a (θ, Φ) ═ vec (a (θ, Φ)) represents representing the UPA direction vector as a column vector;
Figure RE-FDA0003622378170000028
representing Additive White Gaussian Noise (AWGN) at the BS,
Figure RE-FDA0003622378170000029
denotes AWGN at AE;
Figure RE-FDA00036223781700000210
Figure RE-FDA00036223781700000211
ζ represents the radar cross-sectional area (RCS);
step 1.D, obtaining SINR expressions at BS and AE according to the channel and received signal expressions in step 1.B and step 1.C, respectively
Figure RE-FDA0003622378170000031
Wherein the content of the first and second substances,
Figure RE-FDA0003622378170000032
representing the receive beamforming vector, P, of the BS at the time of receiving the superimposed signaluIs the signal transmission power of the user;
step 1.E, the SINR expressions at BS and AE in step 1.D are substituted into the secret ability calculation formula to obtain the secret ability of uplink communication
Cs[n]＝[RB-RE[n]]+ (12)
Wherein R isB＝log2(1+γb),RE[n]＝log2(1+γe[n])[·]+Denotes max {. 0}
And step 1, F, establishing a communication and perception integrated model shown in formulas (1), (2), (3), (4), (9) and (10), realizing that the base station BS performs communication with a user, and simultaneously, applying the perception function of a radar signal to track and interfere AE, and simultaneously, expressing the uplink communication confidentiality capability of the ISAC system as shown in formula (12).
3.A perception-based upstream communication security method for a mobile eavesdropper on the air as claimed in claim 2, wherein: the second step is realized by the method that,
step 2. A: in a communication and perception integrated model, observable parameters are obtained through a Matched Filter (MF) principle based on radar echo signals
Figure RE-FDA0003622378170000033
Wherein the content of the first and second substances,
Figure RE-FDA0003622378170000034
and
Figure RE-FDA0003622378170000035
represents the round trip time delay and doppler shift in time slot n;
step 2. B: according to the observable parameters obtained in the step 2.A
Figure RE-FDA0003622378170000036
Establishing the relationship between the observable parameters and the AE state to obtain the state measurement model of the AE
Figure RE-FDA0003622378170000037
Equation (13) is written in the following form
Figure RE-FDA0003622378170000038
Wherein
Figure RE-FDA0003622378170000039
Figure RE-FDA00036223781700000310
Indicates the state of AE at time slot n; g is a radical of formulanIn (1)
Figure RE-FDA00036223781700000311
fcAnd c represents the carrier frequency and speed of light; w [ n ]]＝[wτ[n],wμ[n],wsinθ[n],wcosθ[n],wsinφ[n]]TRepresenting a zero mean gaussian noise vector whose covariance matrix is expressed as
Figure RE-FDA0003622378170000041
Figure RE-FDA0003622378170000042
aiI ═ 1,2,3,4 are constants relevant to the system configuration and the particular signal processing algorithm, G denotes the matched filter gain;
step 2. C: predicting the state of AE in the n +1 time slot and the MSE matrix according to the state of the nth time slot AE obtained in the step 2.B and the MSE matrix obtained by calculation
Figure RE-FDA0003622378170000043
Figure RE-FDA0003622378170000044
Wherein S iscvRepresents a state transition matrix, represented as
Figure RE-FDA0003622378170000045
In the above formula I3And O33 × 3 identity matrix and zero matrix respectively;
Figure RE-FDA0003622378170000046
in the above formula
Figure RE-FDA0003622378170000047
And
Figure RE-FDA0003622378170000048
respectively representing the variances of Gaussian position noise position and speed noise in the directions of x, y and z axes;
step 2. D: calculating a Kalman gain matrix by using the result obtained in the step 2.C
Figure RE-FDA0003622378170000049
Wherein the content of the first and second substances,
Figure RE-FDA0003622378170000051
the specific expression is
Figure RE-FDA0003622378170000052
The slot index is omitted here, in the above formula
Figure RE-FDA0003622378170000053
Figure RE-FDA0003622378170000054
Step 2. E: correcting and updating the state and MSE matrix of AE in the n +1 time slot, which are obtained by predicting in the step 2.C according to formulas (20) and (21), predicting the track of the AE of the mobile aerial eavesdropper and the CSI between the base station and the AE, realizing accurate tracking of the AE of the mobile aerial eavesdropper, and obtaining accurate CSI between the base station and the AE in real time;
Figure RE-FDA0003622378170000055
M[n+1]＝(I-K[n+1]Gn)M[n+1∣n] (21) 。
4.a perception-based upstream communication security method for a mobile eavesdropper on the air as claimed in claim 3, wherein: the third step is realized by the method that,
step 3.A, given x, using the SINR of the maximized BS as the objective function and the power of the received wave beam as the constraint condition, the optimization problem is as follows
Figure RE-FDA0003622378170000056
s.t.||u||＝1 (23)
And step 3.B, obtaining the closed optimal solution of u in the step 3.A according to Rayleigh-Ritz theorem, and obtaining the optimized receiving beam former u.
5. The perception-based upstream communication security method for a mobile eavesdropper on the air of claim 4, wherein: the implementation method of the fourth step is that,
step 4.A, given u, taking the SINR of the maximized BS as an objective function, taking the SINR of AE, tracking Mean Square Error (MSE) and radar transmission signal power budget as constraint conditions, and optimizing the problem as follows:
Figure RE-FDA0003622378170000061
s.t.γe[n+1]≤γbp, (25)
Tr(M[n+1])≤Γm， (26)
||x||2≤PR (27)
wherein, γbpIs a pre-designed SINR threshold, Γ, of AEmIs a maximum tolerated tracking MSE threshold, PRRepresents the maximum transmit power of the radar signal;
step 4.B first constraint (25) to step 4.AAt a given point
Figure RE-FDA0003622378170000062
Using a first order Taylor series expansion to obtain
Figure RE-FDA0003622378170000063
Step 4. D-for the second constraint (26) in step 4.A, at a given point
Figure RE-FDA0003622378170000064
Applying a first order Taylor series expansion to M-1[n+1]Conversion to the following linear form
Figure RE-FDA0003622378170000065
Wherein, the first and the second end of the pipe are connected with each other,
Figure RE-FDA0003622378170000066
thus, the constraint is expressed as
Figure RE-FDA0003622378170000067
Wherein v isiNot less than 0, i ═ {1, …,6} is an introduced auxiliary variable, eiIs I6The ith column vector of (1);
step 4.D, converting the problem in the step 4.A into the following semi-definite planning SDP problem
Figure RE-FDA0003622378170000068
Figure RE-FDA0003622378170000071
Step 4. E: and D, solving the convex optimization problem in the step 4.D, and updating x.
6. The perception-based upstream communication security method for a mobile eavesdropper on the air of claim 5, wherein: the fifth step is to realize that the method is that,
judging whether the fraction increase of the value in the fourth step compared with the last iteration is smaller than a convergence precision threshold or not, or whether the fraction increase reaches the maximum iteration number, if not, jumping to the third step for iteration, if so, ending the iteration, outputting the radar signal and the receiving beam former after the joint design optimization, realizing the balance optimization between the interference AE and the user communication, namely, ensuring the interference capability of the AE signal and the interference and noise ratio SINR of the aerial eavesdropper to maximize the noise ratio SINR of the BS under the constraints of the tracking mean square error MSE and the power budget, ensuring that the BS and the user communication condition, accurately tracking and predicting the AE of the mobile aerial eavesdropper by using a Kalman filtering method, further enhancing the interference capability of the radar signal to the AE, and improving the security of reliable communication.
CN202210164916.5A 2022-02-23 2022-02-23 Perception-based uplink communication security method for mobile aerial eavesdropper Active CN114584235B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210164916.5A CN114584235B (en) 2022-02-23 2022-02-23 Perception-based uplink communication security method for mobile aerial eavesdropper

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210164916.5A CN114584235B (en) 2022-02-23 2022-02-23 Perception-based uplink communication security method for mobile aerial eavesdropper

Publications (2)

Publication Number Publication Date
CN114584235A true CN114584235A (en) 2022-06-03
CN114584235B CN114584235B (en) 2023-02-03

Family

ID=81774648

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210164916.5A Active CN114584235B (en) 2022-02-23 2022-02-23 Perception-based uplink communication security method for mobile aerial eavesdropper

Country Status (1)

Country Link
CN (1) CN114584235B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114980032A (en) * 2022-06-06 2022-08-30 厦门大学 V2V beam tracking method based on sense-pass integration
CN115173903A (en) * 2022-06-30 2022-10-11 重庆邮电大学 Power distribution method for common-sense integrated system
WO2024020714A1 (en) * 2022-07-25 2024-02-01 Qualcomm Incorporated Rcs variance in isac systems

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110912597A (en) * 2019-11-07 2020-03-24 南京邮电大学 Robust safety beam forming method based on multi-objective optimization
US20210203433A1 (en) * 2017-12-31 2021-07-01 Istanbul Medipol Universitesi Automatic repeat-request system for providing absolute safety and authentication in wireless networks
CN113866756A (en) * 2021-09-27 2021-12-31 西南石油大学 Small unmanned aerial vehicle target tracking method based on MIMO radar
CN113973305A (en) * 2021-10-26 2022-01-25 西安电子科技大学 Intelligent reflecting surface position and beam joint optimization method carried on unmanned aerial vehicle

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210203433A1 (en) * 2017-12-31 2021-07-01 Istanbul Medipol Universitesi Automatic repeat-request system for providing absolute safety and authentication in wireless networks
CN110912597A (en) * 2019-11-07 2020-03-24 南京邮电大学 Robust safety beam forming method based on multi-objective optimization
CN113866756A (en) * 2021-09-27 2021-12-31 西南石油大学 Small unmanned aerial vehicle target tracking method based on MIMO radar
CN113973305A (en) * 2021-10-26 2022-01-25 西安电子科技大学 Intelligent reflecting surface position and beam joint optimization method carried on unmanned aerial vehicle

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
SIXIAN LI等: "Robust Secure UAV Communications With the Aid of Reconfigurable Intelligent Surfaces", 《IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS》 *
XINYI WANG等: "Sensing-Assisted Secure Uplink Communications With Full-Duplex Base Station", 《IEEE COMMUNICATIONS LETTERS》 *
周洁等: "轨迹和干扰功率联合优化的无人机主动窃听算法", 《华侨大学学报(自然科学版)》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114980032A (en) * 2022-06-06 2022-08-30 厦门大学 V2V beam tracking method based on sense-pass integration
CN115173903A (en) * 2022-06-30 2022-10-11 重庆邮电大学 Power distribution method for common-sense integrated system
CN115173903B (en) * 2022-06-30 2024-03-26 深圳泓越信息科技有限公司 Power distribution method of general sense integrated system
WO2024020714A1 (en) * 2022-07-25 2024-02-01 Qualcomm Incorporated Rcs variance in isac systems

Also Published As

Publication number Publication date
CN114584235B (en) 2023-02-03

Similar Documents

Publication Publication Date Title
CN114584235B (en) Perception-based uplink communication security method for mobile aerial eavesdropper
Liu et al. Joint radar and communication design: Applications, state-of-the-art, and the road ahead
Yuan et al. Bayesian predictive beamforming for vehicular networks: A low-overhead joint radar-communication approach
Chen et al. A tutorial on terahertz-band localization for 6G communication systems
Chen et al. Performance of joint sensing-communication cooperative sensing UAV network
Solanki et al. On the performance of IRS-aided UAV networks with NOMA
Shan et al. Machine learning-based field data analysis and modeling for drone communications
Wei et al. Safeguarding UAV networks through integrated sensing, jamming, and communications
Jing et al. ISAC from the sky: UAV trajectory design for joint communication and target localization
US20230144796A1 (en) Estimating direction of arrival of electromagnetic energy using machine learning
Yi et al. Beam training and tracking in mmwave communication: A survey
Nnamani et al. Joint beamforming and location optimization for secure data collection in wireless sensor networks with UAV-carried intelligent reflecting surface
Miao et al. Location-based robust beamforming design for cellular-enabled UAV communications
Wang et al. Multi-vehicle tracking and id association based on integrated sensing and communication signaling
Herschfelt et al. Vehicular rf convergence: Simultaneous radar, communications, and pnt for urban air mobility and automotive applications
CN116600314A (en) Communication control method and system for high-speed rail millimeter wave communication system
Liu et al. Securing multi-user uplink communications against mobile aerial eavesdropper via sensing
Liu et al. Joint localization and predictive beamforming in vehicular networks: Power allocation beyond water-filling
Mohammadi et al. Location-aware beamforming for MIMO-enabled UAV communications: An unknown input observer approach
Yu et al. Measurement-based propagation channel modeling for communication between a UAV and a USV
Rastorgueva-Foi et al. Networking and positioning co-design in multi-connectivity industrial mmw systems
CN113364554A (en) Perception-assisted uplink secure communication method
Khoshafa et al. Securing LPWANs: A Reconfigurable Intelligent Surface (RIS) Assisted UAV Approach
Shoudha et al. WiFi 5GHz CSI-Based Single-AP Localization with Centimeter-Level Median Error
Que et al. Joint beam management and SLAM for mmWave communication systems

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant