CN116744256A - Method for maximizing minimum security rate of RIS-assisted unmanned aerial vehicle NOMA network - Google Patents

Method for maximizing minimum security rate of RIS-assisted unmanned aerial vehicle NOMA network Download PDF

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CN116744256A
CN116744256A CN202310627872.XA CN202310627872A CN116744256A CN 116744256 A CN116744256 A CN 116744256A CN 202310627872 A CN202310627872 A CN 202310627872A CN 116744256 A CN116744256 A CN 116744256A
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aerial vehicle
unmanned aerial
ris
rate
constraint
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CN116744256B (en
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王正强
何鲁娜
青思雨
万晓榆
樊自甫
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Chongqing University of Post and Telecommunications
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    • 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]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/12—Detection or prevention of fraud
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/18—Negotiating wireless communication parameters
    • H04W28/22—Negotiating communication rate
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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  • Computer Security & Cryptography (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The invention discloses a method for optimizing the safety rate of an unmanned aerial vehicle NOMA network assisted by RIS, which is used for realizing the maximum minimum safety rate of the unmanned aerial vehicle NOMA network under the condition of meeting the safety interruption probability, the maximum transmitting power of the unmanned aerial vehicle, SIC decoding sequence and RIS phase shift constraint by considering fairness among legal users and statistical CSI of eavesdropping channels. The invention establishes a maximized minimum safe rate model of an unmanned aerial vehicle NOMA network based on RIS assistance, firstly converts a safe interruption probability constraint into a deterministic constraint by referring to an auxiliary variable, equivalently converts a non-convex optimization problem into a convex optimization problem based on methods such as punishment, successive convex approximation and the like, and then uses an alternate iterative algorithm to maximize the minimum safe rate among users of a combination method. The invention has the advantages of low calculation complexity and strong channel adaptability, and simultaneously considers the fairness of the users, thereby being particularly suitable for RIS-assisted unmanned aerial vehicle NOMA networks in actual eavesdropping scenes.

Description

Method for maximizing minimum security rate of RIS-assisted unmanned aerial vehicle NOMA network
Technical Field
The invention belongs to the field of power control in NOMA networks, in particular to a method for maximizing minimum safe rate of an unmanned aerial vehicle NOMA network based on RIS auxiliary of fairness among legal users.
Background
With the rapid development of the mobile internet and the internet of things, the requirements of communication scenes of high spectrum efficiency, large-scale connection and safe and reliable transmission are higher and higher. Unmanned aerial vehicles are becoming increasingly popular as aerial communication platforms to enhance the coverage, capacity and energy efficiency of existing wireless networks due to high mobility and cost-effective deployment. In particular, the unmanned aerial vehicle is more likely to establish a line-of-sight link, thereby improving air-to-ground communication quality. In order to meet the massive connectivity of future networks and the higher demands of users on traffic, non-orthogonal multiple access technology (Non-Orthogonal Multiple Access, NOMA) has the advantage of improving spectral efficiency and supporting large-scale connections, and is considered to be one of the promising technologies to address these challenges. NOMA allows multiple users to simultaneously use the same time-frequency code resource, allocate different signal powers according to different user channel quality, and decode at the receiving end using serial interference cancellation (Successive Interference Cancellation, SIC) to distinguish between different signals. Compared with the traditional orthogonal multiple access technology, NOMA can transmit data of a large number of users, so that the spectrum efficiency is remarkably improved, and the throughput of the system and the fairness of the users are better balanced. Physical layer security is becoming an increasingly important issue in research because of the severe threat to the security of high rate transmission of information due to the complex wireless environment, the complexity of the network infrastructure, and the broadcast nature of the wireless channel. The intelligent subsurface (Reconfigurable Intelligence Surface, RIS) can improve the strength of legitimate signals or destructively suppress interference on eavesdroppers due to its outstanding ability to achieve green communications and enhance physical layer security, introducing it into unmanned aerial vehicle NOMA networks, thereby reducing information leakage.
Currently, in the resource allocation method in unmanned aerial vehicle networks, the article entitled "IRS-Assisted Secure UAV Transmission via Joint Trajectory and Beamforming Design" is published by Xiaowei Pang et al in IEEE Transactions on Communications,2022,70 (2): 1140-1152, only the case of a single legitimate user is considered, and the eavesdropper channel is a perfect channel; na Tang et al published an article entitled "Cognitive NOMA for UAV-Enabled Secure Communications:Joint 3D Trajectory Design and Power Allocation" on IEEE Access,2020,8:159965-159978, jointly optimizing unmanned aerial vehicle trajectory and power allocation to maximize the worst-case average safe rate for all auxiliary receivers; hui-Ming Wang et al, IEEE Transactions on Communications,2020,68 (9): 5732-5746, published an article entitled "UAV Secure Downlink NOMA Transmissions: A Secure Users Oriented Perspective" that categorizes a plurality of legitimate users as safety-requiring users and quality of service-requiring users, maximizing the minimum safety rate of safety-requiring users.
As can be seen from the above results, most students currently study that in the resource allocation based on the maximization of the security rate in the unmanned aerial vehicle network, only the communication environment where the channel of the eavesdropper is the ideal CSI is considered, but in the actual eavesdropping scenario, the eavesdropper usually keeps silent during eavesdropping, and it is difficult to obtain the instantaneous CSI of the eavesdropping channel. Therefore, the invention considers the fairness among legal users and the statistical CSI condition of the eavesdropping channel, and researches the method for maximizing the minimum safe rate of the unmanned plane NOAM network assisted by RIS.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. A method for maximizing minimum security rate of a RIS-assisted unmanned aerial vehicle NOMA network is provided. The technical scheme of the invention is as follows:
a method of maximizing minimum security rate for a RIS-assisted unmanned NOMA network, comprising the steps of:
101. under the condition of considering legal user fairness and statistical CSI of eavesdropping channels, establishing a maximized minimum safe rate model of an RIS-assisted unmanned aerial vehicle NOMA network, and adopting a safe interruption probability as a safety measure;
102. initializing relevant parameters of a target problem, namely, a position q of the unmanned aerial vehicle, a phase shift matrix Θ of the RIS, power distribution P of the unmanned aerial vehicle, SIC decoding sequence A, a minimum safety rate judgment threshold ζ, and converting safety interruption probability constraint into deterministic constraint by using auxiliary variables;
103. according to the given unmanned aerial vehicle power distribution, unmanned aerial vehicle positions and SIC decoding sequences, solving a RIS phase shift matrix, and updating the RIS phase shift matrix;
104. according to the given unmanned aerial vehicle position, SIC decoding sequence, RIS phase shift matrix, solving unmanned aerial vehicle power distribution, and updating unmanned aerial vehicle power distribution;
105. according to a given RIS phase shift matrix, unmanned aerial vehicle power distribution is carried out, unmanned aerial vehicle positions and SIC decoding sequences are solved, and the unmanned aerial vehicle positions and SIC decoding sequences are updated;
106. and (3) judging the update convergence of the minimum safe rate: if the absolute value of the difference value of the safety rate of the two times is not greater than the safety rate judgment threshold, the safety rate converges, the maximum minimum safety rate is given, and the method is ended; if the absolute value of the difference value of the two safety rates is larger than the safety rate judgment threshold, the safety rate at the moment is saved, and the step 103 is skipped until the safety rate meets the condition, and the maximum minimum safety rate is given.
Further, in step 101, under the condition of considering fairness of legal users and statistical CSI of eavesdropping channels, the safety rate maximization objective optimization problem of the unmanned aerial vehicle NOMA network assisted by the RIS is established as follows
s.t.C1:
C2:
C3:
C4:
C5:
C6:
Wherein C1 is the maximum transmitting power constraint of the unmanned aerial vehicle, p k Transmitting information to legal user U for unmanned aerial vehicle k Power, P of max The maximum transmitting power of the unmanned plane is set; c2 is RIS phase shift constraint, θ n Phase shift for the RIS nth reflective element; c3 is a safety interrupt probability constraint, R e,k Eavesdropping on legitimate user U for eavesdropper k Rate of R k For legal users U k Rate of R s,k For legal users U k Is a safety rate of Θ=diag (θ 1 ,θ 2 ,...θ N ) Phase shift matrix for RIS, P is P k Q is the position of the unmanned aerial vehicle, A is alpha i,j Mu, mu max,k For legal user U k Maximum safe outage probability of (2); C4-C6 is SIC decoding constraint of legal user, variableα i,j When=1, it indicates the legal user U i Decoding legal user U j ，w u,k For legal users U k Is a position of (2);
in the middle ofFor legal users U k Wherein h is ak For unmanned aerial vehicle to legal user U k Channel of->For RIS to legal user U k Θg is the unmanned to RIS channel; />Combined channel for eavesdropper, h ae G is the channel from the unmanned aerial vehicle to the eavesdropper re Sigma for RIS to eavesdropper channel 2 Gaussian noise for legitimate users or eavesdroppers.
Further, in step 102, the position of the unmanned aerial vehicle is initialized to be q= [ x, y, H ], the RIS phase shift matrix is Θ, the transmitting power of the unmanned aerial vehicle is P, the SIC decoding sequence is a, and the minimum safety rate decision threshold is ζ, wherein x, y, H are respectively the abscissa, ordinate and flying height of the unmanned aerial vehicle;
problem P 1 The specific steps of converting the medium constraint C3 safe interrupt probability constraint into the deterministic constraint are as follows:
combined channel h of eavesdropper e Rewritable as
In the middle ofWherein-> Large-scale path loss factor for a drone to eavesdropper link +.>Large-scale path loss factor for drone to RIS link, +.>For the large-scale path loss factor of RIS to eavesdropper link, I is the identity matrix, N is the number of RIS reflecting elements, ρ 0 Represents the path loss at a reference distance of 1m, beta 0 Represents the path fading index, w e Representing the location of an eavesdropper, r representing the location of the RIS;
|h e | 2 following an exponential distribution, according to |h e | 2 Is introduced into the auxiliary variable z by the probability distribution function of (2) k Converting the safe interrupt probability constraint into:
so the objective is optimized to the problem P 1 Conversion to
s.t.C1:
C2:
C3:
C4:
C5：
In the method, in the process of the invention,is R e,k Is transformed by a safe interrupt probability constraint, wherein (ζ) e (q)) 2 ＝N|L re L ar | 2 ，L ae (q) is the large-scale path loss factor of the unmanned to eavesdropper link, L ar (q) is the large-scale path loss factor, L, of the unmanned aerial vehicle to RIS link re (q) is the large-scale path loss factor of the RIS to eavesdropper link and N is the RIS reflective element number.
Further, in step 103, given unmanned plane power distribution, unmanned plane position, SIC decoding order, and optimization of RIS phase shift, the specific steps are:
introducing an auxiliary variable t 1 Optimizing problem P with objective 2 Conversion to
P 3 :
s.t.C1：
C2：
Wherein, constraint C1
For legal users U k Lower bound for rate SCA acquisition, +.>The method comprises the steps of taking the form of a eavesdropping rate concave function difference, and utilizing taylor expansion to obtain an upper bound for a first term; p is p k Transmitting information to legal user U for unmanned aerial vehicle k Power, P of max The maximum transmitting power of the unmanned plane is set; wherein-> For the auxiliary variables introduced, v (n) 、V, < > in the nth iteration>Values.
Further, in step 104, given the unmanned plane position, SIC decoding order, RIS phase shift, the unmanned plane power allocation is optimized, specifically:
introducing an auxiliary variable t 2 Optimizing problem P with objective 2 Conversion to
P 4 :
s.t.C1：
C2：
Wherein, constraint C1
For legal users U k The lower bound achieved by the rate SCA.
Further, in step 105, given the RIS phase shift, the unmanned aerial vehicle power distribution optimizes the unmanned aerial vehicle position and the SIC decoding order, specifically:
introducing an auxiliary variable t 3 Optimizing problem P with objective 2 Conversion to
P 5 :
s.t.C1:
C2:u r (q)≥||q-r||
C3:
C4:
C5:
C6:
C7:
C8:||q-q (n) ||≤δ
C9:||q (n) -w e ||+q-q (n) ≥l e
C10:||q (n) -r||+q-q (n) ≥l r
C11:
C12:
C13:
In the formula, χ= { u k ,l e ,u r ,l r ,η k (q),For the set of auxiliary variables introduced, u k For unmanned plane and legal user U k Upper bound of distance l e Is the lower bound of the distance between the unmanned plane and the eavesdropper, u r Representing the upper bound of the distance between the unmanned aerial vehicle and the RIS, l r A lower bound representing a distance of the drone from the RIS; in constraint C3η k (q)＝f k (u k ,u r )+B k g k (u k ,u r ) For unmanned aerial vehicle to legal user U k Is a lower bound for the desired channel gain,η k (q) wherein,A k wherein the method comprises the steps ofLOS component for unmanned aerial vehicle to RIS channel, +.>For RIS to legal user U k LOS component of channel, R 2 Is the Lais factor, < >>Is R k In the division of (2), the numerator and denominator are divided simultaneouslyη k The denominator after (q); constraint C4 +.>Is thatz k L in the expression of (q, A) ae (q)| 2 +(ξ e (q)) 2 An expanded upper bound of (2); constraint C5 +.>For legal users U k Is the rate of (2)Point +.>First order Taylor expansion, X e,k (A, q) isz k The numerator and denominator in (q, A) being divided by +.>The denominator of the latter and at the point of the nth iteration of SCA +.>A first-order taylor expansion; constraint C7 +.>Is thatη k (q) at the nth iteration point of SCA->A first order Taylor expansion, wherein +.> Constraint q in C8 (n) Representing the value of the nth iteration of the SCA, wherein delta is the maximum allowable displacement of the unmanned aerial vehicle in each SCA iteration; constraint C9, C10 at SCA nth iteration point q (n) Respectively expanding the first-order Taylor to obtain convex sets; constraint C13 is a non-convex set alpha i,j α j,k ≤α i,k After conversion to form of convex function difference, at the nth iteration point of SCA +.>A first-order taylor expansion; for non-convex set alpha i,j And the xi > 0 is used as a penalty term to be written into the objective function, and is used as a penalty coefficient.
Further, in step 106, the comparison is performedMagnitude of security rate decision threshold ζ, wherein +_>For the maximum minimum safe rate of the nth iteration,/v>A maximum minimum safe rate for the n-1 th iteration; if->If not more than ζ, the safety rate converges, the maximum minimum safety rate is given, and the method is ended; if->If the security rate is greater than ζ, saving the security rate at the moment, and jumping to the step 103 until the security rate meets the condition, and giving the maximum minimum securityRate.
The invention has the advantages and beneficial effects as follows:
under the condition of considering fairness among legal users and statistical CSI of eavesdropping channels, the invention converts the non-convex optimization problem into the convex optimization problem equivalently according to variable substitution, penalty function, SCA and other methods under the condition of meeting safety interruption probability, maximum transmitting power of an unmanned aerial vehicle, RIS phase shift and SIC decoding constraint, and then solves the maximum minimum safety rate. The invention is innovative in that in actual eavesdropping scene, eavesdroppers usually keep silent during eavesdropping, and it is difficult to acquire instantaneous CSI of eavesdropping channels, if only the known state of the eavesdropping channels CSI is considered, RIS can only enhance signal reception according to legal user channels CSI, and eavesdropping can be facilitated. In addition, the method does not directly assume the channel gain ordering of legal users, jointly optimizes the unmanned plane position and SIC decoding sequence, and simultaneously considers fairness among the legal users. Compared with the scheme of orthogonal multiple access and no RIS, the invention improves the safety rate, is particularly suitable for the RIS-assisted unmanned aerial vehicle NOMA network under the actual eavesdropping scene, and has better practicability and feasibility.
Drawings
FIG. 1 is a system model of a RIS assisted unmanned NOMA network of the present invention, which provides a preferred embodiment;
FIG. 2 is an iterative convergence diagram of the present invention at different unmanned transmit powers and RIS reflection element numbers;
FIG. 3 is a graph showing the minimum safe rate of the system for different RIS reflective element counts for the present invention and the comparative method;
FIG. 4 shows minimum safe rates of the system at different maximum transmit powers of the unmanned aerial vehicle according to the present invention and the comparative method;
fig. 5 is a flow chart of a method for maximizing minimum safe rate for a preferred embodiment RIS-assisted unmanned NOMA network in accordance with the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and specifically described below with reference to the drawings in the embodiments of the present invention. The described embodiments are only a few embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
fig. 5 discloses a method for maximizing minimum safe rate of a RIS-assisted unmanned aerial vehicle NOMA network. Which comprises the following steps:
the first step: under the condition of considering legal user fairness and statistical CSI of eavesdropping channels, establishing a maximized minimum safe rate model of an RIS-assisted unmanned aerial vehicle NOMA network, and adopting a safe interruption probability as a safety measure;
and a second step of: initializing relevant parameters of a target problem, namely, a position q of the unmanned aerial vehicle, a phase shift matrix Θ of the RIS, power distribution P of the unmanned aerial vehicle, SIC decoding sequence A, a minimum safety rate judgment threshold ζ, and converting safety interruption probability constraint into deterministic constraint by using auxiliary variables;
and a third step of: according to the given unmanned aerial vehicle power distribution, unmanned aerial vehicle positions and SIC decoding sequences, solving a RIS phase shift matrix, and updating the RIS phase shift matrix;
fourth step: according to the given unmanned aerial vehicle position, SIC decoding sequence, RIS phase shift matrix, solving unmanned aerial vehicle power distribution, and updating unmanned aerial vehicle power distribution;
fifth step: according to a given RIS phase shift matrix, unmanned aerial vehicle power distribution is carried out, unmanned aerial vehicle positions and SIC decoding sequences are solved, and the unmanned aerial vehicle positions and SIC decoding sequences are updated;
sixth step: and (3) judging the update convergence of the minimum safe rate: if the absolute value of the difference value of the safety rate of the two times is not greater than the safety rate judgment threshold, the safety rate converges, the maximum minimum safety rate is given, and the method is ended; if the absolute value of the difference value of the safety rate of the two times is larger than the safety rate judgment threshold, the safety rate at the moment is saved, and the third step is skipped until the safety rate meets the condition, and the maximum minimum safety rate is given.
Further, the maximizing the minimum safe rate objective optimization problem of the unmanned aerial vehicle NOMA network assisted by the RIS established in the first step is as follows:
P 1 :
s.t.C1:
C2:
C3:
C4:
C5:
C6:
wherein C1 is the maximum transmitting power constraint of the unmanned aerial vehicle, p k Transmitting information to legal user U for unmanned aerial vehicle k Power, P of max The maximum transmitting power of the unmanned plane is set; c2 is RIS phase shift constraint, θ n Phase shift for the RIS nth reflective element; c3 is a safety interrupt probability constraint, R e,k Eavesdropping on legitimate user U for eavesdropper k Rate of R k For legal users U k Rate of R s,k For legal users U k Is a safety rate of Θ=diag (θ 1 ,θ 2 ,...θ N ) Phase shift matrix for RIS, P is P k Q is the position of the unmanned aerial vehicle, A is alpha i,j Mu, mu max,k For legal user U k Maximum safe outage probability of (2); C4-C6 is SIC decoding constraint of legal user, variableα i When j=1, it indicates the legal user U i Decoding legal user U j ，w u,k For legal users U k Is of the order of (2)And (5) placing.
Further, the second step initializes the related parameters q, Θ, P, A, ζ, and uses the auxiliary variables to solve the problem P 1 The medium constraint C3 safe interrupt probability constraint is converted into deterministic constraint, and the target optimization problem P is solved 1 Conversion to
P 2 :
s.t.C1:
C2:
C3:
C4:
C5:
In the method, in the process of the invention,for the combined channel gain of a legitimate user,
is R e,k Is transformed by a safe interrupt probability constraint, wherein (ζ) e (q)) 2 ＝N|L re L ar | 2 ，L ae (q) is the large-scale path loss factor of the unmanned to eavesdropper link, L ar (q) is the large-scale path loss factor, L, of the unmanned aerial vehicle to RIS link re (q) is the large-scale path loss factor of the RIS to eavesdropper link and N is the RIS reflective element number.
Further, the firstThree steps of giving unmanned plane power distribution, unmanned plane position, SIC decoding sequence, optimizing RIS phase shift and introducing auxiliary variable t 1 Optimizing problem P with objective 2 Conversion to
P 3 :
s.t.C1：
C2：
Wherein, constraint C1
For legal users U k Lower bound for rate SCA acquisition, +.>The method comprises the steps of taking the form of a eavesdropping rate concave function difference, and utilizing taylor expansion to obtain an upper bound for a first term; p is p k Transmitting information to legal user U for unmanned aerial vehicle k Power, P of max The maximum transmitting power of the unmanned plane is set; wherein-> For the auxiliary variables introduced, v (n) 、/>V, < > in the nth iteration>Value of sigma 2 Gaussian noise for legitimate users or eavesdroppers.
Further, the fourth step is to give the unmanned aerial vehicle position, SIC decoding sequence, RIS phase shift, optimize unmanned aerial vehicle power distribution and introduce auxiliary materialsAuxiliary variable t 2 Optimizing problem P with objective 2 Conversion to
P 4 :
s.t.C1：
C2：
Wherein, constraint C1
For legal users U k The lower bound achieved by the rate SCA.
Further, the fifth step is to give RIS phase shift, allocate power of unmanned aerial vehicle, optimize and fix unmanned aerial vehicle position and SIC decoding sequence, introduce auxiliary variable t 3 Optimizing problem P with objective 2 Conversion to
P 5 :
s.t.C1:
C2:u r (q)≥||q-r||
C3:
C4:
C5:
C6:
C7:
C8:||q-q (n) ||≤δ
C9:||q (n) -w e ||+q-q (n) ≥l e
C10:||q (n) -r||+q-q (n) ≥l r
C11:
C12:
In the formula, χ= { u k ,l e ,u r ,l r ,η k (q),For the set of auxiliary variables introduced, u k For unmanned plane and legal user U k Upper bound of distance l e Being the lower bound of the distance between the unmanned aerial vehicle and the eavesdropper, ur represents the upper bound of the distance between the unmanned aerial vehicle and the RIS, l r A lower bound representing a distance of the drone from the RIS; in constraint C3η k (q)＝f k (u k ,u r )+B k g k (u k ,u r ) For unmanned aerial vehicle to legal user U k Is a lower bound for the desired channel gain,η k (q) wherein,A k wherein the method comprises the steps ofLOS component for unmanned aerial vehicle to RIS channel, +.>For RIS to legal user U k LOS component of the channel, R is the Lais factor,>is R k In the division of (2), the numerator and denominator are divided simultaneouslyη k The denominator after (q); constraint C4 +.>Is thatz k L in the expression of (q, A) ae (q)| 2 +(ξ e (q)) 2 An expanded upper bound of (2); constraint C5 ψ k (A, q) is legal user U k At the point +.>First order Taylor expansion, X e,k (A, q) isz k The numerator and denominator in (q, A) being divided by +.>The denominator of the latter and at the point of the nth iteration of SCA +.>A first-order taylor expansion; constraint C7 +.>Is thatη k (q) at the nth iteration point of SCAA first-order taylor expansion; constraint q in C8 (n) Representing the value of the nth iteration of the SCA, wherein delta is the maximum allowable displacement of the unmanned aerial vehicle in each SCA iteration; constraint C9, C10 at SCA nth iteration point q (n) Respectively expanding the first-order Taylor to obtain convex sets; constraint C13 is a non-convex set alpha i,j α j,k ≤α i,k After conversion to form of convex function difference, at the nth iteration point of SCA +.>A first-order taylor expansion; to not be convexSet alpha i,j And the xi > 0 is used as a penalty term to be written into the objective function, and is used as a penalty coefficient.
Further, the sixth step of comparisonMagnitude of security rate decision threshold ζ, wherein +_>For the maximum minimum safe rate of the nth iteration,/v>A maximum minimum safe rate for the n-1 th iteration; if->If not more than ζ, the safety rate converges, the maximum minimum safety rate is given, and the method is ended; if->And if the safety rate is greater than ζ, saving the safety rate at the moment, and jumping to the third step until the safety rate meets the condition, and giving the maximum minimum safety rate.
Under the condition of considering fairness among legal users and statistics CSI of eavesdropping channels, the invention converts non-convex optimization problems into convex optimization problems in an equivalent way according to methods such as variable substitution, penalty function, SCA and the like under the condition of meeting safety interruption probability, unmanned aerial vehicle transmitting power, RIS phase shift and SIC decoding constraint, and then solves the maximum minimum safety rate, so that the method provided by the invention is more in line with the actual eavesdropping scene, has the advantages of low complexity and strong channel adaptability compared with other traditional schemes, improves the safety rate, considers fairness among legal users, is particularly suitable for an RIS-assisted unmanned aerial vehicle NOMA network under the actual eavesdropping scene, and has better practicability and feasibility.
The embodiment is a method for maximizing minimum safe rate of RIS-assisted unmanned aerial vehicle NOMA network, wherein in the RIS-assisted unmanned aerial vehicle NOMA network, the number of legal users is K=7, and the legal users are randomly distributed in a half way by taking (0, 0) m as a circle centerIn a circular area with a diameter of 10m, RIS has a coordinate of (-10, 20) m, eavesdropper has a coordinate of (80,80,0) m, and the probability of safe interruption is μ max,k =0.1, the number of ris reflecting elements is n=80, and the maximum transmitting power of the unmanned aerial vehicle is P max =10dbm, unmanned deployment height h=100deg.m, rice factor r=10db, path loss index β 0 =2.2, the channel gain at the reference distance is ρ 0 = -30dB with noise power σ 2 The method comprises the steps that the channel modeling from the unmanned aerial vehicle to a legal user and the channel modeling from the unmanned aerial vehicle to the RIS to the legal user is a rice channel, the channel modeling from the unmanned aerial vehicle to the RIS is a LoS channel, and the channel modeling from the unmanned aerial vehicle to an eavesdropper and the channel modeling from the RIS to the eavesdropper are performed through statistical CSI.
In this example, fig. 1 is a system model of a preferred embodiment RIS-assisted unmanned NOMA network provided by the present invention, in which a drone acts as an air base station, a single-antenna equipped drone sends signals to legitimate users over a direct link and RIS with N reflective elements; FIG. 2 is an iterative convergence diagram of the present invention at different unmanned transmit powers and RIS reflection element numbers; FIG. 3 is a graph of minimum safe rates for a system of the present invention versus a comparison method (NOMA without RIS, OMA with RIS, and OMA without RIS) at different numbers of reflective elements; fig. 4 is a graph of the minimum safe rate of the system of the present invention versus the comparison method under different conditions of maximum transmit power of the drone. As can be seen from fig. 2, the method provided by the invention converges with the increase of the iteration times, the maximum transmitting power of the unmanned aerial vehicle has a larger influence on the minimum safety rate than the number of RIS reflecting elements, and fig. 3 shows that the method provided by the invention is superior to the comparison method, and the minimum safety rate of the system of all the methods increases with the increase of the number of reflecting elements; fig. 3 shows that the minimum safety rate of the system is increased along with the increase of the maximum transmitting power of the unmanned aerial vehicle, the change of the minimum safety rate slowly and gradually flattens, and the minimum safety rate of the proposed algorithm is higher than that of a comparison method.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The above examples should be understood as illustrative only and not limiting the scope of the invention. Various changes and modifications to the present invention may be made by one skilled in the art after reading the teachings herein, and such equivalent changes and modifications are intended to fall within the scope of the invention as defined in the appended claims.

Claims (7)

1. A method of maximizing minimum security rate for a RIS-assisted unmanned NOMA network, comprising the steps of:
101. under the condition of considering legal user fairness and statistical CSI of eavesdropping channels, establishing a maximized minimum safe rate model of an RIS-assisted unmanned aerial vehicle NOMA network, and adopting a safe interruption probability as a safety measure;
102. initializing relevant parameters of a target problem, namely, a position q of the unmanned aerial vehicle, a phase shift matrix Θ of the RIS, power distribution P of the unmanned aerial vehicle, SIC decoding sequence A, a minimum safety rate judgment threshold ζ, and converting safety interruption probability constraint into deterministic constraint by using auxiliary variables;
103. according to the given unmanned aerial vehicle power distribution, unmanned aerial vehicle positions and SIC decoding sequences, solving a RIS phase shift matrix, and updating the RIS phase shift matrix;
104. according to the given unmanned aerial vehicle position, SIC decoding sequence, RIS phase shift matrix, solving unmanned aerial vehicle power distribution, and updating unmanned aerial vehicle power distribution;
105. according to a given RIS phase shift matrix, unmanned aerial vehicle power distribution is carried out, unmanned aerial vehicle positions and SIC decoding sequences are solved, and the unmanned aerial vehicle positions and SIC decoding sequences are updated;
106. and (3) judging the update convergence of the minimum safe rate: if the absolute value of the difference value of the safety rate of the two times is not greater than the safety rate judgment threshold, the safety rate converges, the maximum minimum safety rate is given, and the method is ended; if the absolute value of the difference value of the two safety rates is larger than the safety rate judgment threshold, the safety rate at the moment is saved, and the step 103 is skipped until the safety rate meets the condition, and the maximum minimum safety rate is given.
2. The method for maximizing the minimum safe rate of a RIS-assisted unmanned aerial vehicle NOMA network according to claim 1, wherein the step 101, under the statistical CSI condition that considers the fairness of legal users and the eavesdropping channel, establishes the safe rate maximization objective optimization problem of the RIS-assisted unmanned aerial vehicle NOMA network as follows
P 1 :
s.t.C1:
C2:
C3:
C4:
C5:
C6:
In the middle ofC1 is the maximum emission power constraint of the unmanned aerial vehicle, p k Transmitting information to legal user U for unmanned aerial vehicle k Power, P of max The maximum transmitting power of the unmanned plane is set; c2 is RIS phase shift constraint, θ n Phase shift for the RIS nth reflective element; c3 is a safety interrupt probability constraint, R e,k Eavesdropping on legitimate user U for eavesdropper k Rate of R k For legal users U k Rate of R s,k For legal users U k Is a safety rate of Θ=diag (θ 1 ,θ 2 ,...θ N ) Phase shift matrix for RIS, P is P k Q is the position of the unmanned aerial vehicle, A is alpha i,j Mu, mu max,k For legal user U k Maximum safe outage probability of (2); C4-C6 is SIC decoding constraint of legal user, variableα i,j When=1, it indicates the legal user U i Decoding legal user U j ，w u,k For legal users U k Is a position of (2);
in the middle ofFor legal users U k Wherein h is ak For unmanned aerial vehicle to legal user U k Channel of->For RIS to legal user U k G is the unmanned to RIS channel; />Combined channel for eavesdropper, h ae G is the channel from the unmanned aerial vehicle to the eavesdropper re Sigma for RIS to eavesdropper channel 2 Gaussian noise for legitimate users or eavesdroppers.
3. The method for maximizing minimum safe rate of a rima-assisted unmanned aerial vehicle NOMA network according to claim 2, wherein in step 102, the position of the unmanned aerial vehicle is initialized to q= [ x, y, H ], the RIS phase shift matrix is Θ, the unmanned aerial vehicle transmit power is P, the SIC decoding order is a, the minimum safe rate decision threshold is ζ, wherein x, y, H are the abscissa, ordinate, and altitude of the unmanned aerial vehicle, respectively;
problem P 1 The specific steps of converting the medium constraint C3 safe interrupt probability constraint into the deterministic constraint are as follows:
combined channel h of eavesdropper e Rewritable as
In the middle ofWherein-> Large-scale path loss factor for a drone to eavesdropper link +.>Large-scale path loss factor for drone to RIS link, +.>For the large-scale path loss factor of RIS to eavesdropper link, I is the identity matrix, N is the number of RIS reflecting elements, ρ 0 Representing the path loss at a reference distance of 1m，β 0 Represents the path fading index, w e Representing the location of an eavesdropper, r representing the location of the RIS;
|h e | 2 following an exponential distribution, according to |h e | 2 Is introduced into the auxiliary variable z by the probability distribution function of (2) k Converting the safe interrupt probability constraint into:
so the objective is optimized to the problem P 1 Conversion to
P 2 :
s.t.C1:
C2:
C3:
C4:
C5:
In the method, in the process of the invention,is R e,k Is transformed by a safe interrupt probability constraint, wherein (ζ) e (q)) 2 ＝N|L re L ar | 2 ，L ae (q) is the large-scale path loss factor of the unmanned to eavesdropper link, L ar (q) is the large-scale path loss factor, L, of the unmanned aerial vehicle to RIS link re (q) is the large-scale path loss factor of the RIS to eavesdropper link and N is the RIS reflective element number.
4. A method for maximizing minimum safe rate of a rima-assisted unmanned aerial vehicle NOMA network according to claim 3, wherein the steps of 103, given unmanned aerial vehicle power allocation, unmanned aerial vehicle position, SIC decoding order, optimizing the RIS phase shift are:
introducing an auxiliary variable t 1 Optimizing problem P with objective 2 Conversion to
P 3 :
s.t.C1：
C2：
Wherein, constraint C1
For legal users U k The lower bound that the rate SCA takes,in the form of a difference in the eavesdropping rate concave function and using taylor expansion for the first term to obtain an upper bound, p k Transmitting information to legal user U for unmanned aerial vehicle k Power, P of max The maximum transmitting power of the unmanned plane is set; wherein-> For the auxiliary variables introduced, v (n) 、/>V, < > in the nth iteration>Values.
5. The method for maximizing minimum safe rate of a rima-assisted unmanned aerial vehicle NOMA network according to claim 4, wherein the step 104, given the unmanned aerial vehicle location, SIC decoding order, RIS phase shift, optimizes the unmanned aerial vehicle power allocation, is specifically:
P 4 :
s.t.C1：
C2：
wherein, constraint C1
For legal users U k The lower bound achieved by the rate SCA.
6. The method for maximizing the minimum safe rate of a RIS-assisted unmanned aerial vehicle NOMA network according to claim 5, wherein the step 105, given the RIS phase shift, the unmanned aerial vehicle power allocation, optimizes the unmanned aerial vehicle position and the SIC decoding order, is specifically:
introducing an auxiliary variable t 3 Optimizing problem P with objective 2 Conversion to
P 5 :
s.t.C1:
C2:u r (q)≥||q-r||
C3:
C4:
C5:
C6:
C7:
C8:||q-q (n) ||≤δ
C9:||q (n) -w e ||+q-q (n) ≥l e
C10:||q (n) -r||+q-q (n) ≥l r
C11:
C12:
C13:
In the middle ofFor the set of auxiliary variables introduced, u k For unmanned plane and legal user U k Upper bound of distance l e Is the lower bound of the distance between the unmanned plane and the eavesdropper, u r Representing the upper bound of the distance between the unmanned aerial vehicle and the RIS, l r A lower bound representing a distance of the drone from the RIS; in constraint C3η k (q)＝f k (u k ,u r )+B k g k (u k ,u r ) For unmanned aerial vehicle to legal user U k Is a lower bound for the desired channel gain,η k (q) wherein,A k wherein the method comprises the steps of LOS component for unmanned aerial vehicle to RIS channel, +.>For RIS to legal user U k LOS component of channel, R 2 Is the Lais factor, < >>Is R k In the division of (2), the numerator and denominator are divided simultaneouslyη k The denominator after (q); constraint C4 +.>Is thatz k L in the expression of (q, A) ae (q)| 2 +(ξ e (q)) 2 An expanded upper bound of (2); constraint C5 +.>For legal users U k Is the rate of (2)Point +.>First order Taylor expansion, X e,k (A, q) isz k The numerator and denominator in (q, A) being divided by +.>The denominator of the latter and at the point of the nth iteration of SCA +.>A first-order taylor expansion; constraint C7 +.>Is thatη k (q) at the nth iteration point of SCA->A first order Taylor expansion, wherein +.> Constraint q in C8 (n) Representing the value of the nth iteration of the SCA, wherein delta is the maximum allowable displacement of the unmanned aerial vehicle in each SCA iteration; constraint C9, C10 at SCA nth iteration point q (n) Respectively expanding the first-order Taylor to obtain convex sets; constraint C13 is a non-convex set alpha i,j α j,k ≤α i,k After conversion to form of convex function difference, at the nth iteration point of SCA +.>A first-order taylor expansion; for non-convex set alpha i,j And the xi > 0 is used as a penalty term to be written into the objective function, and is used as a penalty coefficient.
7. A method of maximising a minimum security rate for a RIS-assisted unmanned aerial vehicle NOMA network according to claim 6, wherein theStep 106, comparingMagnitude of security rate decision threshold ζ, wherein +_>For the maximum minimum safe rate of the nth iteration,/v>A maximum minimum safe rate for the n-1 th iteration; if it isIf not more than ζ, the safety rate converges, the maximum minimum safety rate is given, and the method is ended; if it isAnd if so, saving the safety rate at the moment, and jumping to the step 103 until the safety rate meets the condition, and giving the maximum minimum safety rate.
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