CN110708698A - Physical layer secure transmission method of heterogeneous wireless sensor network based on wireless energy-carrying communication - Google Patents

Physical layer secure transmission method of heterogeneous wireless sensor network based on wireless energy-carrying communication Download PDF

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CN110708698A
CN110708698A CN201910633542.5A CN201910633542A CN110708698A CN 110708698 A CN110708698 A CN 110708698A CN 201910633542 A CN201910633542 A CN 201910633542A CN 110708698 A CN110708698 A CN 110708698A
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energy
capacity
convex
channel
legal
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朱政宇
王忠勇
张贝贝
孙刚灿
郝万明
王宁
高向川
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Zhengzhou University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/02—Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04K—SECRET COMMUNICATION; JAMMING OF COMMUNICATION
    • H04K1/00—Secret communication
    • H04K1/02—Secret communication by adding a second signal to make the desired signal unintelligible

Abstract

The invention provides a physical layer secure transmission method of a heterogeneous wireless sensor network based on wireless energy-carrying communication, which comprises the following steps: acquiring a channel estimation matrix of each node in a network; determining a wireless energy-carrying communication system safe transmission scheme based on artificial noise; deducing a system secret capacity expression by taking the secret capacity as a performance index; determining an optimization problem according to the deduced secret capacity expression; introducing a relaxation variable and converting a non-convex problem into a second-order cone programming problem by using a series of convex optimization methods; and solving the convex optimization problem by using a CVX tool box, and gradually approaching to the optimal beam forming vector and the artificial noise vector. The invention utilizes the reasonable beamforming vector of additive artificial noise in the communication system, solves the problem of protecting the legal user under the condition of potential eavesdroppers and achieves the aim of enhancing the system safety.

Description

Physical layer secure transmission method of heterogeneous wireless sensor network based on wireless energy-carrying communication
Technical Field
The invention relates to the technical field of communication, in particular to a method for secure transmission of a physical layer of a heterogeneous wireless sensor network based on wireless energy-carrying communication.
Background
The wireless Energy-carrying communication (SWIPT) technology is based on the existing wireless power supply technology, and realizes efficient and reliable communication while completing Energy transmission and collection (EH) by a certain technical means, thereby fully utilizing precious transmitting power. Because of the remarkable advantage that information and energy can be transmitted in parallel, the SWIPT technology is expected to be widely applied to information exchange and energy transmission among Radio Frequency Identification (RFID), the Internet of things and various mobile terminals, and when high-speed information exchange is realized, power is effectively fed to various terminals by extracting energy in received signals, so that inconvenience caused by traditional wired or battery power supply is replaced, the size and cost of terminal equipment are reduced, the standby time of the terminal equipment is prolonged, and the terminal equipment is widely concerned and researched by academic circles in recent years.
Wireless Sensor Networks (WSNs) can be widely applied to various fields such as military, national defense engineering, industrial and agricultural control and the like, and integrate a logical information world with a real physical world. However, the nodes of the wireless sensor network are generally powered by batteries, the periodic replacement of the batteries will greatly increase the maintenance cost of the network, and the replacement of the batteries is not possible because many sensor networks (such as the structural health monitoring sensor network) need to work in special environments for a long time. The sensor nodes may be classified into different categories according to sensing capability, computing capability, communication capability, energy, and the like. Heterogeneous sensor networks (HWSN) refer to networks made up of many different types of sensor nodes; on the contrary, a network composed of the same type of sensor nodes is called a homogeneous sensor network. When the actual sensor network is configured and applied, the heterogeneity of the sensor network must be considered sometimes, for example, most sensor network nodes adopt a battery power supply mode, and the energy is limited. And for some important nodes, a mains supply or a solar rechargeable power supply is used for supplying power, so that the energy consumption requirement of the whole network is reduced, and the life cycle of the whole sensor network is greatly improved.
In a wireless communication system, a wireless communication medium has an open basic characteristic such that it is more difficult to secure information than a wired communication system. In the cooperative wireless communication system, the transmission information is received by a third-party node which is not strictly supervised by both legal communication parties except both legal communication parties, and the safety problem of the cooperative wireless communication system is more complicated due to additional threats introduced to information processing and forwarding.
The traditional encryption method based on the cryptosystem exists at the upper layer of the layered model, the security of the traditional encryption method mainly depends on the complexity of calculation, and along with the development and application of the quantum computer technology with infinite computing power, the security performance of the traditional encryption system can meet the serious challenge. When the method has strong enough computing power, once an eavesdropper receives plaintext information, the eavesdropper can acquire ciphertext in a short time through a cryptoanalysis means, and the information security cannot be guaranteed.
The wireless physical layer security technology utilizes the broadcast property, the channel fading property, the Doppler effect and other adverse factors of a wireless channel, and from the information theory perspective, the wireless communication confidentiality problem is solved. The aim of the security design of the physical layer of the wireless system is to improve the security capacity of the system, and the essence of the security capacity improvement is to ensure that the received signal-to-interference-and-noise ratio of a legal user channel is as better as possible than that of an eavesdropping channel, so that the ambiguity of the security information received by an eavesdropper is improved as much as possible. Therefore, the increase of the security capacity can be considered from two aspects, on one hand, the reception of the legal user is improved, and on the other hand, the eavesdropper reception is deteriorated through an active interference blocking scheme, so that the ambiguity of the received information is increased.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a physical layer security transmission method of a heterogeneous wireless sensor network based on wireless energy-carrying communication, wherein a legal originating is utilized to assist in sending beamforming matrix information containing artificial noise AN in a system, and the problem of system physical layer security under the condition of wiretapping is solved. Meanwhile, the legal terminal adopts a power splitting mode, so that information and energy are transmitted in parallel, and the problem of inconvenient power supply in the traditional mode is solved.
In a first aspect, the invention provides a method for secure transmission of a physical layer of a heterogeneous wireless sensor network based on wireless energy-carrying communication, which includes:
s1: acquiring a channel estimation matrix of each node in a network;
s2: determining a wireless energy-carrying communication system safe transmission scheme based on artificial noise;
s3: obtaining a system secret capacity expression by taking the secret capacity as a performance index;
s4: determining an optimization problem according to the deduced secret capacity expression;
s5: introducing a relaxation variable and converting a non-convex problem into a second-order cone programming problem by using a series of convex optimization methods;
s6: solving the convex optimization problem by using a CVX tool box, and gradually approaching to the optimal beam forming vector and artificial noise vector;
preferably, the step S1 specifically includes:
and acquiring a channel estimation matrix of each user in the system through the channel reciprocity of the system.
Preferably, the step S2 specifically includes:
the reception information of each terminal is determined. For example: legal terminal M-SNmReceive information ymLegal terminal HP-SNkReceive information yc,k(ii) a Illegal terminal LP-SNlReceive information ye,l(ii) a Due to HP-SNkUsing power splitting scheme to decode signal and collect energy simultaneously, HP-SNkWherein the received information for signal decoding is
Figure RE-GDA0002308330420000031
The received information for energy harvesting is
Figure RE-GDA0002308330420000032
Preferably, the step S3 specifically includes:
M-SNmthe channel capacity of (a) is:
Figure RE-GDA0002308330420000033
LP-SNldecoding M-SNmChannel capacity of desired signal:
Figure RE-GDA0002308330420000034
M-SNmthe secret capacity of (c) is:
wherein:
Figure RE-GDA0002308330420000036
gmfor macro base station to mth macro user (M-SN)m) Channel information of hmFor micro base stations to M-SNmChannel information of gc,kFor micro base stations to the l-th micro ID user (legal terminal, HP-SN)k) Channel information of hc,kFor macro base stations to HP-SNkThe channel information of (a); he,lFor micro base stations to the l-th micro EH user (illegal terminal, LP-SN)l) Channel information of Ge,lFor macro base stations to LP-SNlThe channel information of (a); v. ofmFor macro base stations to M-SNmTransmit beamforming vector of wkFor micro base stations to HP-SNkTransmit beamforming vectors of (a); z is an artificial noise vector.
Preferably, the step S4 specifically includes determining an optimization problem (P1):
wherein:for legal terminals HP-SNkThe value of the minimum secret capacity of (c),
Figure RE-GDA0002308330420000043
for legal terminals HP-SNkThe minimum value of the collection energy of (c),
Figure RE-GDA0002308330420000044
for illegal terminals LP-SNlP is the threshold value of the total transmission power of the micro base station, P1Threshold value of transmission power, P, for a micro base station for conveying useful information2Is the transmission power threshold of the macro base station.
Since the coupling phenomenon of the optimization variables occurs in both the objective function and the constraint condition of the optimization problem (P1), the problem is also an NP-hard problem, and the optimal solution cannot be obtained by directly using the existing convex optimization method.
Using the formula, the problem is converted to (P2):
Figure RE-GDA0002308330420000045
preferably, step S5 specifically includes:
and (4) converting an optimization thought, wherein a non-convex optimization problem is converted into a second-order cone programming problem.
Preferably, the step S6 specifically includes:
and solving the convex optimization problem by using a CVX tool box, and gradually approaching to the optimal beam forming vector and the artificial noise vector.
According to the technical scheme, the method for the secure transmission of the physical layer of the heterogeneous wireless sensor network based on the wireless energy-carrying communication solves the problem of the security of the physical layer of the system under the condition of wiretapping by using the artificial noise auxiliary technology to send the beam forming information by the legal transmitter in the system. Meanwhile, the legal terminal adopts a power splitting mode, so that information and energy are transmitted in parallel, and the problem of inconvenient power supply in the traditional mode is solved.
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In order to more clearly illustrate the embodiments or technical solutions of the present invention in the prior art, the drawings used in the description of the embodiments or prior art are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a double-layer heterogeneous wireless sensor network based on SWIPT;
FIG. 2 is HP-SNkThe power allocation scheme of (1);
FIG. 3 shows the transmit power P at the macro base station2Under different conditions, the average minimum privacy capacity of macro users in the invention is a graph of the change along with the iteration times;
FIG. 4 shows the transmit power P at the macro base station2Under the changing conditions, the SCA algorithm and the traditional algorithm (no-ANMSCM indicates that the micro base station does not adopt the artificial noise technology, null-AN MSCM indicates that the micro base station adopts the special artificial noise technology and MSCMfix rho)k0.5 represents one half of the received power of the micro-ID user for signal decoding and the other half for energy harvesting), and the average minimum secret capacity of the macro user is recorded along with P)2(ii) a change in (c);
FIG. 5 is a graph showing the relationship between HP-SN andkthe average minimum privacy capacity of macro users varies according to different energy collection requirements. Comparing the SCA algorithm provided by the invention with the traditional algorithm in a simulation way;
fig. 6 is a flowchart illustrating a method for secure transmission of a physical layer of a heterogeneous wireless sensor network based on wireless energy-carrying communication according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 6, a method for secure transmission of a physical layer of a heterogeneous wireless sensor network based on wireless portable communication according to an embodiment of the present invention includes the following steps:
s1: acquiring a channel estimation matrix of each node in a network;
s2: determining a wireless energy-carrying communication system safe transmission scheme based on artificial noise;
s3: obtaining a system secret capacity expression by taking the secret capacity as a performance index;
s4: determining an optimization problem according to the deduced secret capacity expression;
s5: introducing a relaxation variable and converting a non-convex problem into a second-order cone programming problem by using a series of convex optimization methods;
s6: solving the convex optimization problem by using a CVX tool box, and gradually approaching to the optimal beam forming vector and artificial noise vector;
in this embodiment, the specific process of step S1 is as follows:
as shown in fig. 1, the method described in this embodiment is applied to a dual-layer heterogeneous wireless sensor network based on SWIPT, and includes 1 micro base station, K single-antenna macro users (legal users) HP-SN k1 macro base station, M single antenna micro ID users (legal users) M-SNmAnd L micro EH users (eavesdroppers) LP-SNl. There are two different types of receivers in the present system: (1) a group of legal users HP-SNk(e.g., low power receiver) and is noted as K ═ HP-SN1,...,HP-SNkThe micro base station is far away from the micro base station; (2) group of eavesdroppers LP-SNl(e.g., sensor) and is noted as L ═ { LP-SN1,...,LP-SNLAnd (4) the micro base station is close to the micro base station. Suppose LP-SNlWith NEReceiving antennas, and the macro base station and the micro base station have N respectivelyM≥M、NF≥K+LNEA root antenna.
In this embodiment, the specific process of step S1 is as follows:
and acquiring a channel estimation matrix of each user in the system through the channel reciprocity of the system.
It should be noted that the double-layer heterogeneous wireless sensor network based on the SWIPT obtains the channel estimation matrix by using the channel reciprocity of the system.
In this embodiment, as shown in fig. 1, step S2 specifically includes:
s21: the macro base station and the micro base station broadcast information and legal users M-SNm、HP-SNkAnd an eavesdropper LP-SNlA signal is received. Specifically, macro base station broadcast information s and micro base station broadcast information xsCan be expressed as:
Figure RE-GDA0002308330420000071
wherein: smAnd ss,kAre respectively passed to M-SNmAnd HP-SNkIs determined by the information of (a) a,
Figure RE-GDA0002308330420000073
vmfor macro base stations to M-SNmTransmit beamforming vector of wkFor micro base stations to HP-SNkTransmit beamforming vectors of (a); z is the energy-carrying artificial noise vector.
S22: in the process, the legal user M-SNmOnly the information is received and no processing is performed on the received information.Legal user HP-SNkThe power is split into energy acquisition and information transfer parts according to a certain proportion. Legal user M-SNmWith HP-SNkThe received signals may be represented as:
Figure RE-GDA0002308330420000074
Figure RE-GDA0002308330420000075
wherein: gmFor macro base stations to M-SNmChannel gain of hmFor micro base stations to M-SNmChannel of gc,kFor macro base stations to HP-SNkChannel of (a), hc,kFor micro base stations to HP-SNkThe channel of (2); smIs from macro base station to M-SNmA signal carrying information on the basis of a signal,further, n ism，nc,kAre respectively M-SNmAnd HP-SNkComplex Additive White Gaussian Noise (AWGN) and satisfies
Figure RE-GDA0002308330420000077
S23: due to the broadcast nature of the radio signal, the eavesdropper LP-SNlSignals broadcast by the macro base station and signals broadcast by the micro base station may also be received. Then the eavesdropper LP-SNlThe received signal is represented as:
Figure RE-GDA0002308330420000078
wherein: ge,lFor macro base stations to LP-SNlOf the channel coefficient of (A), He,lFor base stations to LP-SNlThe channel coefficient of (a); further, n ise,lIs LP-SNlComplex Additive White Gaussian Noise (AWGN) and satisfies
Figure RE-GDA00023083304200000711
S24: HP-SN due to legal userkSplitting power into Energy Harvesting (EH) and information transfer (ID) portions in a proportion, the proportion ρk∈(0,1]. Then the legal user HP-SNkThe splitting of power into Energy Harvesting (EH) and information transfer (ID) signals in a certain proportion can be expressed as:
Figure RE-GDA0002308330420000079
Figure RE-GDA00023083304200000710
wherein: n isp,kIs HP-SNkComplex Additive White Gaussian Noise (AWGN) at the rf conversion of the ID part and satisfies
In this embodiment, step S3 specifically includes:
s31: calculation of HP-SNkThe channel capacity of (a).
In particular, HP-SNkThe channel capacity of (a) is:
Figure RE-GDA0002308330420000082
wherein:
Figure RE-GDA0002308330420000083
s32: calculation of LP-SNlDecoding HP-SNkThe channel capacity of the target signal.
In particular, LP-SNlDecoding HP-SNkThe channel capacity of the target signal is:
Figure RE-GDA0002308330420000084
s33: calculating M-SNmThe channel capacity of (a).
In particular, M-SNmThe channel capacity of (a) may be expressed as:
Figure RE-GDA0002308330420000085
wherein:
Figure RE-GDA0002308330420000086
s34: calculation of LP-SNlDecoding M-SNmThe channel capacity of the target signal.
In particular, an eavesdropper LP-SNlDecoding M-SNmThe channel capacity of the target signal may be expressed as:
s35: from the definition of the security capacity, an expression of the security capacity under the model can be obtained.
In particular, HP-SNkThe security capacity of (a) may be expressed as:
Figure RE-GDA0002308330420000088
M-SNmthe security capacity of (a) may be expressed as:
s36: calculation of HP-SNkAnd LP-SNlTo collect energy.
In particular, due to HP-SNkA power splitter is used to convert a portion of the radio frequency signal into energy. Then HP-SNkAnd LP-SNlThe energy collected at can be expressed as:
Figure RE-GDA0002308330420000091
Figure RE-GDA0002308330420000092
wherein: eta of 0 ≦c,kEta is not less than 1 and 0 is not less thane,l1 or less represents HP-SNkAnd LP-SNlEnergy conversion rate of (1).
In this embodiment, step S4 specifically includes:
s41: the target of optimization is determined by equation (13).
Specifically, observing equation (13) reveals that there are four variables w thereink,z,vmAnd ρkThe objective of the optimization is to find the appropriate wk,z,vmAnd ρkIn HP-SNsHP-SN under constraint of secret capacitysAnd LP-SNlUnder the constraint condition of collected energy and the constraint condition of transmitting power of macro base station and micro base station, the M-SN is enabled to bemIs that the security capacity gets the maximum. Namely:
Figure RE-GDA0002308330420000093
wherein:for legal terminals HP-SNkThe minimum security capacity of the network element,
Figure RE-GDA0002308330420000095
for legal terminals HP-SNkThe minimum amount of energy to be harvested,for illegal terminals LP-SNlP is the total transmission power of the micro base station, P1Transmission power, P, for micro base stations for communicating information2The total transmission power of the macro base station of the legal terminal.
S42: formula (15) may first be described as
Figure RE-GDA0002308330420000097
In this embodiment, step S5 specifically includes:
s51: the inequality | I + A | ≧ 1+ tr (A) is adopted in the formula (16a), and preparation is made for solving the non-convex problem.
Specifically, a relaxation variable γ is introducedcAnd gammaeUsing the above inequality, the problem (16) can be converted into:
Figure RE-GDA0002308330420000102
wherein:and
Figure RE-GDA0002308330420000104
s52: the expression (16a) is converted into a convex form by adopting an SCA theory, a first-order Taylor series expansion and other modes.
Specifically, a new matrix is defined:
Figure RE-GDA0002308330420000105
and
Figure RE-GDA0002308330420000106
introducing an auxiliary variable vm,wm,um,tl,el,qlConstraints (17b) and (17c) can be translated into a number of simple constraints:
Figure RE-GDA0002308330420000107
Figure RE-GDA0002308330420000108
Figure RE-GDA0002308330420000109
vm-wm≥um(18d)
Figure RE-GDA0002308330420000112
tl-el≤ql(19d)
according to the convex optimization theory, the following can be known: when the constraint is in the form of a concave function equal to or greater than a convex function, the constraint is in the form of a convex. Thus, it can be concluded that (18a) and (19b) are convex, while (18b), (18c), (19a) and (19c) are still non-convex constraints. The observation results are that: due to the right side of (18b) and (18c)
Figure RE-GDA0002308330420000114
Andis a convex function. Similarly, (19a) and (19c) on the left
Figure RE-GDA0002308330420000116
And
Figure RE-GDA0002308330420000117
also a convex function.
The inequality constraints (18b), (18c), (19a) and (19c) are solved using a successive convex approximation technique (SCA). Definition of
Figure RE-GDA0002308330420000118
To becomeMeasurement of
Figure RE-GDA0002308330420000119
The invention is based on the nth iteration value of the SCA iteration algorithm. And will be
Figure RE-GDA00023083304200001110
Is obtained by expansion of a first-order Taylor series formula
Figure RE-GDA00023083304200001111
And
Figure RE-GDA00023083304200001112
then the non-convex constraints (18b), (18c), (19a) and (19c) can be given their linear constraints of
Figure RE-GDA00023083304200001113
Figure RE-GDA00023083304200001114
Figure RE-GDA00023083304200001115
Figure RE-GDA00023083304200001116
S53: and aiming at the constraint condition (16b), converting the constraint (16b) into a convex form by adopting the modes of SPCA theory, first-order Taylor series expansion and the like.
In particular by introducing two relaxation variables s1＞0,s2> 0 and the inequality | I + A | ≧ 1+ tr (A), (16b) can be converted into:
Figure RE-GDA00023083304200001117
Figure RE-GDA00023083304200001118
Figure RE-GDA00023083304200001119
wherein:
Figure RE-GDA00023083304200001120
and
Figure RE-GDA00023083304200001121
after finishing, the method can be further simplified into the following steps:
Figure RE-GDA0002308330420000123
in addition, (22a) constraint is equivalent to
Figure RE-GDA0002308330420000124
Convert it into a second order tapered form of
Figure RE-GDA0002308330420000125
The inequality constraints (22b) and (22c) can be transformed into:
Figure RE-GDA0002308330420000126
Figure RE-GDA0002308330420000127
the observation can obtain: although the above formula is a non-convex function, the right side in the formula is a functional form of quadratic form divided by linear (QoL), which is a convex function. According to the sequential function convex approximation (SPCA) theory, the QoL function is equivalent to one-order Taylor series expansion and is converted into a convex approximation form.
First, a generalized QoL function is defined:
wherein: y is more than or equal to B and B is more than or equal to 0. F is thenB,b(w, y) at the pointThe first order Taylor series expansion of (A) is:
using the above results, for a specific pointConstraints (24a) and (24b) can be transformed into the following convex forms, respectively:
Figure RE-GDA00023083304200001213
Figure RE-GDA0002308330420000131
s54: and (3) converting the constraints (16c) (16d) (15d) into a convex form by adopting an SPCA theory, a first-order Taylor series expansion and the like.
Specifically, the constraint (16c) may be translated into:
Figure RE-GDA0002308330420000132
next, in order to obtain a convex approximation form of (28),using SPCA theory to define
Figure RE-GDA0002308330420000133
Figure RE-GDA0002308330420000134
And
Figure RE-GDA0002308330420000135
substituting the left function of (28) yields:
Figure RE-GDA0002308330420000136
in the above process, the inequality ignores the quadratic term of the beam forming error
Figure RE-GDA0002308330420000137
ΔzHHkΔ z and
Figure RE-GDA0002308330420000138
thus, the linear approximation of the constraint (28) can be expressed as:
Figure RE-GDA0002308330420000139
similarly, the constraint (16d) can be converted into:
Figure RE-GDA00023083304200001310
in addition, the constraint (15d) can equivalently be converted into:
s54: and integrating the results to obtain the final convex programming problem.
Specifically, in the nth iteration, the original optimization problem (15) translates into the following Second Order Cone Programming (SOCP) problem:
Figure RE-GDA0002308330420000141
from convex optimization theory, the problem (33) is a convex programming problem.
In this embodiment, step S6 specifically includes:
easy to obtain: given a
Figure RE-GDA0002308330420000142
The convex programming problem (33) is an SOCP problem and can be effectively solved by convex optimization software such as CVX. According to the SPCA theory, the convex approximation of the optimal solution can be updated by iteration. This indicates that the original optimization problem (15) can be obtained to approach the optimal result. Due to the power constraint of equation (32), the objective function of the final second order cone planning problem (33) is still bounded. Therefore, the algorithm based on the SCA theory provided by the invention can ensure iterative convergence, thereby obtaining a global optimal solution. The specific algorithm is shown in the following table:
Figure RE-GDA0002308330420000143
therefore, the method for the secure transmission of the physical layer of the heterogeneous wireless sensor network based on the wireless energy-carrying communication, which is provided by the embodiment, solves the problem of the security of the physical layer of the system under the condition of eavesdropping by using the artificial noise auxiliary technology. The purpose of improving the safety performance of the system is achieved by obtaining the maximum secrecy capacity of the system. Meanwhile, the legal terminal adopts a power splitting mode, so that information and energy are transmitted in parallel, and the problem of inconvenient power supply in the traditional mode is solved.
Fig. 3 shows the convergence performance of the SCA-based iterative algorithm proposed by the present invention at different iteration numbers. In HP-SNkMinimum security capacity
Figure RE-GDA0002308330420000151
HP-SNkMinimum harvested energy
Figure RE-GDA0002308330420000152
LP-SNlMinimum harvested energy
Figure RE-GDA0002308330420000153
The total transmitting power P of the micro base station is 60dBm, and the transmitting power P of the micro base station for transmitting information1In the case of 40dBm, it can be observed from the figure that convergence can be reached in all cases only in 3 iterations. In addition, the average Minimum Safe Capacity (MSC) is dependent on the total transmission power P of the macro base station2Is increased.
Fig. 4 shows the average minimum safety capacity of macro users at different target transmit powers at the macro base station. HP-SNkMinimum secret capacity set to
Figure RE-GDA0002308330420000154
HP-SNkMinimum energy to harvest is set toLP-SNlMinimum energy to harvest is set to
Figure RE-GDA0002308330420000156
The total transmitting power of the micro base station is set to be P60 dBm, and the transmitting power of the micro base station for transmitting information is set to be P 140 dBm. With the increase of the transmitting power of the macro base station, the performance of the SCA-based iterative algorithm provided by the invention is superior to that of all other traditional schemes. Compared with the cases of non-ANMSCM and null-ANMSCM, the average minimum safe capacity of the SCA-based iterative algorithm provided by the invention is respectively higher than 1.1bps/Hz and 0.7 bps/Hz.
FIG. 5 shows HP-SNkAverage minimum safe capacity curve of macro user under different target collection power. HP-SNkMinimum secret capacity set to
Figure RE-GDA0002308330420000157
LP-SNlMinimum energy to harvest is set to
Figure RE-GDA0002308330420000158
The total transmitting power of the micro base station is set to be P70 dBm, and the transmitting power of the micro base station for transmitting useful information is P1Total transmit power of macro base station is set to P at 50dBm250 dBm. As can be seen from FIG. 5, as
Figure RE-GDA0002308330420000159
The average minimum safe capacity of all algorithms is slowly reduced, and the safety performance of the algorithm is better than that of the other three traditional algorithms. In addition, the performance of the SCA-based iterative algorithm provided by the invention is superior to rhok0.5 protocol 0.8 bps/Hz.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims (7)

1. A physical layer secure transmission method of a heterogeneous wireless sensor network based on wireless energy carrying communication is characterized by comprising the following steps:
s1: acquiring a channel estimation matrix of each node in a network;
s2: determining a wireless energy-carrying communication system safe transmission scheme based on artificial noise;
s3: obtaining a system secret capacity expression by taking the secret capacity as a performance index;
s4: determining an optimization problem according to the deduced secret capacity expression;
s5: introducing a relaxation variable and converting a non-convex problem into a second-order cone programming problem by using a series of convex optimization methods;
s6: and solving the convex optimization problem by using a CVX tool box, and gradually approaching to the optimal beam forming vector and the artificial noise vector.
2. The method according to claim 1, wherein the step S1 specifically includes:
and acquiring a channel estimation matrix of each user in the system through the channel reciprocity of the system.
3. The method according to claim 1, wherein the step S2 specifically includes:
the reception information of each terminal is determined. For example: legal terminal M-SNmReceive information ymLegal terminal HP-SNkReceive information yc,k(ii) a Illegal terminal LP-SNlReceive information ye,l(ii) a Due to HP-SNkUsing power splitting scheme to decode signal and collect energy simultaneously, HP-SNkWherein the received information for signal decoding is
Figure RE-FDA0002251580000000011
The received information for energy harvesting is
Figure RE-FDA0002251580000000012
4. The method according to claim 3, wherein the step S3 specifically includes:
HP-SNkthe channel capacity of (a) is:
Figure RE-FDA0002251580000000013
wherein:
Figure RE-FDA0002251580000000014
LP-SNldecoding HP-SNkThe channel capacity of the desired signal is:
Figure RE-FDA0002251580000000015
HP-SNkthe secret capacity of (c) is:
Figure RE-FDA0002251580000000017
the channel capacity of (a) is:
Figure RE-FDA0002251580000000021
wherein:
Figure RE-FDA0002251580000000022
LP-SNldecoding M-SNmChannel capacity of desired signal:
Figure RE-FDA0002251580000000023
M-SNmthe secret capacity of (c) is:
Figure RE-FDA0002251580000000024
HP-SNkthe energy of the radio frequency signal is collected as follows:
Figure RE-FDA0002251580000000025
LP-SNlthe energy of the radio frequency signal is collected as follows:
Figure RE-FDA0002251580000000026
wherein: eta of 0 ≦c,kHP-SN of legal terminal with less than or equal to 1kThe energy conversion efficiency of (a); eta of 0 ≦e,lLess than or equal to 1 is an illegal terminal LP-SNlEnergy conversion efficiency of (1).
5. The method according to claim 1, wherein the step S4 specifically includes:
determining an optimization problem from the derived privacy capacity expression (P1):
(P1)
Figure RE-FDA0002251580000000028
Figure RE-FDA0002251580000000029
Figure RE-FDA00022515800000000210
0≤ρk≤1.
wherein:
Figure RE-FDA00022515800000000212
for legal terminals HP-SNkThe value of the minimum secret capacity of (c),
Figure RE-FDA00022515800000000213
for legal terminals HP-SNkThe minimum value of the collection energy of (c),
Figure RE-FDA00022515800000000214
for illegal terminals LP-SNlP is the maximum value of the total transmission power of the micro base station 3, P1Maximum transmit power for micro base stations to communicate useful informationValue, P2Is the transmission power threshold of the macro base station. Because the coupling phenomenon of the optimization variables occurs in the objective function and the constraint condition of the optimization problem (P1), the problem is also an NP-hard problem, and the optimal solution can not be obtained by directly utilizing the existing convex optimization algorithm;
first, the optimization problem (P1) is described as (P2):
(P2)
Figure RE-FDA0002251580000000031
Figure RE-FDA0002251580000000033
Figure RE-FDA0002251580000000034
Figure RE-FDA0002251580000000035
0≤ρk≤1.。
6. the method according to claim 1, wherein the step S5 specifically includes:
and introducing a relaxation variable and converting a non-convex optimization problem into a second-order cone programming problem by using a series of convex optimization methods.
7. The method according to claim 1, wherein the step S6 specifically includes: and solving the convex optimization problem by using a CVX tool box, and gradually approaching the optimal beamforming vector and the artificial noise vector through loop iteration.
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